@article{Aspremont2007Direct,
author = {d'Aspremont, A. and El Ghaoui, L. and Jordan, M. I. and Lanckriet,
G. R. G.},
title = {A Direct Formulation for Sparse {PCA} Using Semidefinite Programming},
journal = {SIAM Review},
year = {2007},
volume = {49},
pages = {434--448},
number = {3},
doi = {10.1137/050645506},
owner = {jp},
timestamp = {2013.01.07},
url = {http://dx.doi.org/10.1137/050645506}
}
@article{Veer2008Enabling,
author = {{van't Veer}, L. J. and Bernards, R.},
title = {Enabling personalized cancer medicine through analysis of gene-expression
patterns.},
journal = {Nature},
year = {2008},
volume = {452},
pages = {564--570},
number = {7187},
month = {Apr},
abstract = {Therapies for patients with cancer have changed gradually over the
past decade, moving away from the administration of broadly acting
cytotoxic drugs towards the use of more-specific therapies that are
targeted to each tumour. To facilitate this shift, tests need to
be developed to identify those individuals who require therapy and
those who are most likely to benefit from certain therapies. In particular,
tests that predict the clinical outcome for patients on the basis
of the genes expressed by their tumours are likely to increasingly
affect patient management, heralding a new era of personalized medicine.},
doi = {10.1038/nature06915},
pdf = {../local/Veer2008Enabling.pdf},
file = {Veer2008Enabling.pdf:Veer2008Enabling.pdf:PDF},
institution = {Agendia BV, Louwesweg 6, 1066 EC Amsterdam, The Netherlands.},
keywords = {csbcbook, csbcbook-ch3},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {nature06915},
pmid = {18385730},
timestamp = {2011.11.30},
url = {http://dx.doi.org/10.1038/nature06915}
}
@article{Tayrac2009Simultaneous,
author = {{de Tayrac}, M. and L\^e, S. and Aubry, M. and Mosser, J. and Husson,
F.},
title = {Simultaneous analysis of distinct Omics data sets with integration
of biological knowledge: Multiple Factor Analysis approach.},
journal = {BMC Genomics},
year = {2009},
volume = {10},
pages = {32},
abstract = {Genomic analysis will greatly benefit from considering in a global
way various sources of molecular data with the related biological
knowledge. It is thus of great importance to provide useful integrative
approaches dedicated to ease the interpretation of microarray data.Here,
we introduce a data-mining approach, Multiple Factor Analysis (MFA),
to combine multiple data sets and to add formalized knowledge. MFA
is used to jointly analyse the structure emerging from genomic and
transcriptomic data sets. The common structures are underlined and
graphical outputs are provided such that biological meaning becomes
easily retrievable. Gene Ontology terms are used to build gene modules
that are superimposed on the experimentally interpreted plots. Functional
interpretations are then supported by a step-by-step sequence of
graphical representations.When applied to genomic and transcriptomic
data and associated Gene Ontology annotations, our method prioritize
the biological processes linked to the experimental settings. Furthermore,
it reduces the time and effort to analyze large amounts of 'Omics'
data.},
doi = {10.1186/1471-2164-10-32},
institution = {CNRS UMR 6061, Université de Rennes 1, IFR 140, Faculté de Médecine,
CS 34317, 35043 Rennes, France. marie.de-tayrac@univ-rennes1.fr},
keywords = {Animals; Comparative Genomic Hybridization; Factor Analysis, Statistical;
Gene Expression Profiling, methods; Genomics, methods; Glioma, genetics;
Humans; Mice; Models, Biological; Oligonucleotide Array Sequence
Analysis, methods},
language = {eng},
medline-pst = {epublish},
owner = {jp},
pii = {1471-2164-10-32},
pmid = {19154582},
timestamp = {2012.02.29},
url = {http://dx.doi.org/10.1186/1471-2164-10-32}
}
@article{Consortium2010map,
author = {{1000 Genomes Project Consortium}},
title = {A map of human genome variation from population-scale sequencing.},
journal = {Nature},
year = {2010},
volume = {467},
pages = {1061--1073},
number = {7319},
month = {Oct},
abstract = {The 1000 Genomes Project aims to provide a deep characterization of
human genome sequence variation as a foundation for investigating
the relationship between genotype and phenotype. Here we present
results of the pilot phase of the project, designed to develop and
compare different strategies for genome-wide sequencing with high-throughput
platforms. We undertook three projects: low-coverage whole-genome
sequencing of 179 individuals from four populations; high-coverage
sequencing of two mother-father-child trios; and exon-targeted sequencing
of 697 individuals from seven populations. We describe the location,
allele frequency and local haplotype structure of approximately 15
million single nucleotide polymorphisms, 1 million short insertions
and deletions, and 20,000 structural variants, most of which were
previously undescribed. We show that, because we have catalogued
the vast majority of common variation, over 95\% of the currently
accessible variants found in any individual are present in this data
set. On average, each person is found to carry approximately 250
to 300 loss-of-function variants in annotated genes and 50 to 100
variants previously implicated in inherited disorders. We demonstrate
how these results can be used to inform association and functional
studies. From the two trios, we directly estimate the rate of de
novo germline base substitution mutations to be approximately 10(-8)
per base pair per generation. We explore the data with regard to
signatures of natural selection, and identify a marked reduction
of genetic variation in the neighbourhood of genes, due to selection
at linked sites. These methods and public data will support the next
phase of human genetic research.},
doi = {10.1038/nature09534},
keywords = {Calibration; Chromosomes, Human, Y, genetics; Computational Biology;
DNA Mutational Analysis; DNA, Mitochondrial, genetics; Evolution,
Molecular; Female; Genetic Association Studies; Genetic Variation,
genetics; Genetics, Population, methods; Genome, Human, genetics;
Genome-Wide Association Study; Genomics, methods; Genotype; Haplotypes,
genetics; Humans; Male; Mutation, genetics; Pilot Projects; Polymorphism,
Single Nucleotide, genetics; Recombination, Genetic, genetics; Sample
Size; Selection, Genetic, genetics; Sequence Alignment; Sequence
Analysis, DNA, methods},
language = {eng},
medline-pst = {ppublish},
owner = {philippe},
pii = {nature09534},
pmid = {20981092},
timestamp = {2012.02.24},
url = {http://dx.doi.org/10.1038/nature09534}
}
@article{Razzak1992Applications,
author = {A-Razzak, M. and Glen, R. C.},
title = {Applications of rule-induction in the derivation of quantitative
structure-activity relationships.},
journal = {J. Comput. Aided Mol. Des.},
year = {1992},
volume = {6},
pages = {349--383},
number = {4},
month = {Aug},
abstract = {Recently, methods have been developed in the field of Artificial Intelligence
(AI), specifically in the expert systems area using rule-induction,
designed to extract rules from data. We have applied these methods
to the analysis of molecular series with the objective of generating
rules which are predictive and reliable. The input to rule-induction
consists of a number of examples with known outcomes (a training
set) and the output is a tree-structured series of rules. Unlike
most other analysis methods, the results of the analysis are in the
form of simple statements which can be easily interpreted. These
are readily applied to new data giving both a classification and
a probability of correctness. Rule-induction has been applied to
in-house generated and published QSAR datasets and the methodology,
application and results of these analyses are discussed. The results
imply that in some cases it would be advantageous to use rule-induction
as a complementary technique in addition to conventional statistical
and pattern-recognition methods.},
keywords = {Algorithms, Anticonvulsants, Antimalarials, Artificial Intelligence,
Cardiotonic Agents, Software, Structure-Activity Relationship, gamma-Aminobutyric
Acid, 1403028},
owner = {mahe},
pmid = {1403028},
timestamp = {2006.09.06}
}
@article{Abeel2010Robust,
author = {Abeel, T. and Helleputte, T. and Van de Peer, Y. and Dupont, P. and
Saeys, Y.},
title = {Robust biomarker identification for cancer diagnosis with ensemble
feature selection methods},
journal = {Bioinformatics},
year = {2010},
volume = {26},
pages = {392--398},
month = {Nov},
abstract = {MOTIVATION: Biomarker discovery is an important topic in biomedical
applications of computational biology, including applications such
as gene and SNP selection from high dimensional data. Surprisingly,
the stability with respect to sampling variation or robustness of
such selection processes has received attention only recently. However,
robustness of biomarkers is an important issue, as it may greatly
influence subsequent biological validations. In addition, a more
robust set of markers may strengthen the confidence of an expert
in the results of a selection method. RESULTS: Our first contribution
is a general framework for the analysis of the robustness of a biomarker
selection algorithm. Secondly, we conducted a large-scale analysis
of the recently introduced concept of ensemble feature selection,
where multiple feature selections are combined in order to increase
the robustness of the final set of selected features. We focus on
selection methods that are embedded in the estimation of support
vector machines (SVMs). SVMs are powerful classification models that
have shown state-of-the-art performance on several diagnosis and
prognosis tasks on biological data. Their feature selection extensions
also offered good results for gene selection tasks. We show that
the robustness of SVMs for biomarker discovery can be substantially
increased by using ensemble feature selection techniques, while at
the same time improving upon classification performances. The proposed
methodology is evaluated on four microarray data sets showing increases
of up to almost 30\% in robustness of the selected biomarkers, along
with an improvement of about 15\% in classification performance.
The stability improvement with ensemble methods is particularly noticeable
for small signature sizes (a few tens of genes), which is most relevant
for the design of a diagnosis or prognosis model from a gene signature.
CONTACT: yvan.saeys@psb.ugent.be.},
doi = {10.1093/bioinformatics/btp630},
pdf = {../local/Abeel2009Robust.pdf},
file = {Abeel2009Robust.pdf:Abeel2009Robust.pdf:PDF},
institution = {Department of Plant Systems Biology, VIB, Technologiepark 927, 9052
Gent, Belgium.},
language = {eng},
medline-pst = {aheadofprint},
owner = {jp},
pii = {btp630},
pmid = {19942583},
timestamp = {2010.01.09},
url = {http://dx.doi.org/10.1093/bioinformatics/btp630}
}
@article{Abernethy2008new,
author = {Abernethy, J. and Bach, F. and Evgeniou, T. and Vert, J.-P.},
title = {A new approach to collaborative filtering: operator estimation with
spectral regularization},
journal = {J. Mach. Learn. Res.},
year = {2009},
volume = {10},
pages = {803--826},
issn = {1532-4435},
publisher = {JMLR.org}
}
@techreport{Abernethy2008New-techreport,
author = {Abernethy, J. and Bach, F. and Evgeniou, T. and Vert, J.-P.},
title = {A New Approach to Collaborative Filtering: Operator Estimation with
Spectral Regularization},
institution = {HAL},
year = {2008},
number = {00250231},
timestamp = {2008.03.31}
}
@techreport{Abernethy2006Low-rank,
author = {Abernethy, J. and Bach, F. and Evgeniou, T. and Vert, J.-P.},
title = {Low-rank matrix factorization with attributes},
institution = {arXiv},
year = {2006},
number = {cs/0611124},
owner = {jp},
timestamp = {2008.06.06}
}
@article{Abernethy2008Eliciting,
author = {Abernethy, J. and Evgeniou, T. and Toubia, O. and Vert, J.-P.},
title = {Eliciting consumer preferences using robust adaptive choice questionnaires},
journal = {IEEE Trans. Knowl. Data Eng.},
year = {2008},
volume = {20},
pages = {145--155},
number = {2},
abstract = {We propose a framework for designing adaptive choice-based conjoint
questionnaires that are robust to response error. It is developed
based on a combination of experimental design and statistical learning
theory principles. We implement and test a specific case of this
framework using Regularization Networks. We also formalize within
this framework the polyhedral methods recently proposed in marketing.
We use simulations as well as an online market research experiment
with 500 participants to compare the proposed method to benchmark
methods. Both experiments show that the proposed adaptive questionnaires
outperform existing ones in most cases. This work also indicates
the potential of using machine learning methods in marketing.},
doi = {10.1109/TKDE.2007.190632},
pdf = {../local/Abernethy2008Eliciting.pdf},
file = {Abernethy2008Eliciting.pdf:Abernethy2008Eliciting.pdf:PDF},
owner = {jp},
timestamp = {2009.02.26},
url = {http://dx.doi.org/10.1109/TKDE.2007.190632}
}
@techreport{Abernethy2004optimization,
author = {Abernethy, J. and Evgeniou, T. and Vert, J.-P.},
title = {An optimization framework for adaptive conjoint questionnaire design},
institution = {INSEAD},
year = {2004},
owner = {vert}
}
@article{Abraham2010Prediction,
author = {Abraham, Gad and Kowalczyk, Adam and Loi, Sherene and Haviv, Izhak
and Zobel, Justin},
title = {Prediction of breast cancer prognosis using gene set statistics provides
signature stability and biological context},
journal = {BMC Bioinformatics},
year = {2010},
volume = {11},
pages = {277},
number = {1},
doi = {10.1186/1471-2105-11-277},
pdf = {../local/Abraham2010Prediction.pdf},
file = {Abraham2010Prediction.pdf:Abraham2010Prediction.pdf:PDF},
owner = {jp},
timestamp = {2011.01.13},
url = {http://dx.doi.org/10.1186/1471-2105-11-277}
}
@article{Abrahamian2003Efficient,
author = {E. Abrahamian and P. C. Fox and L. Naerum and I. T. Christensen and
H. Th{\o}gersen and R. D. Clark},
title = {{E}fficient generation, storage, and manipulation of fully flexible
pharmacophore multiplets and their use in 3-{D} similarity searching.},
journal = {J. Chem. Inf. Comput. Sci.},
year = {2003},
volume = {43},
pages = {458--468},
number = {2},
abstract = {Pharmacophore triplets and quartets have been used by many groups
in recent years, primarily as a tool for molecular diversity analysis.
In most cases, slow processing speeds and the very large size of
the bitsets generated have forced researchers to compromise in terms
of how such multiplets were stored, manipulated, and compared, e.g.,
by using simple unions to represent multiplets for sets of molecules.
Here we report using bitmaps in place of bitsets to reduce storage
demands and to improve processing speed. Here, a bitset is taken
to mean a fully enumerated string of zeros and ones, from which a
compressed bitmap is obtained by replacing uniform blocks ("runs")
of digits in the bitset with a pair of values identifying the content
and length of the block (run-length encoding compression). High-resolution
multiplets involving four features are enabled by using 64 bit executables
to create and manipulate bitmaps, which "connect" to the 32 bit executables
used for database access and feature identification via an extensible
mark-up language (XML) data stream. The encoding system used supports
simple pairs, triplets, and quartets; multiplets in which a privileged
substructure is used as an anchor point; and augmented multiplets
in which an additional vertex is added to represent a contingent
feature such as a hydrogen bond extension point linked to a complementary
feature (e.g., a donor or an acceptor atom) in a base pair or triplet.
It can readily be extended to larger, more complex multiplets as
well. Database searching is one particular potential application
for this technology. Consensus bitmaps built up from active ligands
identified in preliminary screening can be used to generate hypothesis
bitmaps, a process which includes allowance for differential weighting
to allow greater emphasis to be placed on bits arising from multiplets
expected to be particularly discriminating. Such hypothesis bitmaps
are shown to be useful queries for database searching, successfully
retrieving active compounds across a range of structural classes
from a corporate database. The current implementation allows multiconformer
bitmaps to be obtained from pregenerated conformations or by random
perturbation on-the-fly. The latter application involves random sampling
of the full range of conformations not precluded by steric clashes,
which limits the usefulness of classical fingerprint similarity measures.
A new measure of similarity, The Stochastic Cosine, is introduced
here to address this need. This new similarity measure uses the average
number of bits common to independently drawn conformer sets to normalize
the cosine coefficient. Its use frees the user from having to ensure
strict comparability of starting conformations and having to use
fixed torsional increments, thereby allowing fully flexible characterization
of pharmacophoric patterns.},
doi = {10.1021/ci025595r},
pdf = {../local/Abrahamian2003Efficient.pdf},
file = {Abrahamian2003Efficient.pdf:Abrahamian2003Efficient.pdf:PDF},
owner = {mahe},
pmid = {12653509},
timestamp = {2006.08.22},
url = {http://dx.doi.org/10.1021/ci025595r}
}
@article{Abramovich2006Adapting,
author = {Abramovich, F. and Benjamini, Y. and Donoho, D. L. and Johnstone,
I. M.},
title = {Adapting to unknown sparsity by controlling the false discovery rate},
journal = {Ann. Stat.},
year = {2006},
volume = {34},
pages = {584--653},
number = {2},
doi = {10.1214/009053606000000074},
pdf = {../local/Abramovich2006Adapting.pdf},
file = {Abramovich2006Adapting.pdf:Abramovich2006Adapting.pdf:PDF},
owner = {jp},
timestamp = {2008.12.09},
url = {http://dx.doi.org/10.1214/009053606000000074}
}
@article{Achard2001XML,
author = {F. Achard and G. Vaysseix and E. Barillot},
title = {XML, bioinformatics and data integration.},
journal = {Bioinformatics},
year = {2001},
volume = {17},
pages = {115--125},
number = {2},
month = {Feb},
abstract = {Motivation: The eXtensible Markup Language (XML) is an emerging standard
for structuring documents, notably for the World Wide Web. In this
paper, the authors present XML and examine its use as a data language
for bioinformatics. In particular, XML is compared to other languages,
and some of the potential uses of XML in bioinformatics applications
are presented. The authors propose to adopt XML for data interchange
between databases and other sources of data. Finally the discussion
is illustrated by a test case of a pedigree data model in XML. Contact:
Emmanuel.Barillot@infobiogen.fr},
institution = {CRI Infobiogen, 523 place des terrasses de l'agora, 91000 Evry, France.},
keywords = {Computational Biology; Humans; Information Storage and Retrieval;
Internet; Programming Languages},
language = {eng},
medline-pst = {ppublish},
owner = {phupe},
pmid = {11238067},
timestamp = {2011.06.01}
}
@article{Adie2005Speeding,
author = {Adie, E. A. and Adams, R. R. and Evans, K. L. and Porteous, D. J.
and Pickard, B. S.},
title = {Speeding disease gene discovery by sequence based candidate prioritization},
journal = {BMC Bioinformatics},
year = {2005},
volume = {6},
pages = {55},
abstract = {BACKGROUND: Regions of interest identified through genetic linkage
studies regularly exceed 30 centimorgans in size and can contain
hundreds of genes. Traditionally this number is reduced by matching
functional annotation to knowledge of the disease or phenotype in
question. However, here we show that disease genes share patterns
of sequence-based features that can provide a good basis for automatic
prioritization of candidates by machine learning. RESULTS: We examined
a variety of sequence-based features and found that for many of them
there are significant differences between the sets of genes known
to be involved in human hereditary disease and those not known to
be involved in disease. We have created an automatic classifier called
PROSPECTR based on those features using the alternating decision
tree algorithm which ranks genes in the order of likelihood of involvement
in disease. On average, PROSPECTR enriches lists for disease genes
two-fold 77\% of the time, five-fold 37\% of the time and twenty-fold
11\% of the time. CONCLUSION: PROSPECTR is a simple and effective
way to identify genes involved in Mendelian and oligogenic disorders.
It performs markedly better than the single existing sequence-based
classifier on novel data. PROSPECTR could save investigators looking
at large regions of interest time and effort by prioritizing positional
candidate genes for mutation detection and case-control association
studies.},
doi = {10.1186/1471-2105-6-55},
pdf = {../local/Adie2005Speeding.pdf},
file = {Adie2005Speeding.pdf:Adie2005Speeding.pdf:PDF},
institution = {Medical Genetics Section, Department of Medical Sciences, The University
of Edinburgh, Edinburgh, UK. euan.adie@ed.ac.uk},
owner = {jp},
pii = {1471-2105-6-55},
pmid = {15766383},
timestamp = {2009.03.18},
url = {http://dx.doi.org/10.1186/1471-2105-6-55}
}
@article{Aebersold2003Mass,
author = {Aebersold, R. and Mann, M.},
title = {Mass spectrometry-based proteomics},
journal = {Nature},
year = {2003},
volume = {422},
pages = {198-207},
number = {6928},
month = {Mar},
abstract = {Recent successes illustrate the role of mass spectrometry-based proteomics
as an indispensable tool for molecular and cellular biology and for
the emerging field of systems biology. {T}hese include the study
of protein-protein interactions via affinity-based isolations on
a small and proteome-wide scale, the mapping of numerous organelles,
the concurrent description of the malaria parasite genome and proteome,
and the generation of quantitative protein profiles from diverse
species. {T}he ability of mass spectrometry to identify and, increasingly,
to precisely quantify thousands of proteins from complex samples
can be expected to impact broadly on biology and medicine.},
comment = {A good ref for the detection of protein-protein interactions by coimmunoprecipitation
followed by mass spectrometry},
doi = {10.1038/nature01511},
pdf = {../local/Aebersold2003Mass.pdf},
file = {Aebersold2003Mass.pdf:Aebersold2003Mass.pdf:PDF},
keywords = {bio},
owner = {vert},
url = {http://dx.doi.org/10.1038/nature01511}
}
@article{Aerts2006Gene,
author = {Aerts, S. and Lambrechts, D. and Maity, S. and Van Loo, P. and Coessens,
B. and De Smet, F. and Tranchevent, L.-C. and De Moor, B. and Marynen,
P. and Hassan, B. and Carmeliet, P. and Moreau, Y.},
title = {Gene prioritization through genomic data fusion},
journal = {Nat. Biotechnol.},
year = {2006},
volume = {24},
pages = {537--544},
number = {5},
month = {May},
abstract = {The identification of genes involved in health and disease remains
a challenge. We describe a bioinformatics approach, together with
a freely accessible, interactive and flexible software termed Endeavour,
to prioritize candidate genes underlying biological processes or
diseases, based on their similarity to known genes involved in these
phenomena. Unlike previous approaches, ours generates distinct prioritizations
for multiple heterogeneous data sources, which are then integrated,
or fused, into a global ranking using order statistics. In addition,
it offers the flexibility of including additional data sources. Validation
of our approach revealed it was able to efficiently prioritize 627
genes in disease data sets and 76 genes in biological pathway sets,
identify candidates of 16 mono- or polygenic diseases, and discover
regulatory genes of myeloid differentiation. Furthermore, the approach
identified a novel gene involved in craniofacial development from
a 2-Mb chromosomal region, deleted in some patients with DiGeorge-like
birth defects. The approach described here offers an alternative
integrative method for gene discovery.},
doi = {10.1038/nbt1203},
pdf = {../local/Aerts2006Gene.pdf},
file = {Aerts2006Gene.pdf:Aerts2006Gene.pdf:PDF},
institution = {artment of Human Genetics, Flanders Interuniversity Institute for
Biotechnology (VIB), University of Leuven, Herestraat 49, bus 602,
3000 Leuven, Belgium. stein.aerts@med.kuleuven.be},
owner = {jp},
pii = {nbt1203},
pmid = {16680138},
timestamp = {2009.03.18},
url = {http://dx.doi.org/10.1038/nbt1203}
}
@inproceedings{Agarwal2009Ranking,
author = {Agarwal, S. and Sengupta, S.},
title = {Ranking Genes By Relevance to a Disease},
booktitle = {Proceedings of the 8th Annual International Conference on Computational
Systems Bioinformatics},
year = {2009},
owner = {mordelet},
timestamp = {2010.10.01}
}
@article{Aguda2001Chaos,
author = {Aguda, B. D.},
title = {Kick-starting the cell cycle: From growth-factor stimulation to initiation
of DNA replication},
journal = {Chaos},
year = {2001},
volume = {11},
pages = {269-276},
number = {1},
abstract = {The essential genes, proteins and associated regulatory networks involved
in the entry into the mammalian cell cycle are identified, from activation
of growth-factor receptors to intracellular signal transduction pathways
that impinge on the cell cycle machinery and ultimately on the initiation
of DNA replication. Signaling pathways mediated by the oncoproteins
Ras and Myc induce the activation of cyclin-dependent kinases CDK4
and CDK2, and the assembly and firing of pre-replication complexes
require a collaboration among E2F, CDK2, and Cdc7 kinase. A proposed
core mechanism of the restriction point, the major checkpoint prior
to commitment to DNA synthesis, involves cyclin E/CDK2, the phosphatase
Cdc25A, and the CDK inhibitor p27Kip1. (c) 2001 American Institute
of Physics.}
}
@article{Aguda1999Oncogene,
author = {Aguda, B. D.},
title = {Instabilities in phosphorylation-dephosphorylation cascades and cell
cycle checkpoints},
journal = {Oncogene},
year = {1999},
volume = {18},
pages = {2846-51},
number = {18},
abstract = {The G2-M checkpoint in the cell cycle is identified with a set of
phosphorylation-dephosphorylation (PD) cycles involving Cdc25 and
the maturation-promoting factor (MPF); these PD cycles are coupled
in a way that generates an instability. This instability arises out
of a transcritical bifurcation which could be exploited by the G2
DNA damage checkpoint pathway in order to arrest or delay entry into
mitosis. The coupling between PD cycles involving Wee1 and MPF does
not lead to an instability and therefore Wee1 may not be a crucial
target of the checkpoint pathway. A set of PD cycles exhibiting transcritical
bifurcation also possesses the integrative ability of a checkpoint
for 'checking' that prerequisites are satisfied prior to the next
cell cycle event. Such a set of coupled PD cycles is suggested to
be a core mechanism of cell cycle checkpoints.},
keywords = {csbcbook}
}
@article{Aguda1999PNAS,
author = {Aguda, B. D.},
title = {A quantitative analysis of the kinetics of the G(2) DNA damage checkpoint
system},
journal = {Proc Natl Acad Sci U S A},
year = {1999},
volume = {96},
pages = {11352-7},
number = {20},
abstract = {A detailed model of the G(2) DNA damage checkpoint (G2DDC) system
is presented that includes complex regulatory networks of the mitotic
kinase Cdc2, phosphatase Cdc25, Wee1 kinase, and damage signal transduction
pathways involving Chk1 and p53. Assumptions on the kinetic equations
of the G2DDC are made, and computer simulations are carried out to
demonstrate how the various subsystems operate to delay or arrest
cell cycle progression. The detailed model could be used to explain
various experiments relevant to G2DDC reported recently, including
the nuclear export of 14-3-3-bound Cdc25, the down-regulation of
cyclin B1 expression by p53, the effect of Chk1 and p53 on Cdc25
levels, and Wee1 degradation. It also is shown that, under certain
conditions, p53 is necessary to sustain a G(2) arrest.},
keywords = {csbcbook}
}
@article{Aguda2003CellCycle,
author = {Aguda, B. D. and Algar, C. K.},
title = {A structural analysis of the qualitative networks regulating the
cell cycle and apoptosis},
journal = {Cell Cycle},
year = {2003},
volume = {2},
pages = {538-44},
number = {6},
abstract = {This paper proposes an integration and modular organization of the
complex regulatory networks involved in the mammalian cell cycle,
apoptosis, and related intracellular signaling cascades. A common
node linking the cell cycle and apoptosis permits the possibility
of coordinate control between the initiation of these two cellular
processes. From this node, pathways emanate that lead to the activation
of cyclin-dependent kinases (in the cell cycle) and caspases (in
apoptosis). Computer simulations are carried out to demonstrate that
the proposed network architecture and certain module-module interactions
can account for the experimentally observed sequence of cellular
events (quiescence, cell cycle, and apoptosis) as the transcriptional
activities of E2F-1 and c-Myc are increased. Despite the lack of
quantitative kinetic data on most of the pathways, it is demonstrated
that there can be meaningful conclusions regarding system stability
that arise from the topology of the network. It is shown that only
cycles in the network graph determine stability. Thus, several positive
and negative feedback loops are identified from a literature review
of the major pathways involved in the initiation of the cell cycle
and of apoptosis.},
keywords = {csbcbook}
}
@article{Aguda2007PLOSCompBiol,
author = {Aguda, B. D. and Goryachev, A. B.},
title = {From pathways databases to network models of switching behavior},
journal = {PLoS Comput Biol},
year = {2007},
volume = {3},
pages = {1674-8},
number = {9},
keywords = {csbcbook}
}
@article{Aguda1999CellProl,
author = {Aguda, B. D. and Tang, Y.},
title = {The kinetic origins of the restriction point in the mammalian cell
cycle},
journal = {Cell Prolif},
year = {1999},
volume = {32},
pages = {321-35},
number = {5},
abstract = {A detailed model mechanism for the G1/S transition in the mammalian
cell cycle is presented and analysed by computer simulation to investigate
whether the kinetic origins of the restriction point (R-point) can
be identified. The R-point occurs in mid-to-late G1 phase and marks
the transition between mitogen-dependent to mitogen-independent progression
of the cell cycle. For purposes of computer simulations, the R-point
is defined as the first point in time after mitosis where cutting
off mitogen stimulation does not prevent the cell reaching the threshold
activity of cyclin-E/cdk2 required for entry into S phase. The key
components of the network that generate a dynamic switching behaviour
associated with the R-point include a positive feedback loop between
cyclin-E/cdk2 and Cdc25A, along with the mutually negative interaction
between the cdk inhibitor p27Kip1 and cyclin-E/cdk2. Simulations
of the passage through the R-point were carried out and the factors
affecting the position of the R-point in G1 are determined. The detailed
model also shows various points in the network where the activation
of cyclin-E/cdk2 can be initiated with or without the involvement
of the retinoblastoma protein.},
keywords = {csbcbook}
}
@article{Aguilera2008Genome,
author = {Aguilera, A. and G{\'o}mez-Gonz{\'a}lez, B.},
title = {Genome instability: a mechanistic view of its causes and consequences.},
journal = {Nat. Rev. Genet.},
year = {2008},
volume = {9},
pages = {204--217},
number = {3},
month = {Mar},
abstract = {Genomic instability in the form of mutations and chromosome rearrangements
is usually associated with pathological disorders, and yet it is
also crucial for evolution. Two types of elements have a key role
in instability leading to rearrangements: those that act in trans
to prevent instability--among them are replication, repair and S-phase
checkpoint factors--and those that act in cis--chromosomal hotspots
of instability such as fragile sites and highly transcribed DNA sequences.
Taking these elements as a guide, we review the causes and consequences
of instability with the aim of providing a mechanistic perspective
on the origin of genomic instability.},
doi = {10.1038/nrg2268},
pdf = {../local/Aguilera2008Genome.pdf},
file = {Aguilera2008Genome.pdf:Aguilera2008Genome.pdf:PDF},
institution = {Centro Andaluz de Biologia Molecular y Medicina Regenerativa CABIMER,
Universidad de Sevilla-CSIC, Avd. Américo Vespucio s/n, 41092 Sevilla,
Spain. aguilo@us.es},
keywords = {csbcbook},
owner = {jp},
pii = {nrg2268},
pmid = {18227811},
timestamp = {2009.10.08},
url = {http://dx.doi.org/10.1038/nrg2268}
}
@article{Aires-de-Sousa2005Prediction,
author = {Aires-de-Sousa, J. and Gasteiger, J.},
title = {Prediction of enantiomeric excess in a combinatorial library of catalytic
enantioselective reactions.},
journal = {J {C}omb {C}hem},
year = {2005},
volume = {7},
pages = {298-301},
number = {2},
abstract = {A quantitative structure-enantioselectivity relationship was established
for a combinatorial library of enantioselective reactions performed
by addition of diethyl zinc to benzaldehyde. {C}hiral catalysts and
additives were encoded by their chirality codes and presented as
input to neural networks. {T}he networks were trained to predict
the enantiomeric excess. {W}ith independent test sets, predictions
of enantiomeric excess could be made with an average error as low
as 6\% ee. {M}ultilinear regression, perceptrons, and support vector
machines were also evaluated as modeling tools. {T}he method is of
interest for the computer-aided design of combinatorial libraries
involving chiral compounds or enantioselective reactions. {T}his
is the first example of a quantitative structure-property relationship
based on chirality codes.},
doi = {10.1021/cc049961q},
pdf = {../local/Aires-de-Sousa2005Prediction.pdf},
file = {Aires-de-Sousa2005Prediction.pdf:local/Aires-de-Sousa2005Prediction.pdf:PDF},
keywords = {biosvm chemoinformatics},
url = {http://dx.doi.org/10.1021/cc049961q}
}
@article{Aizerman1964Theoretical,
author = {Aizerman, M. A. and Braverman, E. M. and Rozono{\'e}r, L. I.},
title = {Theoretical foundations of the potential function method in pattern
recognition learning},
journal = {Automation and {R}emote {C}ontrol},
year = {1964},
volume = {25},
pages = {821--837},
owner = {vert},
timestamp = {2006.02.02}
}
@inproceedings{Akaike1973Information,
author = {Hirotogu Akaike},
title = {Information theory and an extension of the maximum likelihood principle},
booktitle = {Proc. of the 2nd Int. Symp. on Information Theory},
year = {1973},
editor = {Petrov B. N. and Csaki F.},
pages = {267--281},
abstract = {The problem of estimating the dimensionality of a model occurs in
various forms in applied statistics. There is estimating the number
of factor in factor analysis, estimating the degree of a polynomial
describing the data, selecting the variables to},
keywords = {conf, Akaike Information Criterion, criteria, AIC, model, modelling,
parameters, complexity, overfitting, c1973, c197x, c19xx}
}
@article{Akutsu2000Algorithms,
author = {T. Akutsu and S. Miyano and S. Kuhara},
title = {{A}lgorithms for identifying {B}oolean networks and related biological
networks based on matrix multiplication and fingerprint function},
journal = {J. Comput. Biol.},
year = {2000},
volume = {7},
pages = {331--343},
number = {3-4},
abstract = {Due to the recent progress of the DNA microarray technology, a large
number of gene expression profile data are being produced. How to
analyze gene expression data is an important topic in computational
molecular biology. Several studies have been done using the Boolean
network as a model of a genetic network. This paper proposes efficient
algorithms for identifying Boolean networks of bounded indegree and
related biological networks, where identification of a Boolean network
can be formalized as a problem of identifying many Boolean functions
simultaneously. For the identification of a Boolean network, an O(mnD+1)
time naive algorithm and a simple O (mnD) time algorithm are known,
where n denotes the number of nodes, m denotes the number of examples,
and D denotes the maximum in degree. This paper presents an improved
O(momega-2nD + mnD+omega-3) time Monte-Carlo type randomized algorithm,
where omega is the exponent of matrix multiplication (currently,
omega < 2.376). The algorithm is obtained by combining fast matrix
multiplication with the randomized fingerprint function for string
matching. Although the algorithm and its analysis are simple, the
result is nontrivial and the technique can be applied to several
related problems.},
doi = {10.1089/106652700750050817},
pdf = {../local/Akutsu2000Algorithms.pdf},
file = {Akutsu2000Algorithms.pdf:Akutsu2000Algorithms.pdf:PDF},
pmid = {11108466},
timestamp = {2008.02.04},
url = {http://dx.doi.org/10.1089/106652700750050817}
}
@article{Akutsu2000Inferring,
author = {T. Akutsu and S. Miyano and S. Kuhara},
title = {Inferring qualitative relations in genetic networks and metabolic
pathways},
journal = {Bioinformatics},
year = {2000},
volume = {16},
pages = {727--734},
number = {8},
pdf = {../local/Akutsu2000Inferring.pdf},
file = {Akutsu2000Inferring.pdf:local/Akutsu2000Inferring.pdf:PDF},
subject = {bionet},
url = {http://bioinformatics.oxfordjournals.org/cgi/content/abstract/16/8/727}
}
@article{Ala2008Prediction,
author = {Ala, U. and Piro, R.M. and Grassi, E. and Damasco, C. and Silengo,
L. and Oti, M. and Provero, P. and Di Cunto, F.},
title = {Prediction of human disease genes by human-mouse conserved coexpression
analysis.},
journal = {PLoS Comput. Biol.},
year = {2008},
volume = {4},
pages = {e1000043},
number = {3},
month = {Mar},
abstract = {BACKGROUND: Even in the post-genomic era, the identification of candidate
genes within loci associated with human genetic diseases is a very
demanding task, because the critical region may typically contain
hundreds of positional candidates. Since genes implicated in similar
phenotypes tend to share very similar expression profiles, high throughput
gene expression data may represent a very important resource to identify
the best candidates for sequencing. However, so far, gene coexpression
has not been used very successfully to prioritize positional candidates.
METHODOLOGY/PRINCIPAL FINDINGS: We show that it is possible to reliably
identify disease-relevant relationships among genes from massive
microarray datasets by concentrating only on genes sharing similar
expression profiles in both human and mouse. Moreover, we show systematically
that the integration of human-mouse conserved coexpression with a
phenotype similarity map allows the efficient identification of disease
genes in large genomic regions. Finally, using this approach on 850
OMIM loci characterized by an unknown molecular basis, we propose
high-probability candidates for 81 genetic diseases. CONCLUSION:
Our results demonstrate that conserved coexpression, even at the
human-mouse phylogenetic distance, represents a very strong criterion
to predict disease-relevant relationships among human genes.},
doi = {10.1371/journal.pcbi.1000043},
institution = {Molecular Biotechnology Center, Department of Genetics, Biology and
Biochemistry, University of Turin, Turin, Italy.},
keywords = {Algorithms; Animals; Biological Markers; Chromosome Mapping; Conserved
Sequence; Diagnosis, Computer-Assisted; Gene Expression Profiling;
Genetic Diseases, Inborn; Genetic Predisposition to Disease; Humans;
Mice; Proteome},
owner = {mordelet},
pmid = {18369433},
timestamp = {2010.09.28},
url = {http://dx.doi.org/10.1371/journal.pcbi.1000043}
}
@article{Albeck2008PLOSBiol,
author = {Albeck, John G. and Burke, John M. and Spencer, Sabrina L. and Lauffenburger,
Douglas A. and Sorger, Peter K.},
title = {Modeling a Snap-Action, Variable-Delay Switch Controlling Extrinsic
Cell Death},
journal = {PLoS Biol},
year = {2008},
volume = {6},
pages = {e299},
number = {12},
abstract = {A combination of single-cell experiments and mathematical modeling
reveals the mechanisms underlying all-or-none caspase activation
during receptor-induced apoptosis.}
}
@article{Albert2008SCBM,
author = {Albert, I. and Thakar, J. and Li, S. and Zhang, R. and Albert, R.},
title = {Boolean network simulations for life scientists},
journal = {Source Code Biol Med},
year = {2008},
volume = {3},
pages = {16},
abstract = {ABSTRACT: Modern life sciences research increasingly relies on computational
solutions, from large scale data analyses to theoretical modeling.
Within the theoretical models Boolean networks occupy an increasing
role as they are eminently suited at mapping biological observations
and hypotheses into a mathematical formalism. The conceptual underpinnings
of Boolean modeling are very accessible even without a background
in quantitative sciences, yet it allows life scientists to describe
and explore a wide range of surprisingly complex phenomena. In this
paper we provide a clear overview of the concepts used in Boolean
simulations, present a software library that can perform these simulations
based on simple text inputs and give three case studies. The large
scale simulations in these case studies demonstrate the Boolean paradigms
and their applicability as well as the advanced features and complex
use cases that our software package allows. Our software is distributed
via a liberal Open Source license and is freely accessible from http://booleannet.googlecode.com.}
}
@article{Albert2002Statistical,
author = {Albert, R. and Barab{\'a}si, A.L.},
title = {Statistical mechanics of complex networks},
journal = {Rev. {M}od. {P}hys.},
year = {2002},
volume = {74},
pages = {47--97},
pdf = {../local/albe02.pdf},
file = {albe02.pdf:local/albe02.pdf:PDF},
subject = {compnet},
url = {http://www.nd.edu/~networks/PDF/rmp.pdf}
}
@article{Albert2000Attack,
author = {Albert, R. and Jeong, H. and Barab{\'a}si, A.-L.},
title = {Attack and error tolerance in complex networks},
journal = {Nature},
year = {2000},
volume = {406},
pages = {378--381},
pdf = {../local/albe00.pdf},
file = {albe00.pdf:local/albe00.pdf:PDF},
subject = {compnet},
url = {http://www.nd.edu/~networks/Papers/nature_attack.pdf}
}
@article{Albert1999Diameter,
author = {Albert, R. and Jeong, H. and Barab{\'a}si, A.-L.},
title = {Diameter of the {W}orld-{W}ide {W}eb},
journal = {Nature},
year = {1999},
volume = {401},
pages = {130--131},
pdf = {../local/albe99.pdf},
file = {albe99.pdf:local/albe99.pdf:PDF},
subject = {compnet},
url = {http://www.nd.edu/~networks/Papers/401130A0.pdf}
}
@book{Alberts2002Molecular,
title = {Molecular Biology of the Cell},
publisher = {Garland Science, Taylor \& Francis Group, LLC},
year = {2002},
author = {Alberts, B. and Johnson, A. and Lewis, J. and Raff, M. and Roberts,
K. and Walter, P.},
note = {Fourth Edition},
annote = {Fourth Edition},
keywords = {csbcbook}
}
@article{Albertson2003Chromosome,
author = {Albertson, D. G. and Collins, C. and McCormick, F. and Gray, J. W.},
title = {Chromosome aberrations in solid tumors},
journal = {Nat. Genet.},
year = {2003},
volume = {34},
pages = {369--376},
number = {4},
month = {Aug},
abstract = {Chromosome aberrations in human solid tumors are hallmarks of gene
deregulation and genome instability. This review summarizes current
knowledge regarding aberrations, discusses their functional importance,
suggests mechanisms by which aberrations may form during cancer progression
and provides examples of clinical advances that have come from studies
of chromosome aberrations.},
doi = {10.1038/ng1215},
pdf = {../local/Albertson2003Chromosome.pdf},
file = {Albertson2003Chromosome.pdf:Albertson2003Chromosome.pdf:PDF},
institution = {Cancer Research Institute, University of California San Francisco,
San Francisco, California 94143-0808, USA. albertson@cc.ucsf.edu},
keywords = {csbcbook},
owner = {jp},
pii = {ng1215},
pmid = {12923544},
timestamp = {2009.10.08},
url = {http://dx.doi.org/10.1038/ng1215}
}
@article{Albertson2003Genomic,
author = {Albertson, D. G. and Pinkel, D.},
title = {Genomic microarrays in human genetic disease and cancer},
journal = {Hum. Mol. Genet.},
year = {2003},
volume = {12 Spec No 2},
pages = {R145--R152},
month = {Oct},
abstract = {Alterations in the genome that lead to changes in DNA sequence copy
number are a characteristic of solid tumors and are found in association
with developmental abnormalities and/or mental retardation. Comparative
genomic hybridization (CGH) can be used to detect and map these changes.
Recent improvements in the resolution and sensitivity of CGH have
been possible through implementation of microarray-based CGH (array
CGH). Here we discuss the performance characteristics of different
array platforms and review some of the recent applications of array
CGH in cancer and medical genetics.},
doi = {10.1093/hmg/ddg261},
pdf = {../local/Albertson2003Genomic.pdf},
file = {Albertson2003Genomic.pdf:Albertson2003Genomic.pdf:PDF},
institution = {Department of Laboratory Medicine, University of California San Francisco,
San Francisco, CA 94143-0808,USA. albertson@cc.ucsf.edu},
keywords = {csbcbook},
owner = {jp},
pii = {ddg261},
pmid = {12915456},
timestamp = {2009.10.08},
url = {http://dx.doi.org/10.1093/hmg/ddg261}
}
@inproceedings{Aldea2007Image,
author = {Aldea, E. and Atif, J. and Bloch, I.},
title = {Image classification using marginalized kernels for graphs},
booktitle = {Graph-Based Representations in Pattern Recognition},
year = {2007},
volume = {4538/2007},
series = {Lecture Notes in Computer Science},
pages = {103--113},
publisher = {Springer Berlin / Heidelberg},
abstract = {We propose in this article an image classification technique based
on kernel methods and graphs. Our work explores the possibility of
applying marginalized kernels to image processing. In machine learning,
performant algorithms have been developed for data organized as real
valued arrays; these algorithms are used for various purposes like
classification or regression. However, they are inappropriate for
direct use on complex data sets. Our work consists of two distinct
parts. In the first one we model the images by graphs to be able
to represent their structural properties and inherent attributes.
In the second one, we use kernel functions to project the graphs
in a mathematical space that allows the use of performant classification
algorithms. Experiments are performed on medical images acquired
with various modalities and concerning different parts of the body.},
doi = {10.1007/978-3-540-72903-7_10},
pdf = {../local/Aldea2007Image.pdf},
file = {Aldea2007Image.pdf:local/Aldea2007Image.pdf:PDF},
keywords = {image},
timestamp = {2008.07.29},
url = {http://dx.doi.org/10.1007/978-3-540-72903-7_10}
}
@article{Alexandersson2003SLAM,
author = {Alexandersson, M. and Cawley, S. and Pachter, L.},
title = {S{LAM}: cross-species gene finding and alignment with a generalized
pair hidden {M}arkov model.},
journal = {Genome {R}es.},
year = {2003},
volume = {13},
pages = {496--502},
number = {3},
month = {Mar},
abstract = {Comparative-based gene recognition is driven by the principle that
conserved regions between related organisms are more likely than
divergent regions to be coding. {W}e describe a probabilistic framework
for gene structure and alignment that can be used to simultaneously
find both the gene structure and alignment of two syntenic genomic
regions. {A} key feature of the method is the ability to enhance
gene predictions by finding the best alignment between two syntenic
sequences, while at the same time finding biologically meaningful
alignments that preserve the correspondence between coding exons.
{O}ur probabilistic framework is the generalized pair hidden {M}arkov
model, a hybrid of (1). generalized hidden {M}arkov models, which
have been used previously for gene finding, and (2). pair hidden
{M}arkov models, which have applications to sequence alignment. {W}e
have built a gene finding and alignment program called {SLAM}, which
aligns and identifies complete exon/intron structures of genes in
two related but unannotated sequences of {DNA}. {SLAM} is able to
reliably predict gene structures for any suitably related pair of
organisms, most notably with fewer false-positive predictions compared
to previous methods (examples are provided for {H}omo sapiens/{M}us
musculus and {P}lasmodium falciparum/{P}lasmodium vivax comparisons).
{A}ccuracy is obtained by distinguishing conserved noncoding sequence
({CNS}) from conserved coding sequence. {CNS} annotation is a novel
feature of {SLAM} and may be useful for the annotation of {UTR}s,
regulatory elements, and other noncoding features.},
doi = {10.1101/gr.424203},
pdf = {../local/Alexandersson2003SLAM.pdf},
file = {Alexandersson2003SLAM.pdf:local/Alexandersson2003SLAM.pdf:PDF},
keywords = {biogm},
owner = {vert},
pmid = {12618381},
timestamp = {2006.01.18},
url = {http://dx.doi.org/10.1101/gr.424203}
}
@article{Algoet1994strong,
author = {Algoet, P.H.},
title = {The strong law of large numbers for sequential decisions under uncertainty},
journal = {I{EEE} {T}rans. {I}nform. {T}heory},
year = {1994},
volume = {40},
pages = {609-633},
number = {3},
month = {May},
abstract = {Combines optimization and ergodic theory to characterize the optimum
long-run average performance that can be asymptotically attained
by nonanticipating sequential decisions. {L}et {{X}t} be a stationary
ergodic process, and suppose an action bt must be selected in a space
&{B}scr; with knowledge of the t-past ({X}0, ···, {X}t-1) at the
beginning of every period t⩾0. {A}ction bt will incur a loss
l(bt, {X}t) at the end of period t when the random variable {X}t
is revealed. {T}he author proves under mild integrability conditions
that the optimum strategy is to select actions that minimize the
conditional expected loss given the currently available information
at each step. {T}he minimum long-run average loss per decision can
be approached arbitrarily closely by strategies that are finite-order
{M}arkov, and under certain continuity conditions, it is equal to
the minimum expected loss given the infinite past. {I}f the loss
l(b, x) is bounded and continuous and if the space &{B}scr; is compact,
then the minimum can be asymptotically attained, even if the distribution
of the process {{X}t} is unknown a priori and must be learned from
experience },
doi = {10.1109/18.335876},
pdf = {../local/Algoet1994strong.pdf},
file = {Algoet1994strong.pdf:local/Algoet1994strong.pdf:PDF},
keywords = {information-theory},
owner = {vert},
url = {http://dx.doi.org/10.1109/18.335876}
}
@inproceedings{Aliferis2002Machine,
author = {Aliferis, C.F. and Hardin, D.P. and Massion, P.},
title = {Machine {L}earning {M}odels {F}or {L}ung {C}ancer {C}lassification
{U}sing {A}rray {C}omparative {G}enomic {H}ybridization},
booktitle = {Proceedings of the 2002 {A}merican {M}edical {I}nformatics {A}ssociation
({AMIA}) {A}nnual {S}ymposium},
year = {2002},
pages = {7-11},
abstract = {Array {CGH} is a recently introduced technology that measures changes
in the gene copy number of hundreds of genes in a single experiment.
{T}he primary goal of this study was to develop machine learning
models that classify non-small {L}ung {C}ancers according to histopathology
types and to compare several machine learning methods in this learning
task. {DNA} from tumors of 37 patients (21 squamous carcinomas, and
16 adenocarcinomas) were extracted and hybridized onto a 452 {BAC}
clone array. {T}he following algorithms were used: {KNN}, {D}ecision
{T}ree {I}nduction, {S}upport {V}ector {M}achines and {F}eed-{F}orward
{N}eural {N}etworks. {P}erformance was measured via leave-one-out
classification accuracy. {T}he best multi-gene model found had a
leave-one-out accuracy of 89.2\%. {D}ecision {T}rees performed poorer
than the other methods in this learning task and dataset. {W}e conclude
that gene copy numbers as measured by array {CGH} are, collectively,
an excellent indicator of histological subtype. {S}everal interesting
research directions are discussed.},
pdf = {../local/Aliferis2002Machine.pdf},
file = {Aliferis2002Machine.pdf:local/Aliferis2002Machine.pdf:PDF},
keywords = {biosvm microarray, cgh},
owner = {jeanphilippevert}
}
@article{Alizadeh2000Distinct,
author = {Alizadeh, A. A. and Eisen, M. B. and Davis, R. E. and Ma, C. and
Lossos, I. S. and Rosenwald, A. and Boldrick, J. C. and Sabet, H.
and Tran, T. and Yu, X. and Powell, J. I. and Yang, L. and Marti,
G. E. and Moore, T. and Hudson, J. and Lu, L. and Lewis, D. B. and
Tibshirani, R. and Sherlock, G. and Chan, W. C. and Greiner, T. C.
and Weisenburger, D. D. and Armitage, J. O. and Warnke, R. and Levy,
R. and Wilson, W. and Grever, M. R. and Byrd, J. C. and Botstein,
D. and Brown, P. O. and Staudt, L. M.},
title = {Distinct types of diffuse large {B}-cell lymphoma identified by gene
expression profiling},
journal = {Nature},
year = {2000},
volume = {403},
pages = {503--511},
number = {6769},
month = {Feb},
abstract = {Diffuse large B-cell lymphoma (DLBCL), the most common subtype of
non-Hodgkin's lymphoma, is clinically heterogeneous: 40\% of patients
respond well to current therapy and have prolonged survival, whereas
the remainder succumb to the disease. We proposed that this variability
in natural history reflects unrecognized molecular heterogeneity
in the tumours. Using DNA microarrays, we have conducted a systematic
characterization of gene expression in B-cell malignancies. Here
we show that there is diversity in gene expression among the tumours
of DLBCL patients, apparently reflecting the variation in tumour
proliferation rate, host response and differentiation state of the
tumour. We identified two molecularly distinct forms of DLBCL which
had gene expression patterns indicative of different stages of B-cell
differentiation. One type expressed genes characteristic of germinal
centre B cells ('germinal centre B-like DLBCL'); the second type
expressed genes normally induced during in vitro activation of peripheral
blood B cells ('activated B-like DLBCL'). Patients with germinal
centre B-like DLBCL had a significantly better overall survival than
those with activated B-like DLBCL. The molecular classification of
tumours on the basis of gene expression can thus identify previously
undetected and clinically significant subtypes of cancer.},
doi = {10.1038/35000501},
pdf = {../local/Alizadeh2000Distinct.pdf},
file = {Alizadeh2000Distinct.pdf:local/Alizadeh2000Distinct.pdf:PDF},
institution = {Department of Biochemistry, Stanford University School of Medicine,
California 94305, USA.},
keywords = {csbcbook},
owner = {jp},
pmid = {10676951},
timestamp = {2008.11.15},
url = {http://dx.doi.org/10.1038/35000501}
}
@article{Alkan2009Personalized,
author = {Can Alkan and Jeffrey M Kidd and Tomas Marques-Bonet and Gozde Aksay
and Francesca Antonacci and Fereydoun Hormozdiari and Jacob O Kitzman
and Carl Baker and Maika Malig and Onur Mutlu and S. Cenk Sahinalp
and Richard A Gibbs and Evan E Eichler},
title = {Personalized copy number and segmental duplication maps using next-generation
sequencing.},
journal = {Nat. Genet.},
year = {2009},
volume = {41},
pages = {1061--1067},
number = {10},
month = {Oct},
abstract = {Despite their importance in gene innovation and phenotypic variation,
duplicated regions have remained largely intractable owing to difficulties
in accurately resolving their structure, copy number and sequence
content. We present an algorithm (mrFAST) to comprehensively map
next-generation sequence reads, which allows for the prediction of
absolute copy-number variation of duplicated segments and genes.
We examine three human genomes and experimentally validate genome-wide
copy number differences. We estimate that, on average, 73-87 genes
vary in copy number between any two individuals and find that these
genic differences overwhelmingly correspond to segmental duplications
(odds ratio = 135; P < 2.2 x 10(-16)). Our method can distinguish
between different copies of highly identical genes, providing a more
accurate assessment of gene content and insight into functional constraint
without the limitations of array-based technology.},
doi = {10.1038/ng.437},
pdf = {../local/Alkan2009Personalized.pdf},
file = {Alkan2009Personalized.pdf:Alkan2009Personalized.pdf:PDF},
institution = {Department of Genome Sciences, University of Washington School of
Medicine, Seattle, Washington, USA.},
keywords = {ngs},
owner = {jp},
pii = {ng.437},
pmid = {19718026},
timestamp = {2009.10.09},
url = {http://dx.doi.org/10.1038/ng.437}
}
@book{Allgoweri1990Numerical,
title = {Numerical continuation methods},
publisher = {Springer},
year = {1990},
author = {E.L. Allgower and K.Georg},
isbn = {3-540-12760-7}
}
@article{Almohamad1993linear,
author = {Almohamad, H.A. and Duffuaa, S.O.},
title = {A linear programming approach for the weighted graph matching problem},
journal = {IEEE Trans. Pattern Anal. Mach. Intell.},
year = {1993},
volume = {15},
pages = {522--525},
number = {5},
month = {May },
doi = {10.1109/34.211474},
pdf = {../local/Almohamad1993linear.pdf},
file = {Almohamad1993linear.pdf:Almohamad1993linear.pdf:PDF},
owner = {jp},
timestamp = {2008.10.05},
url = {http://dx.doi.org/10.1109/34.211474}
}
@article{Alon1998Finding,
author = {Alon, N. and Krivelevich, M. and Sudakov, B.},
title = {Finding a large hidden clique in a random graph},
journal = {Random Struct. Algorithm.},
year = {1998},
volume = {13},
pages = {457--466},
doi = {10.1002/(SICI)1098-2418(199810/12)13:3/4<457::AID-RSA14>3.0.CO;2-W},
pdf = {../local/Alon1998Finding.pdf},
file = {Alon1998Finding.pdf:Alon1998Finding.pdf:PDF},
owner = {jp},
timestamp = {2013.01.07},
url = {http://dx.doi.org/10.1002/(SICI)1098-2418(199810/12)13:3/4<457::AID-RSA14>3.0.CO;2-W}
}
@article{Alon2007Network,
author = {Alon, U.},
title = {Network motifs: theory and experimental approaches.},
journal = {Nat Rev Genet},
year = {2007},
volume = {8},
pages = {450--461},
number = {6},
month = {Jun},
abstract = {Transcription regulation networks control the expression of genes.
The transcription networks of well-studied microorganisms appear
to be made up of a small set of recurring regulation patterns, called
network motifs. The same network motifs have recently been found
in diverse organisms from bacteria to humans, suggesting that they
serve as basic building blocks of transcription networks. Here I
review network motifs and their functions, with an emphasis on experimental
studies. Network motifs in other biological networks are also mentioned,
including signalling and neuronal networks.},
doi = {10.1038/nrg2102},
pdf = {../local/Alon2007Network.pdf},
file = {Alon2007Network.pdf:Alon2007Network.pdf:PDF},
institution = {Department of Molecular Cell Biology, Weizmann Institute of Science,
Rehovot 76100, Israel. urialon@weizmann.ac.il},
language = {eng},
medline-pst = {ppublish},
owner = {Andrei Zinovyev},
pii = {nrg2102},
pmid = {17510665},
timestamp = {2011.04.08},
url = {http://dx.doi.org/10.1038/nrg2102}
}
@article{Alter2000Singular,
author = {Alter, O. and Brown, P. O. and Botstein, D.},
title = {Singular value decomposition for genome-wide expression data processing
and modeling.},
journal = {Proc Natl Acad Sci U S A},
year = {2000},
volume = {97},
pages = {10101--10106},
number = {18},
month = {Aug},
abstract = {We describe the use of singular value decomposition in transforming
genome-wide expression data from genes x arrays space to reduced
diagonalized "eigengenes" x "eigenarrays" space, where the eigengenes
(or eigenarrays) are unique orthonormal superpositions of the genes
(or arrays). Normalizing the data by filtering out the eigengenes
(and eigenarrays) that are inferred to represent noise or experimental
artifacts enables meaningful comparison of the expression of different
genes across different arrays in different experiments. Sorting the
data according to the eigengenes and eigenarrays gives a global picture
of the dynamics of gene expression, in which individual genes and
arrays appear to be classified into groups of similar regulation
and function, or similar cellular state and biological phenotype,
respectively. After normalization and sorting, the significant eigengenes
and eigenarrays can be associated with observed genome-wide effects
of regulators, or with measured samples, in which these regulators
are overactive or underactive, respectively.},
doi = {10.1073/pnas.97.18.10101},
pdf = {../local/Alter2000Singular.pdf},
file = {Alter2000Singular.pdf:Alter2000Singular.pdf:PDF},
institution = {Departments of Genetics and Biochemistry, Stanford University, Stanford,
CA 94305, USA. orly@genome.stanford.edu},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {97/18/10101},
pmid = {10963673},
timestamp = {2011.12.30},
url = {http://dx.doi.org/10.1073/pnas.97.18.10101}
}
@article{Altschul1990basic,
author = {S.F. Altschul and W.~Gish and W.~Miller and E.W. Myers and D.J. Lipman},
title = {A basic local alignment search tool},
journal = {J. {M}ol. {B}iol.},
year = {1990},
volume = {215},
pages = {403--410}
}
@article{Altschul1997Gapped,
author = {S.F. Altschul and T.L. Madden and A.A. Schaffer and J. Zhang and
Z. Zhang and W. Miller and D.J. Lipman},
title = {Gapped {BLAST} and {PSI}-{BLAST}: {A} new generation of protein database
search programs},
journal = {Nucleic {A}cids {R}esearch},
year = {1997},
volume = {25},
pages = {3389--3402},
pdf = {../local/alts97.pdf},
file = {alts97.pdf:local/alts97.pdf:PDF},
subject = {biocasp},
url = {http://nar.oupjournals.org/cgi/reprint/25/17/3389.pdf}
}
@inproceedings{Altun2003Large,
author = {Altun, Y. and Hofmann, T.},
title = {Large {M}argin {M}ethods for {L}abel {S}equence {L}earning},
booktitle = { 8th {E}uropean {C}onference on {S}peech {C}ommunication and {T}echnology
({E}uro{S}peech)},
year = {2003},
abstract = {Label sequence learning is the problem of inferring a state sequence
from an observation sequence, where the state sequence may encode
a labeling, annotation or segmentation of the sequence. {I}n this
paper we give an overview of discriminative methods developed for
this problem. {S}pecial emphasis is put on large margin methods by
generalizing multiclass {S}upport {V}ector {M}achines and {A}da{B}oost
to the case of label sequences.{A}n experimental evaluation demonstrates
the advantages over classical approaches like {H}idden {M}arkov {M}odels
and the competitiveness with methods like {C}onditional {R}andom
{F}ields.},
pdf = {../local/Altun2003Large.pdf},
file = {Altun2003Large.pdf:local/Altun2003Large.pdf:PDF},
keywords = {conditional-random-field},
owner = {vert}
}
@inproceedings{Altun2004Gaussian,
author = {Altun, Y. and Hofmann, T. and Smola, A.J.},
title = {Gaussian process classification for segmenting and annotating sequences},
booktitle = {Twenty-first international conference on {M}achine learning},
year = {2004},
publisher = {ACM Press},
abstract = {Many real-world classification tasks involve the prediction of multiple,
inter-dependent class labels. {A} prototypical case of this sort
deals with prediction of a sequence of labels for a sequence of observations.
{S}uch problems arise naturally in the context of annotating and
segmenting observation sequences. {T}his paper generalizes {G}aussian
{P}rocess classification to predict multiple labels by taking dependencies
between neighboring labels into account. {O}ur approach is motivated
by the desire to retain rigorous probabilistic semantics, while overcoming
limitations of parametric methods like {C}onditional {R}andom {F}ields,
which exhibit conceptual and computational difficulties in high-dimensional
input spaces. {E}xperiments on named entity recognition and pitch
accent prediction tasks demonstrate the competitiveness of our approach.},
doi = {10.1145/1015330.1015433},
pdf = {../local/Altun2004Gaussian.pdf},
file = {Altun2004Gaussian.pdf:local/Altun2004Gaussian.pdf:PDF},
isbn = {1-58113-828-5},
keywords = {conditional-random-field},
location = {Banff, Alberta, Canada},
url = {http://doi.acm.org/10.1145/1015330.1015433}
}
@inproceedings{Altun2004Exponential,
author = {Altun, Y. and Smola, A. and Hofmann, T.},
title = {Exponential {F}amilies for {C}onditional {R}andom {F}ields},
booktitle = {20th {C}onference on {U}ncertainty in {A}rtificial {I}ntelligennce},
year = {2004},
pdf = {../local/Altun2004Exponential.pdf},
file = {Altun2004Exponential.pdf:local/Altun2004Exponential.pdf:PDF},
keywords = {conditional-random-field},
owner = {vert}
}
@article{Amaral2000Classes,
author = {L. A. N. Amaral and A. Scala and M. Barth{\'e}l{\'e}my and H. E.
Stanley },
title = {Classes of small-world networks},
journal = {Proc. {N}atl. {A}cad. {S}ci. {USA}},
year = {2000},
volume = {97},
pages = {11149--11152},
number = {21},
pdf = {../local/amar00.pdf},
file = {amar00.pdf:local/amar00.pdf:PDF},
subject = {compnet},
url = {http://www.pnas.org/cgi/content/full/97/21/11149}
}
@article{Amari2001Information,
author = {Amari, S.-I.},
title = {Information geometry on hierarchy of probability distributions},
journal = {I{EEE} {T}rans. {I}nform. {T}heory},
year = {2001},
volume = {47},
pages = {1701--1711},
number = {5},
month = {July},
pdf = {../local/amar01.pdf},
file = {amar01.pdf:local/amar01.pdf:PDF},
subject = {stat},
url = {http://www.islab.brain.riken.go.jp/~amari/pub/IGHI.ps.gz}
}
@article{Amari1998Natural,
author = {Amari, S.-I.},
title = {Natural {G}radient {W}orks {E}fficiently in {L}earning},
journal = {Neural {C}omputation},
year = {1998},
volume = {10},
pages = {251-276},
number = {2},
pdf = {../local/amar98.pdf},
file = {amar98.pdf:local/amar98.pdf:PDF},
subject = {ml},
url = {http://www.islab.brain.riken.go.jp/amari/pub/am45-2.ps.gz}
}
@book{Amari2001Methods,
title = {Methods of information geometry},
publisher = {AMS vol. 191},
year = {2001},
author = {Amari, S.-I. and Nagaoka, H.}
}
@article{Amari1999Improving,
author = {Amari, S.-I. and Wu, S.},
title = {Improving support vector machine classifiers by modifying kernel
functions},
journal = {Neural {N}etworks},
year = {1999},
volume = {12},
pages = {783--789},
number = {6},
month = {Jul},
abstract = {We propose a method of modifying a kernel function to improve the
performance of a support vector machine classifier. {T}his is based
on the structure of the {R}iemannian geometry induced by the kernel
function. {T}he idea is to enlarge the spatial resolution around
the separating boundary surface, by a conformal mapping, such that
the separability between classes is increased. {E}xamples are given
specifically for modifying {G}aussian {R}adial {B}asis {F}unction
kernels. {S}imulation results for both artificial and real data show
remarkable improvement of generalization errors, supporting our idea.},
pdf = {../local/amar99.pdf},
file = {amar99.pdf:local/amar99.pdf:PDF},
subject = {kernel},
url = {http://www.islab.brain.riken.go.jp/wusi/GKSVM.ps}
}
@article{Amarzguioui2004algorithm,
author = {Amarzguioui, M. and Prydz, H.},
title = {An algorithm for selection of functional si{RNA} sequences.},
journal = {Biochem. {B}iophys. {R}es. {C}ommun.},
year = {2004},
volume = {316},
pages = {1050-8},
number = {4},
month = {Apr},
abstract = {Randomly designed si{RNA} targeting different positions within the
same m{RNA} display widely differing activities. {W}e have performed
a statistical analysis of 46 si{RNA}, identifying various features
of the 19bp duplex that correlate significantly with functionality
at the 70\% knockdown level and verified these results against an
independent data set of 34 si{RNA} recently reported by others. {F}eatures
that consistently correlated positively with functionality across
the two data sets included an asymmetry in the stability of the duplex
ends (measured as the {A}/{U} differential of the three terminal
basepairs at either end of the duplex) and the motifs {S}1, {A}6,
and {W}19. {T}he presence of the motifs {U}1 or {G}19 was associated
with lack of functionality. {A} selection algorithm based on these
findings strongly differentiated between the two functional groups
of si{RNA} in both data sets and proved highly effective when used
to design si{RNA} targeting new endogenous human genes.},
doi = {10.1016/j.bbrc.2004.02.157},
keywords = {sirna},
pii = {S0006291X04004425},
url = {http://dx.doi.org/10.1016/j.bbrc.2004.02.157}
}
@article{Amato2006multi-step,
author = {R. Amato and A. Ciaramella and N. Deniskina and C. Del Mondo and
D. di Bernardo and C. Donalek and G. Longo and G. Mangano and G.
Miele and G. Raiconi and A. Staiano and R. Tagliaferri},
title = {A multi-step approach to time series analysis and gene expression
clustering.},
journal = {Bioinformatics},
year = {2006},
volume = {22},
pages = {589--596},
number = {5},
month = {Mar},
abstract = {MOTIVATION: The huge growth in gene expression data calls for the
implementation of automatic tools for data processing and interpretation.
RESULTS: We present a new and comprehensive machine learning data
mining framework consisting in a non-linear PCA neural network for
feature extraction, and probabilistic principal surfaces combined
with an agglomerative approach based on Negentropy aimed at clustering
gene microarray data. The method, which provides a user-friendly
visualization interface, can work on noisy data with missing points
and represents an automatic procedure to get, with no a priori assumptions,
the number of clusters present in the data. Cell-cycle dataset and
a detailed analysis confirm the biological nature of the most significant
clusters. AVAILABILITY: The software described here is a subpackage
part of the ASTRONEURAL package and is available upon request from
the corresponding author. SUPPLEMENTARY INFORMATION: Supplementary
data are available at Bioinformatics online.},
doi = {10.1093/bioinformatics/btk026},
institution = {Dipartimento di Scienze Fisiche, University of Naples Federico II,
Naples, Italy.},
owner = {fantine},
pii = {btk026},
pmid = {16397005},
timestamp = {2010.10.21},
url = {http://dx.doi.org/10.1093/bioinformatics/btk026}
}
@inproceedings{Ambauen2003Graph,
author = {R. Ambauen and S. Fischer and H. Bunke},
title = {Graph Edit Distance with Node Splitting and Merging, and Its Application
to Diatom Idenfication},
booktitle = {GbRPR},
year = {2003},
pages = {95-106},
bibsource = {DBLP, http://dblp.uni-trier.de},
ee = {http://springerlink.metapress.com/openurl.asp?genre=article{\&}issn=0302-9743{\&}volume=2726{\&}spage=95}
}
@article{Ambroise2002Selection,
author = {Ambroise, C. and McLachlan, G.J.},
title = {Selection bias in gene extraction on the basis of microarray gene-expression
data},
journal = {Proc. {N}atl. {A}cad. {S}ci. {USA}},
year = {2002},
volume = {99},
pages = {6562-6566},
number = {10},
abstract = {In the context of cancer diagnosis and treatment, we consider the
problem of constructing an accurate prediction rule on the basis
of a relatively small number of tumor tissue samples of known type
containing the expression data on very many (possibly thousands)
genes. {R}ecently, results have been presented in the literature
suggesting that it is possible to construct a prediction rule from
only a few genes such that it has a negligible prediction error rate.
{H}owever, in these results the test error or the leave-one-out cross-validated
error is calculated without allowance for the selection bias. {T}here
is no allowance because the rule is either tested on tissue samples
that were used in the first instance to select the genes being used
in the rule or because the cross-validation of the rule is not external
to the selection process; that is, gene selection is not performed
in training the rule at each stage of the cross-validation process.
{W}e describe how in practice the selection bias can be assessed
and corrected for by either performing a cross-validation or applying
the bootstrap external to the selection process. {W}e recommend using
10-fold rather than leave-one-out cross-validation, and concerning
the bootstrap, we suggest using the so-called .632+ bootstrap error
estimate designed to handle overfitted prediction rules. {U}sing
two published data sets, we demonstrate that when correction is made
for the selection bias, the cross-validated error is no longer zero
for a subset of only a few genes.},
pdf = {../local/Ambroise2002Selection.pdf},
file = {Ambroise2002Selection.pdf:local/Ambroise2002Selection.pdf:PDF},
keywords = {featureselection biosvm},
owner = {jeanphilippevert},
url = {http://www.pnas.org/cgi/content/abstract/99/10/6562}
}
@article{Ameres2007Molecular,
author = {Stefan Ludwig Ameres and Javier Martinez and Renée Schroeder},
title = {Molecular basis for target RNA recognition and cleavage by human
RISC.},
journal = {Cell},
year = {2007},
volume = {130},
pages = {101--112},
number = {1},
month = {Jul},
abstract = {The RNA-Induced Silencing Complex (RISC) is a ribonucleoprotein particle
composed of a single-stranded short interfering RNA (siRNA) and an
endonucleolytically active Argonaute protein, capable of cleaving
mRNAs complementary to the siRNA. The mechanism by which RISC cleaves
a target RNA is well understood, however it remains enigmatic how
RISC finds its target RNA. Here, we show, both in vitro and in vivo,
that the accessibility of the target site correlates directly with
the efficiency of cleavage, demonstrating that RISC is unable to
unfold structured RNA. In the course of target recognition, RISC
transiently contacts single-stranded RNA nonspecifically and promotes
siRNA-target RNA annealing. Furthermore, the 5' part of the siRNA
within RISC creates a thermodynamic threshold that determines the
stable association of RISC and the target RNA. We therefore provide
mechanistic insights by revealing features of RISC and target RNAs
that are crucial to achieve efficiency and specificity in RNA interference.},
doi = {10.1016/j.cell.2007.04.037},
pdf = {../local/Ameres2007Molecular.pdf},
file = {Ameres2007Molecular.pdf:Ameres2007Molecular.pdf:PDF},
institution = {Max F. Perutz Laboratories, University of Vienna, Vienna, Austria.},
keywords = {sirna},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {S0092-8674(07)00583-1},
pmid = {17632058},
timestamp = {2009.10.28},
url = {http://dx.doi.org/10.1016/j.cell.2007.04.037}
}
@inproceedings{Amit2007Uncovering,
author = {Yonatan Amit and Michael Fink and Nathan Srebro and Shimon Ullman},
title = {Uncovering shared structures in multiclass classification},
booktitle = {ICML '07: Proceedings of the 24th international conference on Machine
learning},
year = {2007},
pages = {17--24},
address = {New York, NY, USA},
publisher = {ACM},
doi = {http://doi.acm.org/10.1145/1273496.1273499},
isbn = {978-1-59593-793-3},
location = {Corvalis, Oregon}
}
@article{Anderson2003new,
author = {Anderson, D.C. and Li, W. and Payan, D.G. and Noble, W.S.},
title = {A new algorithm for the evaluation of shotgun peptide sequencing
in proteomics: support vector machine classification of peptide {{MS}/{MS}}
spectra and {SEQUEST} scores.},
journal = {J {P}roteome {R}es},
year = {2003},
volume = {2},
pages = {137-146},
number = {2},
abstract = {Shotgun tandem mass spectrometry-based peptide sequencing using programs
such as {SEQUEST} allows high-throughput identification of peptides,
which in turn allows the identification of corresponding proteins.
{W}e have applied a machine learning algorithm, called the support
vector machine, to discriminate between correctly and incorrectly
identified peptides using {SEQUEST} output. {E}ach peptide was characterized
by {SEQUEST}-calculated features such as delta {C}n and {X}corr,
measurements such as precursor ion current and mass, and additional
calculated parameters such as the fraction of matched {MS}/{MS} peaks.
{T}he trained {SVM} classifier performed significantly better than
previous cutoff-based methods at separating positive from negative
peptides. {P}ositive and negative peptides were more readily distinguished
in training set data acquired on a {QTOF}, compared to an ion trap
mass spectrometer. {T}he use of 13 features, including four new parameters,
significantly improved the separation between positive and negative
peptides. {U}se of the support vector machine and these additional
parameters resulted in a more accurate interpretation of peptide
{MS}/{MS} spectra and is an important step toward automated interpretation
of peptide tandem mass spectrometry data in proteomics.},
pdf = {../local/Anderson2003new.pdf},
file = {Anderson2003new.pdf:local/Anderson2003new.pdf:PDF},
keywords = {biosvm proteomics},
owner = {jeanphilippevert}
}
@article{Andersson2007Multivariate,
author = {C. D. Andersson and E. Thysell and A. Lindstr{\"o}m and M. Bylesj{\"o}
and F. Raubacher and A. Linusson},
title = {A multivariate approach to investigate docking parameters' effects
on docking performance.},
journal = {J. Chem. Inform. Model.},
year = {2007},
volume = {47},
pages = {1673--1687},
number = {4},
abstract = {Increasingly powerful docking programs for analyzing and estimating
the strength of protein-ligand interactions have been developed in
recent decades, and they are now valuable tools in drug discovery.
Software used to perform dockings relies on a number of parameters
that affect various steps in the docking procedure. However, identifying
the best choices of the settings for these parameters is often challenging.
Therefore, the settings of the parameters are quite often left at
their default values, even though scientists with long experience
with a specific docking tool know that modifying certain parameters
can improve the results. In the study presented here, we have used
statistical experimental design and subsequent regression based on
root-mean-square deviation values using partial least-square projections
to latent structures (PLS) to scrutinize the effects of different
parameters on the docking performance of two software packages: FRED
and GOLD. Protein-ligand complexes with a high level of ligand diversity
were selected from the PDBbind database for the study, using principal
component analysis based on 1D and 2D descriptors, and space-filling
design. The PLS models showed quantitative relationships between
the docking parameters and the ability of the programs to reproduce
the ligand crystallographic conformation. The PLS models also revealed
which of the parameters and what parameter settings were important
for the docking performance of the two programs. Furthermore, the
variation in docking results obtained with specific parameter settings
for different protein-ligand complexes in the diverse set examined
indicates that there is great potential for optimizing the parameter
settings for selected sets of proteins.},
doi = {10.1021/ci6005596},
institution = {Department of Chemistry, Umeå University, SE-901 87 Umeå, Sweden.},
owner = {bricehoffmann},
pmid = {17559207},
timestamp = {2009.02.13},
url = {http://dx.doi.org/10.1021/ci6005596}
}
@article{Ando2005A,
author = {Rie Kubota Ando and Tong Zhang and Peter Bartlett},
title = {A framework for learning predictive structures from multiple tasks
and unlabeled data},
journal = {Journal of Machine Learning Research},
year = {2005},
volume = {6},
pages = {1817--1853}
}
@article{Andrea1991Applications,
author = {T. A. Andrea and H. Kalayeh},
title = {{A}pplications of neural networks in quantitative structure-activity
relationships of dihydrofolate reductase inhibitors.},
journal = {J. Med. Chem.},
year = {1991},
volume = {34},
pages = {2824--2836},
number = {9},
month = {Sep},
abstract = {Back propagation neural networks is a new technology useful for modeling
nonlinear functions of several variables. This paper explores their
applications in the field of quantitative structure-activity relationships.
In particular, their ability to fit biological activity surfaces,
predict activity, and determine the "functional forms" of its dependence
on physical properties is compared to well-established methods in
the field. A dataset of 256 5-phenyl-3,4-diamino-6,6-dimethyldihydrotriazines
that inhibit dihydrofolate reductase enzyme is used as a basis for
comparison. It is found that neural networks lead to enhanced surface
fits and predictions relative to standard regression methods. Moreover,
they circumvent the need for ad hoc indicator variables, which account
for a significant part of the variance in linear regression models.
Additionally, they lead to the elucidation of nonlinear and "cross-products"
effects that correspond to trade-offs between physical properties
in their effect on biological activity. This is the first demonstration
of the latter two findings. On the other hand, due to the complexity
of the resulting models, an understanding of the local, but not the
global, structure-activity relationships is possible. The latter
must await further developments. Furthermore, the longer computational
time required to train the networks is somewhat inconveniencing,
although not restrictive.},
keywords = {Animals, Carcinoma 256, Cultured, Experimental, Folic Acid Antagonists,
Leukemia, Neural Pathways, Neurons, Regression Analysis, Structure-Activity
Relationship, Tumor Cells, Walker, 1895302},
owner = {mahe},
pmid = {1895302},
timestamp = {2006.09.06}
}
@inproceedings{Andrews2002Multiple,
author = {Andrews, S. and Hofmann, T. and Tsochantaridis, I.},
title = {Multiple {I}nstance {L}earning with {G}eneralized {S}upport {V}ector
{M}achines},
booktitle = {Proceedings of the {E}ighteenth {N}ational {C}onference on {A}rtificial
{I}ntelligence},
year = {2002},
pages = {943-944},
publisher = {American Association for Artificial Intelligence},
keywords = {kernel-theory},
owner = {mahe},
timestamp = {2006.08.09}
}
@article{Anguita2003Quantum,
author = {Davide Anguita and Sandro Ridella and Fabio Rivieccio and Rodolfo
Zunino},
title = {Quantum optimization for training support vector machines.},
journal = {Neural {N}etw.},
year = {2003},
volume = {16},
pages = {763-70},
number = {5-6},
abstract = {Refined concepts, such as {R}ademacher estimates of model complexity
and nonlinear criteria for weighting empirical classification errors,
represent recent and promising approaches to characterize the generalization
ability of {S}upport {V}ector {M}achines ({SVM}s). {T}he advantages
of those techniques lie in both improving the {SVM} representation
ability and yielding tighter generalization bounds. {O}n the other
hand, they often make {Q}uadratic-{P}rogramming algorithms no longer
applicable, and {SVM} training cannot benefit from efficient, specialized
optimization techniques. {T}he paper considers the application of
{Q}uantum {C}omputing to solve the problem of effective {SVM} training,
especially in the case of digital implementations. {T}he presented
research compares the behavioral aspects of conventional and enhanced
{SVM}s; experiments in both a synthetic and real-world problems support
the theoretical analysis. {A}t the same time, the related differences
between {Q}uadratic-{P}rogramming and {Q}uantum-based optimization
techniques are considered.},
doi = {10.1016/S0893-6080(03)00087-X},
pdf = {../local/Anguita2003Quantum.pdf},
file = {Anguita2003Quantum.pdf:local/Anguita2003Quantum.pdf:PDF},
pii = {S089360800300087X},
url = {http://dx.doi.org/10.1016/S0893-6080(03)00087-X}
}
@article{Anstreicheir2001new,
author = {K. M. Anstreicher and N. W. Brixius},
title = {A New Bound for the Quadratic Assignment Problem Based on Convex
Quadratic Programming},
journal = {Math. Program.},
year = {2001},
volume = {89},
pages = {341--357},
number = {3}
}
@article{Antes2006DynaPred,
author = {Iris Antes and Shirley W I Siu and Thomas Lengauer},
title = {{D}yna{P}red: a structure and sequence based method for the prediction
of {MHC} class {I} binding peptide sequences and conformations.},
journal = {Bioinformatics},
year = {2006},
volume = {22},
pages = {e16--e24},
number = {14},
month = {Jul},
abstract = {MOTIVATION: The binding of endogenous antigenic peptides to MHC class
I molecules is an important step during the immunologic response
of a host against a pathogen. Thus, various sequence- and structure-based
prediction methods have been proposed for this purpose. The sequence-based
methods are computationally efficient, but are hampered by the need
of sufficient experimental data and do not provide a structural interpretation
of their results. The structural methods are data-independent, but
are quite time-consuming and thus not suited for screening of whole
genomes. Here, we present a new method, which performs sequence-based
prediction by incorporating information obtained from molecular modeling.
This allows us to perform large databases screening and to provide
structural information of the results. RESULTS: We developed a SVM-trained,
quantitative matrix-based method for the prediction of MHC class
I binding peptides, in which the features of the scoring matrix are
energy terms retrieved from molecular dynamics simulations. At the
same time we used the equilibrated structures obtained from the same
simulations in a simple and efficient docking procedure. Our method
consists of two steps: First, we predict potential binders from sequence
data alone and second, we construct protein-peptide complexes for
the predicted binders. So far, we tested our approach on the HLA-A0201
allele. We constructed two prediction models, using local, position-dependent
(DynaPred(POS)) and global, position-independent (DynaPred) features.
The former model outperformed the two sequence-based methods used
in our evaluation; the latter shows a much higher generalizability
towards other alleles than the position-dependent models. The constructed
peptide structures can be refined within seconds to structures with
an average backbone RMSD of 1.53 A from the corresponding experimental
structures.},
doi = {10.1093/bioinformatics/btl216},
keywords = {immunoinformatics},
pii = {22/14/e16},
pmid = {16873467},
timestamp = {2007.01.25},
url = {http://dx.doi.org/10.1093/bioinformatics/btl216}
}
@article{Aoki2005score,
author = {Aoki, K. F. and Mamitsuka, H. and Akutsu, T. and Kanehisa, M.},
title = {A score matrix to reveal the hidden links in glycans},
journal = {Bioinformatics},
year = {2005},
volume = {21},
pages = {1457-63},
number = {8},
month = {Apr},
abstract = {M{OTIVATION}: {G}lycans are the third major class of biomolecules
following {DNA} and proteins. {T}hey are extremely vital for the
functioning of multicellular organisms. {H}owever, comparing the
fast development of sequence analysis techniques, informatics work
on glycans have a long way to go. {A}lignment algorithms for glycan
tree structures are one of the foremost concerns. {I}n addition,
the statistical analysis of these algorithms in terms of biological
significance needs to be addressed. {RESULTS}: {W}e developed a tree-structure
alignment algorithm for glycans and performed a statistical analysis
of these alignment scores such that biologically interesting features
could be captured into a score matrix for glycans. {W}e generated
our score matrix in a manner similar to {BLOSUM}, but with slight
variations to accomodate our glycan data, including the incorporation
of linkage information. {W}e verified the effectiveness of our new
glycan score matrix by illustrating how well the resulting score
matrix entries correspond with biological knowledge. {F}uture work
for even better improvements with the use of a variety of score matrices
for different subclasses of glycans due to their complexity is also
discussed. {CONTACT}: mami@kuicr.kyoto-u.ac.jp {SUPPLEMENTARY} {INFORMATION}:
{T}he glycan score matrix can be downloaded from http://kanehisa.kuicr.kyoto-u.ac.jp/{P}aper/kcam/glycan{M}atrix0.1.txt.},
doi = {10.1093/bioinformatics/bti193},
keywords = {glycans},
pii = {bti193},
url = {http://dx.doi.org/10.1093/bioinformatics/bti193}
}
@article{Aoki2004KCaM,
author = {Aoki, K. F. and Yamaguchi, A. and Ueda, N. and Akutsu, T. and Mamitsuka,
H. and Goto, S. and Kanehisa, M.},
title = {K{C}a{M} ({KEGG} {C}arbohydrate {M}atcher): a software tool for analyzing
the structures of carbohydrate sugar chains.},
journal = {Nucleic {A}cids {R}es.},
year = {2004},
volume = {32},
pages = {W267-72},
number = {Web Server issue},
month = {Jul},
abstract = {K{C}a{M} ({KEGG} {C}arbohydrate {M}atcher) is a tool for the analysis
of carbohydrate sugar chains, or glycans. {I}t consists of a web-based
graphical user interface that allows users to enter glycans easily
with the mouse. {T}he glycan structure is then transformed into our
{KCF} ({KEGG} {C}hemical {F}unction) file format and sent to our
program which implements an efficient tree-structure alignment algorithm,
similar to sequence alignment algorithms but for branched tree structures.
{U}sers can also retrieve glycan tree structures in {KCF} format
from their local computers for visualization over the web. {T}he
tree-matching algorithm provides several options for performing different
types of tree-matching procedures on glycans. {T}hese options consist
of whether to incorporate gaps in a match, whether to take the linkage
information into consideration and local versus global alignment.
{T}he results of this program are returned as a list of glycan structures
in order of similarity based on these options. {T}he actual alignment
can be viewed graphically, and the annotation information can also
be viewed easily since all this information is linked with {KEGG}'s
comprehensive suite of genomic data. {A}nalogously to {BLAST}, users
are thus able to compare glycan structures of interest with glycans
from different glycan databases using a variety of tree-alignment
options. {KC}a{M} is currently available at http://glycan.genome.ad.jp.},
doi = {10.1093/nar/gkh473},
keywords = {glycans},
pii = {32/suppl_2/W267},
url = {http://dx.doi.org/10.1093/nar/gkh473}
}
@article{Aouba2007Les,
author = {Aouba, A. and P{\'e}quignot, F. and Le Toullec, A. and Jougla, E.},
title = {Les causes médicales de d{\'e}c{\`e}s en {France} en 2004 et leur
évolution 1980-2004},
journal = {Bulletin {\'e}pid{\'e}miologique hebdomadaire},
year = {2007},
volume = {35-36},
pages = {308--314},
pdf = {../local/Aouba2007Les.pdf},
file = {Aouba2007Les.pdf:Aouba2007Les.pdf:PDF},
keywords = {csbcbook},
url = {http://www.invs.sante.fr/beh/2007/35_36/beh_35_36_2007.pdf}
}
@article{Aoyama1990Neural,
author = {T. Aoyama and Y. Suzuki and H. Ichikawa},
title = {{N}eural networks applied to quantitative structure-activity relationship
analysis.},
journal = {J. Med. Chem.},
year = {1990},
volume = {33},
pages = {2583--2590},
number = {9},
month = {Sep},
abstract = {An application of the neural network to quantitative structure-activity
relationship (QSAR) analysis has been studied. The new method was
compared with the linear multiregression analysis in various ways.
It was found that the neural network can be a potential tool in the
routine work of QSAR analysis. The mathematical relationship of operation
between the neural network and the multiregression analysis was described.
It was shown that the neural network can exceed the level of the
linear multiregression analysis.},
keywords = {Animals, Azirines, Benzodiazepines, Carbazilquinone, Comparative Study,
Nerve Net, Nervous System, Regression Analysis, Structure-Activity
Relationship, 2202830},
owner = {mahe},
pmid = {2202830},
timestamp = {2006.09.07}
}
@article{Aphinyanaphongs2005Text,
author = {Yindalon Aphinyanaphongs and Ioannis Tsamardinos and Alexander Statnikov
and Douglas Hardin and Constantin F Aliferis},
title = {Text categorization models for high-quality article retrieval in
internal medicine.},
journal = {J. {A}m. {M}ed. {I}nform. {A}ssoc.},
year = {2005},
volume = {12},
pages = {207-16},
number = {2},
abstract = {O{BJECTIVE} {F}inding the best scientific evidence that applies to
a patient problem is becoming exceedingly difficult due to the exponential
growth of medical publications. {T}he objective of this study was
to apply machine learning techniques to automatically identify high-quality,
content-specific articles for one time period in internal medicine
and compare their performance with previous {B}oolean-based {P}ub{M}ed
clinical query filters of {H}aynes et al. {DESIGN} {T}he selection
criteria of the {ACP} {J}ournal {C}lub for articles in internal medicine
were the basis for identifying high-quality articles in the areas
of etiology, prognosis, diagnosis, and treatment. {N}aive {B}ayes,
a specialized {A}da{B}oost algorithm, and linear and polynomial support
vector machines were applied to identify these articles. {MEASUREMENTS}
{T}he machine learning models were compared in each category with
each other and with the clinical query filters using area under the
receiver operating characteristic curves, 11-point average recall
precision, and a sensitivity/specificity match method. {RESULTS}
{I}n most categories, the data-induced models have better or comparable
sensitivity, specificity, and precision than the clinical query filters.
{T}he polynomial support vector machine models perform the best among
all learning methods in ranking the articles as evaluated by area
under the receiver operating curve and 11-point average recall precision.
{CONCLUSION} {T}his research shows that, using machine learning methods,
it is possible to automatically build models for retrieving high-quality,
content-specific articles using inclusion or citation by the {ACP}
{J}ournal {C}lub as a gold standard in a given time period in internal
medicine that perform better than the 1994 {P}ub{M}ed clinical query
filters.},
doi = {10.1197/jamia.M1641},
pdf = {../local/Aphinyanaphongs2005Text.pdf},
file = {Aphinyanaphongs2005Text.pdf:local/Aphinyanaphongs2005Text.pdf:PDF},
keywords = {biosvm nlp},
pii = {M1641},
url = {http://dx.doi.org/10.1197/jamia.M1641}
}
@book{Applegate06Traveling,
title = {The Traveling Salesman Problem: A Computational Study (Princeton
Series in Applied Mathematics)},
publisher = {Princeton University Press},
year = {2007},
author = {Applegate, D. L. and Bixby, R. E. and Chvatal, V. and Cook, W. J.
},
month = {January},
abstract = {This book presents the latest findings on one of the most intensely
investigated subjects in computational mathematics--the traveling
salesman problem. It sounds simple enough: given a set of cities
and the cost of travel between each pair of them, the problem challenges
you to find the cheapest route by which to visit all the cities and
return home to where you began. Though seemingly modest, this exercise
has inspired studies by mathematicians, chemists, and physicists.
Teachers use it in the classroom. It has practical applications in
genetics, telecommunications, and neuroscience.
The authors of this book are the same pioneers who for nearly two
decades have led the investigation into the traveling salesman problem.
They have derived solutions to almost eighty-six thousand cities,
yet a general solution to the problem has yet to be discovered. Here
they describe the method and computer code they used to solve a broad
range of large-scale problems, and along the way they demonstrate
the interplay of applied mathematics with increasingly powerful computing
platforms. They also give the fascinating history of the problem--how
it developed, and why it continues to intrigue us.},
citeulike-article-id = {3991575},
howpublished = {Hardcover},
isbn = {0691129932},
posted-at = {2009-02-01 17:26:20},
priority = {2}
}
@misc{Concorde:website,
author = {Applegate, D. L. and Bixby, R. E. and Chvatal, V. and Cook, W. J.
},
title = {Concorde TSP solver},
howpublished = {\url{http://www.tsp.gatech.edu/concorde.html}},
year = {2005}
}
@article{Arakawa2003Application,
author = {M. Arakawa and K. Hasegawa and K. Funatsu},
title = {{A}pplication of the novel molecular alignment method using the {H}opfield
{N}eural {N}etwork to 3{D}-{QSAR}.},
journal = {J Chem Inf Comput Sci},
year = {2003},
volume = {43},
pages = {1396--1402},
number = {5},
abstract = {Recently, we investigated and proposed the novel molecular alignment
method with the Hopfield Neural Network (HNN). Molecules are represented
by four kinds of chemical properties (hydrophobic group, hydrogen-bonding
acceptor, hydrogen-bonding donor, and hydrogen-bonding donor/acceptor),
and then those properties between two molecules correspond to each
other using HNN. The 12 pairs of enzyme-inhibitors were used for
validation, and our method could successfully reproduce the real
molecular alignments obtained from X-ray crystallography. In this
paper, we apply the molecular alignment method to three-dimensional
quantitative structure-activity relationship (3D-QSAR) analysis.
The two data sets (human epidermal growth factor receptor-2 inhibitors
and cyclooxygenase-2 inhibitors) were investigated to validate our
method. As a result, the robust and predictive 3D-QSAR models were
successfully obtained in both data sets.},
doi = {10.1021/ci030005q},
keywords = {Chemical, Cyclooxygenase 2, Cyclooxygenase 2 Inhibitors, Cyclooxygenase
Inhibitors, Databases, Enzyme Inhibitors, Epidermal Growth Factor,
Factual, Humans, Isoenzymes, Membrane Proteins, Models, Molecular,
Neural Networks (Computer), Prostaglandin-Endoperoxide Synthases,
Quantitative Structure-Activity Relationship, Receptor, 14502472},
owner = {mahe},
pmid = {14502472},
timestamp = {2006.08.22},
url = {http://dx.doi.org/10.1021/ci030005q}
}
@article{Aranda2010IntAct,
author = {B. Aranda and P. Achuthan and Y. Alam-Faruque and I. Armean and A.
Bridge and C. Derow and M. Feuermann and A. T. Ghanbarian and S.
Kerrien and J. Khadake and J. Kerssemakers and C. Leroy and M. Menden
and M. Michaut and L. Montecchi-Palazzi and S. N. Neuhauser and S.
Orchard and V. Perreau and B. Roechert and K. van Eijk and H. Hermjakob},
title = {The IntAct molecular interaction database in 2010.},
journal = {Nucleic Acids Res},
year = {2010},
volume = {38},
pages = {D525--D531},
number = {Database issue},
month = {Jan},
abstract = {IntAct is an open-source, open data molecular interaction database
and toolkit. Data is abstracted from the literature or from direct
data depositions by expert curators following a deep annotation model
providing a high level of detail. As of September 2009, IntAct contains
over 200.000 curated binary interaction evidences. In response to
the growing data volume and user requests, IntAct now provides a
two-tiered view of the interaction data. The search interface allows
the user to iteratively develop complex queries, exploiting the detailed
annotation with hierarchical controlled vocabularies. Results are
provided at any stage in a simplified, tabular view. Specialized
views then allows 'zooming in' on the full annotation of interactions,
interactors and their properties. IntAct source code and data are
freely available at http://www.ebi.ac.uk/intact.},
doi = {10.1093/nar/gkp878},
institution = {EMBL Outstation, European Bioinformatics Institute, Wellcome Trust
Genome Campus Hinxton, Cambridge CB10 1SD, UK.},
keywords = {Animals; Computational Biology; Databases, Genetic; Databases, Protein;
False Positive Reactions; Humans; Information Storage and Retrieval;
Internet; Programming Languages; Protein Interaction Mapping; Protein
Structure, Tertiary; Proteins; Software; User-Computer Interface;
Vocabulary, Controlled},
owner = {fantine},
pii = {gkp878},
pmid = {19850723},
timestamp = {2010.10.21},
url = {http://dx.doi.org/10.1093/nar/gkp878}
}
@inproceedings{Argyriou2007Multi-task,
author = {Argyriou, A. and Evgeniou, T. and Pontil, M.},
title = {Multi-task feature learning},
booktitle = {Adv. Neural. Inform. Process Syst. 19},
year = {2007},
editor = {Sch\"{o}lkopf, B. and Platt, J. and Hoffman, T.},
pages = {41--48},
address = {Cambridge, MA},
publisher = {MIT Press},
timestamp = {2007.10.22}
}
@article{Argyriou2008Convex,
author = {Argyriou, A. and Evgeniou, T. and Pontil, M.},
title = {Convex multi-task feature learning},
journal = {Mach. Learn.},
year = {2008},
volume = {73},
pages = {243--272},
number = {3},
note = {To appear.},
owner = {jp},
timestamp = {2008.07.09}
}
@article{Argyriou2012Sparse,
author = {Argyriou, A. and Foygel, R. and Srebro, N.},
title = {Sparse Prediction with the k-Overlap Norm},
journal = {arXiv preprint arXiv:1204.5043},
year = {2012}
}
@article{Argyriou2008When,
author = {Andreas Argyriou and Charles A. Micchelli and Massimiliano Pontil},
title = {When is there a representer theorem? Vector versus matrix regularizers},
journal = {CoRR},
year = {2008},
volume = {abs/0809.1590},
bibsource = {DBLP, http://dblp.uni-trier.de},
ee = {http://arxiv.org/abs/0809.1590}
}
@incollection{Argyriou2008A,
author = {Andreas Argyriou and Charles A. Micchelli and Massimiliano Pontil
and Yiming Ying},
title = {A Spectral Regularization Framework for Multi-Task Structure Learning},
booktitle = {Advances in Neural Information Processing Systems 20},
publisher = {MIT Press},
year = {2008},
editor = {J.C. Platt and D. Koller and Y. Singer and S. Roweis},
pages = {25--32},
address = {Cambridge, MA}
}
@article{Arimoto2005Development,
author = {Rieko Arimoto and Madhu-Ashni Prasad and Eric M Gifford},
title = {Development of {CYP}3{A}4 inhibition models: comparisons of machine-learning
techniques and molecular descriptors.},
journal = {J {B}iomol {S}creen},
year = {2005},
volume = {10},
pages = {197-205},
number = {3},
month = {Apr},
abstract = {Computational models of cytochrome {P}450 3{A}4 inhibition were developed
based on high-throughput screening data for 4470 proprietary compounds.
{M}ultiple models differentiating inhibitors ({IC}(50) <3 micro{M})
and noninhibitors were generated using various machine-learning algorithms
(recursive partitioning [{RP}], {B}ayesian classifier, logistic regression,
k-nearest-neighbor, and support vector machine [{SVM}]) with structural
fingerprints and topological indices. {N}ineteen models were evaluated
by internal 10-fold cross-validation and also by an independent test
set. {T}hree most predictive models, {B}arnard {C}hemical {I}nformation
({BCI})-fingerprint/{SVM}, {MDL}-keyset/{SVM}, and topological indices/{RP},
correctly classified 249, 248, and 236 compounds of 291 noninhibitors
and 135, 137, and 147 compounds of 179 inhibitors in the validation
set. {T}heir overall accuracies were 82\%, 82\%, and 81\%, respectively.
{I}nvestigating applicability of the {BCI}/{SVM} model found a strong
correlation between the predictive performance and the structural
similarity to the training set. {U}sing {T}animoto similarity index
as a confidence measurement for the predictions, the limitation of
the extrapolation was 0.7 in the case of the {BCI}/{SVM} model. {T}aking
consensus of the 3 best models yielded a further improvement in predictive
capability, kappa = 0.65 and accuracy = 83\%. {T}he consensus model
could also be tuned to minimize either false positives or false negatives
depending on the emphasis of the screening.},
doi = {10.1177/1087057104274091},
keywords = {biosvm chemoinformatics},
pii = {10/3/197},
url = {http://dx.doi.org/10.1177/1087057104274091}
}
@article{Arkin1997test,
author = {A. Arkin and P. Shen and J. Ross},
title = {A Test Case of Correlation Metric Construction of a Reaction Pathway
from Measurements},
journal = {Science},
year = {1997},
volume = {277},
pages = {1275--1279},
number = {5330},
abstract = {A method for the prediction of the interactions within complex reaction
networks from experimentally measured time series of the concentration
of the species composing the system has been tested experimentally
on the first few steps of the glycolytic pathway. The reconstituted
reaction system, containing eight enzymes and 14 metabolic intermediates,
was kept away from equilibrium in a continuous-flow, stirred-tank
reactor. Input concentrations of adenosine monophosphate and citrate
were externally varied over time, and their concentrations in the
reactor and the response of eight other species were measured. Multidimensional
scaling analysis and heuristic algorithms applied to two-species
time-lagged correlation functions derived from the time series yielded
a diagram from which the interactions among all of the species could
be deduced. The diagram predicts essential features of the known
reaction network in regard to chemical reactions and interactions
among the measured species. The approach is applicable to many complex
reaction systems.},
keywords = {reconstruction, kinetic, metabolism, analysis, multidimensional scaling,
Correlation Metric Construction },
url = {http://www.sciencemag.org/cgi/reprint/277/5330/1275.pdf}
}
@misc{Armstrong1999review,
author = {J. W. Armstrong},
title = {A review of high-throughput screening approaches for drug discovery},
howpublished = {Application note},
year = {1999},
owner = {mahe},
timestamp = {2006.09.05}
}
@article{Arodz2005Pattern,
author = {Tomasz Arod{\'z} and Marcin Kurdziel and Erik O D Sevre and David
A Yuen},
title = {Pattern recognition techniques for automatic detection of suspicious-looking
anomalies in mammograms.},
journal = {Comput. {M}ethods {P}rograms {B}iomed.},
year = {2005},
volume = {79},
pages = {135-49},
number = {2},
month = {Aug},
abstract = {We have employed two pattern recognition methods used commonly for
face recognition in order to analyse digital mammograms. {T}he methods
are based on novel classification schemes, the {A}da{B}oost and the
support vector machines ({SVM}). {A} number of tests have been carried
out to evaluate the accuracy of these two algorithms under different
circumstances. {R}esults for the {A}da{B}oost classifier method are
promising, especially for classifying mass-type lesions. {I}n the
best case the algorithm achieved accuracy of 76\% for all lesion
types and 90\% for masses only. {T}he {SVM} based algorithm did not
perform as well. {I}n order to achieve a higher accuracy for this
method, we should choose image features that are better suited for
analysing digital mammograms than the currently used ones.},
doi = {10.1016/j.cmpb.2005.03.009},
pdf = {../local/Arodz2005Pattern.pdf},
file = {Arodz2005Pattern.pdf:local/Arodz2005Pattern.pdf:PDF},
keywords = {biosvm image},
pii = {S0169-2607(05)00083-0},
url = {http://dx.doi.org/10.1016/j.cmpb.2005.03.009}
}
@article{Aronov2005Predictive,
author = {Aronov, A. M.},
title = {{P}redictive in silico modeling for h{ERG} channel blockers.},
journal = {Drug Discov. Today},
year = {2005},
volume = {10},
pages = {149--155},
number = {2},
month = {Jan},
abstract = {hERG-mediated sudden death as a side effect of non-antiarrhythmic
drugs has been receiving increased regulatory attention. Perhaps
owing to the unique shape of the ligand-binding site and its hydrophobic
character, the hERG channel has been shown to interact with pharmaceuticals
of widely varying structure. Several in silico approaches have attempted
to predict hERG channel blockade. Some of these approaches are aimed
primarily at filtering out potential hERG blockers in the context
of virtual libraries, others involve understanding structure-activity
relationships governing hERG-drug interactions. This review summarizes
the most recent efforts in this emerging field.},
doi = {10.1016/S1359-6446(04)03278-7},
pdf = {../local/Aronov2005Predictive.pdf},
file = {Aronov2005Predictive.pdf:Aronov2005Predictive.pdf:PDF},
keywords = {chemoinformatics herg},
pii = {S1359644604032787},
pmid = {15718164},
timestamp = {2007.02.03},
url = {http://dx.doi.org/10.1016/S1359-6446(04)03278-7}
}
@article{Aronszajn1950Theory,
author = {Aronszajn, N.},
title = {Theory of reproducing kernels},
journal = {Trans. {A}m. {M}ath. {S}oc.},
year = {1950},
volume = {68},
pages = {337~-~404},
pdf = {../local/Aronszajn1950Theory.pdf},
file = {Aronszajn1950Theory.pdf:local/Aronszajn1950Theory.pdf:PDF},
keywords = {kernel-theory},
subject = {kernelml}
}
@article{Asefa2005Support,
author = {Tirusew Asefa and Mariush Kemblowski and Gilberto Urroz and Mac McKee},
title = {Support vector machines ({SVM}s) for monitoring network design.},
journal = {Ground {W}ater},
year = {2005},
volume = {43},
pages = {413-22},
number = {3},
abstract = {In this paper we present a hydrologic application of a new statistical
learning methodology called support vector machines ({SVM}s). {SVM}s
are based on minimization of a bound on the generalized error (risk)
model, rather than just the mean square error over a training set.
{D}ue to {M}ercer's conditions on the kernels, the corresponding
optimization problems are convex and hence have no local minima.
{I}n this paper, {SVM}s are illustratively used to reproduce the
behavior of {M}onte {C}arlo-based flow and transport models that
are in turn used in the design of a ground water contamination detection
monitoring system. {T}he traditional approach, which is based on
solving transient transport equations for each new configuration
of a conductivity field, is too time consuming in practical applications.
{T}hus, there is a need to capture the behavior of the transport
phenomenon in random media in a relatively simple manner. {T}he objective
of the exercise is to maximize the probability of detecting contaminants
that exceed some regulatory standard before they reach a compliance
boundary, while minimizing cost (i.e., number of monitoring wells).
{A}pplication of the method at a generic site showed a rather promising
performance, which leads us to believe that {SVM}s could be successfully
employed in other areas of hydrology. {T}he {SVM} was trained using
510 monitoring configuration samples generated from 200 {M}onte {C}arlo
flow and transport realizations. {T}he best configurations of well
networks selected by the {SVM} were identical with the ones obtained
from the physical model, but the reliabilities provided by the respective
networks differ slightly.},
doi = {10.1111/j.1745-6584.2005.0050.x},
pdf = {../local/Asefa2005Support.pdf},
file = {Asefa2005Support.pdf:local/Asefa2005Support.pdf:PDF},
keywords = {Adult, Aged, Aging, Algorithms, Apoptosis, Artificial Intelligence,
Automated, Computer-Assisted, Female, Foot, Gait, Gene Expression
Profiling, Humans, Image Interpretation, Male, Neoplasms, Non-U.S.
Gov't, Oligonucleotide Array Sequence Analysis, Pattern Recognition,
Polymerase Chain Reaction, Proteins, Reproducibility of Results,
Research Support, Sensitivity and Specificity, Subcellular Fractions,
Unknown Primary, 15882333},
pii = {GWAT50},
url = {http://dx.doi.org/10.1111/j.1745-6584.2005.0050.x}
}
@article{Ashburner2000Gene,
author = {M. Ashburner and C. A. Ball and J. A. Blake and D. Botstein and H.
Butler and J. M. Cherry and A. P. Davis and K. Dolinski and S. S.
Dwight and J. T. Eppig and M. A. Harris and D. P. Hill and L. Issel-Tarver
and A. Kasarskis and S. Lewis and J. C. Matese and J. E. Richardson
and M. Ringwald and G. M. Rubin and G. Sherlock},
title = {Gene ontology: tool for the unification of biology. The Gene Ontology
Consortium.},
journal = {Nat Genet},
year = {2000},
volume = {25},
pages = {25--29},
number = {1},
month = {May},
doi = {10.1038/75556},
institution = {Department of Genetics, Stanford University School of Medicine, California,
USA. cherry@stanford.edu},
keywords = {Animals; Computer Communication Networks; Databases, Factual; Eukaryotic
Cells; Genes; Humans; Metaphysics; Mice; Molecular Biology; Sequence
Analysis, DNA; Terminology as Topic},
owner = {fantine},
pmid = {10802651},
timestamp = {2010.10.25},
url = {http://dx.doi.org/10.1038/75556}
}
@article{Atalay2005Implicit,
author = {Atalay, V. and Cetin-Atalay, R.},
title = {Implicit motif distribution based hybrid computational kernel for
sequence classification},
journal = {Bioinformatics},
year = {2005},
volume = {21},
pages = {1429-1436},
number = {8},
month = {Apr},
abstract = {M{OTIVATION}: {W}e designed a general computational kernel for classification
problems that require specific motif extraction and search from sequences.
{I}nstead of searching for explicit motifs, our approach finds the
distribution of implicit motifs and uses as a feature for classification.
{I}mplicit motif distribution approach may be used as modus operandi
for bioinformatics problems that require specific motif extraction
and search, which is otherwise computationally prohibitive. {RESULTS}:
{A} system named {P}2{SL} that infer protein subcellular targeting
was developed through this computational kernel. {T}argeting-signal
was modeled by the distribution of subsequence occurrences (implicit
motifs) using self-organizing maps. {T}he boundaries among the classes
were then determined with a set of support vector machines. {P}2{SL}
hybrid computational system achieved approximately 81\% of prediction
accuracy rate over {ER} targeted, cytosolic, mitochondrial and nuclear
protein localization classes. {P}2{SL} additionally offers the distribution
potential of proteins among localization classes, which is particularly
important for proteins, shuttle between nucleus and cytosol. {AVAILABILITY}:
http://staff.vbi.vt.edu/volkan/p2sl and http://www.i-cancer.fen.bilkent.edu.tr/p2sl
{CONTACT}: rengul@bilkent.edu.tr.},
doi = {10.1093/bioinformatics/bti212},
pdf = {../local/Atalay2005Implicit.pdf},
file = {Atalay2005Implicit.pdf:local/Atalay2005Implicit.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://dx.doi.org/10.1093/bioinformatics/bti212}
}
@article{Attwood2003PRINTS,
author = {T. K. Attwood and P. Bradley and D. R. Flower and A. Gaulton and
N. Maudling and A. L. Mitchell and G. Moulton and A. Nordle and K.
Paine and P. Taylor and A. Uddin and C. Zygouri},
title = {PRINTS and its automatic supplement, prePRINTS.},
journal = {Nucleic Acids Res.},
year = {2003},
volume = {31},
pages = {400--402},
number = {1},
month = {Jan},
abstract = {The PRINTS database houses a collection of protein fingerprints. These
may be used to assign uncharacterised sequences to known families
and hence to infer tentative functions. The September 2002 release
(version 36.0) includes 1800 fingerprints, encoding approximately
11 000 motifs, covering a range of globular and membrane proteins,
modular polypeptides and so on. In addition to its continued steady
growth, we report here the development of an automatic supplement,
prePRINTS, designed to increase the coverage of the resource and
reduce some of the manual burdens inherent in its maintenance. The
databases are accessible for interrogation and searching at http://www.bioinf.man.ac.uk/dbbrowser/PRINTS/.},
keywords = {Amino Acid Motifs; Animals; Automation; Conserved Sequence; Databases,
Protein; Proteins; Software},
owner = {laurent},
pmid = {12520033},
timestamp = {2007.09.22}
}
@article{Auer2010Statistical,
author = {Auer, P. L. and Doerge, R. W.},
title = {Statistical design and analysis of RNA sequencing data.},
journal = {Genetics},
year = {2010},
volume = {185},
pages = {405--416},
number = {2},
month = {Jun},
abstract = {Next-generation sequencing technologies are quickly becoming the preferred
approach for characterizing and quantifying entire genomes. Even
though data produced from these technologies are proving to be the
most informative of any thus far, very little attention has been
paid to fundamental design aspects of data collection and analysis,
namely sampling, randomization, replication, and blocking. We discuss
these concepts in an RNA sequencing framework. Using simulations
we demonstrate the benefits of collecting replicated RNA sequencing
data according to well known statistical designs that partition the
sources of biological and technical variation. Examples of these
designs and their corresponding models are presented with the goal
of testing differential expression.},
doi = {10.1534/genetics.110.114983},
institution = {Department of Statistics, Purdue University, West Lafayette, Indiana
47907, USA.},
language = {eng},
medline-pst = {ppublish},
owner = {phupe},
pii = {genetics.110.114983},
pmid = {20439781},
timestamp = {2011.04.08},
url = {http://dx.doi.org/10.1534/genetics.110.114983}
}
@article{Auliac2008Evolutionary,
author = {C{\'e}dric Auliac and Vincent Frouin and Xavier Gidrol and Florence
d'Alch{\'e}-Buc},
title = {Evolutionary approaches for the reverse-engineering of gene regulatory
networks: A study on a biologically realistic dataset},
journal = {BMC Bioinformatics},
year = {2008},
volume = {9},
bibsource = {DBLP, http://dblp.uni-trier.de},
ee = {http://dx.doi.org/10.1186/1471-2105-9-91}
}
@article{Avidan2004Support,
author = {Shai Avidan},
title = {Support vector tracking.},
journal = {I{EEE} {T}rans {P}attern {A}nal {M}ach {I}ntell},
year = {2004},
volume = {26},
pages = {1064-72},
number = {8},
month = {Aug},
abstract = {Support {V}ector {T}racking ({SVT}) integrates the {S}upport {V}ector
{M}achine ({SVM}) classifier into an optic-flow-based tracker. {I}nstead
of minimizing an intensity difference function between successive
frames, {SVT} maximizes the {SVM} classification score. {T}o account
for large motions between successive frames, we build pyramids from
the support vectors and use a coarse-to-fine approach in the classification
stage. {W}e show results of using {SVT} for vehicle tracking in image
sequences.},
doi = {10.1109/TPAMI.2004.53},
pdf = {../local/Avidan2004Support.pdf},
file = {Avidan2004Support.pdf:local/Avidan2004Support.pdf:PDF},
url = {http://dx.doi.org/10.1109/TPAMI.2004.53}
}
@article{Avlani2007Critical,
author = {Avlani, V. A. and Gregory, K. J. and Morton, C. J. and Parker, M.
W. and Sexton, P. M. and Christopoulos, A.},
title = {Critical role for the second extracellular loop in the binding of
both orthosteric and allosteric G protein-coupled receptor ligands.},
journal = {J. Biol. Chem.},
year = {2007},
volume = {282},
pages = {25677--25686},
number = {35},
month = {Aug},
abstract = {The second extracellular (E2) loop of G protein-coupled receptors
(GPCRs) plays an essential but poorly understood role in the binding
of non-peptidic small molecules. We have utilized both orthosteric
ligands and allosteric modulators of the M2 muscarinic acetylcholine
receptor, a prototypical Family A GPCR, to probe possible E2 loop
binding dynamics. We developed a homology model based on the crystal
structure of bovine rhodopsin and predicted novel cysteine substitutions
that should dramatically reduce E2 loop flexibility via disulfide
bond formation and significantly inhibit the binding of both types
of ligands. This prediction was validated experimentally using radioligand
binding, dissociation kinetics, and cell-based functional assays.
The results argue for a flexible "gatekeeper" role of the E2 loop
in the binding of both allosteric and orthosteric GPCR ligands.},
doi = {10.1074/jbc.M702311200},
keywords = {chemogenomics},
owner = {laurent},
pii = {M702311200},
pmid = {17591774},
timestamp = {2008.07.21},
url = {http://dx.doi.org/10.1074/jbc.M702311200}
}
@article{Azencott2007One,
author = {Azencott, C.-A. and Ksikes, A. and Swamidass, S. J. and Chen, J.
H. and Ralaivola, L. and Baldi, P.},
title = {One- to four-dimensional kernels for virtual screening and the prediction
of physical, chemical, and biological properties.},
journal = {J. Chem. Inform. Model.},
year = {2007},
volume = {47},
pages = {965--974},
number = {3},
abstract = {Many chemoinformatics applications, including high-throughput virtual
screening, benefit from being able to rapidly predict the physical,
chemical, and biological properties of small molecules to screen
large repositories and identify suitable candidates. When training
sets are available, machine learning methods provide an effective
alternative to ab initio methods for these predictions. Here, we
leverage rich molecular representations including 1D SMILES strings,
2D graphs of bonds, and 3D coordinates to derive efficient machine
learning kernels to address regression problems. We further expand
the library of available spectral kernels for small molecules developed
for classification problems to include 2.5D surface and 3D kernels
using Delaunay tetrahedrization and other techniques from computational
geometry, 3D pharmacophore kernels, and 3.5D or 4D kernels capable
of taking into account multiple molecular configurations, such as
conformers. The kernels are comprehensively tested using cross-validation
and redundancy-reduction methods on regression problems using several
available data sets to predict boiling points, melting points, aqueous
solubility, octanol/water partition coefficients, and biological
activity with state-of-the art results. When sufficient training
data are available, 2D spectral kernels in general tend to yield
the best and most robust results, better than state-of-the art. On
data sets containing thousands of molecules, the kernels achieve
a squared correlation coefficient of 0.91 for aqueous solubility
prediction and 0.94 for octanol/water partition coefficient prediction.
Averaging over conformations improves the performance of kernels
based on the three-dimensional structure of molecules, especially
on challenging data sets. Kernel predictors for aqueous solubility
(kSOL), LogP (kLOGP), and melting point (kMELT) are available over
the Web through: http://cdb.ics.uci.edu.},
doi = {10.1021/ci600397p},
pdf = {../local/Azencott2007One.pdf},
file = {Azencott2007One.pdf:Azencott2007One.pdf:PDF},
owner = {pmahe},
pmid = {17338509},
timestamp = {2007.07.13},
url = {http://dx.doi.org/10.1021/ci600397p}
}
@book{Bohm2003Protein-ligand,
title = {Protein-ligand interactions},
publisher = {Wiley},
year = {2003},
author = {H.-J. B\"{o}hm and G. Schneider and R. Mannhold and H. Kubinyi and
G. Folkers},
keywords = {chemoinformatics},
owner = {mahe},
timestamp = {2006.02.03}
}
@article{Babur2004,
author = {Babur, O and Demir, E and Ayaz, A and Dogrusoz, U and Sakarya, O},
title = {Pathway activity inference using microarray data},
journal = {Technical report, {B}ilkent {C}enter for {B}ioinformatics ({BCBI})},
year = {2004}
}
@inproceedings{Bach2009Exploring,
author = {Bach, F.},
title = {Exploring large feature spaces with hierarchical multiple kernel
learning},
booktitle = {Adv. Neural. Inform. Process Syst.},
year = {2009},
volume = {21},
pdf = {../local/Bach2009Exploring.pdf},
file = {Bach2009Exploring.pdf:Bach2009Exploring.pdf:PDF},
owner = {jp},
timestamp = {2009.01.25}
}
@article{Bach2008Consistencya,
author = {Bach, F.},
title = {Consistency of the group lasso and multiple kernel learning},
journal = {J. Mach. Learn. Res.},
year = {2008},
volume = {9},
pages = {1179--1225},
abstract = {We consider the least-square regression problem with regularization
by a block l1-norm, that is, a sum of Euclidean norms over spaces
of dimensions larger than one. This problem, referred to as the group
Lasso, extends the usual regularization by the l1-norm where all
spaces have dimension one, where it is commonly referred to as the
Lasso. In this paper, we study the asymptotic group selection consistency
of the group Lasso. We derive necessary and sufficient conditions
for the consistency of group Lasso under practical assumptions, such
as model mis specification. When the linear predictors and Euclidean
norms are replaced by functions and reproducing kernel Hilbert norms,
the problem is usually referred to as multiple kernel learning and
is commonly used for learning from heterogeneous data sources and
for non linear variable selection. Using tools from functional analysis,
and in particular covar iance operators, we extend the consistency
results to this infinite dimensional case and also propose an adaptive
scheme to obtain a consistent model estimate, even when the necessary
condition required for the non adaptive scheme is not satisfied.},
pdf = {../local/Bach2008Consistencya.pdf},
file = {Bach2008Consistencya.pdf:Bach2008Consistencya.pdf:PDF},
keywords = {lasso},
owner = {jp},
timestamp = {2008.12.21},
url = {http://jmlr.csail.mit.edu/papers/v9/bach08b.html}
}
@article{Bach2011Optimization,
author = {Bach, F. and Jenatton, R. and Mairal, J. and Obozinski, G.},
title = {Optimization with sparsity-inducing penalties},
journal = {Foundations and Trends{\textregistered} in Machine Learning},
year = {2011},
volume = {4},
pages = {1--106},
number = {1},
doi = {10.1561/2200000015},
pdf = {../local/Bach2011Optimization.pdf},
file = {Bach2011Optimization.pdf:Bach2011Optimization.pdf:PDF},
url = {http://dx.doi.org/10.1561/2200000015}
}
@article{Bach2011Structured,
author = {Bach, F. and Jenatton, R. and Mairal, J. and Obozinski, G.},
title = {Structured sparsity through convex optimization},
journal = {arXiv preprint arXiv:1109.2397},
year = {2011}
}
@article{Bach2002Kernel,
author = {Bach, F.R. and Jordan, M.I.},
title = {Kernel independent component analysis},
journal = {J. Mach. Learn. Res.},
year = {2002},
volume = {3},
pages = {1--48},
pdf = {../local/Bach2002Kernel.pdf},
file = {Bach2002Kernel.pdf:local/Bach2002Kernel.pdf:PDF},
html = {http://www.ai.mit.edu/projects/jmlr/papers/volume3/bshouty02a/abstract.html},
subject = {kernel},
url = {http://jmlr.csail.mit.edu/papers/v3/bach02a.html}
}
@inproceedings{Bach2008Bolasso,
author = {Bach, F. R.},
title = {Bolasso: model consistent {Lasso} estimation through the bootstrap},
booktitle = {Proceedings of the 25th international conference on Machine learning},
year = {2008},
editor = {William W. Cohen and Andrew McCallum and Sam T. Roweis},
volume = {308},
series = {ACM International Conference Proceeding Series},
pages = {33--40},
address = {New York, NY, USA},
publisher = {ACM},
doi = {http://doi.acm.org/10.1145/1390156.1390161},
isbn = {978-1-60558-205-4},
location = {Helsinki, Finland}
}
@article{Bach2008Consistency,
author = {Bach, F. R.},
title = {Consistency of trace norm minimization},
journal = {J. Mach. Learn. Res.},
year = {2008},
volume = {9},
pages = {1019--1048},
abstract = {Regularization by the sum of singular values, also referred to as
the trace norm, is a popular tech-
nique for estimating low rank rectangular matrices. In this paper,
we extend some of the consis-
tency results of the Lasso to provide necessary and sufficient conditions
for rank consistency of
trace norm minimization with the square loss. We also provide an adaptive
version that is rank
consistent even when the necessary condition for the non adaptive
version is not fulfilled.},
pdf = {../local/Bach2008Consistency.pdf},
file = {Bach2008Consistency.pdf:Bach2008Consistency.pdf:PDF},
url = {http://jmlr.csail.mit.edu/papers/volume9/bach08a/bach08a.pdf}
}
@inproceedings{Bach2005Predictive,
author = {Bach, F. R. and Jordan, M. I.},
title = {Predictive low-rank decomposition for kernel methods},
booktitle = {ICML '05: Proceedings of the 22nd international conference on Machine
learning},
year = {2005},
pages = {33--40},
address = {New York, NY, USA},
publisher = {ACM},
abstract = {Low-rank matrix decompositions are essential tools in the application
of kernel methods to large-scale learning problems. These decompositions
have generally been treated as black boxes---the decomposition of
the kernel matrix that they deliver is independent of the specific
learning task at hand---and this is a potentially significant source
of inefficiency. In this paper, we present an algorithm that can
exploit side information (e.g., classification labels, regression
responses) in the computation of low-rank decompositions for kernel
matrices. Our algorithm has the same favorable scaling as state-of-the-art
methods such as incomplete Cholesky decomposition---it is linear
in the number of data points and quadratic in the rank of the approximation.
We present simulation results that show that our algorithm yields
decompositions of significantly smaller rank than those found by
incomplete Cholesky decomposition.},
doi = {http://doi.acm.org/10.1145/1102351.1102356}
}
@inproceedings{Bach2003Learning,
author = {Francis R. Bach and Michael I. Jordan},
title = {Learning Spectral Clustering},
booktitle = {Advances in Neural Information Processing Systems 16},
year = {2003},
publisher = {MIT Press}
}
@techreport{Bach2004Fast,
author = {Bach, F. R. and Lanckriet, G. and Jordan, M. I.},
title = {Fast kernel learning using sequential minimal optimization},
institution = {Computer Science Division, UC Berkeley},
year = {2004},
number = {UCB/CSD-04-1307},
month = {February},
pdf = {../local/Bach2004Fast.pdf},
file = {Bach2004Fast.pdf:Bach2004Fast.pdf:PDF},
owner = {jp},
timestamp = {2009.01.05}
}
@inproceedings{Bach2004Multiple,
author = {Bach, F. R. and Lanckriet, G. R. G. and Jordan, M. I.},
title = {Multiple Kernel Learning, Conic Duality, and the {SMO} Algorithm},
booktitle = {Proceedings of the Twenty-First International Conference on Machine
Learning},
year = {2004},
pages = {6},
address = {New York, NY, USA},
publisher = {ACM},
abstract = {While classical kernel-based classifiers are based on a single kernel,
in practice it is often desirable to base classifiers on combinations
of multiple kernels. Lanckriet et al. (2004) considered conic combinations
of kernel matrices for the support vector machine (SVM), and showed
that the optimization of the coefficients of such a combination reduces
to a convex optimization problem known as a quadratically-constrained
quadratic program (QCQP). Unfortunately, current convex optimization
toolboxes can solve this problem only for a small number of kernels
and a small number of data points; moreover, the sequential minimal
optimization (SMO) techniques that are essential in large-scale implementations
of the SVM cannot be applied because the cost function is non-differentiable.
We propose a novel dual formulation of the QCQP as a second-order
cone programming problem, and show how to exploit the technique of
Moreau-Yosida regularization to yield a formulation to which SMO
techniques can be applied. We present experimental results that show
that our SMO-based algorithm is significantly more efficient than
the general-purpose interior point methods available in current optimization
toolboxes.},
doi = {http://doi.acm.org/10.1145/1015330.1015424},
pdf = {../local/Bach2004Multiple.pdf},
file = {Bach2004Multiple.pdf:Bach2004Multiple.pdf:PDF}
}
@inproceedings{Bach2005Computing,
author = {F. R. Bach and R. Thibaux and M. I. Jordan},
title = {Computing Regularization Paths for Learning Multiple Kernels},
booktitle = {Advances in Neural Information Processing Systems 17},
year = {2005},
editor = {Saul, L. K. and Weiss, Y. and Bottou, L.},
pages = {73-80},
address = {Cambridge, MA},
publisher = {MIT Press}
}
@article{Bacilieri2006Ligand,
author = {Magdalena Bacilieri and Stefano Moro},
title = {Ligand-based drug design methodologies in drug discovery process:
an overview.},
journal = {Curr Drug Discov Technol},
year = {2006},
volume = {3},
pages = {155--165},
number = {3},
month = {Sep},
abstract = {Ligand-based drug design represents an important research field in
the drug discovery and optimisation process. This review provides
an overview about the theoretical background of the quantitative
structure activity relationship (QSAR) models.},
institution = {Molecular Modeling Section, Dipartimento di Scienze Farmaceutiche,
Università di Padova, Via Marzolo 5, I-35131 Padova, Italy.},
keywords = {Drug Design; Ligands; Models, Theoretical; Molecular Structure; Pharmaceutical
Preparations, chemistry/metabolism; Quantitative Structure-Activity
Relationship; Technology, Pharmaceutical, methods},
owner = {bricehoffmann},
pmid = {17311561},
timestamp = {2009.02.13}
}
@article{Bader2003BIND,
author = {Bader, G.D. and Betel, D. and Hogue, C.W.V.},
title = {BIND: the Biomolecular Interaction Network Database.},
journal = {Nucleic Acids Res},
year = {2003},
volume = {31},
pages = {248--250},
number = {1},
month = {Jan},
abstract = {The Biomolecular Interaction Network Database (BIND: http://bind.ca)
archives biomolecular interaction, complex and pathway information.
A web-based system is available to query, view and submit records.
BIND continues to grow with the addition of individual submissions
as well as interaction data from the PDB and a number of large-scale
interaction and complex mapping experiments using yeast two hybrid,
mass spectrometry, genetic interactions and phage display. We have
developed a new graphical analysis tool that provides users with
a view of the domain composition of proteins in interaction and complex
records to help relate functional domains to protein interactions.
An interaction network clustering tool has also been developed to
help focus on regions of interest. Continued input from users has
helped further mature the BIND data specification, which now includes
the ability to store detailed information about genetic interactions.
The BIND data specification is available as ASN.1 and XML DTD.},
institution = {Department of Biochemistry, Samuel Lunenfeld Research Institute,
University of Toronto, Toronto M5G 1X5, Canada.},
owner = {fantine},
pmid = {12519993},
timestamp = {2010.10.21}
}
@article{Baek2008impact,
author = {Daehyun Baek and Judit Villén and Chanseok Shin and Fernando D Camargo
and Steven P Gygi and David P Bartel},
title = {The impact of microRNAs on protein output.},
journal = {Nature},
year = {2008},
volume = {455},
pages = {64--71},
number = {7209},
month = {Sep},
abstract = {MicroRNAs are endogenous approximately 23-nucleotide RNAs that can
pair to sites in the messenger RNAs of protein-coding genes to downregulate
the expression from these messages. MicroRNAs are known to influence
the evolution and stability of many mRNAs, but their global impact
on protein output had not been examined. Here we use quantitative
mass spectrometry to measure the response of thousands of proteins
after introducing microRNAs into cultured cells and after deleting
mir-223 in mouse neutrophils. The identities of the responsive proteins
indicate that targeting is primarily through seed-matched sites located
within favourable predicted contexts in 3' untranslated regions.
Hundreds of genes were directly repressed, albeit each to a modest
degree, by individual microRNAs. Although some targets were repressed
without detectable changes in mRNA levels, those translationally
repressed by more than a third also displayed detectable mRNA destabilization,
and, for the more highly repressed targets, mRNA destabilization
usually comprised the major component of repression. The impact of
microRNAs on the proteome indicated that for most interactions microRNAs
act as rheostats to make fine-scale adjustments to protein output.},
doi = {10.1038/nature07242},
pdf = {../local/Baek2008impact.pdf},
file = {Baek2008impact.pdf:Baek2008impact.pdf:PDF},
institution = {Whitehead Institute for Biomedical Research, 9 Cambridge Center,
Cambridge, Massachusetts 02142, USA.},
keywords = {sirna},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {nature07242},
pmid = {18668037},
timestamp = {2009.10.28},
url = {http://dx.doi.org/10.1038/nature07242}
}
@article{Bagci2008PLOS1,
author = {Bagci, E. Z. and Vodovotz, Y. and Billiar, T. R. and Ermentrout,
B. and Bahar, I.},
title = {Computational insights on the competing effects of nitric oxide in
regulating apoptosis},
journal = {PLoS One},
year = {2008},
volume = {3},
pages = {e2249},
number = {5},
abstract = {Despite the establishment of the important role of nitric oxide (NO)
on apoptosis, a molecular-level understanding of the origin of its
dichotomous pro- and anti-apoptotic effects has been elusive. We
propose a new mathematical model for simulating the effects of nitric
oxide (NO) on apoptosis. The new model integrates mitochondria-dependent
apoptotic pathways with NO-related reactions, to gain insights into
the regulatory effect of the reactive NO species N(2)O(3), non-heme
iron nitrosyl species (FeL(n)NO), and peroxynitrite (ONOO(-)). The
biochemical pathways of apoptosis coupled with NO-related reactions
are described by ordinary differential equations using mass-action
kinetics. In the absence of NO, the model predicts either cell survival
or apoptosis (a bistable behavior) with shifts in the onset time
of apoptotic response depending on the strength of extracellular
stimuli. Computations demonstrate that the relative concentrations
of anti- and pro-apoptotic reactive NO species, and their interplay
with glutathione, determine the net anti- or pro-apoptotic effects
at long time points. Interestingly, transient effects on apoptosis
are also observed in these simulations, the duration of which may
reach up to hours, despite the eventual convergence to an anti-apoptotic
state. Our computations point to the importance of precise timing
of NO production and external stimulation in determining the eventual
pro- or anti-apoptotic role of NO.},
keywords = {csbcbook}
}
@article{Bagci2006BiophysJ,
author = {Bagci, E. Z. and Vodovotz, Y. and Billiar, T. R. and Ermentrout,
G. B. and Bahar, I.},
title = {Bistability in apoptosis: roles of bax, bcl-2, and mitochondrial
permeability transition pores},
journal = {Biophys J},
year = {2006},
volume = {90},
pages = {1546-59},
number = {5},
abstract = {We propose a mathematical model for mitochondria-dependent apoptosis,
in which kinetic cooperativity in formation of the apoptosome is
a key element ensuring bistability. We examine the role of Bax and
Bcl-2 synthesis and degradation rates, as well as the number of mitochondrial
permeability transition pores (MPTPs), on the cell response to apoptotic
stimuli. Our analysis suggests that cooperative apoptosome formation
is a mechanism for inducing bistability, much more robust than that
induced by other mechanisms, such as inhibition of caspase-3 by the
inhibitor of apoptosis (IAP). Simulations predict a pathological
state in which cells will exhibit a monostable cell survival if Bax
degradation rate is above a threshold value, or if Bax expression
rate is below a threshold value. Otherwise, cell death or survival
occur depending on initial caspase-3 levels. We show that high expression
rates of Bcl-2 can counteract the effects of Bax. Our simulations
also demonstrate a monostable (pathological) apoptotic response if
the number of MPTPs exceeds a threshold value. This study supports
our contention, based on mathematical modeling, that cooperativity
in apoptosome formation is critically important for determining the
healthy responses to apoptotic stimuli, and helps define the roles
of Bax, Bcl-2, and MPTP vis-a-vis apoptosome formation.},
keywords = {csbcbook}
}
@article{Bagga2005Quantitative,
author = {Harmohina Bagga and David S Greenfield and William J Feuer},
title = {Quantitative assessment of atypical birefringence images using scanning
laser polarimetry with variable corneal compensation.},
journal = {Am {J} {O}phthalmol},
year = {2005},
volume = {139},
pages = {437-46},
number = {3},
month = {Mar},
abstract = {P{URPOSE}: {T}o define the clinical characteristics of atypical birefringence
images and to describe a quantitative method for their identification.
{DESIGN}: {P}rospective, comparative, clinical observational study.
{METHODS}: {N}ormal and glaucomatous eyes underwent complete examination,
standard automated perimetry, scanning laser polarimetry with variable
corneal compensation ({GD}x-{VCC}), and optical coherence tomography
({OCT}) of the macula, peripapillary retinal nerve fiber layer ({RNFL}),
and optic disk. {E}yes were classified into two groups: normal birefringence
pattern ({NBP}) and atypical birefringence pattern ({ABP}). {C}linical,
functional, and structural characteristics were assessed separately.
{A} multiple logistic regression model was used to predict eyes with
{ABP} on the basis of a quantitative scan score generated by a support
vector machine ({SVM}) with {GD}x-{VCC}. {RESULTS}: {S}ixty-five
eyes of 65 patients were enrolled. {ABP} images were observed in
5 of 20 (25\%) normal eyes and 23 of 45 (51\%) glaucomatous eyes.
{C}ompared with eyes with {NBP}, glaucomatous eyes with {ABP} demonstrated
significantly lower {SVM} scores ({P} < .0001, < 0.0001, 0.008, 0.03,
and 0.03, respectively) and greater temporal, mean, inferior, and
nasal {RNFL} thickness using {GD}x-{VCC}; and a weaker correlation
with {OCT} generated {RNFL} thickness ({R}(2) = .75 vs .27). {ABP}
images were significantly correlated with older age ({R}(2) = .16,
{P} = .001). {T}he {SVM} score was the only significant ({P} < .0001)
predictor of {ABP} images and provided high discriminating power
between eyes with {NBP} and {ABP} (area under the receiver operator
characteristic curve = 0.98). {CONCLUSIONS}: {ABP} images exist in
a subset of normal and glaucomatous eyes, are associated with older
patient age, and produce an artifactual increase in {RNFL} thickness
using {GD}x-{VCC}. {T}he {SVM} score is highly predictive of {ABP}
images.},
doi = {10.1016/j.ajo.2004.10.019},
pdf = {../local/Bagga2005Quantitative.pdf},
file = {Bagga2005Quantitative.pdf:locql/Bagga2005Quantitative.pdf:PDF},
keywords = {80 and over, Adult, Aged, Algorithms, Amino Acids, Animals, Area Under
Curve, Artifacts, Automated, Birefringence, Brain Chemistry, Brain
Neoplasms, Comparative Study, Computer-Assisted, Cornea, Cross-Sectional
Studies, Decision Trees, Diagnosis, Diagnostic Imaging, Diagnostic
Techniques, Discriminant Analysis, Evolution, Face, Female, Genetic,
Glaucoma, Humans, Intraocular Pressure, Lasers, Least-Squares Analysis,
Magnetic Resonance Imaging, Magnetic Resonance Spectroscopy, Male,
Middle Aged, Models, Molecular, Nerve Fibers, Non-U.S. Gov't, Numerical
Analysis, Ophthalmological, Optic Nerve Diseases, Optical Coherence,
P.H.S., Pattern Recognition, Photic Stimulation, Prospective Studies,
Protein, ROC Curve, Regression Analysis, Research Support, Retinal
Ganglion Cells, Sensitivity and Specificity, Sequence Analysis, Statistics,
Tomography, U.S. Gov't, Visual Fields, beta-Lactamases, 15767051},
pii = {S0002-9394(04)01265-6},
url = {http://dx.doi.org/10.1016/j.ajo.2004.10.019}
}
@article{Bagirov2003New,
author = {A. M. Bagirov and B. Ferguson and S. Ivkovic and G. Saunders and
J. Yearwood},
title = {New algorithms for multi-class cancer diagnosis using tumor gene
expression signatures.},
journal = {Bioinformatics},
year = {2003},
volume = {19},
pages = {1800-7},
number = {14},
month = {Sep},
abstract = {M{OTIVATION}: {T}he increasing use of {DNA} microarray-based tumor
gene expression profiles for cancer diagnosis requires mathematical
methods with high accuracy for solving clustering, feature selection
and classification problems of gene expression data. {RESULTS}: {N}ew
algorithms are developed for solving clustering, feature selection
and classification problems of gene expression data. {T}he clustering
algorithm is based on optimization techniques and allows the calculation
of clusters step-by-step. {T}his approach allows us to find as many
clusters as a data set contains with respect to some tolerance. {F}eature
selection is crucial for a gene expression database. {O}ur feature
selection algorithm is based on calculating overlaps of different
genes. {T}he database used, contains over 16 000 genes and this number
is considerably reduced by feature selection. {W}e propose a classification
algorithm where each tissue sample is considered as the center of
a cluster which is a ball. {T}he results of numerical experiments
confirm that the classification algorithm in combination with the
feature selection algorithm perform slightly better than the published
results for multi-class classifiers based on support vector machines
for this data set. {AVAILABILITY}: {A}vailable on request from the
authors.},
pdf = {../local/Bagirov2003New.pdf},
file = {Bagirov2003New.pdf:local/Bagirov2003New.pdf:PDF},
keywords = {Algorithms, Amino Acid Sequence, Anion Exchange Resins, Antigen-Antibody
Complex, Artificial Intelligence, Automated, Automatic Data Processing,
Biological, Blood Cells, Chemical, Chromatography, Cluster Analysis,
Comparative Study, Computational Biology, Computer Simulation, Computer-Assisted,
DNA, Data Interpretation, Databases, Decision Making, Decision Trees,
Diffusion Magnetic Resonance Imaging, English Abstract, Epitopes,
Expert Systems, Factual, Fuzzy Logic, Gene Expression Profiling,
Gene Expression Regulation, Gene Targeting, Genetic, Genome, Histocompatibility
Antigens Class I, Humans, Image Interpretation, Image Processing,
In Vitro, Indicators and Reagents, Information Storage and Retrieval,
Ion Exchange, Least-Squares Analysis, Liver Cirrhosis, Magnetic Resonance
Imaging, Male, Models, Molecular Sequence Data, Neoplasms, Neoplastic,
Neural Networks (Computer), Non-P.H.S., Non-U.S. Gov't, Nonl, Nucleic
Acid Conformation, Oligonucleotide Array Sequence Analysis, P.H.S.,
Pattern Recognition, Pro, Prostatic Neoplasms, Protein, Protein Binding,
Protein Interaction Mapping, Proteins, Quantitative Structure-Activity
Relationship, RNA, ROC Curve, Reproducibility of Results, Research
Support, Sensitivity and Specificity, Sequence Alignment, Sequence
Analysis, Severity of Illness Index, Statistical, Structure-Activity
Relationship, Subtraction Technique, T-Lymphocyte, Transcription
Factors, Transfer, Treatment Outcome, Tumor Markers, U.S. Gov't,
User-Computer Interface, inear Dynamics, teome, 14512351},
url = {http://bioinformatics.oxfordjournals.org/cgi/content/abstract/19/14/1800}
}
@article{Bagos2005Evaluation,
author = {Pantelis G Bagos and Theodore D Liakopoulos and Stavros J Hamodrakas},
title = {Evaluation of methods for predicting the topology of beta-barrel
outer membrane proteins and a consensus prediction method.},
journal = {B{MC} {B}ioinformatics},
year = {2005},
volume = {6},
pages = {7},
number = {1},
month = {Jan},
abstract = {B{ACKGROUND}: {P}rediction of the transmembrane strands and topology
of beta-barrel outer membrane proteins is of interest in current
bioinformatics research. {S}everal methods have been applied so far
for this task, utilizing different algorithmic techniques and a number
of freely available predictors exist. {T}he methods can be grossly
divided to those based on {H}idden {M}arkov {M}odels ({HMM}s), on
{N}eural {N}etworks ({NN}s) and on {S}upport {V}ector {M}achines
({SVM}s). {I}n this work, we compare the different available methods
for topology prediction of beta-barrel outer membrane proteins. {W}e
evaluate their performance on a non-redundant dataset of 20 beta-barrel
outer membrane proteins of gram-negative bacteria, with structures
known at atomic resolution. {A}lso, we describe, for the first time,
an effective way to combine the individual predictors, at will, to
a single consensus prediction method. {RESULTS}: {W}e assess the
statistical significance of the performance of each prediction scheme
and conclude that {H}idden {M}arkov {M}odel based methods, {HMM}-{B}2{TMR},
{P}rof{TMB} and {PRED}-{TMBB}, are currently the best predictors,
according to either the per-residue accuracy, the segments overlap
measure ({SOV}) or the total number of proteins with correctly predicted
topologies in the test set. {F}urthermore, we show that the available
predictors perform better when only transmembrane beta-barrel domains
are used for prediction, rather than the precursor full-length sequences,
even though the {HMM}-based predictors are not influenced significantly.
{T}he consensus prediction method performs significantly better than
each individual available predictor, since it increases the accuracy
up to 4\% regarding {SOV} and up to 15\% in correctly predicted topologies.
{CONCLUSIONS}: {T}he consensus prediction method described in this
work, optimizes the predicted topology with a dynamic programming
algorithm and is implemented in a web-based application freely available
to non-commercial users at http://bioinformatics.biol.uoa.gr/{C}on{BBPRED}.},
doi = {10.1186/1471-2105-6-7},
pdf = {../local/Bagos2005Evaluation.pdf},
file = {Bagos2005Evaluation.pdf:local/Bagos2005Evaluation.pdf:PDF},
keywords = {Algorithms, Cell Nucleus, Cytoplasm, Databases, Genetic Vectors, Humans,
Internet, Mitochondria, Models, Non-U.S. Gov't, Peptides, Protein,
Proteins, Proteomics, Reproducibility of Results, Research Support,
Software, Theoretical, 15647112},
pii = {1471-2105-6-7},
url = {http://dx.doi.org/10.1186/1471-2105-6-7}
}
@article{Bailey1994Fitting,
author = {Bailey, T. L. and Elkan, C.},
title = {Fitting a mixture model by expectation maximization to discover motifs
in biopolymers.},
journal = {Proc Int Conf Intell Syst Mol Biol},
year = {1994},
volume = {2},
pages = {28--36},
abstract = {The algorithm described in this paper discovers one or more motifs
in a collection of DNA or protein sequences by using the technique
of expectation maximization to fit a two-component finite mixture
model to the set of sequences. Multiple motifs are found by fitting
a mixture model to the data, probabilistically erasing the occurrences
of the motif thus found, and repeating the process to find successive
motifs. The algorithm requires only a set of unaligned sequences
and a number specifying the width of the motifs as input. It returns
a model of each motif and a threshold which together can be used
as a Bayes-optimal classifier for searching for occurrences of the
motif in other databases. The algorithm estimates how many times
each motif occurs in each sequence in the dataset and outputs an
alignment of the occurrences of the motif. The algorithm is capable
of discovering several different motifs with differing numbers of
occurrences in a single dataset.},
institution = {Department of Computer Science and Engineering, University of California
at San Diego, La Jolla 92093-0114, USA.},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pmid = {7584402},
timestamp = {2010.02.21},
url = {http://view.ncbi.nlm.nih.gov/pubmed/7584402}
}
@article{Bailey2006MEME,
author = {Bailey, Timothy L. and Williams, Nadya and Misleh, Chris and Li,
Wilfred W.},
title = {MEME: discovering and analyzing DNA and protein sequence motifs},
journal = {Nucl. Acids Res.},
year = {2006},
volume = {34},
pages = {W369--373},
number = {suppl\_2},
month = {July},
abstract = {MEME (Multiple EM for Motif Elicitation) is one of the most widely
used tools for searching for novel signals' in sets of biological
sequences. Applications include the discovery of new transcription
factor binding sites and protein domains. MEME works by searching
for repeated, ungapped sequence patterns that occur in the DNA or
protein sequences provided by the user. Users can perform MEME searches
via the web server hosted by the National Biomedical Computation
Resource (http://meme.nbcr.net) and several mirror sites. Through
the same web server, users can also access the Motif Alignment and
Search Tool to search sequence databases for matches to motifs encoded
in several popular formats. By clicking on buttons in the MEME output,
users can compare the motifs discovered in their input sequences
with databases of known motifs, search sequence databases for matches
to the motifs and display the motifs in various formats. This article
describes the freely accessible web server and its architecture,
and discusses ways to use MEME effectively to find new sequence patterns
in biological sequences and analyze their significance. 10.1093/nar/gkl198},
address = {Institute of Molecular Bioscience, The University of Queensland,
St Lucia, QLD 4072, Australia. t.bailey@imb.uq.edu.au},
doi = {10.1093/nar/gkl198},
issn = {1362-4962},
keywords = {motif-identification, sequence-pattern-recognition, software},
posted-at = {2009-09-18 19:38:23},
priority = {2},
url = {http://dx.doi.org/10.1093/nar/gkl198}
}
@inproceedings{Baird1993Document,
author = {Baird, H.},
title = {Document image defect models and their uses},
booktitle = {Proceedings of the Second International Conference on Document Analysis
and Recognition ICDAR-93},
year = {1993},
pdf = {../local/Baird1993Document.pdf},
file = {Baird1993Document.pdf:Baird1993Document.pdf:PDF},
owner = {jp},
timestamp = {2008.12.22}
}
@article{Bajorath2002Integration,
author = {J. Bajorath},
title = {{I}ntegration of virtual and high-throughput screening.},
journal = {Nat Rev Drug Discov},
year = {2002},
volume = {1},
pages = {882--894},
number = {11},
month = {Nov},
abstract = {High-throughput and virtual screening are important components of
modern drug discovery research. Typically, these screening technologies
are considered distinct approaches, as one is experimental and the
other is theoretical in nature. However, given their similar tasks
and goals, these approaches are much more complementary to each other
than often thought. Various statistical, informatics and filtering
methods have recently been introduced to foster the integration of
experimental and in silico screening and maximize their output in
drug discovery. Although many of these ideas and efforts have not
yet proceeded much beyond the conceptual level, there are several
success stories and good indications that early-stage drug discovery
will benefit greatly from a more unified and knowledge-based approach
to biological screening, despite the many technical advances towards
even higher throughput that are made in the screening arena.},
doi = {10.1038/nrd941},
keywords = {Animals, Cluster Analysis, Computer Simulation, DNA Fingerprinting,
Drug Design, Drug Evaluation, Humans, Pharmaceutical, Preclinical,
Quantitative Structure-Activity Relationship, Structure-Activity
Relationship, Technology, 12415248},
owner = {mahe},
pii = {nrd941},
pmid = {12415248},
timestamp = {2006.08.15},
url = {http://dx.doi.org/10.1038/nrd941}
}
@article{Bajorath2001Selected,
author = {Bajorath, J.},
title = {Selected concepts and investigations in compound classification,
molecular descriptor analysis, and virtual screening.},
journal = {J {C}hem {I}nf {C}omput {S}ci},
year = {2001},
volume = {41},
pages = {233--245},
number = {2},
keywords = {chemoinformatics},
owner = {vert},
pmid = {11277704},
timestamp = {2006.01.19}
}
@article{Baker2010Next,
author = {Baker, Monya},
title = {Next-generation sequencing: adjusting to data overload free},
journal = {Nature Methods},
year = {2010},
volume = {7},
pages = {495-499},
owner = {philippe},
timestamp = {2010.07.27}
}
@article{Bakker2003Task,
author = {Bakker, B. and Heskes, T.},
title = {Task clustering and gating for bayesian multitask learning},
journal = {J. Mach. Learn. Res.},
year = {2003},
volume = {4},
pages = {83--99},
address = {Cambridge, MA, USA},
issn = {1533-7928},
publisher = {MIT Press}
}
@article{Balakin2002Property-based,
author = {Balakin, K. V. and Tkachenko, S. E. and Lang, S. A. and Okun, I.
and Ivashchenko, A. A. and Savchuk, N. P.},
title = {Property-based design of {GPCR}-targeted library.},
journal = {J. Chem. Inf. Comput. Sci.},
year = {2002},
volume = {42},
pages = {1332--1342},
number = {6},
abstract = {The design of a GPCR-targeted library, based on a scoring scheme for
the classification of molecules into "GPCR-ligand-like" and "non-GPCR-ligand-like",
is outlined. The methodology is a valuable tool that can aid in the
selection and prioritization of potential GPCR ligands for bioscreening
from large collections of compounds. It is based on the distillation
of knowledge from large databases of GPCR and non-GPCR active agents.
The method employed a set of descriptors for encoding the molecular
structures and by training of a neural network for classifying the
molecules. The molecular requirements were profiled and validated
by using available databases of GPCR- and non-GPCR-active agents
[5736 diverse GPCR-active molecules and 7506 diverse non-GPCR-active
molecules from the Ensemble Database (Prous Science, 2002)]. The
method enables efficient qualification or disqualification of a molecule
as a potential GPCR ligand and represents a useful tool for constraining
the size of GPCR-targeted libraries that will help speed up the development
of new GPCR-active drugs.},
keywords = {chemogenomics},
owner = {laurent},
pii = {ci025538y},
pmid = {12444729},
timestamp = {2007.09.22}
}
@article{Balasubramanian2002isomap,
author = {Balasubramanian, M. and Schwartz, E. L.},
title = {The isomap algorithm and topological stability},
journal = {Science},
year = {2002},
volume = {295},
pages = {7},
number = {5552},
month = {Jan},
doi = {10.1126/science.295.5552.7a},
pdf = {../local/Balasubramanian2002isomap.pdf},
file = {Balasubramanian2002isomap.pdf:local/Balasubramanian2002isomap.pdf:PDF},
keywords = {dimred},
pii = {295/5552/7a},
url = {http://dx.doi.org/10.1126/science.295.5552.7a}
}
@book{Baldi2001Bioinformatcs,
title = {Bioinformatcs, the machine learning approach},
publisher = {MIT Press},
year = {2001},
author = {Baldi, P. and Brunak, S.},
owner = {jp},
timestamp = {2010.10.12}
}
@article{Baldi1999Exploiting,
author = {Baldi, P. and Brunak, S. and Frasconi, P. and Soda, G. and Pollastri,
G.},
title = {Exploiting the past and the future in protein secondary structure
prediction},
journal = {Bioinformatics},
year = {1999},
volume = {15},
pages = {937--946},
pdf = {../local/bald99.pdf},
file = {bald99.pdf:local/bald99.pdf:PDF},
subject = {biocasp},
url = {http://bioinformatics.oupjournals.org/cgi/reprint/15/11/937.pdf}
}
@article{Baldi1994Hidden,
author = {Baldi, P. and Chauvin, Y. and Hunkapiller, T. and Mc{C}lure, M.A.},
title = {Hidden {M}arkov models of biological primary sequence information},
journal = {Proc. {N}atl. {A}cad. {S}ci. {USA}},
year = {1994},
volume = {91(3)},
pages = {1053--1063},
pdf = {../local/Baldi1994Hidden.pdf},
file = {Baldi1994Hidden.pdf:local/Baldi1994Hidden.pdf:PDF}
}
@article{Ballesteros2001G,
author = {J. Ballesteros and K. Palczewski},
title = {G protein-coupled receptor drug discovery: implications from the
crystal structure of rhodopsin.},
journal = {Curr. Opin. Drug Discov. Devel.},
year = {2001},
volume = {4},
pages = {561--574},
number = {5},
month = {Sep},
abstract = {G protein-coupled receptors (GPCRs) are a functionally diverse group
of membrane proteins that play a critical role in signal transduction.
Because of the lack of a high-resolution structure, the heptahelical
transmembrane bundle within the N-terminal extracellular and C-terminal
intracellular region of these receptors has initially been modeled
based on the high-resolution structure of bacterial retinal-binding
protein, bacteriorhodopsin. However, the low-resolution structure
of rhodopsin, a prototypical GPCR, revealed that there is a minor
relationship between GPCRs and bacteriorhodopsins. The high-resolution
crystal structure of the rhodopsin ground state and further refinements
of the model provide the first structural information about the entire
organization of the polypeptide chain and post-translational moieties.
These studies provide a structural template for Family 1 GPCRs that
has the potential to significantly improve structure-based approaches
to GPCR drug discovery.},
keywords = {Amino Acid Sequence; Animals; Crystallography, X-Ray; Drug Design;
GTP-Binding Proteins; Humans; Models, Molecular; Molecular Sequence
Data; Receptors, Drug; Rhodopsin},
owner = {laurent},
pmid = {12825452},
timestamp = {2007.09.22}
}
@article{Balmain2003genetics,
author = {Balmain, A. and Gray, J. and Ponder, B.},
title = {The genetics and genomics of cancer},
journal = {Nat. {G}enet.},
year = {2003},
volume = {33},
pages = {238-244},
abstract = {The past decade has seen great strides in our understanding of the
genetic basis of human disease. {A}rguably, the most profound impact
has been in the area of cancer genetics, where the explosion of genomic
sequence and molecular profiling data has illustrated the complexity
of human malignancies. {I}n a tumor cell, dozens of different genes
may be aberrant in structure or copy number, and hundreds or thousands
of genes may be differentially expressed. {A} number of familial
cancer genes with high-penetrance mutations have been identified,
but the contribution of low-penetrance genetic variants or polymorphisms
to the risk of sporadic cancer development remains unclear. {S}tudies
of the complex somatic genetic events that take place in the emerging
cancer cell may aid the search for the more elusive germline variants
that confer increased susceptibility. {I}nsights into the molecular
pathogenesis of cancer have provided new strategies for treatment,
but a deeper understanding of this disease will require new statistical
and computational approaches for analysis of the genetic and signaling
networks that orchestrate individual cancer susceptibility and tumor
behavior.},
doi = {doi:10.1038/ng1107},
pdf = {../local/Balmain2003genetics.pdf},
file = {Balmain2003genetics.pdf:local/Balmain2003genetics.pdf:PDF},
url = {http://dx.doi.org/10.1038/ng1107}
}
@article{Bandyopadhyay2006Systematic,
author = {Bandyopadhyay, S. and Sharan, R. and Ideker, T.},
title = {Systematic identification of functional orthologs based on protein
network comparison},
journal = {Genome Res.},
year = {2006},
volume = {16},
pages = {428--435},
number = {3},
month = {Mar},
abstract = {Annotating protein function across species is an important task that
is often complicated by the presence of large paralogous gene families.
Here, we report a novel strategy for identifying functionally related
proteins that supplements sequence-based comparisons with information
on conserved protein-protein interactions. First, the protein interaction
networks of two species are aligned by assigning proteins to sequence
homology clusters using the Inparanoid algorithm. Next, probabilistic
inference is performed on the aligned networks to identify pairs
of proteins, one from each species, that are likely to retain the
same function based on conservation of their interacting partners.
Applying this method to Drosophila melanogaster and Saccharomyces
cerevisiae, we analyze 121 cases for which functional orthology assignment
is ambiguous when sequence similarity is used alone. In 61 of these
cases, the network supports a different protein pair than that favored
by sequence comparisons. These results suggest that network analysis
can be used to provide a key source of information for refining sequence-based
homology searches.},
doi = {10.1101/gr.4526006},
pdf = {../local/Bandyopadhyay2006Systematic.pdf},
file = {Bandyopadhyay2006Systematic.pdf:local/Bandyopadhyay2006Systematic.pdf:PDF},
institution = {Program in Bioinformatics, University of California at San Diego,
La Jolla, California 92093, USA.},
owner = {jp},
pii = {16/3/428},
pmid = {16510899},
timestamp = {2008.10.02},
url = {http://dx.doi.org/10.1101/gr.4526006}
}
@article{Banerjee2008Model,
author = {Banerjee, O. and El Ghaoui, L. and d'Aspremont, A.},
title = {Model Selection Through Sparse Maximum Likelihood Estimation for
Multivariate Gaussian or Binary Data},
journal = {J. Mach. Learn. Res.},
year = {2008},
volume = {9},
pages = {485--516},
pdf = {../local/Banerjee2008Model.pdf},
file = {Banerjee2008Model.pdf:Banerjee2008Model.pdf:PDF},
owner = {jp},
timestamp = {2012.03.20},
url = {http://jmlr.csail.mit.edu/papers/volume9/banerjee08a/banerjee08a.pdf}
}
@article{Bansal2007How,
author = {Bansal, M. and Belcastro, V. and Ambesi-Impiombato, A. and di Bernardo,
D.},
title = {How to infer gene networks from expression profiles},
journal = {Mol. Syst. Biol.},
year = {2007},
volume = {3},
pages = {78},
abstract = {Inferring, or 'reverse-engineering', gene networks can be defined
as the process of identifying gene interactions from experimental
data through computational analysis. Gene expression data from microarrays
are typically used for this purpose. Here we compared different reverse-engineering
algorithms for which ready-to-use software was available and that
had been tested on experimental data sets. We show that reverse-engineering
algorithms are indeed able to correctly infer regulatory interactions
among genes, at least when one performs perturbation experiments
complying with the algorithm requirements. These algorithms are superior
to classic clustering algorithms for the purpose of finding regulatory
interactions among genes, and, although further improvements are
needed, have reached a discreet performance for being practically
useful.},
doi = {10.1038/msb4100120},
pdf = {../local/Bansal2007How.pdf},
file = {Bansal2007How.pdf:Bansal2007How.pdf:PDF},
institution = {Telethon Institute of Genetics and Medicine, Via P Castellino, Naples,
Italy.},
owner = {fantine},
pii = {msb4100120},
pmid = {17299415},
timestamp = {2008.02.07},
url = {http://dx.doi.org/10.1038/msb4100120}
}
@article{Bansal2006Inference,
author = {Bansal, M. and Della Gatta, G. and Bernardo, D.},
title = {Inference of gene regulatory networks and compound mode of action
from time course gene expression profiles},
journal = {Bioinformatics},
year = {2006},
volume = {22},
pages = {815--822},
number = {7},
month = {Apr},
abstract = {MOTIVATION: Time series expression experiments are an increasingly
popular method for studying a wide range of biological systems. Here
we developed an algorithm that can infer the local network of gene-gene
interactions surrounding a gene of interest. This is achieved by
a perturbation of the gene of interest and subsequently measuring
the gene expression profiles at multiple time points. We applied
this algorithm to computer simulated data and to experimental data
on a nine gene network in Escherichia coli. RESULTS: In this paper
we show that it is possible to recover the gene regulatory network
from a time series data of gene expression following a perturbation
to the cell. We show this both on simulated data and on a nine gene
subnetwork part of the DNA-damage response pathway (SOS pathway)
in the bacteria E. coli. CONTACT: dibernardo@tigem.it SUPLEMENTARY
INFORMATION: Supplementary data are available at http://dibernado.tigem.it},
doi = {10.1093/bioinformatics/btl003},
institution = {Telethon Institute of Genetics and Medicine, Via P. Castellino 111,
80131 Naples, Italy.},
owner = {fantine},
pii = {btl003},
pmid = {16418235},
timestamp = {2008.01.30},
url = {http://dx.doi.org/10.1093/bioinformatics/btl003}
}
@article{Bantscheff2007Quantitative,
author = {Marcus Bantscheff and Markus Schirle and Gavain Sweetman and Jens
Rick and Bernhard Kuster},
title = {Quantitative mass spectrometry in proteomics: a critical review.},
journal = {Anal Bioanal Chem},
year = {2007},
volume = {389},
pages = {1017--1031},
number = {4},
month = {Oct},
abstract = {The quantification of differences between two or more physiological
states of a biological system is among the most important but also
most challenging technical tasks in proteomics. In addition to the
classical methods of differential protein gel or blot staining by
dyes and fluorophores, mass-spectrometry-based quantification methods
have gained increasing popularity over the past five years. Most
of these methods employ differential stable isotope labeling to create
a specific mass tag that can be recognized by a mass spectrometer
and at the same time provide the basis for quantification. These
mass tags can be introduced into proteins or peptides (i) metabolically,
(ii) by chemical means, (iii) enzymatically, or (iv) provided by
spiked synthetic peptide standards. In contrast, label-free quantification
approaches aim to correlate the mass spectrometric signal of intact
proteolytic peptides or the number of peptide sequencing events with
the relative or absolute protein quantity directly. In this review,
we critically examine the more commonly used quantitative mass spectrometry
methods for their individual merits and discuss challenges in arriving
at meaningful interpretations of quantitative proteomic data.},
doi = {10.1007/s00216-007-1486-6},
institution = {Cellzome AG, Meyerhofstrasse 1, 69254, Heidelberg, Germany.},
keywords = {Automatic Data Processing; Isotope Labeling; Mass Spectrometry; Peptides;
Proteins; Proteome; Proteomics; Reference Standards},
owner = {phupe},
pmid = {17668192},
timestamp = {2010.08.13},
url = {http://dx.doi.org/10.1007/s00216-007-1486-6}
}
@article{Bao2005Identifying,
author = {Lei Bao},
title = {Identifying genes related to chemosensitivity using support vector
machine.},
journal = {Methods {M}ol {M}ed},
year = {2005},
volume = {111},
pages = {233-40},
abstract = {In an effort to identify genes involved in chemosensitivity and to
evaluate the functional relationships between genes and anticancer
drugs acting by the same mechanism, a supervised machine learning
approach called support vector machine ({SVM}) is used to associate
genes with any of five predefined anticancer drug mechanistic categories.
{T}he drug activity profiles are used as training examples to train
the {SVM} and then the gene expression profiles are used as test
examples to predict their associated mechanistic categories. {T}his
method of correlating drugs and genes provides a strategy for finding
novel biologically significant relationships for molecular pharmacology.},
keywords = {biosvm},
pii = {1-59259-889-7:233}
}
@article{Bao2005Prediction,
author = {Lei Bao and Yan Cui},
title = {Prediction of the phenotypic effects of non-synonymous single nucleotide
polymorphisms using structural and evolutionary information.},
journal = {Bioinformatics},
year = {2005},
volume = {21},
pages = {2185-90},
number = {10},
month = {May},
abstract = {M{OTIVATION}: {T}here has been great expectation that the knowledge
of an individual's genotype will provide a basis for assessing susceptibility
to diseases and designing individualized therapy. {N}on-synonymous
single nucleotide polymorphisms (ns{SNP}s) that lead to an amino
acid change in the protein product are of particular interest because
they account for nearly half of the known genetic variations related
to human inherited diseases. {T}o facilitate the identification of
disease-associated ns{SNP}s from a large number of neutral ns{SNP}s,
it is important to develop computational tools to predict the phenotypic
effects of ns{SNP}s. {RESULTS}: {W}e prepared a training set based
on the variant phenotypic annotation of the {S}wiss-{P}rot database
and focused our analysis on ns{SNP}s having homologous 3{D} structures.
{S}tructural environment parameters derived from the 3{D} homologous
structure as well as evolutionary information derived from the multiple
sequence alignment were used as predictors. {T}wo machine learning
methods, support vector machine and random forest, were trained and
evaluated. {W}e compared the performance of our method with that
of the {SIFT} algorithm, which is one of the best predictive methods
to date. {A}n unbiased evaluation study shows that for ns{SNP}s with
sufficient evolutionary information (with not <10 homologous sequences),
the performance of our method is comparable with the {SIFT} algorithm,
while for ns{SNP}s with insufficient evolutionary information (<10
homologous sequences), our method outperforms the {SIFT} algorithm
significantly. {T}hese findings indicate that incorporating structural
information is critical to achieving good prediction accuracy when
sufficient evolutionary information is not available. {AVAILABILITY}:
{T}he codes and curated dataset are available at http://compbio.utmem.edu/snp/dataset/},
doi = {10.1093/bioinformatics/bti365},
pdf = {../local/Bao2005Prediction.pdf},
file = {Bao2005Prediction.pdf:local/Bao2005Prediction.pdf:PDF},
keywords = {biosvm},
pii = {bti365},
url = {http://dx.doi.org/10.1093/bioinformatics/bti365}
}
@article{Bao2002Identifying,
author = {Bao, L. and Sun, Z.},
title = {Identifying genes related to drug anticancer mechanisms using support
vector machine},
journal = {F{EBS} {L}ett.},
year = {2002},
volume = {521},
pages = {109--114},
abstract = {In an effort to identify genes related to the cell line chemosensitivity
and to evaluate the functional relationships between genes and anticancer
drugs acting by the same mechanism, a supervised machine learning
approach called support vector machine was used to label genes into
any of the five predefined anticancer drug mechanistic categories.
{A}mong dozens of unequivocally categorized genes, many were known
to be causally related to the drug mechanisms. {F}or example, a few
genes were found to be involved in the biological process triggered
by the drugs (e.g. {DNA} polymerase epsilon was the direct target
for the drugs from {DNA} antimetabolites category). {DNA} repair-related
genes were found to be enriched for about eight-fold in the resulting
gene set relative to the entire gene set. {S}ome uncharacterized
transcripts might be of interest in future studies. {T}his method
of correlating the drugs and genes provides a strategy for finding
novel biologically significant relationships for molecular pharmacology.},
pdf = {../local/bao02.pdf},
file = {bao02.pdf:local/bao02.pdf:PDF},
keywords = {biosvm microarray},
subject = {biokernel},
url = {http://www.elsevier.com/febs/402/19/42/article.html}
}
@article{Barabasi1999Emergence,
author = {Barab{\'a}si, A.-L. and Albert, R.},
title = {Emergence of scaling in random networks},
journal = {Science},
year = {1999},
volume = {286},
pages = {509--512},
abstract = {Systems as diverse as genetic networks or the World Wide Web are best
described as networks with complex topology. A common property of
many large networks is that the vertex connectivities follow a scale-free
power-law distribution. This feature was found to be a consequence
of two generic mechanisms: (i) networks expand continuously by the
addition of new vertices, and (ii) new vertices attach preferentially
to sites that are already well connected. A model based on these
two ingredients reproduces the observed stationary scale-free distributions,
which indicates that the development of large networks is governed
by robust self-organizing phenomena that go beyond the particulars
of the individual systems.},
pdf = {../local/Barabasi1999Emergence.pdf},
file = {Barabasi1999Emergence.pdf:Barabasi1999Emergence.pdf:PDF},
subject = {bionet},
url = {http://www.sciencemag.org/cgi/reprint/286/5439/509.pdf}
}
@unpublished{Barabasi2001Deterministic,
author = {Barab{\'a}si, A.-L. and Ravasz, E.},
title = {Deterministic scale-free networks},
note = {E-print cond-mat/0107419},
year = {2001},
pdf = {../local/bara01.pdf},
file = {bara01.pdf:local/bara01.pdf:PDF},
subject = {compnet},
url = {http://xxx.lanl.gov/abs/cond-mat/0107419}
}
@article{Baranger1971Matrices,
author = {Baranger, J. and Duc-Jacquet, M.},
title = {Matrices tridiagonales sym\'etriques et matrices factorisables},
journal = {Revue fran\c caise d'informatique et de recherche op\'erationnelle,
s\'erie rouge},
year = {1971},
volume = {5},
pages = {61--66},
number = {3},
pdf = {../local/Baranger1971Matrices.pdf},
file = {Baranger1971Matrices.pdf:Baranger1971Matrices.pdf:PDF},
owner = {jp},
timestamp = {2010.08.03},
url = {http://www.numdam.org/item?id=M2AN_1971__5_3_61_0}
}
@article{Barash2010Deciphering,
author = {Yoseph Barash and John A Calarco and Weijun Gao and Qun Pan and Xinchen
Wang and Ofer Shai and Benjamin J Blencowe and Brendan J Frey},
title = {Deciphering the splicing code.},
journal = {Nature},
year = {2010},
volume = {465},
pages = {53--59},
number = {7294},
month = {May},
abstract = {Alternative splicing has a crucial role in the generation of biological
complexity, and its misregulation is often involved in human disease.
Here we describe the assembly of a 'splicing code', which uses combinations
of hundreds of RNA features to predict tissue-dependent changes in
alternative splicing for thousands of exons. The code determines
new classes of splicing patterns, identifies distinct regulatory
programs in different tissues, and identifies mutation-verified regulatory
sequences. Widespread regulatory strategies are revealed, including
the use of unexpectedly large combinations of features, the establishment
of low exon inclusion levels that are overcome by features in specific
tissues, the appearance of features deeper into introns than previously
appreciated, and the modulation of splice variant levels by transcript
structure characteristics. The code detected a class of exons whose
inclusion silences expression in adult tissues by activating nonsense-mediated
messenger RNA decay, but whose exclusion promotes expression during
embryogenesis. The code facilitates the discovery and detailed characterization
of regulated alternative splicing events on a genome-wide scale.},
doi = {10.1038/nature09000},
institution = {Biomedical Engineering, Department of Electrical and Computer Engineering,
University of Toronto, 10 King's College Road, Toronto M5S 3G4, Canada.},
keywords = {Alternative Splicing, genetics; Animals; Gene Expression Regulation;
Gene Silencing; Genetic Code, genetics; Humans; Mice; Models, Genetic;
RNA, Messenger, metabolism; Reproducibility of Results},
language = {eng},
medline-pst = {ppublish},
owner = {philippe},
pii = {nature09000},
pmid = {20445623},
timestamp = {2011.06.04},
url = {http://dx.doi.org/10.1038/nature09000}
}
@article{Baraud2009Gaussian,
author = {Yannick Baraud and Christophe Giraud and Sylvie Huet},
title = {Gaussian model selection with an unknown variance},
journal = {{A}nnals {O}f {S}tatistics, to appear},
year = {2009},
volume = {37},
pages = {630},
url = {http://doi:10.1214/07-AOS573}
}
@article{Barkai1997Robustness,
author = {Barkai, N. and Leibler, S.},
title = {Robustness in simple biochemical networks.},
journal = {Nature},
year = {1997},
volume = {387},
pages = {913--917},
number = {6636},
month = {Jun},
abstract = {Cells use complex networks of interacting molecular components to
transfer and process information. These "computational devices of
living cells" are responsible for many important cellular processes,
including cell-cycle regulation and signal transduction. Here we
address the issue of the sensitivity of the networks to variations
in their biochemical parameters. We propose a mechanism for robust
adaptation in simple signal transduction networks. We show that this
mechanism applies in particular to bacterial chemotaxis. This is
demonstrated within a quantitative model which explains, in a unified
way, many aspects of chemotaxis, including proper responses to chemical
gradients. The adaptation property is a consequence of the network's
connectivity and does not require the 'fine-tuning' of parameters.
We argue that the key properties of biochemical networks should be
robust in order to ensure their proper functioning.},
doi = {10.1038/43199},
pdf = {../local/Barkai1997Robustness.pdf},
file = {Barkai1997Robustness.pdf:Barkai1997Robustness.pdf:PDF},
institution = {Department of Physics, Princeton University, New Jersey 08544, USA.},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pmid = {9202124},
timestamp = {2013.01.25},
url = {http://dx.doi.org/10.1038/43199}
}
@article{Barretina2012Cancer,
author = {Barretina, Jordi and Caponigro, Giordano and Stransky, Nicolas and
Venkatesan, Kavitha and Margolin, Adam A. and Kim, Sungjoon and Wilson,
Christopher J. and Lehár, Joseph and Kryukov, Gregory V. and Sonkin,
Dmitriy and Reddy, Anupama and Liu, Manway and Murray, Lauren and
Berger, Michael F. and Monahan, John E. and Morais, Paula and Meltzer,
Jodi and Korejwa, Adam and Jané-Valbuena, Judit and Mapa, Felipa
A. and Thibault, Joseph and Bric-Furlong, Eva and Raman, Pichai and
Shipway, Aaron and Engels, Ingo H. and Cheng, Jill and Yu, Guoying
K. and Yu, Jianjun and Aspesi, Jr, Peter and {de Silva}, Melanie
and Jagtap, Kalpana and Jones, Michael D. and Wang, Li and Hatton,
Charles and Palescandolo, Emanuele and Gupta, Supriya and Mahan,
Scott and Sougnez, Carrie and Onofrio, Robert C. and Liefeld, Ted
and MacConaill, Laura and Winckler, Wendy and Reich, Michael and
Li, Nanxin and Mesirov, Jill P. and Gabriel, Stacey B. and Getz,
Gad and Ardlie, Kristin and Chan, Vivien and Myer, Vic E. and Weber,
Barbara L. and Porter, Jeff and Warmuth, Markus and Finan, Peter
and Harris, Jennifer L. and Meyerson, Matthew and Golub, Todd R.
and Morrissey, Michael P. and Sellers, William R. and Schlegel, Robert
and Garraway, Levi A.},
title = {The Cancer Cell Line Encyclopedia enables predictive modelling of
anticancer drug sensitivity.},
journal = {Nature},
year = {2012},
volume = {483},
pages = {603--607},
number = {7391},
month = {Mar},
abstract = {The systematic translation of cancer genomic data into knowledge of
tumour biology and therapeutic possibilities remains challenging.
Such efforts should be greatly aided by robust preclinical model
systems that reflect the genomic diversity of human cancers and for
which detailed genetic and pharmacological annotation is available.
Here we describe the Cancer Cell Line Encyclopedia (CCLE): a compilation
of gene expression, chromosomal copy number and massively parallel
sequencing data from 947 human cancer cell lines. When coupled with
pharmacological profiles for 24 anticancer drugs across 479 of the
cell lines, this collection allowed identification of genetic, lineage,
and gene-expression-based predictors of drug sensitivity. In addition
to known predictors, we found that plasma cell lineage correlated
with sensitivity to IGF1 receptor inhibitors; AHR expression was
associated with MEK inhibitor efficacy in NRAS-mutant lines; and
SLFN11 expression predicted sensitivity to topoisomerase inhibitors.
Together, our results indicate that large, annotated cell-line collections
may help to enable preclinical stratification schemata for anticancer
agents. The generation of genetic predictions of drug response in
the preclinical setting and their incorporation into cancer clinical
trial design could speed the emergence of 'personalized' therapeutic
regimens.},
doi = {10.1038/nature11003},
pdf = {../local/Barretina2012Cancer.pdf},
file = {Barretina2012Cancer.pdf:Barretina2012Cancer.pdf:PDF},
institution = {The Broad Institute of Harvard and MIT, Cambridge, Massachusetts
02142, USA.},
language = {eng},
medline-pst = {epublish},
owner = {jp},
pii = {nature11003},
pmid = {22460905},
timestamp = {2012.04.06},
url = {http://dx.doi.org/10.1038/nature11003}
}
@article{Barrett2011NCBI,
author = {Barrett, T. and Troup, D.B. and Wilhite, S.E. and Ledoux, P. and
Evangelista, C. and Kim, I.F. and Tomashevsky, M. and Marshall, K.A.
and Phillippy, K.H. and Sherman, P.M. and others},
title = {NCBI GEO: archive for functional genomics data sets - 10 years on},
journal = {Nucleic acids research},
year = {2011},
volume = {39},
pages = {D1005--D1010},
number = {suppl 1},
publisher = {Oxford Univ Press}
}
@article{Barrett2009NCBI,
author = {Barrett, T. and Troup, D.B. and Wilhite, S.E. and Ledoux, P. and
Rudnev, D. and Evangelista, C. and Kim, I.F. and Soboleva, A. and
Tomashevsky, M. and Marshall, K.A. and others},
title = {NCBI GEO: archive for high-throughput functional genomic data},
journal = {Nucleic acids research},
year = {2009},
volume = {37},
pages = {D885--D890},
number = {suppl 1},
publisher = {Oxford Univ Press}
}
@article{Barron1993Universal,
author = {Barron, A.R. },
title = {Universal approximation bounds for superpositions of a sigmoidal
function},
journal = {I{EEE} {T}rans. {I}nform. {T}heory},
year = {1993},
volume = {39},
pages = {930-945},
number = {3},
month = {May},
abstract = {Approximation properties of a class of artificial neural networks
are established. {I}t is shown that feedforward networks with one
layer of sigmoidal nonlinearities achieve integrated squared error
of order {O} (1/n), where n is the number of nodes. {T}he approximated
function is assumed to have a bound on the first moment of the magnitude
distribution of the {F}ourier transform. {T}he nonlinear parameters
associated with the sigmoidal nodes, as well as the parameters of
linear combination, are adjusted in the approximation. {I}n contrast,
it is shown that for series expansions with n terms, in which only
the parameters of linear combination are adjusted, the integrated
squared approximation error cannot be made smaller than order 1/n2d/
uniformly for functions satisfying the same smoothness assumption,
where d is the dimension of the input to the function. {F}or the
class of functions examined, the approximation rate and the parsimony
of the parameterization of the networks are shown to be advantageous
in high-dimensional settings },
pdf = {../local/Barron1993Universal.pdf},
file = {Barron1993Universal.pdf:local/Barron1993Universal.pdf:PDF},
keywords = {information-theory},
owner = {vert}
}
@article{Barron1991Minimum,
author = {Barron, A.R. and Cover, T.M. },
title = {Minimum complexity density estimation},
journal = {I{EEE} {T}rans. {I}nform. {T}heory},
year = {1991},
volume = {37},
pages = {1034-1054},
number = {4},
month = {Jul},
abstract = {The authors introduce an index of resolvability that is proved to
bound the rate of convergence of minimum complexity density estimators
as well as the information-theoretic redundancy of the corresponding
total description length. {T}he results on the index of resolvability
demonstrate the statistical effectiveness of the minimum description-length
principle as a method of inference. {T}he minimum complexity estimator
converges to true density nearly as fast as an estimator based on
prior knowledge of the true subclass of densities. {I}nterpretations
and basic properties of minimum complexity estimators are discussed.
{S}ome regression and classification problems that can be examined
from the minimum description-length framework are considered },
pdf = {../local/Barron1991Minimum.pdf},
file = {Barron1991Minimum.pdf:local/Barron1991Minimum.pdf:PDF},
keywords = {information-theory},
owner = {vert}
}
@article{Barron1988bound,
author = {Barron, A.R. and Cover, T.M. },
title = {A bound on the financial value of information},
journal = {IEEE Trans. Inform. Theory},
year = {1988},
volume = {34},
pages = {1097-1100},
number = {5},
month = {Sep},
abstract = {It is shown that each bit of information at most doubles the resulting
wealth in the general stock-market setup. {T}his information bound
on the growth of wealth is actually attained for certain probability
distributions on the market investigated by {J}. {K}elly (1956).
{T}he bound is shown to be a special case of the result that the
increase in exponential growth of wealth achieved with true knowledge
of the stock market distribution {F} over that achieved with incorrect
knowledge {G} is bounded above by the entropy of {F} relative to
{G} },
pdf = {../local/Barron1988bound.pdf},
file = {Barron1988bound.pdf:local/Barron1988bound.pdf:PDF},
keywords = {information-theory},
owner = {vert}
}
@article{Barron1992Distribution,
author = {Barron, A.R. and Gy{\"o}rfi, L. and van der Meulen, E.C.},
title = {Distribution estimation consistent in total variation and in two
types of information divergence},
journal = {I{EEE} {T}rans. {I}nform. {T}heory},
year = {1992},
volume = {38},
pages = {1437-1454},
number = {5},
month = {Sep},
abstract = {The problem of the nonparametric estimation of a probability distribution
is considered from three viewpoints: the consistency in total variation,
the consistency in information divergence, and consistency in reversed-order
information divergence. {T}hese types of consistencies are relatively
strong criteria of convergence, and a probability distribution cannot
be consistently estimated in either type of convergence without any
restrictions on the class of probability distributions allowed. {H}istogram-based
estimators of distribution are presented which, under certain conditions,
converge in total variation, in information divergence, and in reversed-order
information divergence to the unknown probability distribution. {S}ome
a priori information about the true probability distribution is assumed
in each case. {A}s the concept of consistency in information divergence
is stronger than that of convergence in total variation, additional
assumptions are imposed in the cases of informational divergences},
pdf = {../local/Barron1992Distribution.pdf},
file = {Barron1992Distribution.pdf:local/Barron1992Distribution.pdf:PDF},
owner = {vert}
}
@article{Barron1998minimum,
author = {Barron, A. and Rissanen, J. and Bin Yu},
title = {The minimum description length principle in coding and modeling},
journal = {I{EEE} {T}rans. {I}nform. {T}heory},
year = {1998},
volume = {44},
pages = {2743-2760},
number = {6},
month = {Oct},
abstract = {We review the principles of minimum description length and stochastic
complexity as used in data compression and statistical modeling.
{S}tochastic complexity is formulated as the solution to optimum
universal coding problems extending {S}hannon's basic source coding
theorem. {T}he normalized maximized likelihood, mixture, and predictive
codings are each shown to achieve the stochastic complexity to within
asymptotically vanishing terms. {W}e assess the performance of the
minimum description length criterion both from the vantage point
of quality of data compression and accuracy of statistical inference.
{C}ontext tree modeling, density estimation, and model selection
in {G}aussian linear regression serve as examples },
pdf = {../local/Barron1998minimum.pdf},
file = {Barron1998minimum.pdf:local/Barron1998minimum.pdf:PDF},
keywords = {information-theory},
owner = {vert}
}
@article{Barry2005Significance,
author = {Barry, W. T. and Nobel, A. B. and Wright, F. A.},
title = {Significance analysis of functional categories in gene expression
studies: a structured permutation approach},
journal = {Bioinformatics},
year = {2005},
volume = {21},
pages = {1943--1949},
number = {9},
month = {May},
abstract = {MOTIVATION: In high-throughput genomic and proteomic experiments,
investigators monitor expression across a set of experimental conditions.
To gain an understanding of broader biological phenomena, researchers
have until recently been limited to post hoc analyses of significant
gene lists.Method: We describe a general framework, significance
analysis of function and expression (SAFE), for conducting valid
tests of gene categories ab initio. SAFE is a two-stage, permutation-based
method that can be applied to various experimental designs, accounts
for the unknown correlation among genes and enables permutation-based
estimation of error rates. RESULTS: The utility and flexibility of
SAFE is illustrated with a microarray dataset of human lung carcinomas
and gene categories based on Gene Ontology and the Protein Family
database. Significant gene categories were observed in comparisons
of (1) tumor versus normal tissue, (2) multiple tumor subtypes and
(3) survival times. AVAILABILITY: Code to implement SAFE in the statistical
package R is available from the authors. SUPPLEMENTARY INFORMATION:
http://www.bios.unc.edu/~fwright/SAFE.},
doi = {10.1093/bioinformatics/bti260},
institution = {Department of Biostatistics, University of North Carolina at Chapel
Hill, 27599-7420, USA.},
owner = {jp},
pii = {bti260},
pmid = {15647293},
timestamp = {2008.12.05},
url = {http://dx.doi.org/10.1093/bioinformatics/bti260}
}
@article{Bartel2004MicroRNAs,
author = {David P Bartel},
title = {MicroRNAs: genomics, biogenesis, mechanism, and function.},
journal = {Cell},
year = {2004},
volume = {116},
pages = {281--297},
number = {2},
month = {Jan},
abstract = {MicroRNAs (miRNAs) are endogenous approximately 22 nt RNAs that can
play important regulatory roles in animals and plants by targeting
mRNAs for cleavage or translational repression. Although they escaped
notice until relatively recently, miRNAs comprise one of the more
abundant classes of gene regulatory molecules in multicellular organisms
and likely influence the output of many protein-coding genes.},
pdf = {../local/Bartel2004MicroRNAs.pdf},
file = {Bartel2004MicroRNAs.pdf:Bartel2004MicroRNAs.pdf:PDF},
institution = {Whitehead Institute for Biomedical Research, 9 Cambridge Center,
Cambridge, MA 02142, USA. dbartel@wi.mit.edu},
keywords = {sirna},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {S0092867404000455},
pmid = {14744438},
timestamp = {2009.10.28}
}
@techreport{Bartlett2003Convexity,
author = {Bartlett, P.I. and Jordan, M.I. and McAuliffe, J.D.},
title = {Convexity, classification and risk bounds},
institution = {UC Berkeley Statistics},
year = {2003},
number = {638},
pdf = {../local/Bartlett2003Convexity.pdf},
file = {Bartlett2003Convexity.pdf:local/Bartlett2003Convexity.pdf:PDF},
owner = {jeanphilippevert}
}
@article{Bartlett2005Local,
author = {Bartlett, P. L. and Bousquet, O. and Mendelson, S.},
title = {Local {R}ademacher {C}omplexities},
journal = {Ann. {S}tat.},
year = {2005},
volume = {33},
pages = {1497-1537},
number = {4},
doi = {10.1214/009053605000000282},
pdf = {../local/Bartlett2005Local.pdf},
file = {Bartlett2005Local.pdf:local/Bartlett2005Local.pdf:PDF},
url = {http://dx.doi.org/10.1214/009053605000000282}
}
@article{Bartlett2002Rademacher,
author = {Bartlett, P. L. and Mendelson, S.},
title = {Rademacher and {G}aussian complexities: risk bounds and structural
results},
journal = {J. Mach. Learn. Res.},
year = {2002},
volume = {3},
pages = {463--482},
pdf = {../local/Bartlett2002Rademacher.pdf},
file = {Bartlett2002Rademacher.pdf:Bartlett2002Rademacher.pdf:PDF},
owner = {jp},
timestamp = {2012.04.15},
url = {http://jmlr.csail.mit.edu/papers/volume3/bartlett02a/bartlett02a.pdf}
}
@article{Bartlett2007Sparseness,
author = {Bartlett, P. L. and Tewari, A.},
title = {Sparseness vs Estimating Conditional Probabilities: Some Asymptotic
Results},
journal = {J. Mach. Learn. Res.},
year = {2007},
volume = {8},
pages = {775--790},
abstract = {One of the nice properties of kernel classifiers such as SVMs is that
they often produce sparse solutions. However, the decision functions
of these classifiers cannot always be used to estimate the conditional
probability of the class label. We investigate the relationship between
these two properties and show that these are intimately related:
sparseness does not occur when the conditional probabilities can
be unambiguously estimated. We consider a family of convex loss functions
and derive sharp asymptotic results for the fraction of data that
becomes support vectors. This enables us to characterize the exact
trade-off between sparseness and the ability to estimate conditional
probabilities for these loss functions.},
keywords = {PUlearning},
owner = {jp},
timestamp = {2010.02.01},
url = {http://jmlr.csail.mit.edu/papers/v8/bartlett07a.html}
}
@inproceedings{Bartlett2004Sparseness,
author = {P. L. Bartlett and A. Tewari},
title = {Sparseness vs estimating conditional probabilities: {S}ome asymptotic
results},
booktitle = {Lecture {N}otes in {C}omputer {S}cience},
year = {2004},
volume = {3120},
pages = {564-578},
publisher = {Springer}
}
@article{Basak1988Determining,
author = {S.C. Basak and V.R. Magnuson and G.J. Niemi and R.R. Regal},
title = {Determining {S}tructural {S}imilarity of {C}hemicals {U}sing {G}raph
{T}heoretic {I}ndices},
journal = {Discrete {A}ppl. {M}ath.},
year = {1988},
volume = {19},
pages = {17-44},
owner = {mahe},
timestamp = {2006.09.01}
}
@article{Bashir2008Evaluation,
author = {Ali Bashir and Stanislav Volik and Colin Collins and Vineet Bafna
and Benjamin J Raphael},
title = {Evaluation of paired-end sequencing strategies for detection of genome
rearrangements in cancer.},
journal = {PLoS Comput. Biol.},
year = {2008},
volume = {4},
pages = {e1000051},
number = {4},
month = {Apr},
abstract = {Paired-end sequencing is emerging as a key technique for assessing
genome rearrangements and structural variation on a genome-wide scale.
This technique is particularly useful for detecting copy-neutral
rearrangements, such as inversions and translocations, which are
common in cancer and can produce novel fusion genes. We address the
question of how much sequencing is required to detect rearrangement
breakpoints and to localize them precisely using both theoretical
models and simulation. We derive a formula for the probability that
a fusion gene exists in a cancer genome given a collection of paired-end
sequences from this genome. We use this formula to compute fusion
gene probabilities in several breast cancer samples, and we find
that we are able to accurately predict fusion genes in these samples
with a relatively small number of fragments of large size. We further
demonstrate how the ability to detect fusion genes depends on the
distribution of gene lengths, and we evaluate how different parameters
of a sequencing strategy impact breakpoint detection, breakpoint
localization, and fusion gene detection, even in the presence of
errors that suggest false rearrangements. These results will be useful
in calibrating future cancer sequencing efforts, particularly large-scale
studies of many cancer genomes that are enabled by next-generation
sequencing technologies.},
doi = {10.1371/journal.pcbi.1000051},
pdf = {../local/Bashir2008Evaluation.pdf},
file = {Bashir2008Evaluation.pdf:Bashir2008Evaluation.pdf:PDF},
institution = {Bioinformatics Graduate Program, University of California San Diego,
San Diego, California, United States of America. abashir@ucsd.edu},
keywords = {ngs},
owner = {jp},
pmid = {18404202},
timestamp = {2009.10.09},
url = {http://dx.doi.org/10.1371/journal.pcbi.1000051}
}
@book{Basseville1993Detection,
title = {Detection of abrupt changes: theory and application},
publisher = {Prentice Hall Information},
year = {1993},
author = {Basseville, M. and Nikiforov, N.},
series = {Information and System Sciences Series},
pdf = {../local/Basseville1993Detection.pdf},
file = {Basseville1993Detection.pdf:Basseville1993Detection.pdf:PDF},
owner = {jp},
timestamp = {2010.06.02}
}
@article{Baudat2000Generalized,
author = {G. Baudat and F. Anouar},
title = {Generalized discriminant analysis using a kernel approach.},
journal = {Neural Comput.},
year = {2000},
volume = {12},
pages = {2385-404},
number = {10},
month = {Oct},
abstract = {We present a new method that we call generalized discriminant analysis
({GDA}) to deal with nonlinear discriminant analysis using kernel
function operator. {T}he underlying theory is close to the support
vector machines ({SVM}) insofar as the {GDA} method provides a mapping
of the input vectors into high-dimensional feature space. {I}n the
transformed space, linear properties make it easy to extend and generalize
the classical linear discriminant analysis ({LDA}) to nonlinear discriminant
analysis. {T}he formulation is expressed as an eigenvalue problem
resolution. {U}sing a different kernel, one can cover a wide class
of nonlinearities. {F}or both simulated data and alternate kernels,
we give classification results, as well as the shape of the decision
function. {T}he results are confirmed using real data to perform
seed classification.},
doi = {10.1162/089976600300014980},
pdf = {../local/Baudat2000Generalized.pdf},
file = {Baudat2000Generalized.pdf:Baudat2000Generalized.pdf:PDF},
url = {http://dx.doi.org/10.1162/089976600300014980}
}
@article{Bauknecht1996Locating,
author = {H. Bauknecht and A. Zell and H. Bayer and P. Levi and M. Wagener
and J. Sadowski and J. Gasteiger},
title = {{L}ocating biologically active compounds in medium-sized heterogeneous
datasets by topological autocorrelation vectors: dopamine and benzodiazepine
agonists.},
journal = {J Chem Inf Comput Sci},
year = {1996},
volume = {36},
pages = {1205--1213},
number = {6},
abstract = {Electronic properties located on the atoms of a molecule such as partial
atomic charges as well as electronegativity and polarizability values
are encoded by an autocorrelation vector accounting for the constitution
of a molecule. This encoding procedure is able to distinguish between
compounds being dopamine agonists and those being benzodiazepine
receptor agonists even after projection into a two-dimensional self-organizing
network. The two types of compounds can still be distinguished if
they are buried in a dataset of 8323 compounds of a chemical supplier
catalog comprising a wide structural variety. The maps obtained by
this sequence of events, calculation of empirical physicochemical
effects, encoding in a topological autocorrelation vector, and projection
by a self-organizing neural network, can thus be used for searching
for structural similarity, and, in particular, for finding new lead
structures with biological activity.},
keywords = {Animals, Chemical, Chemistry, Databases, Dopamine Agonists, Drug Design,
Electrochemistry, Factual, GABA-A, Logistic Models, Models, Molecular
Structure, Neural Networks (Computer), Non-U.S. Gov't, Phenols, Physical,
Receptors, Research Support, Structure-Activity Relationship, Tetrahymena
pyriformis, 8941996},
owner = {mahe},
pmid = {8941996},
timestamp = {2006.08.17}
}
@article{Baulcombe1996RNA,
author = {Baulcombe, D. C.},
title = {{RNA} as a target and an initiator of post-transcriptional gene silencing
in transgenic plants.},
journal = {Plant Mol. Biol.},
year = {1996},
volume = {32},
pages = {79--88},
number = {1-2},
month = {Oct},
abstract = {Post-transcriptional gene silencing in transgenic plants is the manifestation
of a mechanism that suppresses RNA accumulation in a sequence-specific
manner. The target RNA species may be the products of transgenes,
endogenous plant genes or viral RNAs. For an RNA to be a target it
is necessary only that it has sequence homology to the sense RNA
product of the transgene. There are three current hypotheses to account
for the mechanism of post transcriptional gene silencing. These models
all require production of an antisense RNA of the RNA targets to
account for the specificity of the mechanism. There could be either
direct transcription of the antisense RNA from the transgene, antisense
RNA produced in response to over expression of the transgene or antisense
RNA produced in response to the production of an aberrant sense RNA
product of the transgene. To determine which of these models is correct
it will be necessary to find out whether transgene methylation, which
is frequently associated with the potential of transgenes to confer
post-transcriptional gene silencing, is a cause or a consequence
of the process.},
doi = {10.1007/BF00039378},
pdf = {../local/Baulcombe1996RNA.pdf},
file = {Baulcombe1996RNA.pdf:Baulcombe1996RNA.pdf:PDF},
owner = {vert},
pmid = {8980475},
timestamp = {2006.03.28},
url = {http://dx.doi.org/10.1007/BF00039378}
}
@article{Baumgartner2004Supervised,
author = {Baumgartner, C. and Bohm, C. and Baumgartner, D. and Marini, G. and
Weinberger, K. and Olgemoller, B. and Liebl, B. and Roscher, A. A.},
title = {Supervised machine learning techniques for the classification of
metabolic disorders in newborns},
journal = {Bioinformatics},
year = {2004},
volume = {20},
pages = {2985-2996},
number = {17},
abstract = {Motivation: {D}uring the {B}avarian newborn screening programme all
newborns have been tested for about 20 inherited metabolic disorders.
{O}wing to the amount and complexity of the generated experimental
data, machine learning techniques provide a promising approach to
investigate novel patterns in high-dimensional metabolic data which
form the source for constructing classification rules with high discriminatory
power. {R}esults: {S}ix machine learning techniques have been investigated
for their classification accuracy focusing on two metabolic disorders,
phenylketo nuria ({PKU}) and medium-chain acyl-{C}o{A} dehydrogenase
deficiency ({MCADD}). {L}ogistic regression analysis led to superior
classification rules (sensitivity >96.8%, specificity >99.98%) compared
to all investigated algorithms. {I}ncluding novel constellations
of metabolites into the models, the positive predictive value could
be strongly increased ({PKU} 71.9% versus 16.2%, {MCADD} 88.4% versus
54.6% compared to the established diagnostic markers). {O}ur results
clearly prove that the mined data confirm the known and indicate
some novel metabolic patterns which may contribute to a better understanding
of newborn metabolism. {A}vailability: {WEKA} machine learning package:
www.cs.waikato.ac.nz/~ml/weka and statistical software package {ADE}-4:
http://pbil.univ-lyon1.fr/{ADE}-4},
doi = {10.1093/bioinformatics/bth343},
pdf = {../local/Baumgartner2004Supervised.pdf},
file = {Baumgartner2004Supervised.pdf:local/Baumgartner2004Supervised.pdf:PDF},
keywords = {biosvm proteomics},
owner = {jeanphilippevert},
url = {http://bioinformatics.oupjournals.org/cgi/content/abstract/20/17/2985}
}
@article{Baumgartner2004Unsupervised,
author = {R. Baumgartner and R. Somorjai and C. Bowman and T. C. Sorrell and
C. E. Mountford and U. Himmelreich},
title = {Unsupervised feature dimension reduction for classification of {MR}
spectra.},
journal = {Magn {R}eson {I}maging},
year = {2004},
volume = {22},
pages = {251-6},
number = {2},
month = {Feb},
abstract = {We present an unsupervised feature dimension reduction method for
the classification of magnetic resonance spectra. {T}he technique
preserves spectral information, important for disease profiling.
{W}e propose to use this technique as a preprocessing step for computationally
demanding wrapper-based feature subset selection. {W}e show that
the classification accuracy on an independent test set can be sustained
while achieving considerable feature reduction. {O}ur method is applicable
to other classification techniques, such as neural networks, support
vector machines, etc.},
doi = {10.1016/j.mri.2003.08.033},
pdf = {../local/Baumgartner2004Unsupervised.pdf},
file = {Baumgartner2004Unsupervised.pdf:local/Baumgartner2004Unsupervised.pdf:PDF},
keywords = {Algorithms, Ambergris, Candida, Candida albicans, Combinatorial Chemistry
Techniques, Eye Enucleation, Humans, Magnetic Resonance Spectroscopy,
Melanoma, Models, Molecular, Molecular Conformation, Non-U.S. Gov't,
Odors, P.H.S., Perfume, Predictive Value of Tests, Prognosis, Prospective
Studies, Quantitative Structure-Activity Relationship, Research Support,
U.S. Gov't, Uveal Neoplasms, 15010118},
pii = {S0730725X03003503},
url = {http://dx.doi.org/10.1016/j.mri.2003.08.033}
}
@article{Baxter2000Model,
author = {Jonathan Baxter},
title = {A Model of Inductive Bias Learning},
journal = {Journal of Artificial Intelligence Research},
year = {2000},
volume = {12},
pages = {149-198},
url = {http://citeseer.ist.psu.edu/article/baxter00model.html}
}
@inproceedings{Baxter1997A,
author = {Jonathan Baxter},
title = {A Bayesian/information theoretic model of learning to learn via multiple
task sampling},
booktitle = {Machine Learning},
year = {1997},
pages = {7--39}
}
@inproceedings{Baxter1996Bayesian/information,
author = {Jonathan Baxter},
title = {A Bayesian/information theoretic model of bias learning},
booktitle = {COLT '96: Proceedings of the ninth annual conference on Computational
learning theory},
year = {1996},
pages = {77--88},
address = {New York, NY, USA},
publisher = {ACM Press},
doi = {http://doi.acm.org/10.1145/238061.238071},
isbn = {0-89791-811-8},
location = {Desenzano del Garda, Italy}
}
@inproceedings{Baxter1996Learning,
author = {Jonathan Baxter},
title = {Learning Model Bias},
booktitle = {Advances in Neural Information Processing Systems},
year = {1996},
pages = {169--175},
publisher = {MIT Press}
}
@article{Bazzani2001SVM,
author = {A. Bazzani and A. Bevilacqua and D. Bollini and R. Brancaccio and
R. Campanini and N. Lanconelli and A. Riccardi and D. Romani},
title = {An {SVM} classifier to separate false signals from microcalcifications
in digital mammograms.},
journal = {Phys {M}ed {B}iol},
year = {2001},
volume = {46},
pages = {1651-63},
number = {6},
month = {Jun},
abstract = {In this paper we investigate the feasibility of using an {SVM} (support
vector machine) classifier in our automatic system for the detection
of clustered microcalcifications in digital mammograms. {SVM} is
a technique for pattern recognition which relies on the statistical
learning theory. {I}t minimizes a function of two terms: the number
of misclassified vectors of the training set and a term regarding
the generalization classifier capability. {W}e compare the {SVM}
classifier with an {MLP} (multi-layer perceptron) in the false-positive
reduction phase of our detection scheme: a detected signal is considered
either microcalcification or false signal, according to the value
of a set of its features. {T}he {SVM} classifier gets slightly better
results than the {MLP} one ({A}z value of 0.963 against 0.958) in
the presence of a high number of training data; the improvement becomes
much more evident ({A}z value of 0.952 against 0.918) in training
sets of reduced size. {F}inally, the setting of the {SVM} classifier
is much easier than the {MLP} one.},
doi = {10.1088/0031-9155/46/6/305},
pdf = {../local/Bazzani2001SVM.pdf},
file = {Bazzani2001SVM.pdf:local/Bazzani2001SVM.pdf:PDF},
keywords = {biosvm image},
url = {http://dx.doi.org/10.1088/0031-9155/46/6/305}
}
@article{Beal2005Bayesian,
author = {Beal, M. J. and Falciani, F. and Ghahramani, Z. and Rangel, C. and
Wild, D. L.},
title = {A {B}ayesian approach to reconstructing genetic regulatory networks
with hidden factors.},
journal = {Bioinformatics},
year = {2005},
volume = {21},
pages = {349--356},
number = {3},
month = {Feb},
abstract = {M{OTIVATION}: {W}e have used state-space models ({SSM}s) to reverse
engineer transcriptional networks from highly replicated gene expression
profiling time series data obtained from a well-established model
of {T} cell activation. {SSM}s are a class of dynamic {B}ayesian
networks in which the observed measurements depend on some hidden
state variables that evolve according to {M}arkovian dynamics. {T}hese
hidden variables can capture effects that cannot be directly measured
in a gene expression profiling experiment, for example: genes that
have not been included in the microarray, levels of regulatory proteins,
the effects of m{RNA} and protein degradation, etc. {RESULTS}: {W}e
have approached the problem of inferring the model structure of these
state-space models using both classical and {B}ayesian methods. {I}n
our previous work, a bootstrap procedure was used to derive classical
confidence intervals for parameters representing 'gene-gene' interactions
over time. {I}n this article, variational approximations are used
to perform the analogous model selection task in the {B}ayesian context.
{C}ertain interactions are present in both the classical and the
{B}ayesian analyses of these regulatory networks. {T}he resulting
models place {J}un{B} and {J}un{D} at the centre of the mechanisms
that control apoptosis and proliferation. {T}hese mechanisms are
key for clonal expansion and for controlling the long term behavior
(e.g. programmed cell death) of these cells. {AVAILABILITY}: {S}upplementary
data is available at http://public.kgi.edu/wild/index.htm and {M}atlab
source code for variational {B}ayesian learning of {SSM}s is available
at http://www.cse.ebuffalo.edu/faculty/mbeal/software.html.},
doi = {10.1093/bioinformatics/bti014},
pdf = {../local/Beal2005Bayesian.pdf},
file = {Beal2005Bayesian.pdf:local/Beal2005Bayesian.pdf:PDF},
keywords = {biogm},
owner = {vert},
pii = {bti014},
pmid = {15353451},
timestamp = {2006.01.18},
url = {http://dx.doi.org/10.1093/bioinformatics/bti014}
}
@article{Beck2009fast,
author = {Beck, A. and Teboulle, M.},
title = {A fast iterative shrinkage-thresholding algorithm for linear inverse
problems},
journal = {SIAM J. Img. Sci.},
year = {2009},
volume = {2},
pages = {183--202},
number = {1},
doi = {10.1137/080716542},
pdf = {../local/Beck2009fast.pdf},
file = {Beck2009fast.pdf:Beck2009fast.pdf:PDF},
owner = {jp},
timestamp = {2010.10.14},
url = {http://dx.doi.org/10.1137/080716542}
}
@article{Becker2004G,
author = {Becker, O. M. and Marantz, Y. and Shacham, S. and Inbal, B. and Heifetz,
A. and Kalid, O. and Bar-Haim, S. and Warshaviak, D. and Fichman,
M. and Noiman, S.},
title = {G protein-coupled receptors: in silico drug discovery in {3D}},
journal = {Proc. Natl. Acad. Sci. USA},
year = {2004},
volume = {101},
pages = {11304--11309},
number = {31},
month = {Aug},
abstract = {The application of structure-based in silico methods to drug discovery
is still considered a major challenge, especially when the x-ray
structure of the target protein is unknown. Such is the case with
human G protein-coupled receptors (GPCRs), one of the most important
families of drug targets, where in the absence of x-ray structures,
one has to rely on in silico 3D models. We report repeated success
in using ab initio in silico GPCR models, generated by the predict
method, for blind in silico screening when applied to a set of five
different GPCR drug targets. More than 100,000 compounds were typically
screened in silico for each target, leading to a selection of <100
"virtual hit" compounds to be tested in the lab. In vitro binding
assays of the selected compounds confirm high hit rates, of 12-21\%
(full dose-response curves, Ki < 5 microM). In most cases, the best
hit was a novel compound (New Chemical Entity) in the 1- to 100-nM
range, with very promising pharmacological properties, as measured
by a variety of in vitro and in vivo assays. These assays validated
the quality of the hits as lead compounds for drug discovery. The
results demonstrate the usefulness and robustness of ab initio in
silico 3D models and of in silico screening for GPCR drug discovery.},
doi = {10.1073/pnas.0401862101},
keywords = {chemogenomics},
owner = {laurent},
pii = {0401862101},
pmid = {15277683},
timestamp = {2008.03.27},
url = {http://dx.doi.org/10.1073/pnas.0401862101}
}
@article{Beer2004Predicting,
author = {Beer, M. A. and Tavazoie, S.},
title = {Predicting gene expression from sequence},
journal = {Cell},
year = {2004},
volume = {117},
pages = {185--198},
pdf = {../local/Beer2004Predicting.pdf},
file = {Beer2004Predicting.pdf:Beer2004Predicting.pdf:PDF},
owner = {jp},
timestamp = {2009.01.05}
}
@article{Beerenwinkel2003Methods,
author = {Beerenwinkel, N. and Lengauer, T. and Daumer, M. and Kaiser, R. and
Walter, H. and Korn, K. and Hoffmann, D. and Selbig, J.},
title = {Methods for optimizing antiviral combination therapies},
journal = {Bioinformatics},
year = {2003},
volume = {19},
pages = {i16-i25},
number = {Suppl. 1},
abstract = {Motivation: {D}espite some progress with antiretroviral combination
therapies, therapeutic success in the management of {HIV}-infected
patients is limited. {T}he evolution of drug-resistant genetic variants
in response to therapy plays a key role in treatment failure and
finding a new potent drug combination after therapy failure is considered
challenging. {R}esults: {T}o estimate the activity of a drug combination
against a particular viral strain, we develop a scoring function
whose independent variables describe a set of antiviral agents and
viral {DNA} sequences coding for the molecular targets of the respective
drugs. {T}he construction of this activity score involves (1) predicting
phenotypic drug resistance from genotypes for each drug individually,
(2) probabilistic modeling of predicted resistance values and integration
into a score for drug combinations, and (3) searching through the
mutational neighborhood of the considered strain in order to estimate
activity on nearby mutants. {F}or a clinical data set, we determine
the optimal search depth and show that the scoring scheme is predictive
of therapeutic outcome. {P}roperties of the activity score and applications
are discussed. {C}ontact: beerenwinkel@mpi-sb.mpg.de {K}eywords:
{HIV}, antiretroviral therapy, drug resistance, {SVM} regression,
therapy optimization, sequence space search.},
pdf = {../local/Beerenwinkel2003Methods.pdf},
file = {Beerenwinkel2003Methods.pdf:local/Beerenwinkel2003Methods.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://bioinformatics.oupjournals.org/cgi/content/abstract/19/suppl_1/i16}
}
@article{Beerenwinkel2001Geno2pheno,
author = {Beerenwinkel, N. and Schmidt, B. and Walter, H. and Kaiser, R. and
Lengauer, T. and Hoffman, D. and Korn, K. and Selbig, J.},
title = {{{G}eno2pheno: {I}nterpreting {G}enotypic {HIV} {D}rug {R}esistance
{T}ests}},
journal = {I{EEE} {I}ntelligent {S}ystems},
year = {2001},
volume = {6},
pages = {35-41},
number = {6},
abstract = {Rapid accumulation of resistance mutations in the genome of the human
immunodeficiency virus ({HIV}) plays a central role in drug treatment
failure in infected patients. {T}he authors have developed geno2pheno,
an intelligent system that uses the information encoded in the viral
genomic sequence to predict resistance or susceptibility of the virus
to 13 antiretroviral agents. {T}o predict phenotypic drug resistance
from genotype, they applied two machine learning techniques: decision
trees and linear support vector machines. {T}hese techniques performed
learning on more than 400 genotype-phenotype pairs for each drug.
{T}he authors compared the generalization performance of the two
families of models in leave-one-out experiments. {E}xcept for three
drugs, all error estimates ranged between 7.25 and 15.5 percent.
{S}upport vector machines performed slightly better for most drugs,
but knowledge extraction was easier for decision trees. {G}eno2pheno
is freely available at http://cartan.gmd.de/geno2pheno.html.},
doi = {10.1109/5254.972080},
pdf = {../local/Beerenwinkel2001Geno2pheno.pdf},
file = {Beerenwinkel2001Geno2pheno.pdf:local/Beerenwinkel2001Geno2pheno.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://dx.doi.org/10.1109/5254.972080}
}
@article{Beers2006Array-CGH,
author = {van Beers, E. and Nederlof, P.},
title = {Array-{CGH} and breast cancer},
journal = {Breast Cancer Research},
year = {2006},
volume = {8},
pages = {210},
number = {3},
abstract = {The introduction of comparative genomic hybridization (CGH) in 1992
opened new avenues in genomic investigation; in particular, it advanced
analysis of solid tumours, including breast cancer, because it obviated
the need to culture cells before their chromosomes could be analyzed.
The current generation of CGH analysis uses ordered arrays of genomic
DNA sequences and is therefore referred to as array-CGH or matrix-CGH.
It was introduced in 1998, and further increased the potential of
CGH to provide insight into the fundamental processes of chromosomal
instability and cancer. This review provides a critical evaluation
of the data published on array-CGH and breast cancer, and discusses
some of its expected future value and developments.},
doi = {10.1186/bcr1510},
pdf = {../local/Beers2006Array-CGH.pdf},
file = {Beers2006Array-CGH.pdf:Beers2006Array-CGH.pdf:PDF},
issn = {1465-5411},
keywords = {breastcancer, cgh},
owner = {jp},
pubmedid = {16817944},
timestamp = {2008.12.08},
url = {http://breast-cancer-research.com/content/8/3/210}
}
@article{Begg2005machine,
author = {R. Begg and J. Kamruzzaman},
title = {A machine learning approach for automated recognition of movement
patterns using basic, kinetic and kinematic gait data.},
journal = {J {B}iomech},
year = {2005},
volume = {38},
pages = {401-8},
number = {3},
month = {Mar},
abstract = {This paper investigated application of a machine learning approach
({S}upport vector machine, {SVM}) for the automatic recognition of
gait changes due to ageing using three types of gait measures: basic
temporal/spatial, kinetic and kinematic. {T}he gaits of 12 young
and 12 elderly participants were recorded and analysed using a synchronized
{PEAK} motion analysis system and a force platform during normal
walking. {A}ltogether, 24 gait features describing the three types
of gait characteristics were extracted for developing gait recognition
models and later testing of generalization performance. {T}est results
indicated an overall accuracy of 91.7\% by the {SVM} in its capacity
to distinguish the two gait patterns. {T}he classification ability
of the {SVM} was found to be unaffected across six kernel functions
(linear, polynomial, radial basis, exponential radial basis, multi-layer
perceptron and spline). {G}ait recognition rate improved when features
were selected from different gait data type. {A} feature selection
algorithm demonstrated that as little as three gait features, one
selected from each data type, could effectively distinguish the age
groups with 100\% accuracy. {T}hese results demonstrate considerable
potential in applying {SVM}s in gait classification for many applications.},
doi = {10.1016/j.jbiomech.2004.05.002},
pdf = {../local/Begg2005machine.pdf},
file = {Begg2005machine.pdf:local/Begg2005machine.pdf:PDF},
pii = {S0021929004002258},
url = {http://dx.doi.org/10.1016/j.jbiomech.2004.05.002}
}
@article{Begg2005Support,
author = {Rezaul K Begg and Marimuthu Palaniswami and Brendan Owen},
title = {Support vector machines for automated gait classification.},
journal = {I{EEE} {T}rans {B}iomed {E}ng},
year = {2005},
volume = {52},
pages = {828-38},
number = {5},
month = {May},
abstract = {Ageing influences gait patterns causing constant threats to control
of locomotor balance. {A}utomated recognition of gait changes has
many advantages including, early identification of at-risk gait and
monitoring the progress of treatment outcomes. {I}n this paper, we
apply an artificial intelligence technique [support vector machines
({SVM})] for the automatic recognition of young-old gait types from
their respective gait-patterns. {M}inimum foot clearance ({MFC})
data of 30 young and 28 elderly participants were analyzed using
a {PEAK}-2{D} motion analysis system during a 20-min continuous walk
on a treadmill at self-selected walking speed. {G}ait features extracted
from individual {MFC} histogram-plot and {P}oincaré-plot images
were used to train the {SVM}. {C}ross-validation test results indicate
that the generalization performance of the {SVM} was on average 83.3\%
(+/-2.9) to recognize young and elderly gait patterns, compared to
a neural network's accuracy of 75.0+/-5.0\%. {A} "hill-climbing"
feature selection algorithm demonstrated that a small subset (3-5)
of gait features extracted from {MFC} plots could differentiate the
gait patterns with 90\% accuracy. {P}erformance of the gait classifier
was evaluated using areas under the receiver operating characteristic
plots. {I}mproved performance of the classifier was evident when
trained with reduced number of selected good features and with radial
basis function kernel. {T}hese results suggest that {SVM}s can function
as an efficient gait classifier for recognition of young and elderly
gait patterns, and has the potential for wider applications in gait
identification for falls-risk minimization in the elderly.},
doi = {10.1109/TBME.2005.845241},
pdf = {../local/Begg2005Support.pdf},
file = {Begg2005Support.pdf:local/Begg2005Support.pdf:PDF},
keywords = {Adult, Aged, Aging, Algorithms, Apoptosis, Artificial Intelligence,
Automated, Computer-Assisted, Female, Foot, Gait, Gene Expression
Profiling, Humans, Image Interpretation, Male, Neoplasms, Non-U.S.
Gov't, Oligonucleotide Array Sequence Analysis, Pattern Recognition,
Polymerase Chain Reaction, Proteins, Reproducibility of Results,
Research Support, Sensitivity and Specificity, Subcellular Fractions,
Unknown Primary, 15887532},
url = {http://dx.doi.org/10.1109/TBME.2005.845241}
}
@unpublished{Behr2011Simultaneous,
author = {Behr, J. and Bonhert, R. and Kahles, A. and R\"atsch, G.},
title = {Simultaneous {RNA}-seq-based transcript inference and quantification
using mixed integer programming},
note = {NIPS Machine Learning in Computational Biology Workshop, Sierra Nevada.},
year = {2011},
owner = {jp},
timestamp = {2012.03.06}
}
@article{Behre2008Structural,
author = {Behre, J. and Wilhelm, T. and {von Kamp}, A. and Ruppin, E. and Schuster,
S.},
title = {Structural robustness of metabolic networks with respect to multiple
knockouts.},
journal = {J Theor Biol},
year = {2008},
volume = {252},
pages = {433--441},
number = {3},
month = {Jun},
abstract = {We present a generalised framework for analysing structural robustness
of metabolic networks, based on the concept of elementary flux modes
(EFMs). Extending our earlier study on single knockouts [Wilhelm,
T., Behre, J., Schuster, S., 2004. Analysis of structural robustness
of metabolic networks. IEE Proc. Syst. Biol. 1(1), 114-120], we are
now considering the general case of double and multiple knockouts.
The robustness measures are based on the ratio of the number of remaining
EFMs after knockout vs. the number of EFMs in the unperturbed situation,
averaged over all combinations of knockouts. With the help of simple
examples we demonstrate that consideration of multiple knockouts
yields additional information going beyond single-knockout results.
It is proven that the robustness score decreases as the knockout
depth increases. We apply our extended framework to metabolic networks
representing amino acid anabolism in Escherichia coli and human hepatocytes,
and the central metabolism in human erythrocytes. Moreover, in the
E. coli model the two subnetworks synthesising amino acids that are
essential and those that are non-essential for humans are studied
separately. The results are discussed from an evolutionary viewpoint.
We find that E. coli has the most robust metabolism of all the cell
types studied here. Considering only the subnetwork of the synthesis
of non-essential amino acids, E. coli and the human hepatocyte show
about the same robustness.},
doi = {10.1016/j.jtbi.2007.09.043},
pdf = {../local/Behre2008Structural.pdf},
file = {Behre2008Structural.pdf:Behre2008Structural.pdf:PDF},
institution = {Faculty of Biology and Pharmaceutics, Section of Bioinformatics,
Friedrich Schiller University Jena, Ernst-Abbe-Platz 2, D-07743 Jena,
Germany. jbehre@minet.uni-jena.de},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {S0022-5193(07)00472-9},
pmid = {18023456},
timestamp = {2013.01.25},
url = {http://dx.doi.org/10.1016/j.jtbi.2007.09.043}
}
@article{Bejerano2001Variations,
author = {Bejerano, G. and Yona, G. },
title = {Variations on probabilistic suffix trees: statistical modeling and
prediction of protein families},
journal = {Bioinformatics},
year = {2001},
volume = {17},
pages = {23-43},
pdf = {../local/Bejerano2001Variations.pdf},
file = {Bejerano2001Variations.pdf:local/Bejerano2001Variations.pdf:PDF},
owner = {vert},
url = {http://bioinformatics.oxfordjournals.org/cgi/content/abstract/17/1/23}
}
@inproceedings{Bejerano1999Modeling,
author = {Bejerano, G. and Yona, G. },
title = {Modeling protein families using probabilistic suffix trees},
booktitle = {Proceedings of {RECOMB} 1999},
year = {1999},
pages = {15-24},
publisher = {ACM Press},
pdf = {../local/Bejerano1999Modeling.pdf},
file = {Bejerano1999Modeling.pdf:local/Bejerano1999Modeling.pdf:PDF},
owner = {vert}
}
@article{Belkin2003Laplacian,
author = {Belkin, M. and Niyogi, P.},
title = {Laplacian {E}igenmaps for {D}imensionality {R}eduction and {D}ata
{R}epresentation},
journal = {Neural {C}omput.},
year = {2003},
volume = {15},
pages = {1373-1396},
number = {6},
abstract = {One of the central problems in machine learning and pattern recognition
is to develop appropriate representations for complex data. {W}e
consider the problem of constructing a representation for data lying
on a low-dimensional manifold embedded in a high-dimensional space.
{D}rawing on the correspondence between the graph {L}aplacian, the
{L}aplace {B}eltrami operator on the manifold, and the connections
to the heat equation, we propose a geometrically motivated algorithm
for representing the high-dimensional data. {T}he algorithm provides
a computationally efficient approach to nonlinear dimensionality
reduction that has locality-preserving properties and a natural connection
to clustering. {S}ome potential applications and illustrative examples
are discussed.},
pdf = {../local/1373.pdf:http\},
file = {1373.pdf:http\://neco.mitpress.org/cgi/reprint/15/6/1373.pdf:PDF},
keywords = {dimred},
url = {http://neco.mitpress.org/cgi/content/abstract/15/6/1373}
}
@article{Bellman1959adaptive,
author = {Bellman, R. and Kalaba, R.},
title = {On adaptive control processes},
journal = {Automatic Control, IRE Transactions on},
year = {1959},
volume = {4},
pages = {1--9},
number = {2},
publisher = {IEEE}
}
@article{Belongie02Shape,
author = {Belongie, S. and Malik, J. and Puzicha, J.},
title = {Shape matching and object recognition using shape contexts},
journal = {IEEE Trans. Pattern Anal. Mach. Intell.},
year = {2002},
volume = {24},
pages = {509--522},
number = {4},
abstract = {We present a novel approach to measuring similarity between shapes
and exploit it for object recognition. In our framework, the measurement
of similarity is preceded by: (1) solving for correspondences between
points on the two shapes; (2) using the correspondences to estimate
an aligning transform. In order to solve the correspondence problem,
we attach a descriptor, the shape context, to each point. The shape
context at a reference point captures the distribution of the remaining
points relative to it, thus offering a globally discriminative characterization.
Corresponding points on two similar shapes will have similar shape
contexts, enabling us to solve for correspondences as an optimal
assignment problem. Given the point correspondences, we estimate
the transformation that best aligns the two shapes; regularized thin-plate
splines provide a flexible class of transformation maps for this
purpose. The dissimilarity between the two shapes is computed as
a sum of matching errors between corresponding points, together with
a term measuring the magnitude of the aligning transform. We treat
recognition in a nearest-neighbor classification framework as the
problem of finding the stored prototype shape that is maximally similar
to that in the image. Results are presented for silhouettes, trademarks,
handwritten digits, and the COIL data set},
doi = {10.1109/34.993558},
pdf = {../local/Belongie02Shape.pdf},
file = {Belongie02Shape.pdf:Belongie02Shape.pdf:PDF},
publisher = {IEEE},
url = {http://dx.doi.org/10.1109/34.993558}
}
@misc{Ben-David2003Exploiting,
author = {S. Ben-David and R. Schuller},
title = {Exploiting task relatedness for multiple task learning},
year = {2003},
text = {Shai Ben-David and Reba Schuller. Exploiting task relatedness for
multiple task learning. In Proc. of the Sixteenth Annual Conference
on Learning Theory COLT 2003.},
url = {http://citeseer.ist.psu.edu/ben-david03exploiting.html}
}
@article{Ben-Dor2000Tissue,
author = {Ben-Dor, A. and Bruhn, L. and Friedman, N. and Nachman, I. and Schummer,
M. and Yakhini, Z.},
title = {Tissue Classification with Gene Expression Profiles},
journal = {J. Comput. Biol.},
year = {2000},
volume = {7},
pages = {559-583},
number = {3-4},
abstract = {Constantly improving gene expression profiling technologies are expected
to provide understanding and insight into cancer-related cellular
processes. {G}ene expression data is also expected to significantly
aid in the development of efficient cancer diagnosis and classification
platforms. {I}n this work we examine three sets of gene expression
data measured across sets of tumor(s) and normal clinical samples:
{T}he first set consists of 2,000 genes, measured in 62 epithelial
colon samples ({A}lon et al., 1999). {T}he second consists of approximately
equal to 100,000 clones, measured in 32 ovarian samples (unpublished
extension of data set described in {S}chummer et al. (1999)). {T}he
third set consists of approximately equal to 7,100 genes, measured
in 72 bone marrow and peripheral blood samples ({G}olub et al, 1999).
{W}e examine the use of scoring methods, measuring separation of
tissue type (e.g., tumors from normals) using individual gene expression
levels. {T}hese are then coupled with high-dimensional classification
methods to assess the classification power of complete expression
profiles. {W}e present results of performing leave-one-out cross
validation ({LOOCV}) experiments on the three data sets, employing
nearest neighbor classifier, {SVM} ({C}ortes and {V}apnik, 1995),
{A}da{B}oost ({F}reund and {S}chapire, 1997) and a novel clustering-based
classification technique. {A}s tumor samples can differ from normal
samples in their cell-type composition, we also perform {LOOCV} experiments
using appropriately modified sets of genes, attempting to eliminate
the resulting bias. {W}e demonstrate success rate of at least 90%
in tumor versus normal classification, using sets of selected genes,
with, as well as without, cellular-contamination-related members.
{T}hese results are insensitive to the exact selection mechanism,
over a certain range.},
pdf = {../local/Ben-Dor2000Tissue.pdf},
file = {Ben-Dor2000Tissue.pdf:local/Ben-Dor2000Tissue.pdf:PDF},
keywords = {biosvm microarray},
owner = {jeanphilippevert},
url = {http://www.liebertonline.com/doi/abs/10.1089/106652700750050943}
}
@article{Ben-Elazar2013Spatial,
author = {Ben-Elazar, S. and Yakhini, Z. and Yanai, I.},
title = {Spatial localization of co-regulated genes exceeds genomic gene clustering
in the Saccharomyces cerevisiae genome.},
journal = {Nucleic Acids Res},
year = {2013},
volume = {41},
pages = {2191--2201},
number = {4},
month = {Feb},
abstract = {While it has been long recognized that genes are not randomly positioned
along the genome, the degree to which its 3D structure influences
the arrangement of genes has remained elusive. In particular, several
lines of evidence suggest that actively transcribed genes are spatially
co-localized, forming transcription factories; however, a generalized
systematic test has hitherto not been described. Here we reveal transcription
factories using a rigorous definition of genomic structure based
on Saccharomyces cerevisiae chromosome conformation capture data,
coupled with an experimental design controlling for the primary gene
order. We develop a data-driven method for the interpolation and
the embedding of such datasets and introduce statistics that enable
the comparison of the spatial and genomic densities of genes. Combining
these, we report evidence that co-regulated genes are clustered in
space, beyond their observed clustering in the context of gene order
along the genome and show this phenomenon is significant for 64 out
of 117 transcription factors. Furthermore, we show that those transcription
factors with high spatially co-localized targets are expressed higher
than those whose targets are not spatially clustered. Collectively,
our results support the notion that, at a given time, the physical
density of genes is intimately related to regulatory activity.},
doi = {10.1093/nar/gks1360},
pdf = {../local/Ben-Elazar2013Spatial.pdf},
file = {Ben-Elazar2013Spatial.pdf:Ben-Elazar2013Spatial.pdf:PDF},
institution = {Department of Biology, Technion - Israel Institute of Technology,
Haifa, Israel, Department of Computer Science, Technion - Israel
Institute of Technology, Haifa, Israel and Agilent Laboratories,
Tel Aviv, Israel.},
keywords = {ngs, hic},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {gks1360},
pmid = {23303780},
timestamp = {2013.03.29},
url = {http://dx.doi.org/10.1093/nar/gks1360}
}
@article{Ben-Hur2003Remote,
author = {Ben-Hur, A. and Brutlag, D.},
title = {Remote homology detection: a motif based approach},
journal = {Bioinformatics},
year = {2003},
volume = {19},
pages = {i26-i33},
number = {Suppl. 1},
abstract = {Motivation: {R}emote homology detection is the problem of detecting
homology in cases of low sequence similarity. {I}t is a hard computational
problem with no approach that works well in all cases. {R}esults:
{W}e present a method for detecting remote homology that is based
on the presence of discrete sequence motifs. {T}he motif content
of a pair of sequences is used to define a similarity that is used
as a kernel for a {S}upport {V}ector {M}achine ({SVM}) classifier.
{W}e test the method on two remote homology detection tasks: prediction
of a previously unseen {SCOP} family and prediction of an enzyme
class given other enzymes that have a similar function on other substrates.
{W}e find that it performs significantly better than an {SVM} method
that uses {BLAST} or {S}mith-{W}aterman similarity scores as features.
{A}vailability: {T}he software is available from the authors upon
request.},
pdf = {../local/Ben-Hur2003Remote.pdf},
file = {Ben-Hur2003Remote.pdf:local/Ben-Hur2003Remote.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://bioinformatics.oupjournals.org/cgi/content/abstract/19/suppl_1/i26}
}
@article{Ben-Hur2001Support,
author = {Ben-Hur, A. and Horn, D. and Siegelmann, H.T. and Vapnik, V.},
title = {Support {V}ector {C}lustering},
journal = {J. {M}ach. {L}earn. {R}es.},
year = {2001},
volume = {2},
pages = {125--137},
pdf = {../local/benh01.pdf},
file = {benh01.pdf:local/benh01.pdf:PDF},
subject = {kernel},
url = {http://www.ai.mit.edu/projects/jmlr/papers/volume2/horn01a/rev1/horn01a1r.pdf}
}
@article{Ben-Hur2006Choosing,
author = {Ben-Hur, A. and Noble, W. S.},
title = {Choosing negative examples for the prediction of protein-protein
interactions.},
journal = {BMC Bioinformatics},
year = {2006},
volume = {7 Suppl 1},
pages = {S2},
abstract = {The protein-protein interaction networks of even well-studied model
organisms are sketchy at best, highlighting the continued need for
computational methods to help direct experimentalists in the search
for novel interactions. This need has prompted the development of
a number of methods for predicting protein-protein interactions based
on various sources of data and methodologies. The common method for
choosing negative examples for training a predictor of protein-protein
interactions is based on annotations of cellular localization, and
the observation that pairs of proteins that have different localization
patterns are unlikely to interact. While this method leads to high
quality sets of non-interacting proteins, we find that this choice
can lead to biased estimates of prediction accuracy, because the
constraints placed on the distribution of the negative examples makes
the task easier. The effects of this bias are demonstrated in the
context of both sequence-based and non-sequence based features used
for predicting protein-protein interactions.},
doi = {10.1186/1471-2105-7-S1-S2},
institution = {CO, USA. asa@cs.colostate.edu},
owner = {jp},
pii = {1471-2105-7-S1-S2},
pmid = {16723005},
timestamp = {2008.06.01},
url = {http://dx.doi.org/10.1186/1471-2105-7-S1-S2}
}
@article{Ben-Hur2005Kernel,
author = {Ben-Hur, A. and Noble, W. S.},
title = {Kernel methods for predicting protein-protein interactions.},
journal = {Bioinformatics},
year = {2005},
volume = {21},
pages = {i38-i46},
number = {Suppl. 1},
month = {Jun},
abstract = {M{OTIVATION}: {D}espite advances in high-throughput methods for discovering
protein-protein interactions, the interaction networks of even well-studied
model organisms are sketchy at best, highlighting the continued need
for computational methods to help direct experimentalists in the
search for novel interactions. {RESULTS}: {W}e present a kernel method
for predicting protein-protein interactions using a combination of
data sources, including protein sequences, {G}ene {O}ntology annotations,
local properties of the network, and homologous interactions in other
species. {W}hereas protein kernels proposed in the literature provide
a similarity between single proteins, prediction of interactions
requires a kernel between pairs of proteins. {W}e propose a pairwise
kernel that converts a kernel between single proteins into a kernel
between pairs of proteins, and we illustrate the kernel's effectiveness
in conjunction with a support vector machine classifier. {F}urthermore,
we obtain improved performance by combining several sequence-based
kernels based on k-mer frequency, motif and domain content and by
further augmenting the pairwise sequence kernel with features that
are based on other sources of data.{W}e apply our method to predict
physical interactions in yeast using data from the {BIND} database.
{A}t a false positive rate of 1\% the classifier retrieves close
to 80\% of a set of trusted interactions. {W}e thus demonstrate the
ability of our method to make accurate predictions despite the sizeable
fraction of false positives that are known to exist in interaction
databases. {AVAILABILITY}: {T}he classification experiments were
performed using {P}y{ML} available at http://pyml.sourceforge.net.
{D}ata are available at: http://noble.gs.washington.edu/proj/sppi
{CONTACT}: asa@gs.washington.edu.},
doi = {10.1093/bioinformatics/bti1016},
pdf = {../local/Ben-Hur2005Kernel.pdf},
file = {Ben-Hur2005Kernel.pdf:local/Ben-Hur2005Kernel.pdf:PDF},
keywords = {biosvm},
pii = {21/suppl_1/i38},
url = {http://dx.doi.org/10.1093/bioinformatics/bti1016}
}
@article{Bengio2004Learning,
author = {Bengio, Y. and Delalleau, O. and Le Roux, N. and Paiement, J.-F.
and Vincent, P. and Ouimet, M.},
title = {Learning eigenfunctions links spectral embedding and kernel {PCA}.},
journal = {Neural {C}omput.},
year = {2004},
volume = {16},
pages = {2197-219},
number = {10},
month = {Oct},
abstract = {In this letter, we show a direct relation between spectral embedding
methods and kernel principal components analysis and how both are
special cases of a more general learning problem: learning the principal
eigenfunctions of an operator defined from a kernel and the unknown
data-generating density. {W}hereas spectral embedding methods provided
only coordinates for the training points, the analysis justifies
a simple extension to out-of-sample examples (the {N}yström formula)
for multidimensional scaling ({MDS}), spectral clustering, {L}aplacian
eigenmaps, locally linear embedding ({LLE}), and {I}somap. {T}he
analysis provides, for all such spectral embedding methods, the definition
of a loss function, whose empirical average is minimized by the traditional
algorithms. {T}he asymptotic expected value of that loss defines
a generalization performance and clarifies what these algorithms
are trying to learn. {E}xperiments with {LLE}, {I}somap, spectral
clustering, and {MDS} show that this out-of-sample embedding formula
generalizes well, with a level of error comparable to the effect
of small perturbations of the training set on the embedding.},
doi = {10.1162/0899766041732396},
pdf = {../local/Bengio2004Learning.pdf},
file = {Bengio2004Learning.pdf:local/Bengio2004Learning.pdf:PDF},
keywords = {dimred},
url = {http://dx.doi.org/10.1162/0899766041732396}
}
@article{Bengtsson2009Single,
author = {Henrik Bengtsson and Pratyaksha Wirapati and Terence P Speed},
title = {A single-array preprocessing method for estimating full-resolution
raw copy numbers from all Affymetrix genotyping arrays including
GenomeWideSNP 5 \& 6.},
journal = {Bioinformatics},
year = {2009},
volume = {25},
pages = {2149--2156},
number = {17},
month = {Sep},
abstract = {MOTIVATION: High-resolution copy-number (CN) analysis has in recent
years gained much attention, not only for the purpose of identifying
CN aberrations associated with a certain phenotype, but also for
identifying CN polymorphisms. In order for such studies to be successful
and cost effective, the statistical methods have to be optimized.
We propose a single-array preprocessing method for estimating full-resolution
total CNs. It is applicable to all Affymetrix genotyping arrays,
including the recent ones that also contain non-polymorphic probes.
A reference signal is only needed at the last step when calculating
relative CNs. RESULTS: As with our method for earlier generations
of arrays, this one controls for allelic crosstalk, probe affinities
and PCR fragment-length effects. Additionally, it also corrects for
probe sequence effects and co-hybridization of fragments digested
by multiple enzymes that takes place on the latest chips. We compare
our method with Affymetrix's CN5 method and the dChip method by assessing
how well they differentiate between various CN states at the full
resolution and various amounts of smoothing. Although CRMA v2 is
a single-array method, we observe that it performs as well as or
better than alternative methods that use data from all arrays for
their preprocessing. This shows that it is possible to do online
analysis in large-scale projects where additional arrays are introduced
over time.},
doi = {10.1093/bioinformatics/btp371},
institution = {Department of Statistics, University of California, Berkeley, California,
USA. hb@stat.berkeley.edu},
keywords = {Base Pairing, genetics; Chromosomes, Human, Pair 10, genetics; Gene
Dosage, genetics; Genome, Human, genetics; Genotype; Humans; Oligonucleotide
Array Sequence Analysis, methods; Polymorphism, Single Nucleotide,
genetics; ROC Curve},
language = {eng},
medline-pst = {ppublish},
owner = {philippe},
pii = {btp371},
pmid = {19535535},
timestamp = {2010.08.05},
url = {http://dx.doi.org/10.1093/bioinformatics/btp371}
}
@article{Benito2004Adjustment,
author = {Monica Benito and Joel Parker and Quan Du and Junyuan Wu and Dong
Xiang and Charles M Perou and J. S. Marron},
title = {Adjustment of systematic microarray data biases.},
journal = {Bioinformatics},
year = {2004},
volume = {20},
pages = {105--114},
number = {1},
month = {Jan},
abstract = {M{OTIVATION}: {S}ystematic differences due to experimental features
of microarray experiments are present in most large microarray data
sets. {M}any different experimental features can cause biases including
different sources of {RNA}, different production lots of microarrays
or different microarray platforms. {T}hese systematic effects present
a substantial hurdle to the analysis of microarray data. {RESULTS}:
{W}e present here a new method for the identification and adjustment
of systematic biases that are present within microarray data sets.
{O}ur approach is based on modern statistical discrimination methods
and is shown to be very effective in removing systematic biases present
in a previously published breast tumor c{DNA} microarray data set.
{T}he new method of '{D}istance {W}eighted {D}iscrimination ({DWD})'
is shown to be better than {S}upport {V}ector {M}achines and {S}ingular
{V}alue {D}ecomposition for the adjustment of systematic microarray
effects. {I}n addition, it is shown to be of general use as a tool
for the discrimination of systematic problems present in microarray
data sets, including the merging of two breast tumor data sets completed
on different microarray platforms. {AVAILABILITY}: {M}atlab software
to perform {DWD} can be retrieved from https://genome.unc.edu/pubsup/dwd/}
}
@article{Benjamini1995Controlling,
author = {Benjamini, Y. and Hochberg, Y.},
title = {Controlling the False Discovery Rate: a Practical and Powerful Approach
to Multiple Testing},
journal = {J. R. Stat. Soc. Ser. B},
year = {1995},
volume = {57},
pages = {289--300},
pdf = {../local/Benjamini1995Controlling.pdf},
file = {Benjamini1995Controlling.pdf:Benjamini1995Controlling.pdf:PDF},
owner = {jp},
timestamp = {2008.12.09}
}
@article{Bentele2004JCB,
author = {Bentele, M. and Lavrik, I. and Ulrich, M. and Stosser, S. and Heermann,
D. W. and Kalthoff, H. and Krammer, P. H. and Eils, R.},
title = {Mathematical modeling reveals threshold mechanism in CD95-induced
apoptosis},
journal = {J Cell Biol},
year = {2004},
volume = {166},
pages = {839-51},
number = {6},
abstract = {Mathematical modeling is required for understanding the complex behavior
of large signal transduction networks. Previous attempts to model
signal transduction pathways were often limited to small systems
or based on qualitative data only. Here, we developed a mathematical
modeling framework for understanding the complex signaling behavior
of CD95(APO-1/Fas)-mediated apoptosis. Defects in the regulation
of apoptosis result in serious diseases such as cancer, autoimmunity,
and neurodegeneration. During the last decade many of the molecular
mechanisms of apoptosis signaling have been examined and elucidated.
A systemic understanding of apoptosis is, however, still missing.
To address the complexity of apoptotic signaling we subdivided this
system into subsystems of different information qualities. A new
approach for sensitivity analysis within the mathematical model was
key for the identification of critical system parameters and two
essential system properties: modularity and robustness. Our model
describes the regulation of apoptosis on a systems level and resolves
the important question of a threshold mechanism for the regulation
of apoptosis.},
keywords = {csbcbook}
}
@book{Berg1984Harmonic,
title = {Harmonic analysis on semigroups},
publisher = {Springer-Verlag},
year = {1984},
author = {C. Berg and J. P. R. Christensen and P. Ressel},
address = {New-York}
}
@article{Berg2006Cross-species,
author = {Berg, J. and L{\"a}ssig, M.},
title = {Cross-species analysis of biological networks by Bayesian alignment},
journal = {Proc. Natl. Acad. Sci. USA},
year = {2006},
volume = {103},
pages = {10967--10972},
number = {29},
month = {Jul},
abstract = {Complex interactions between genes or proteins contribute a substantial
part to phenotypic evolution. Here we develop an evolutionarily grounded
method for the cross-species analysis of interaction networks by
alignment, which maps bona fide functional relationships between
genes in different organisms. Network alignment is based on a scoring
function measuring mutual similarities between networks, taking into
account their interaction patterns as well as sequence similarities
between their nodes. High-scoring alignments and optimal alignment
parameters are inferred by a systematic Bayesian analysis. We apply
this method to analyze the evolution of coexpression networks between
humans and mice. We find evidence for significant conservation of
gene expression clusters and give network-based predictions of gene
function. We discuss examples where cross-species functional relationships
between genes do not concur with sequence similarity.},
doi = {10.1073/pnas.0602294103},
pdf = {../local/Berg2006Cross-species.pdf},
file = {Berg2006Cross-species.pdf:local/Berg2006Cross-species.pdf:PDF},
institution = {Institut für Theoretische Physik, Universität zu Köln, Zülpicherstrasse
77, 50937 Cologne, Germany. berg@thp.uni-koeln.de},
owner = {jp},
pii = {0602294103},
pmid = {16835301},
timestamp = {2008.10.03},
url = {http://dx.doi.org/10.1073/pnas.0602294103}
}
@book{Berge1959Espaces,
title = {Espaces topologiques et fonctions multivoques},
publisher = {Dunod},
year = {1959},
author = {Berge, C.},
address = {Paris},
owner = {jp},
timestamp = {2011.04.30}
}
@book{Berger1985Statistical,
title = {Statistical Decision Theory and Bayesian Analysis},
publisher = {Springer-Verlag},
year = {1985},
author = {J.O. Berger}
}
@article{Berkum2010HiC,
author = {van Berkum, N. L. and Lieberman-Aiden, E. and Williams, L. and Imakaev,
M. and Gnirke, A. and Mirny, L. A. and Dekker, J. and Lander, E.
S.},
title = {{Hi-C}: a method to study the three-dimensional architecture of genomes.},
journal = {J. Vis. Exp.},
year = {2010},
volume = {39},
pages = {e1869},
abstract = {The three-dimensional folding of chromosomes compartmentalizes the
genome and and can bring distant functional elements, such as promoters
and enhancers, into close spatial proximity (2-6). Deciphering the
relationship between chromosome organization and genome activity
will aid in understanding genomic processes, like transcription and
replication. However, little is known about how chromosomes fold.
Microscopy is unable to distinguish large numbers of loci simultaneously
or at high resolution. To date, the detection of chromosomal interactions
using chromosome conformation capture (3C) and its subsequent adaptations
required the choice of a set of target loci, making genome-wide studies
impossible (7-10). We developed Hi-C, an extension of 3C that is
capable of identifying long range interactions in an unbiased, genome-wide
fashion. In Hi-C, cells are fixed with formaldehyde, causing interacting
loci to be bound to one another by means of covalent DNA-protein
cross-links. When the DNA is subsequently fragmented with a restriction
enzyme, these loci remain linked. A biotinylated residue is incorporated
as the 5' overhangs are filled in. Next, blunt-end ligation is performed
under dilute conditions that favor ligation events between cross-linked
DNA fragments. This results in a genome-wide library of ligation
products, corresponding to pairs of fragments that were originally
in close proximity to each other in the nucleus. Each ligation product
is marked with biotin at the site of the junction. The library is
sheared, and the junctions are pulled-down with streptavidin beads.
The purified junctions can subsequently be analyzed using a high-throughput
sequencer, resulting in a catalog of interacting fragments. Direct
analysis of the resulting contact matrix reveals numerous features
of genomic organization, such as the presence of chromosome territories
and the preferential association of small gene-rich chromosomes.
Correlation analysis can be applied to the contact matrix, demonstrating
that the human genome is segregated into two compartments: a less
densely packed compartment containing open, accessible, and active
chromatin and a more dense compartment containing closed, inaccessible,
and inactive chromatin regions. Finally, ensemble analysis of the
contact matrix, coupled with theoretical derivations and computational
simulations, revealed that at the megabase scale Hi-C reveals features
consistent with a fractal globule conformation.},
doi = {10.3791/1869},
institution = {Program in Gene Function and Expression, Department of Biochemistry
and Molecular Pharmacology, University of Massachusetts Medical School.},
keywords = {ngs, hic},
language = {eng},
medline-pst = {epublish},
owner = {philippe},
pii = {1869},
pmid = {20461051},
timestamp = {2010.07.27},
url = {http://dx.doi.org/10.3791/1869}
}
@book{Berlinet2003Reproducing,
title = {Reproducing Kernel Hilbert Spaces in Probability and Statistics},
publisher = {Springer},
year = {2003},
author = {Berlinet, A. and Thomas-Agnan, C.},
owner = {vert},
timestamp = {2007.08.02}
}
@article{Bern2004Automatic,
author = {Bern, M. and Goldberg, D. and McDonald, W. H. and Yates, J. R., III},
title = {Automatic {Q}uality {A}ssessment of {P}eptide {T}andem {M}ass {S}pectra},
journal = {Bioinformatics},
year = {2004},
volume = {20},
pages = {i49-i54},
number = {Suppl. 1},
abstract = {Motivation: {A} powerful proteomics methodology couples high-performance
liquid chromatography ({HPLC}) with tandem mass spectrometry and
database-search software, such as {SEQUEST}. {S}uch a set-up, however,
produces a large number of spectra, many of which are of too poor
quality to be useful. {H}ence a filter that eliminates poor spectra
before the database search can significantly improve throughput and
robustness. {M}oreover, spectra judged to be of high quality, but
that cannot be identified by database search, are prime candidates
for still more computationally intensive methods, such as de novo
sequencing or wider database searches including post-translational
modifications. {R}esults: {W}e report on two different approaches
to assessing spectral quality prior to identification: binary classification,
which predicts whether or not {SEQUEST} will be able to make an identification,
and statistical regression, which predicts a more universal quality
metric involving the number of b- and y-ion peaks. {T}he best of
our binary classifiers can eliminate over 75% of the unidentifiable
spectra while losing only 10% of the identifiable spectra. {S}tatistical
regression can pick out spectra of modified peptides that can be
identified by a de novo program but not by {SEQUEST}. {I}n a section
of independent interest, we discuss intensity normalization of mass
spectra.},
pdf = {../local/Bern2004Automatic.pdf},
file = {Bern2004Automatic.pdf:local/Bern2004Automatic.pdf:PDF},
keywords = {biosvm proteomics},
owner = {jeanphilippevert},
url = {http://bioinformatics.oupjournals.org/cgi/content/abstract/20/suppl_1/i49}
}
@article{Bernardo2005Chemogenomica,
author = {di Bernardo, D. and Thompson, M.J. and Gardner, T.S. and Chobot,
S.E. and Eastwood, E.L. and Wojtovich, A.P. and Elliott, S.J. and
Schaus, S.E. and Collins, J.J.},
title = {Chemogenomic profiling on a genome-wide scale using reverse-engineered
gene networks.},
journal = {Nat Biotechnol},
year = {2005},
volume = {23},
pages = {377--383},
number = {3},
month = {Mar},
abstract = {A major challenge in drug discovery is to distinguish the molecular
targets of a bioactive compound from the hundreds to thousands of
additional gene products that respond indirectly to changes in the
activity of the targets. Here, we present an integrated computational-experimental
approach for computing the likelihood that gene products and associated
pathways are targets of a compound. This is achieved by filtering
the mRNA expression profile of compound-exposed cells using a reverse-engineered
model of the cell's gene regulatory network. We apply the method
to a set of 515 whole-genome yeast expression profiles resulting
from a variety of treatments (compounds, knockouts and induced expression),
and correctly enrich for the known targets and associated pathways
in the majority of compounds examined. We demonstrate our approach
with PTSB, a growth inhibitory compound with a previously unknown
mode of action, by predicting and validating thioredoxin and thioredoxin
reductase as its target.},
doi = {10.1038/nbt1075},
institution = {Telethon Institute for Genetics and Medicine, Naples, Italy.},
keywords = {Algorithms; Artificial Intelligence; Computer Simulation; Drug Delivery
Systems; Drug Design; Gene Expression Profiling; Gene Expression
Regulation; Models, Biological; Models, Statistical; Protein Engineering;
Protein Interaction Mapping; Saccharomyces cerevisiae; Saccharomyces
cerevisiae Proteins; Signal Transduction; Thioredoxin-Disulfide Reductase;
Thioredoxins},
owner = {fantine},
pii = {nbt1075},
pmid = {15765094},
timestamp = {2010.10.21},
url = {http://dx.doi.org/10.1038/nbt1075}
}
@article{Bernardo2005Chemogenomic,
author = {Bernardo, D. and Thompson, M. J. and Gardner, T. S. and Chobot, S.
E. and Eastwood, E. L. and Wojtovich, A. P. and Elliott, S. J. and
Schaus, S. E. and Collins, J. J.},
title = {Chemogenomic profiling on a genome-wide scale using reverse-engineered
gene networks},
journal = {Nat. Biotechnol.},
year = {2005},
volume = {23},
pages = {377--383},
number = {3},
month = {Mar},
abstract = {A major challenge in drug discovery is to distinguish the molecular
targets of a bioactive compound from the hundreds to thousands of
additional gene products that respond indirectly to changes in the
activity of the targets. Here, we present an integrated computational-experimental
approach for computing the likelihood that gene products and associated
pathways are targets of a compound. This is achieved by filtering
the mRNA expression profile of compound-exposed cells using a reverse-engineered
model of the cell's gene regulatory network. We apply the method
to a set of 515 whole-genome yeast expression profiles resulting
from a variety of treatments (compounds, knockouts and induced expression),
and correctly enrich for the known targets and associated pathways
in the majority of compounds examined. We demonstrate our approach
with PTSB, a growth inhibitory compound with a previously unknown
mode of action, by predicting and validating thioredoxin and thioredoxin
reductase as its target.},
doi = {10.1038/nbt1075},
institution = {Telethon Institute for Genetics and Medicine, Naples, Italy.},
owner = {fantine},
pii = {nbt1075},
pmid = {15765094},
timestamp = {2008.01.22},
url = {http://dx.doi.org/10.1038/nbt1075}
}
@article{Bernstein1977Protein,
author = {F. C. Bernstein and T. F. Koetzle and G. J. Williams and E. F. Meyer
and M. D. Brice and J. R. Rodgers and O. Kennard and T. Shimanouchi
and M. Tasumi},
title = {The Protein Data Bank: a computer-based archival file for macromolecular
structures.},
journal = {J. Mol. Biol.},
year = {1977},
volume = {112},
pages = {535--542},
number = {3},
month = {May},
keywords = {Computers; Great Britain; Information Systems; Japan; Protein Conformation;
Proteins; United States},
owner = {bricehoffmann},
pmid = {875032},
timestamp = {2009.02.13}
}
@inproceedings{Berretti04Graph,
author = {Berretti, S. and Del Bimbo, A. and Pala, P.},
title = {A Graph Edit Distance Based on Node Merging},
booktitle = {Proc. of ACM International Conference on Image and Video Retrieval
(CIVR)},
year = {2004},
pages = {464--472},
address = {Dublin, Ireland},
month = {July},
url = {http://www.micc.unifi.it/publications/2004/BDP04a}
}
@article{Bertoni2008Discovering,
author = {Alberto Bertoni and Giorgio Valentini},
title = {Discovering multi-level structures in bio-molecular data through
the Bernstein inequality.},
journal = {BMC Bioinformatics},
year = {2008},
volume = {9 Suppl 2},
pages = {S4},
abstract = {The unsupervised discovery of structures (i.e. clusterings) underlying
data is a central issue in several branches of bioinformatics. Methods
based on the concept of stability have been recently proposed to
assess the reliability of a clustering procedure and to estimate
the "optimal" number of clusters in bio-molecular data. A major problem
with stability-based methods is the detection of multi-level structures
(e.g. hierarchical functional classes of genes), and the assessment
of their statistical significance. In this context, a chi-square
based statistical test of hypothesis has been proposed; however,
to assure the correctness of this technique some assumptions about
the distribution of the data are needed.To assess the statistical
significance and to discover multi-level structures in bio-molecular
data, a new method based on Bernstein's inequality is proposed. This
approach makes no assumptions about the distribution of the data,
thus assuring a reliable application to a large range of bioinformatics
problems. Results with synthetic and DNA microarray data show the
effectiveness of the proposed method.The Bernstein test, due to its
loose assumptions, is more sensitive than the chi-square test to
the detection of multiple structures simultaneously present in the
data. Nevertheless it is less selective, that is subject to more
false positives, but adding independence assumptions, a more selective
variant of the Bernstein inequality-based test is also presented.
The proposed methods can be applied to discover multiple structures
and to assess their significance in different types of bio-molecular
data.},
doi = {10.1186/1471-2105-9-S2-S4},
institution = {DSI, Dipartimento di Scienze dell' Informazione, Universitá degli
Studi di Milano, Via Comelico 39, Milano, Italy. bertoni@dsi.unimi.it},
language = {eng},
medline-pst = {epublish},
owner = {philippe},
pii = {1471-2105-9-S2-S4},
pmid = {18387206},
timestamp = {2011.05.14},
url = {http://dx.doi.org/10.1186/1471-2105-9-S2-S4}
}
@book{Bertsekas1999Nonlinear,
title = {Nonlinear programming},
publisher = {Athena Scientific},
year = {1999},
author = {D. Bertsekas}
}
@article{Bertucci2006Gene,
author = {Bertucci, F. and Finetti, P. and Cervera, N. and Charafe-Jauffret,
E. and Mamessier, E. and Ad\'ela\"ide, J. and Debono, S. and Houvenaeghel,
G. and Maraninchi, D. and Viens, P. and Charpin, C. and Jacquemier,
J. and Birnbaum, D.},
title = {Gene expression profiling shows medullary breast cancer is a subgroup
of basal breast cancers.},
journal = {Cancer Res.},
year = {2006},
volume = {66},
pages = {4636--4644},
number = {9},
month = {May},
abstract = {Medullary breast cancer (MBC) is a rare but enigmatic pathologic type
of breast cancer. Despite features of aggressiveness, MBC is associated
with a favorable prognosis. Morphologic diagnosis remains difficult
in many cases. Very little is known about the molecular alterations
involved in MBC. Notably, it is not clear whether MBC and ductal
breast cancer (DBC) represent molecularly distinct entities and what
genes/proteins might account for their differences. Using whole-genome
oligonucleotide microarrays, we compared gene expression profiles
of 22 MBCs and 44 grade III DBCs. We show that MBCs are less heterogeneous
than DBCs. Whereas different molecular subtypes (luminal A, luminal
B, basal, ERBB2-overexpressing, and normal-like) exist in DBCs, 95\%
MBCs display a basal profile, similar to that of basal DBCs. Supervised
analysis identified gene expression signatures that discriminated
MBCs from DBCs. Discriminator genes are associated with various cellular
processes related to MBC features, in particular immune reaction
and apoptosis. As compared with MBCs, basal DBCs overexpress genes
involved in smooth muscle cell differentiation, suggesting that MBCs
are a distinct subgroup of basal breast cancer with limited myoepithelial
differentiation. Finally, MBCs overexpress a series of genes located
on the 12p13 and 6p21 chromosomal regions known to contain pluripotency
genes. Our results contribute to a better understanding of MBC and
of mammary oncogenesis in general.},
doi = {10.1158/0008-5472.CAN-06-0031},
pdf = {../local/Bertucci2006Gene.pdf},
file = {Bertucci2006Gene.pdf:Bertucci2006Gene.pdf:PDF},
institution = {Institut de Cancérologie de Marseille, Département d'Oncologie Moléculaire,
Institut Paoli-Calmettes et Unité Mixte de Recherche 599 Institut
National de la Santé et de la Recherche Médicale, Marseilles, France.},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {66/9/4636},
pmid = {16651414},
timestamp = {2011.11.30},
url = {http://dx.doi.org/10.1158/0008-5472.CAN-06-0031}
}
@article{Bhalla1999Emergent,
author = {Bhalla, U. S. and Iyengar, R.},
title = {Emergent Properties of Networks of Biological Signaling Pathways},
journal = {Science},
year = {1999},
volume = {283},
pages = {381-387},
number = {5400},
doi = {10.1126/science.283.5400.381},
eprint = {http://www.sciencemag.org/cgi/reprint/283/5400/381.pdf},
pdf = {../local/Bhalla1999Emergent.pdf},
file = {Bhalla1999Emergent.pdf:Bhalla1999Emergent.pdf:PDF},
keywords = {csbcbook},
url = {http://www.sciencemag.org/cgi/content/abstract/283/5400/381}
}
@article{Bhasin2005GPCRsclass,
author = {Bhasin, M. and Raghava, G. P. S.},
title = {G{PCR}sclass: a web tool for the classification of amine type of
{G}-protein-coupled receptors.},
journal = {Nucleic {A}cids {R}es.},
year = {2005},
volume = {33},
pages = {W143-7},
number = {Web Server issue},
month = {Jul},
abstract = {The receptors of amine subfamily are specifically major drug targets
for therapy of nervous disorders and psychiatric diseases. {T}he
recognition of novel amine type of receptors and their cognate ligands
is of paramount interest for pharmaceutical companies. {I}n the past,
{C}hou and co-workers have shown that different types of amine receptors
are correlated with their amino acid composition and are predictable
on its basis with considerable accuracy [{E}lrod and {C}hou (2002)
{P}rotein {E}ng., 15, 713-715]. {T}his motivated us to develop a
better method for the recognition of novel amine receptors and for
their further classification. {T}he method was developed on the basis
of amino acid composition and dipeptide composition of proteins using
support vector machine. {T}he method was trained and tested on 167
proteins of amine subfamily of {G}-protein-coupled receptors ({GPCR}s).
{T}he method discriminated amine subfamily of {GPCR}s from globular
proteins with {M}atthew's correlation coefficient of 0.98 and 0.99
using amino acid composition and dipeptide composition, respectively.
{I}n classifying different types of amine receptors using amino acid
composition and dipeptide composition, the method achieved an accuracy
of 89.8 and 96.4\%, respectively. {T}he performance of the method
was evaluated using 5-fold cross-validation. {T}he dipeptide composition
based method predicted 67.6\% of protein sequences with an accuracy
of 100\% with a reliability index > or =5. {A} web server {GPCR}sclass
has been developed for predicting amine-binding receptors from its
amino acid sequence [http://www.imtech.res.in/raghava/gpcrsclass/
and http://bioinformatics.uams.edu/raghava/gpersclass/ (mirror site)].},
doi = {10.1093/nar/gki351},
pdf = {../local/Bhasin2005GPCRsclass.pdf},
file = {Bhasin2005GPCRsclass.pdf:local/Bhasin2005GPCRsclass.pdf:PDF},
keywords = {biosvm},
pii = {33/suppl_2/W143},
url = {http://dx.doi.org/10.1093/nar/gki351}
}
@article{Bhasin2005Pcleavage,
author = {Bhasin, M. and Raghava, G. P. S.},
title = {Pcleavage: an {SVM} based method for prediction of constitutive proteasome
and immunoproteasome cleavage sites in antigenic sequences.},
journal = {Nucleic {A}cids {R}es},
year = {2005},
volume = {33},
pages = {W202-7},
number = {Web Server issue},
month = {Jul},
abstract = {This manuscript describes a support vector machine based method for
the prediction of constitutive as well as immunoproteasome cleavage
sites in antigenic sequences. {T}his method achieved {M}atthew's
correlation coefficents of 0.54 and 0.43 on in vitro and major histocompatibility
complex ligand data, respectively. {T}his shows that the performance
of our method is comparable to that of the {N}et{C}hop method, which
is currently considered to be the best method for proteasome cleavage
site prediction. {B}ased on the method, a web server, {P}cleavage,
has also been developed. {T}his server accepts protein sequences
in any standard format and present results in a user-friendly format.
{T}he server is available for free use by all academic users at the
{URL} http://www.imtech.res.in/raghava/pcleavage/ or http://bioinformatics.uams.edu/mirror/pcleavage/.},
doi = {10.1093/nar/gki587},
pdf = {../local/Bhasin2005Pcleavage.pdf},
file = {Bhasin2005Pcleavage.pdf:local/Bhasin2005Pcleavage.pdf:PDF},
keywords = {biosvm immunoinformatics},
url = {http://dx.doi.org/10.1093/nar/gki587}
}
@article{Bhasin2004Analysis,
author = {Bhasin, M. and Raghava, G. P. S.},
title = {Analysis and prediction of affinity of {TAP} binding peptides using
cascade {SVM}},
journal = {Protein {S}ci.},
year = {2004},
volume = {13},
pages = {596-607},
number = {3},
month = {Mar},
abstract = {The generation of cytotoxic {T} lymphocyte ({CTL}) epitopes from an
antigenic sequence involves number of intracellular processes, including
production of peptide fragments by proteasome and transport of peptides
to endoplasmic reticulum through transporter associated with antigen
processing ({TAP}). {I}n this study, 409 peptides that bind to human
{TAP} transporter with varying affinity were analyzed to explore
the selectivity and specificity of {TAP} transporter. {T}he abundance
of each amino acid from {P}1 to {P}9 positions in high-, intermediate-,
and low-affinity {TAP} binders were examined. {T}he rules for predicting
{TAP} binding regions in an antigenic sequence were derived from
the above analysis. {T}he quantitative matrix was generated on the
basis of contribution of each position and residue in binding affinity.
{T}he correlation of r = 0.65 was obtained between experimentally
determined and predicted binding affinity by using a quantitative
matrix. {F}urther a support vector machine ({SVM})-based method has
been developed to model the {TAP} binding affinity of peptides. {T}he
correlation (r = 0.80) was obtained between the predicted and experimental
measured values by using sequence-based {SVM}. {T}he reliability
of prediction was further improved by cascade {SVM} that uses features
of amino acids along with sequence. {A}n extremely good correlation
(r = 0.88) was obtained between measured and predicted values, when
the cascade {SVM}-based method was evaluated through jackknife testing.
{A} {W}eb service, {TAPP}red (http://www.imtech.res.in/raghava/tappred/
or http://bioinformatics.uams.edu/mirror/tappred/), has been developed
based on this approach.},
doi = {10.1110/ps.03373104},
pdf = {../local/Bhasin2004Analysis.pdf},
file = {Bhasin2004Analysis.pdf:local/Bhasin2004Analysis.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://dx.doi.org/10.1110/ps.03373104}
}
@article{Bhasin2004Classification,
author = {Bhasin, M. and Raghava, G. P. S.},
title = {Classification of {N}uclear {R}eceptors {B}ased on {A}mino {A}cid
{C}omposition and {D}ipeptide {C}omposition},
journal = {J. {B}iol. {C}hem.},
year = {2004},
volume = {279},
pages = {23262-23266},
number = {22},
abstract = {Nuclear receptors are key transcription factors that regulate crucial
gene networks responsible for cell growth, differentiation, and homeostasis.
{N}uclear receptors form a superfamily of phylogenetically related
proteins and control functions associated with major diseases (e.g.
diabetes, osteoporosis, and cancer). {I}n this study, a novel method
has been developed for classifying the subfamilies of nuclear receptors.
{T}he classification was achieved on the basis of amino acid and
dipeptide composition from a sequence of receptors using support
vector machines. {T}he training and testing was done on a non-redundant
data set of 282 proteins obtained from the {N}uclea{RDB} data base
(1). {T}he performance of all classifiers was evaluated using a 5-fold
cross validation test. {I}n the 5-fold cross-validation, the data
set was randomly partitioned into five equal sets and evaluated five
times on each distinct set while keeping the remaining four sets
for training. {I}t was found that different subfamilies of nuclear
receptors were quite closely correlated in terms of amino acid composition
as well as dipeptide composition. {T}he overall accuracy of amino
acid composition-based and dipeptide compositionbased classifiers
were 82.6 and 97.5%, respectively. {T}herefore, our results prove
that different subfamilies of nuclear receptors are predictable with
considerable accuracy using amino acid or dipeptide composition.
{F}urthermore, based on above approach, an online web service, {NR}pred,
was developed, which is available at www.imtech.res.in/raghava/nrpred.},
doi = {10.1074/jbc.M401932200},
eprint = {http://www.jbc.org/cgi/reprint/279/22/23262.pdf},
pdf = {../local/Bhasin2004Classification.pdf},
file = {Bhasin2004Classification.pdf:local/Bhasin2004Classification.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://dx.doi.org/10.1074/jbc.M401932200}
}
@article{Bhasin2004ESLpred,
author = {Bhasin, M. and Raghava, G. P. S.},
title = {{{ESL}pred: {SVM}}-based method for subcellular localization of eukaryotic
proteins using dipeptide composition and {{PSI}-{BLAST}}},
journal = {Nucl. {A}cids {R}es.},
year = {2004},
volume = {32},
pages = {W414-419},
number = {Suppl. 2},
abstract = {Automated prediction of subcellular localization of proteins is an
important step in the functional annotation of genomes. {T}he existing
subcellular localization prediction methods are based on either amino
acid composition or {N}-terminal characteristics of the proteins.
{I}n this paper, support vector machine ({SVM}) has been used to
predict the subcellular location of eukaryotic proteins from their
different features such as amino acid composition, dipeptide composition
and physico-chemical properties. {T}he {SVM} module based on dipeptide
composition performed better than the {SVM} modules based on amino
acid composition or physico-chemical properties. {I}n addition, {PSI}-{BLAST}
was also used to search the query sequence against the dataset of
proteins (experimentally annotated proteins) to predict its subcellular
location. {I}n order to improve the prediction accuracy, we developed
a hybrid module using all features of a protein, which consisted
of an input vector of 458 dimensions (400 dipeptide compositions,
33 properties, 20 amino acid compositions of the protein and 5 from
{PSI}-{BLAST} output). {U}sing this hybrid approach, the prediction
accuracies of nuclear, cytoplasmic, mitochondrial and extracellular
proteins reached 95.3, 85.2, 68.2 and 88.9%, respectively. {T}he
overall prediction accuracy of {SVM} modules based on amino acid
composition, physico-chemical properties, dipeptide composition and
the hybrid approach was 78.1, 77.8, 82.9 and 88.0%, respectively.
{T}he accuracy of all the modules was evaluated using a 5-fold cross-validation
technique. {A}ssigning a reliability index (reliability index > or
=3), 73.5% of prediction can be made with an accuracy of 96.4%. {B}ased
on the above approach, an online web server {ESL}pred was developed,
which is available at http://www.imtech.res.in/raghava/eslpred/.},
doi = {10.1093/nar/gkh350},
eprint = {http://nar.oupjournals.org/cgi/reprint/32/suppl_2/W414.pdf},
pdf = {../local/Bhasin2004ESLpred.pdf},
file = {Bhasin2004ESLpred.pdf:local/Bhasin2004ESLpred.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://nar.oupjournals.org/cgi/content/abstract/32/suppl_2/W414}
}
@article{Bhasin2004GPCRpred,
author = {Bhasin, M. and Raghava, G. P. S.},
title = {{{GPCR}pred}: an {SVM}-based method for prediction of families and
subfamilies of {G}-protein coupled receptors},
journal = {Nucl. {A}cids {R}es.},
year = {2004},
volume = {32},
pages = {W383-389},
number = {Supp.2},
abstract = {G-protein coupled receptors ({GPCR}s) belong to one of the largest
superfamilies of membrane proteins and are important targets for
drug design. {I}n this study, a support vector machine ({SVM})-based
method, {GPCR}pred, has been developed for predicting families and
subfamilies of {GPCR}s from the dipeptide composition of proteins.
{T}he dataset used in this study for training and testing was obtained
from http://www.soe.ucsc.edu/research/compbio/gpcr/. {T}he method
classified {GPCR}s and non-{GPCR}s with an accuracy of 99.5% when
evaluated using 5-fold cross-validation. {T}he method is further
able to predict five major classes or families of {GPCR}s with an
overall {M}atthew's correlation coefficient ({MCC}) and accuracy
of 0.81 and 97.5% respectively. {I}n recognizing the subfamilies
of the rhodopsin-like family, the method achieved an average {MCC}
and accuracy of 0.97 and 97.3% respectively. {T}he method achieved
overall accuracy of 91.3% and 96.4% at family and subfamily level
respectively when evaluated on an independent/blind dataset of 650
{GPCR}s. {A} server for recognition and classification of {GPCR}s
based on multiclass {SVM}s has been set up at http://www.imtech.res.in/raghava/gpcrpred/.
{W}e have also suggested subfamilies for 42 sequences which were
previously identified as unclassified {C}lass{A} {GPCR}s. {T}he supplementary
information is available at http://www.imtech.res.in/raghava/gpcrpred/info.html.},
doi = {10.1093/nar/gkh416},
eprint = {http://nar.oupjournals.org/cgi/reprint/32/suppl_2/W383.pdf},
pdf = {../local/Bhasin2004GPCRpred.pdf},
file = {Bhasin2004GPCRpred.pdf:local/Bhasin2004GPCRpred.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://dx.doi.org/10.1093/nar/gkh416}
}
@article{Bhasin2004Prediction,
author = {Bhasin, M. and Raghava, G. P. S.},
title = {Prediction of {CTL} epitopes using {QM}, {SVM} and {ANN} techniques},
journal = {Vaccine},
year = {2004},
volume = {22},
pages = {3195-3204},
number = {23-24},
abstract = {Cytotoxic {T} lymphocyte ({CTL}) epitopes are potential candidates
for subunit vaccine design for various diseases. {M}ost of the existing
{T} cell epitope prediction methods are indirect methods that predict
{MHC} class {I} binders instead of {CTL} epitopes. {I}n this study,
a systematic attempt has been made to develop a direct method for
predicting {CTL} epitopes from an antigenic sequence. {T}his method
is based on quantitative matrix ({QM}) and machine learning techniques
such as {S}upport {V}ector {M}achine ({SVM}) and {A}rtificial {N}eural
{N}etwork ({ANN}). {T}his method has been trained and tested on non-redundant
dataset of {T} cell epitopes and non-epitopes that includes 1137
experimentally proven {MHC} class {I} restricted {T} cell epitopes.
{T}he accuracy of {QM}-, {ANN}- and {SVM}-based methods was 70.0,
72.2 and 75.2%, respectively. {T}he performance of these methods
has been evaluated through {L}eave {O}ne {O}ut {C}ross-{V}alidation
({LOOCV}) at a cutoff score where sensitivity and specificity was
nearly equal. {F}inally, both machine-learning methods were used
for consensus and combined prediction of {CTL} epitopes. {T}he performances
of these methods were evaluated on blind dataset where machine learning-based
methods perform better than {QM}-based method. {W}e also demonstrated
through subgroup analysis that our methods can discriminate between
{T}-cell epitopes and {MHC} binders (non-epitopes). {I}n brief this
method allows prediction of {CTL} epitopes using {QM}, {SVM}, {ANN}
approaches. {T}he method also facilitates prediction of {MHC} restriction
in predicted {T} cell epitopes.},
doi = {10.1016/j.vaccine.2004.02.005},
pdf = {../local/Bhasin2004Prediction.pdf},
file = {Bhasin2004Prediction.pdf:local/Bhasin2004Prediction.pdf:PDF},
keywords = {biosvm immunoinformatics},
owner = {jeanphilippevert},
url = {http://dx.doi.org/10.1016/j.vaccine.2004.02.005}
}
@article{Bhasin2004SVM,
author = {Bhasin, M. and Raghava, G. P. S.},
title = {S{VM} based method for predicting {{HLA}-{DRB}1*0401} binding peptides
in an antigen sequence},
journal = {Bioinformatics},
year = {2004},
volume = {20},
pages = {421-423},
number = {3},
abstract = {Summary: {P}rediction of peptides binding with {MHC} class {II} allele
{HLA}-{DRB}1*0401 can effectively reduce the number of experiments
required for identifying helper {T} cell epitopes. {T}his paper describes
support vector machine ({SVM}) based method developed for identifying
{HLA}-{DRB}1*0401 binding peptides in an antigenic sequence. {SVM}
was trained and tested on large and clean data set consisting of
567 binders and equal number of non-binders. {T}he accuracy of the
method was 86% when evaluated through 5-fold cross-validation technique.
{A}vailable: {A} web server {HLA}-{DR}4{P}red based on above approach
is available at http://www.imtech.res.in/raghava/hladr4pred/ and
http://bioinformatics.uams.edu/mirror/hladr4pred/ ({M}irror {S}ite).
{S}upplementary information: http://www.imtech.res.in/raghava/hladr4pred/info.html},
pdf = {../local/Bhasin2004SVM.pdf},
file = {Bhasin2004SVM.pdf:local/Bhasin2004SVM.pdf:PDF},
keywords = {biosvm immunoinformatics},
owner = {jeanphilippevert},
url = {http://bioinformatics.oupjournals.org/cgi/content/abstract/20/3/421}
}
@article{Bhasin2003MHCBN,
author = {Manoj Bhasin and Harpreet Singh and G. P S Raghava},
title = {{MHCBN}: a comprehensive database of {MHC} binding and non-binding
peptides.},
journal = {Bioinformatics},
year = {2003},
volume = {19},
pages = {665--666},
number = {5},
month = {Mar},
abstract = {MHCBN is a comprehensive database of Major Histocompatibility Complex
(MHC) binding and non-binding peptides compiled from published literature
and existing databases. The latest version of the database has 19
777 entries including 17 129 MHC binders and 2648 MHC non-binders
for more than 400 MHC molecules. The database has sequence and structure
data of (a) source proteins of peptides and (b) MHC molecules. MHCBN
has a number of web tools that include: (i) mapping of peptide on
query sequence; (ii) search on any field; (iii) creation of data
sets; and (iv) online data submission. The database also provides
hypertext links to major databases like SWISS-PROT, PDB, IMGT/HLA-DB,
GenBank and PUBMED.},
keywords = {Amino Acid Sequence; Binding Sites; Database Management Systems; Databases,
Protein; Histocompatibility Antigens; Information Storage and Retrieval;
Macromolecular Substances; Major Histocompatibility Complex; Molecular
Sequence Data; Peptide Fragments; Peptides; Protein Binding; Protein
Conformation; Sequence Alignment; Sequence Analysis, Protein; Structure-Activity
Relationship; User-Computer Interface},
owner = {laurent},
pmid = {12651731},
timestamp = {2007.01.30}
}
@article{Bhattacharjee2001Classification,
author = {Bhattacharjee, A. and Richards, W. G. and Staunton, J. and Li, C.
and Monti, S. and Vasa, P. and Ladd, C. and Beheshti, J. and Bueno,
R. and Gillette, M. and Loda, M. and Weber, G. and Mark, E. J. and
Lander, E. S. and Wong, W. and Johnson, B. E. and Golub, T. R. and
Sugarbaker, D. A. and Meyerson, M.},
title = {Classification of human lung carcinomas by {mRNA} expression profiling
reveals distinct adenocarcinoma subclasses},
journal = {Proc. Natl. Acad. Sci. USA},
year = {2001},
volume = {98},
pages = {13790--13795},
number = {24},
month = {Nov},
abstract = {We have generated a molecular taxonomy of lung carcinoma, the leading
cause of cancer death in the United States and worldwide. Using oligonucleotide
microarrays, we analyzed mRNA expression levels corresponding to
12,600 transcript sequences in 186 lung tumor samples, including
139 adenocarcinomas resected from the lung. Hierarchical and probabilistic
clustering of expression data defined distinct subclasses of lung
adenocarcinoma. Among these were tumors with high relative expression
of neuroendocrine genes and of type II pneumocyte genes, respectively.
Retrospective analysis revealed a less favorable outcome for the
adenocarcinomas with neuroendocrine gene expression. The diagnostic
potential of expression profiling is emphasized by its ability to
discriminate primary lung adenocarcinomas from metastases of extra-pulmonary
origin. These results suggest that integration of expression profile
data with clinical parameters could aid in diagnosis of lung cancer
patients.},
doi = {10.1073/pnas.191502998},
pdf = {../local/Bhattacharjee2001Classification.pdf},
file = {Bhattacharjee2001Classification.pdf:Bhattacharjee2001Classification.pdf:PDF},
institution = {Department of Adult Oncology, Dana-Farber Cancer Institute, Harvard
Medical School, 44 Binney Street, Boston, MA 02115, USA.},
owner = {jp},
pii = {191502998},
pmid = {11707567},
timestamp = {2008.11.15},
url = {http://dx.doi.org/10.1073/pnas.191502998}
}
@article{Bhavani2006Substructure-based,
author = {S. Bhavani and A. Nagargadde and A. Thawani and V. Sridhar and N.
Chandra},
title = {Substructure-based support vector machine classifiers for prediction
of adverse effects in diverse classes of drugs.},
journal = {J. Chem. Inform. Model.},
year = {2006},
volume = {46},
pages = {2478--2486},
number = {6},
abstract = {Unforeseen adverse effects exhibited by drugs contribute heavily to
late-phase failure and even withdrawal of marketed drugs. Torsade
de pointes (TdP) is one such important adverse effect, which causes
cardiac arrhythmia and, in some cases, sudden death, making it crucial
for potential drugs to be screened for torsadogenicity. The need
to tap the power of computational approaches for the prediction of
adverse effects such as TdP is increasingly becoming evident. The
availability of screening data including those in organized databases
greatly facilitates exploration of newer computational approaches.
In this paper, we report the development of a prediction method based
on a support machine vector algorithm. The method uses a combination
of descriptors, encoding both the type of toxicophore as well as
the position of the toxicophore in the drug molecule, thus considering
both the pharmacophore and the three-dimensional shape information
of the molecule. For delineating toxicophores, a novel pattern-recognition
method that utilizes substructures within a molecule has been developed.
The results obtained using the hybrid approach have been compared
with those available in the literature for the same data set. An
improvement in prediction accuracy is clearly seen, with the accuracy
reaching up to 97\% in predicting compounds that can cause TdP and
90\% for predicting compounds that do not cause TdP. The generic
nature of the method has been demonstrated with four data sets available
for carcinogenicity, where prediction accuracies were significantly
higher, with a best receiver operating characteristics (ROC) value
of 0.81 as against a best ROC value of 0.7 reported in the literature
for the same data set. Thus, the method holds promise for wide applicability
in toxicity prediction.},
doi = {10.1021/ci060128l},
keywords = {Algorithms; Carcinogens; Chemistry, Pharmaceutical; Computational
Biology; Drug Evaluation, Preclinical; Drug Industry; Humans; Models,
Chemical; Models, Statistical; Neural Networks (Computer); Pattern
Recognition, Automated; ROC Curve; Sequence Analysis, Protein; Software;
Torsades de Pointes},
owner = {laurent},
pmid = {17125188},
timestamp = {2007.09.18},
url = {http://dx.doi.org/10.1021/ci060128l}
}
@article{Bi2003Dimensionality,
author = {Bi, J. and Bennett, K. and Embrechts, M. and Breneman, C. and Song,
M.},
title = {Dimensionality reduction via sparse support vector machines},
journal = {J. Mach. Learn. Res.},
year = {2003},
volume = {3},
pages = {1229--1243},
owner = {jp},
timestamp = {2011.01.11}
}
@incollection{Biasotti20043D,
author = {S. Biasotti and S. Marini and M. Mortara and G. Patane and M. Spagnuolo
and B. Falcidieno },
title = {3D Shape Matching through Topological Structures},
booktitle = {Discrete Geometry for Computer Imagery},
publisher = {Springer Berlin / Heidelberg},
year = {2004},
pages = {194-203}
}
@article{Biau2012Analysis,
author = {Biau, G.},
title = {Analysis of a Random Forests model},
journal = {The Journal of Machine Learning Research},
year = {2012},
volume = {98888},
pages = {1063--1095},
publisher = {JMLR. org}
}
@article{Biau2006Statistical,
author = {Biau, G. and Bleakley, K.},
title = {Statistical inference on graphs},
journal = {Statistics and Decisions},
year = {2006},
volume = {24},
pages = {209-232},
number = {2},
timestamp = {2007.02.01}
}
@article{Bickel2009Simultaneous,
author = {Bickel, P. J. and Ritov, Y. and Tsybakov, A.},
title = {Simultaneous analysis of {L}asso and {D}antzig selector},
journal = {Ann. Stat.},
year = {2009},
volume = {37},
pages = {1705--1732},
number = {4},
pdf = {../local/BickelSimultaneous.pdf},
file = {BickelSimultaneous.pdf:BickelSimultaneous.pdf:PDF},
keywords = {lasso},
owner = {jp},
timestamp = {2009.05.02}
}
@inproceedings{Bickel2008Multi-task,
author = {Steffen Bickel and Jasmina Bogojeska and Thomas Lengauer and Tobias
Scheffer},
title = {Multi-task learning for HIV therapy screening},
booktitle = {ICML'08: Proceedings of the 25th international conference on Machine
learning},
year = {2008},
pages = {56-63},
bibsource = {DBLP, http://dblp.uni-trier.de},
ee = {http://doi.acm.org/10.1145/1390156.1390164}
}
@inproceedings{Bickel2007Discriminative,
author = {Steffen Bickel and Michael Br{\"u}ckner and Tobias Scheffer},
title = {Discriminative learning for differing training and test distributions},
booktitle = {ICML '07: Proceedings of the 24th international conference on Machine
learning},
year = {2007},
pages = {81--88},
publisher = {ACM Press}
}
@book{Biggs1976Graph,
title = {Graph theory 1736-1936},
publisher = {Oxford University Press},
year = {1976},
author = {N. L. Biggs and E.K. Lloyd and R. J. Wilson}
}
@article{Bild2006Oncogenic,
author = {Bild, A. H. and Yao, G. and Chang, J. T. and Wang, Q. and Potti,
A. and Chasse, D. and Joshi, M. B. and Harpole, D. and Lancaster,
J. M. and Berchuck, A. and Olson, J. A., Jr. and Marks, J. R. and
Dressman, H. K. and West, M. and Nevins, J. R.},
title = {Oncogenic pathway signatures in human cancers as a guide to targeted
therapies},
journal = {Nature},
year = {2006},
volume = {439},
pages = {353-7},
number = {7074},
abstract = {The development of an oncogenic state is a complex process involving
the accumulation of multiple independent mutations that lead to deregulation
of cell signalling pathways central to the control of cell growth
and cell fate. {T}he ability to define cancer subtypes, recurrence
of disease and response to specific therapies using {DNA} microarray-based
gene expression signatures has been demonstrated in multiple studies.
{V}arious studies have also demonstrated the potential for using
gene expression profiles for the analysis of oncogenic pathways.
{H}ere we show that gene expression signatures can be identified
that reflect the activation status of several oncogenic pathways.
{W}hen evaluated in several large collections of human cancers, these
gene expression signatures identify patterns of pathway deregulation
in tumours and clinically relevant associations with disease outcomes.
{C}ombining signature-based predictions across several pathways identifies
coordinated patterns of pathway deregulation that distinguish between
specific cancers and tumour subtypes. {C}lustering tumours based
on pathway signatures further defines prognosis in respective patient
subsets, demonstrating that patterns of oncogenic pathway deregulation
underlie the development of the oncogenic phenotype and reflect the
biology and outcome of specific cancers. {P}redictions of pathway
deregulation in cancer cell lines are also shown to predict the sensitivity
to therapeutic agents that target components of the pathway. {L}inking
pathway deregulation with sensitivity to therapeutics that target
components of the pathway provides an opportunity to make use of
these oncogenic pathway signatures to guide the use of targeted therapeutics.},
doi = {10.1038/nature04296},
pdf = {../local/Bild2006Oncogenic.pdf},
file = {Bild2006Oncogenic.pdf:Bild2006Oncogenic.pdf:PDF},
keywords = {breastcancer},
url = {http://dx.doi.org/10.1038/nature04296}
}
@article{Bilello2004Automatic,
author = {Michel Bilello and Salih Burak Gokturk and Terry Desser and Sandy
Napel and R. Brooke Jeffrey and Christopher F Beaulieu},
title = {Automatic detection and classification of hypodense hepatic lesions
on contrast-enhanced venous-phase {CT}.},
journal = {Med {P}hys},
year = {2004},
volume = {31},
pages = {2584-93},
number = {9},
month = {Sep},
abstract = {The objective of this work was to develop and validate algorithms
for detection and classification of hypodense hepatic lesions, specifically
cysts, hemangiomas, and metastases from {CT} scans in the portal
venous phase of enhancement. {F}ifty-six {CT} sections from 51 patients
were used as representative of common hypodense liver lesions, including
22 simple cysts, 11 hemangiomas, 22 metastases, and 1 image containing
both a cyst and a hemangioma. {T}he detection algorithm uses intensity-based
histogram methods to find central lesions, followed by liver contour
refinement to identify peripheral lesions. {T}he classification algorithm
operates on the focal lesions identified during detection, and includes
shape-based segmentation, edge pixel weighting, and lesion texture
filtering. {S}upport vector machines are then used to perform a pair-wise
lesion classification. {F}or the detection algorithm, 80\% lesion
sensitivity was achieved at approximately 0.3 false positives ({FP})
per slice for central lesions, and 0.5 {FP} per slice for peripheral
lesions, giving a total of 0.8 {FP} per section. {F}or 90\% sensitivity,
the total number of {FP} rises to about 2.2 per section. {T}he pair-wise
classification yielded good discrimination between cysts and metastases
(at 95\% sensitivity for detection of metastases, only about 5\%
of cysts are incorrectly classified as metastases), perfect discrimination
between hemangiomas and cysts, and was least accurate in discriminating
between hemangiomas and metastases (at 90\% sensitivity for detection
of hemangiomas, about 28\% of metastases were incorrectly classified
as hemangiomas). {I}nitial implementations of our algorithms are
promising for automating liver lesion detection and classification.}
}
@article{Billerey1996Etude,
author = {Billerey, C. and Boccon-Gibod, L.},
title = {Etude des variations inter-pathologistes dans l'{\'e}valuation du
grade et du stade des tumeurs v{\'e}sicales},
journal = {Progr{\`e}s en Urologie},
year = {1996},
volume = {6},
pages = {49--57},
pdf = {../local/Billerey1996Etude.pdf},
file = {Billerey1996Etude.pdf:Billerey1996Etude.pdf:PDF},
keywords = {csbcbook, csbcbook-ch3},
url = {http://www.urofrance.org/fileadmin/documents/data/PU/1996/PU-1996-00070049/TEXF-PU-1996-00070049.PDF}
}
@article{Billerey2001Frequent,
author = {Billerey, C. and Chopin D and Aubriot-Lorton MH and Ricol D and Gil
Diez de Medina S and Van Rhijn B and Bralet MP and Lefrere-Belda
MA and Lahaye JB and Abbou CC and Bonaventure J and Zafrani ES and
van der Kwast T and Thiery JP and Radvanyi F.},
title = {Frequent FGFR3 mutations in papillary non-invasive bladder (pTa)
tumors.},
journal = {Am J Pathol.},
year = {2001},
volume = {158},
pages = {1955-1959},
abstract = {We recently identified activating mutations of fibroblast growth factor
receptor 3 (FGFR3) in bladder carcinoma. In this study we assessed
the incidence of FGFR3 mutations in a series of 132 bladder carcinomas:
20 carcinoma in situ (CIS), 50 pTa, 19 pT1, and 43 pT2-4. All 48
mutations identified were identical to the germinal activating mutations
that cause thanatophoric dysplasia, a lethal form of dwarfism. The
S249C mutation, found in 33 of the 48 mutated tumors, was the most
common. The frequency of mutations was higher in pTa tumors (37 of
50, 74%) than in CIS (0 of 20, 0%; P < 0.0001), pT1 (4 of 19, 21%;
P < 0.0001) and pT2-4 tumors (7 of 43, 16%; P < 0.0001). FGFR3 mutations
were detected in 27 of 32 (84%) G1, 16 of 29 (55%) G2, and 5 of 71
(7%) G3 tumors. This association between FGFR3 mutations and low
grade was highly significant (P < 0.0001). FGFR3 is the first gene
found to be mutated at a high frequency in pTa tumors. The absence
of FGFR3 mutations in CIS and the low frequency of FGFR3 mutations
in pT1 and pT2-4 tumors are consistent with the model of bladder
tumor progression in which the most common precursor of pT1 and pT2-4
tumors is CIS.},
owner = {lcalzone},
timestamp = {2010.04.27}
}
@article{Birge2006Minimal,
author = {Birg{\'e}, L. and Massart, P.},
title = {Minimal penalties for Gaussian model selection},
journal = {Probab. Theory Relat. Fields},
year = {2006},
volume = {138},
pages = {33--73},
doi = {10.1007/s00440-006-0011-8},
pdf = {../local/Birge2006Minimal.pdf},
file = {Birge2006Minimal.pdf:Birge2006Minimal.pdf:PDF},
owner = {jp},
timestamp = {2009.05.02},
url = {http://dx.doi.org/10.1007/s00440-006-0011-8}
}
@article{Birge2001Gaussian,
author = {Birg{\'e}, L. and Massart, P.},
title = {Gaussian model selection},
journal = {J. Eur. Math. Soc.},
year = {2001},
volume = {3},
pages = {203--268},
owner = {jp},
timestamp = {2010.06.02}
}
@book{Bishop2006Pattern,
title = {Pattern recognition and machine learning},
publisher = {Springer},
year = {2006},
author = {Bishop, C.M.},
owner = {vert},
timestamp = {2007.08.02}
}
@article{Bissantz2003Protein-based,
author = {Bissantz, C. and Bernard, P. and Hibert, M. and Rognan, D.},
title = {Protein-based virtual screening of chemical databases. {II}. Are
homology models of {G}-Protein Coupled Receptors suitable targets?},
journal = {Proteins},
year = {2003},
volume = {50},
pages = {5--25},
number = {1},
month = {Jan},
abstract = {The aim of the current study is to investigate whether homology models
of G-Protein-Coupled Receptors (GPCRs) that are based on bovine rhodopsin
are reliable enough to be used for virtual screening of chemical
databases. Starting from the recently described 2.8 A-resolution
X-ray structure of bovine rhodopsin, homology models of an "antagonist-bound"
form of three human GPCRs (dopamine D3 receptor, muscarinic M1 receptor,
vasopressin V1a receptor) were constructed. The homology models were
used to screen three-dimensional databases using three different
docking programs (Dock, FlexX, Gold) in combination with seven scoring
functions (ChemScore, Dock, FlexX, Fresno, Gold, Pmf, Score). Rhodopsin-based
homology models turned out to be suitable, indeed, for virtual screening
since known antagonists seeded in the test databases could be distinguished
from randomly chosen molecules. However, such models are not accurate
enough for retrieving known agonists. To generate receptor models
better suited for agonist screening, we developed a new knowledge-
and pharmacophore-based modeling procedure that might partly simulate
the conformational changes occurring in the active site during receptor
activation. Receptor coordinates generated by this new procedure
are now suitable for agonist screening. We thus propose two alternative
strategies for the virtual screening of GPCR ligands, relying on
a different set of receptor coordinates (antagonist-bound and agonist-bound
states).},
doi = {10.1002/prot.10237},
keywords = {chemogenomics},
owner = {laurent},
pmid = {12471595},
timestamp = {2008.03.27},
url = {http://dx.doi.org/10.1002/prot.10237}
}
@article{Bittner2000Molecular,
author = {Bittner, M. and Meltzer, P. and Chen, Y. and Jiang, Y. and Seftor,
E. and Hendrix, M. and Radmacher, M. and Simon, R. and Yakhini, Z.
and Ben-Dor, A. and Sampas, N. and Dougherty, E. and Wang, E. and
Marincola, F. and Gooden, C. and Lueders, J. and Glatfelter, A. and
Pollock, P. and Carpten, J. and Gillanders, E. and Leja, D. and Dietrich,
K. and Beaudry, C. and Berens, M. and Alberts, D. and Sondak, V.},
title = {Molecular classification of cutaneous malignant melanoma by gene
expression profiling.},
journal = {Nature},
year = {2000},
volume = {406},
pages = {536--540},
number = {6795},
month = {Aug},
abstract = {The most common human cancers are malignant neoplasms of the skin.
Incidence of cutaneous melanoma is rising especially steeply, with
minimal progress in non-surgical treatment of advanced disease. Despite
significant effort to identify independent predictors of melanoma
outcome, no accepted histopathological, molecular or immunohistochemical
marker defines subsets of this neoplasm. Accordingly, though melanoma
is thought to present with different 'taxonomic' forms, these are
considered part of a continuous spectrum rather than discrete entities.
Here we report the discovery of a subset of melanomas identified
by mathematical analysis of gene expression in a series of samples.
Remarkably, many genes underlying the classification of this subset
are differentially regulated in invasive melanomas that form primitive
tubular networks in vitro, a feature of some highly aggressive metastatic
melanomas. Global transcript analysis can identify unrecognized subtypes
of cutaneous melanoma and predict experimentally verifiable phenotypic
characteristics that may be of importance to disease progression.},
doi = {10.1038/35020115},
pdf = {../local/Bittner2000Molecular.pdf},
file = {Bittner2000Molecular.pdf:Bittner2000Molecular.pdf:PDF},
institution = {Cancer Genetics Branch, National Human Genome Research Institute,
NIH, Bethesda, Maryland 20892, USA. mbittner@nhgri.nih.gov},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pmid = {10952317},
timestamp = {2012.02.27},
url = {http://dx.doi.org/10.1038/35020115}
}
@book{Blake1987Visual,
title = {Visual Reconstruction},
publisher = {MIT Press},
year = {1987},
author = {A. Blake and A. Zisserman}
}
@article{Blake2000Chemoinformatics,
author = {J. F. Blake},
title = {Chemoinformatics - predicting the physicochemical properties of 'drug-like'
molecules.},
journal = {Curr. Opin. Biotechnol.},
year = {2000},
volume = {11},
pages = {104--107},
number = {1},
month = {Feb},
abstract = {A few major advances have occurred in the area of physicochemical
modeling of organic compounds during the past several years, spurred
on by changes in the pharmaceutical industry. Recent advances include
the ability to categorize and screen the overall physicochemical
properties of potential drug candidates based entirely on their molecular
structures and the ability to model the components that contribute
to the oral absorption characteristics of potential drug candidates.},
doi = {10.1016/S0958-1669(99)00062-2},
pdf = {../local/Blake2000Chemoinformatics.pdf},
file = {Blake2000Chemoinformatics.pdf:Blake2000Chemoinformatics.pdf:PDF},
owner = {vert},
pii = {S0958-1669(99)00062-2},
pmid = {10679344},
timestamp = {2007.08.02},
url = {http://dx.doi.org/10.1016/S0958-1669(99)00062-2}
}
@phdthesis{Blanchard2001Methodes,
author = {Blanchard, G.},
title = {M{\'e}thodes de m{\'e}lange et d'aggregation d'estimateurs en reconnaissance
de formes. {A}pplications aux arbres de decision},
school = {University Paris 13},
year = {2001},
month = jan,
pdf = {../local/blan01.pdf},
file = {blan01.pdf:local/blan01.pdf:PDF},
subject = {ml},
url = {http://www.dma.ens.fr/~gblancha/phd.ps.gz}
}
@unpublished{Blanchard2004Statistical,
author = {Blanchard, G. and Bousquet, O. and Masssart, P.},
title = {Statistical {P}erformance of {S}upport {V}ector {M}achine},
note = {Submitted Ann.Stat.},
year = {2004}
}
@misc{Blaschko2006Conformal,
author = {M.B. Blaschko and T. Hofmann},
title = {Conformal {M}ulti-{I}nstance {K}ernels},
howpublished = {In NIPS 2006 {W}orkshop on {L}earning to {C}ompare {E}xamples},
year = {2006},
owner = {pmahe},
timestamp = {2007.07.13}
}
@article{Blaveri2005Bladder,
author = {Blaveri, E. and Brewer, J. L. and Roydasgupta, R. and Fridlyand,
J. and DeVries, S. and Koppie, T. and Pejavar, S. and Mehta, K. and
Carroll, P. and Simko, J. P. and Waldman, F. M.},
title = {Bladder cancer stage and outcome by array-based comparative genomic
hybridization.},
journal = {Clin. Cancer Res.},
year = {2005},
volume = {11},
pages = {7012--7022},
number = {19 Pt 1},
month = {Oct},
abstract = {PURPOSE: Bladder carcinogenesis is believed to follow alternative
pathways of disease progression driven by an accumulation of genetic
alterations. The purpose of this study was to evaluate associations
between measures of genomic instability and bladder cancer clinical
phenotype. EXPERIMENTAL DESIGN: Genome-wide copy number profiles
were obtained for 98 bladder tumors of diverse stages (29 pT(a),
14 pT1, 55 pT(2-4)) and grades (21 low-grade and 8 high-grade superficial
tumors) by array-based comparative genomic hybridization (CGH). Each
array contained 2,464 bacterial artificial chromosome and P1 clones,
providing an average resolution of 1.5 Mb across the genome. A total
of 54 muscle-invasive cases had follow-up information available.
Overall outcome analysis was done for patients with muscle-invasive
tumors having "good" (alive >2 years) versus "bad" (dead in <2 years)
prognosis. RESULTS: Array CGH analysis showed significant increases
in copy number alterations and genomic instability with increasing
stage and with outcome. The fraction of genome altered (FGA) was
significantly different between tumors of different stages (pT(a)
versus pT1, P = 0.0003; pT(a) versus pT(2-4), P = 0.02; and pT1 versus
pT(2-4), P = 0.03). Individual clones that differed significantly
between different tumor stages were identified after adjustment for
multiple comparisons (false discovery rate < 0.05). For muscle-invasive
tumors, the FGA was associated with patient outcome (bad versus good
prognosis patients, P = 0.002) and was identified as the only independent
predictor of overall outcome based on a multivariate Cox proportional
hazards method. Unsupervised hierarchical clustering separated "good"
and "bad" prognosis muscle-invasive tumors into clusters that showed
significant association with FGA and survival (Kaplan-Meier, P =
0.019). Supervised tumor classification (prediction analysis for
microarrays) had a 71\% classification success rate based on 102
unique clones. CONCLUSIONS: Array-based CGH identified quantitative
and qualitative differences in DNA copy number alterations at high
resolution according to tumor stage and grade. Fraction genome altered
was associated with worse outcome in muscle-invasive tumors, independent
of other clinicopathologic parameters. Measures of genomic instability
add independent power to outcome prediction of bladder tumors.},
doi = {10.1158/1078-0432.CCR-05-0177},
institution = {Department of Laboratory Medicine, University of California San Francisco,
San Francisco, California 94143-0808, USA.},
keywords = {Chromosome Mapping; Chromosomes, Artificial, Bacterial; Cluster Analysis;
DNA; Disease Progression; Gene Deletion; Gene Expression Profiling;
Gene Expression Regulation, Neoplastic; Genome; Humans; Image Processing,
Computer-Assisted; Linkage (Genetics); Multivariate Analysis; Nucleic
Acid Hybridization; Oligonucleotide Array Sequence Analysis; Phenotype;
Prognosis; Proportional Hazards Models; Time Factors; Treatment Outcome;
Urinary Bladder Neoplasms},
owner = {jp},
pii = {11/19/7012},
pmid = {16203795},
timestamp = {2009.10.05},
url = {http://dx.doi.org/10.1158/1078-0432.CCR-05-0177}
}
@article{Bleakley2007Supervised,
author = {Bleakley, K. and Biau, G. and Vert, J.-P.},
title = {Supervised reconstruction of biological networks with local models},
journal = {Bioinformatics},
year = {2007},
volume = {23},
pages = {i57--i65},
number = {13},
month = {Jul},
abstract = {MOTIVATION: Inference and reconstruction of biological networks from
heterogeneous data is currently an active research subject with several
important applications in systems biology. The problem has been attacked
from many different points of view with varying degrees of success.
In particular, predicting new edges with a reasonable false discovery
rate is highly demanded for practical applications, but remains extremely
challenging due to the sparsity of the networks of interest. RESULTS:
While most previous approaches based on the partial knowledge of
the network to be inferred build global models to predict new edges
over the network, we introduce here a novel method which predicts
whether there is an edge from a newly added vertex to each of the
vertices of a known network using local models. This involves learning
individually a certain subnetwork associated with each vertex of
the known network, then using the discovered classification rule
associated with only that vertex to predict the edge to the new vertex.
Excellent experimental results are shown in the case of metabolic
and protein-protein interaction network reconstruction from a variety
of genomic data. AVAILABILITY: An implementation of the proposed
algorithm is available upon request from the authors. CONTACT: Jean-Philippe.Vert@ensmp.fr.},
doi = {10.1093/bioinformatics/btm204},
pdf = {../local/Bleakley2007Supervised.pdf},
file = {Bleakley2007Supervised.pdf:Bleakley2007Supervised.pdf:PDF},
owner = {laurent},
pii = {23/13/i57},
pmid = {17646345},
timestamp = {2007.07.27},
url = {http://dx.doi.org/10.1093/bioinformatics/btm204}
}
@techreport{Bleakley2009Joint,
author = {Bleakley, K. and Vert, J.-P.},
title = {Joint segmentation of many {aCGH} profiles using fast group {LARS}},
institution = {HAL},
year = {2009},
number = {hal-00422430},
month = {October},
abstract = {Array-Based Comparative Genomic Hybridization (aCGH) is a method used
to search for genomic regions with copy numbers variations. For a
given aCGH profile, one challenge is to accurately segment it into
regions of constant copy number. Subjects sharing the same disease
status, for example a type of cancer, often have aCGH profiles with
similar copy number variations, due to duplications and deletions
relevant to that particular disease. We introduce a constrained optimization
algorithm that jointly segments aCGH profiles of many subjects. It
simultaneously penalizes the amount of freedom the set of profiles
have to jump from one level of constant copy number to another, at
genomic locations known as breakpoints. We show that breakpoints
shared by many different profiles tend to be found first by the algorithm,
even in the presence of significant amounts of noise. The algorithm
can be formulated as a group LARS problem. We propose an extremely
fast way to find the solution path, i.e., a sequence of shared breakpoints
in order of importance. For no extra cost the algorithm smoothes
all of the aCGH profiles into piecewise-constant regions of equal
copy number, giving low-dimensional versions of the original data.
These can be shown for all profiles on a single graph, allowing for
intuitive visual interpretation. Simulations and an implementation
of the algorithm on bladder cancer aCGH profiles are provided.},
owner = {jp},
timestamp = {2010.01.11},
url = {http://hal.archives-ouvertes.fr/hal-00422430}
}
@article{Bleicher2003Hit,
author = {K. H. Bleicher and H.-J. B{\"o}hm and K. M{\"u}ller and A. I. Alanine},
title = {{H}it and lead generation: beyond high-throughput screening.},
journal = {Nat Rev Drug Discov},
year = {2003},
volume = {2},
pages = {369--378},
number = {5},
month = {May},
abstract = {The identification of small-molecule modulators of protein function,
and the process of transforming these into high-content lead series,
are key activities in modern drug discovery. The decisions taken
during this process have far-reaching consequences for success later
in lead optimization and even more crucially in clinical development.
Recently, there has been an increased focus on these activities due
to escalating downstream costs resulting from high clinical failure
rates. In addition, the vast emerging opportunities from efforts
in functional genomics and proteomics demands a departure from the
linear process of identification, evaluation and refinement activities
towards a more integrated parallel process. This calls for flexible,
fast and cost-effective strategies to meet the demands of producing
high-content lead series with improved prospects for clinical success.},
doi = {10.1038/nrd1086},
keywords = {Amino Acid Motifs, Combinatorial Chemistry Techniques, Drug Design,
Drug Evaluation, Genomics, Preclinical, Proteomics, 12750740},
owner = {mahe},
pii = {nrd1086},
pmid = {12750740},
timestamp = {2006.08.15},
url = {http://dx.doi.org/10.1038/nrd1086}
}
@article{Blow2008DNA,
author = {Blow, N.},
title = {{DNA} sequencing: generation next-next},
journal = {Nat. Meth.},
year = {2008},
volume = {5},
pages = {267-274},
number = {3},
abstract = {Emboldened by the success of next-generation sequencing, scientists
are pursuing the holy grail of genomics—the '$1,000 genome'—with
single-molecule approaches. Nathan Blow reports.},
doi = {10.1038/nmeth0308-267},
pdf = {../local/Blow2008DNA.pdf},
file = {Blow2008DNA.pdf:Blow2008DNA.pdf:PDF},
keywords = {csbcbook-ch2, csbcbook},
owner = {jp},
url = {http://dx.doi.org/10.1038/nmeth0308-267}
}
@article{Blows2010Subtyping,
author = {Fiona M Blows and Kristy E Driver and Marjanka K Schmidt and Annegien
Broeks and Flora E van Leeuwen and Jelle Wesseling and Maggie C Cheang
and Karen Gelmon and Torsten O Nielsen and Carl Blomqvist and Päivi
Heikkilä and Tuomas Heikkinen and Heli Nevanlinna and Lars A Akslen
and Louis R Bégin and William D Foulkes and Fergus J Couch and Xianshu
Wang and Vicky Cafourek and Janet E Olson and Laura Baglietto and
Graham G Giles and Gianluca Severi and Catriona A McLean and Melissa
C Southey and Emad Rakha and Andrew R Green and Ian O Ellis and Mark
E Sherman and Jolanta Lissowska and William F Anderson and Angela
Cox and Simon S Cross and Malcolm W R Reed and Elena Provenzano and
Sarah-Jane Dawson and Alison M Dunning and Manjeet Humphreys and
Douglas F Easton and Montserrat García-Closas and Carlos Caldas and
Paul D Pharoah and David Huntsman},
title = {Subtyping of breast cancer by immunohistochemistry to investigate
a relationship between subtype and short and long term survival:
a collaborative analysis of data for 10,159 cases from 12 studies.},
journal = {PLoS Med},
year = {2010},
volume = {7},
pages = {e1000279},
number = {5},
month = {May},
abstract = {Immunohistochemical markers are often used to classify breast cancer
into subtypes that are biologically distinct and behave differently.
The aim of this study was to estimate mortality for patients with
the major subtypes of breast cancer as classified using five immunohistochemical
markers, to investigate patterns of mortality over time, and to test
for heterogeneity by subtype.We pooled data from more than 10,000
cases of invasive breast cancer from 12 studies that had collected
information on hormone receptor status, human epidermal growth factor
receptor-2 (HER2) status, and at least one basal marker (cytokeratin
[CK]5/6 or epidermal growth factor receptor [EGFR]) together with
survival time data. Tumours were classified as luminal and nonluminal
tumours according to hormone receptor expression. These two groups
were further subdivided according to expression of HER2, and finally,
the luminal and nonluminal HER2-negative tumours were categorised
according to expression of basal markers. Changes in mortality rates
over time differed by subtype. In women with luminal HER2-negative
subtypes, mortality rates were constant over time, whereas mortality
rates associated with the luminal HER2-positive and nonluminal subtypes
tended to peak within 5 y of diagnosis and then decline over time.
In the first 5 y after diagnosis the nonluminal tumours were associated
with a poorer prognosis, but over longer follow-up times the prognosis
was poorer in the luminal subtypes, with the worst prognosis at 15
y being in the luminal HER2-positive tumours. Basal marker expression
distinguished the HER2-negative luminal and nonluminal tumours into
different subtypes. These patterns were independent of any systemic
adjuvant therapy.The six subtypes of breast cancer defined by expression
of five markers show distinct behaviours with important differences
in short term and long term prognosis. Application of these markers
in the clinical setting could have the potential to improve the targeting
of adjuvant chemotherapy to those most likely to benefit. The different
patterns of mortality over time also suggest important biological
differences between the subtypes that may result in differences in
response to specific therapies, and that stratification of breast
cancers by clinically relevant subtypes in clinical trials is urgently
required.},
doi = {10.1371/journal.pmed.1000279},
pdf = {../local/Blows2010Subtyping.pdf},
file = {Blows2010Subtyping.pdf:Blows2010Subtyping.pdf:PDF},
institution = {Department of Oncology, University of Cambridge, United Kingdom.},
keywords = {Adult; Aged; Aged, 80 and over; Breast Neoplasms, metabolism/mortality/pathology;
Female; Hormones, analysis; Humans; Immunohistochemistry; Keratins;
Middle Aged; Prognosis; Proportional Hazards Models; Receptor, Epidermal
Growth Factor, analysis; Receptors, Cell Surface, metabolism; Tumor
Markers, Biological, analysis; Young Adult},
language = {eng},
medline-pst = {epublish},
owner = {phupe},
pmid = {20520800},
timestamp = {2011.06.01},
url = {http://dx.doi.org/10.1371/journal.pmed.1000279}
}
@inproceedings{Blum1998Combining,
author = {Blum, A. and Mitchell, T.},
title = {Combining labeled and unlabeled data with co-training},
booktitle = {COLT' 98: Proceedings of the eleventh annual conference on Computational
learning theory},
year = {1998},
pages = {92--100},
address = {New York, NY, USA},
publisher = {ACM},
doi = {http://doi.acm.org/10.1145/279943.279962},
isbn = {1-58113-057-0},
location = {Madison, Wisconsin, United States},
owner = {mordelet},
timestamp = {2010.10.27},
url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.114.9164}
}
@article{Bock2005Virtual,
author = {Bock, J. R. and Gough, D. A.},
title = {Virtual screen for ligands of orphan {G} protein-coupled receptors.},
journal = {J. Chem. Inform. Model.},
year = {2005},
volume = {45},
pages = {1402--1414},
number = {5},
abstract = {This paper describes a virtual screening methodology that generates
a ranked list of high-binding small molecule ligands for orphan G
protein-coupled receptors (oGPCRs), circumventing the requirement
for receptor three-dimensional structure determination. Features
representing the receptor are based only on physicochemical properties
of primary amino acid sequence, and ligand features use the two-dimensional
atomic connection topology and atomic properties. An experimental
screen comprised nearly 2 million hypothetical oGPCR-ligand complexes,
from which it was observed that the top 1.96\% predicted affinity
scores corresponded to "highly active" ligands against orphan receptors.
Results representing predicted high-scoring novel ligands for many
oGPCRs are presented here. Validation of the method was carried out
in several ways: (1) A random permutation of the structure-activity
relationship of the training data was carried out; by comparing test
statistic values of the randomized and nonshuffled data, we conclude
that the value obtained with nonshuffled data is unlikely to have
been encountered by chance. (2) Biological activities linked to the
compounds with high cross-target binding affinity were analyzed using
computed log-odds from a structure-based program. This information
was correlated with literature citations where GPCR-related pathways
or processes were linked to the bioactivity in question. (3) Anecdotal,
out-of-sample predictions for nicotinic targets and known ligands
were performed, with good accuracy in the low-to-high "active" binding
range. (4) An out-of-sample consistency check using the commercial
antipsychotic drug olanzapine produced "active" to "highly-active"
predicted affinities for all oGPCRs in our study, an observation
that is consistent with documented findings of cross-target affinity
of this compound for many different GPCRs. It is suggested that this
virtual screening approach may be used in support of the functional
characterization of oGPCRs by identifying potential cognate ligands.
Ultimately, this approach may have implications for pharmaceutical
therapies to modulate the activity of faulty or disease-related cellular
signaling pathways. In addition to application to cell surface receptors,
this approach is a generalized strategy for discovery of small molecules
that may bind intracellular enzymes and involve protein-protein interactions.},
doi = {10.1021/ci050006d},
pdf = {../local/Bock2005Virtual.pdf},
file = {Bock2005Virtual.pdf:Bock2005Virtual.pdf:PDF},
keywords = {chemogenomics},
owner = {laurent},
pmid = {16180917},
timestamp = {2007.07.30},
url = {http://dx.doi.org/10.1021/ci050006d}
}
@article{Bock2003Whole-proteome,
author = {Bock, J. R. and Gough, D. A.},
title = {Whole-proteome interaction mining},
journal = {Bioinformatics},
year = {2003},
volume = {19},
pages = {125-134},
number = {1},
abstract = {Motivation: {A} major post-genomic scientific and technological pursuit
is to describe the functions performed by the proteins encoded by
the genome. {O}ne strategy is to first identify the protein-protein
interactions in a proteome, then determine pathways and overall structure
relating these interactions, and finally to statistically infer functional
roles of individual proteins. {A}lthough huge amounts of genomic
data are at hand, current experimental protein interaction assays
must overcome technical problems to scale-up for high-throughput
analysis. {I}n the meantime, bioinformatics approaches may help bridge
the information gap required for inference of protein function. {I}n
this paper, a previously described data mining approach to prediction
of protein-protein interactions ({B}ock and {G}ough, 2001, {B}ioinformatics,
17, 455-460) is extended to interaction mining on a proteome-wide
scale. {A}n algorithm (the phylogenetic bootstrap) is introduced,
which suggests traversal of a phenogram, interleaving rounds of computation
and experiment, to develop a knowledge base of protein interactions
in genetically-similar organisms. {R}esults: {T}he interaction mining
approach was demonstrated by building a learning system based on
1,039 experimentally validated protein-protein interactions in the
human gastric bacterium {H}elicobacter pylori. {A}n estimate of the
generalization performance of the classifier was derived from 10-fold
cross-validation, which indicated expected upper bounds on precision
of 80% and sensitivity of 69% when applied to related organisms.
{O}ne such organism is the enteric pathogen {C}ampylobacter jejuni,
in which comprehensive machine learning prediction of all possible
pairwise protein-protein interactions was performed. {T}he resulting
network of interactions shares an average protein connectivity characteristic
in common with previous investigations reported in the literature,
offering strong evidence supporting the biological feasibility of
the hypothesized map. {F}or inferences about complete proteomes in
which the number of pairwise non-interactions is expected to be much
larger than the number of actual interactions, we anticipate that
the sensitivity will remain the same but precision may decrease.
{W}e present specific biological examples of two subnetworks of protein-protein
interactions in {C}. jejuni resulting from the application of this
approach, including elements of a two-component signal transduction
systems for thermoregulation, and a ferritin uptake network. {C}ontact:
dgough@bioeng.ucsd.edu},
pdf = {../local/Bock2003Whole-proteome.pdf},
file = {Bock2003Whole-proteome.pdf:local/Bock2003Whole-proteome.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://bioinformatics.oupjournals.org/cgi/content/abstract/19/1/125}
}
@article{Bock2002New,
author = {Bock, J. R. and Gough, D. A.},
title = {A {N}ew {M}ethod to {E}stimate {L}igand-{R}eceptor {E}nergetics},
journal = {Mol {C}ell {P}roteomics},
year = {2002},
volume = {1},
pages = {904-910},
number = {11},
abstract = {In the discovery of new drugs, lead identification and optimization
have assumed critical importance given the number of drug targets
generated from genetic, genomics, and proteomic technologies. {H}igh-throughput
experimental screening assays have been complemented recently by
"virtual screening" approaches to identify and filter potential ligands
when the characteristics of a target receptor structure of interest
are known. {V}irtual screening mandates a reliable procedure for
automatic ranking of structurally distinct ligands in compound library
databases. {C}omputing a rank score requires the accurate prediction
of binding affinities between these ligands and the target. {M}any
current scoring strategies require information about the target three-dimensional
structure. {I}n this study, a new method to estimate the free binding
energy between a ligand and receptor is proposed. {W}e extend a central
idea previously reported ({B}ock, {J}. {R}., and {G}ough, {D}. {A}.
(2001) {P}redicting protein-protein interactions from primary structure.
{B}ioinformatics 17, 455-460; {B}ock, {J}. {R}., and {G}ough, {D}.
{A}. (2002) {W}hole-proteome interaction mining. {B}ioinformatics,
in press) that uses simple descriptors to represent biomolecules
as input examples to train a support vector machine ({S}mola, {A}.
{J}., and {S}cholkopf, {B}. (1998) {A} {T}utorial on {S}upport {V}ector
{R}egression, {N}euro{COLT} {T}echnical {R}eport {NC}-{TR}-98-030,
{R}oyal {H}olloway {C}ollege, {U}niversity of {L}ondon, {UK}) and
the application of the trained system to previously unseen pairs,
estimating their propensity for interaction. {H}ere we seek to learn
the function that maps features of a receptor-ligand pair onto their
equilibrium free binding energy. {T}hese features do not comprise
any direct information about the three-dimensional structures of
ligand or target. {I}n cross-validation experiments, it is demonstrated
that objective measurements of prediction error rate and rank-ordering
statistics are competitive with those of several other investigations,
most of which depend on three-dimensional structural data. {T}he
size of the sample (n = 2,671) indicates that this approach is robust
and may have widespread applicability beyond restricted families
of receptor types. {I}t is concluded that newly sequenced proteins,
or those for which three-dimensional crystal structures are not easily
obtained, can be rapidly analyzed for their binding potential against
a library of ligands using this methodology.},
pdf = {../local/Bock2002New.pdf},
file = {Bock2002New.pdf:local/Bock2002New.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://www.mcponline.org/cgi/content/abstract/1/11/904}
}
@article{Bock2001Predicting,
author = {Bock, J. R. and Gough, D. A.},
title = {Predicting protein-protein interactions from primary structure},
journal = {Bioinformatics},
year = {2001},
volume = {17},
pages = {455--460},
number = {5},
pdf = {../local/bock01.pdf},
file = {bock01.pdf:local/bock01.pdf:PDF},
keywords = {biosvm},
subject = {biokernel},
url = {http://bioinformatics.oupjournals.org/cgi/reprint/17/5/455.pdf}
}
@article{Bock2007Effective,
author = {Mary Ellen Bock and Claudio Garutti and Conettina Guerra},
title = {Effective labeling of molecular surface points for cavity detection
and location of putative binding sites.},
journal = {Comput Syst Bioinformatics Conf},
year = {2007},
volume = {6},
pages = {263--274},
abstract = {We present a method for detecting and comparing cavities on protein
surfaces that is useful for protein binding site recognition. The
method is based on a representation of the protein structures by
a collection of spin-images and their associated spin-image profiles.
Results of the cavity detection procedure are presented for a large
set of non-redundant proteins and compared with SURFNET-ConSurf.
Our comparison method is used to find a surface region in one cavity
of a protein that is geometrically similar to a surface region in
the cavity of another protein. Such a finding would be an indication
that the two regions likely bind to the same ligand. Our overall
approach for cavity detection and comparison is benchmarked on several
pairs of known complexes, obtaining a good coverage of the atoms
of the binding sites.},
institution = {Department of Statistics, Purdue University 150 N. University Street,
West Lafayette, IN 47907-2067, USA. mbock@purdue.edu},
keywords = {Binding Sites; Computer Simulation; Models, Chemical; Models, Molecular;
Protein Binding; Protein Conformation; Protein Folding; Proteins,
chemistry/ultrastructure; Sequence Analysis, Protein, methods; Surface
Properties},
owner = {bricehoffmann},
pii = {9781860948732_0028},
pmid = {17951830},
timestamp = {2009.02.13}
}
@article{Bockaert1999Molecular,
author = {Bockaert, J. and Pin, J. P.},
title = {Molecular tinkering of {G} protein-coupled receptors: an evolutionary
success},
journal = {EMBO J.},
year = {1999},
volume = {18},
pages = {1723--1729},
number = {7},
month = {Apr},
abstract = {Among membrane-bound receptors, the G protein-coupled receptors (GPCRs)
are certainly the most diverse. They have been very successful during
evolution, being capable of transducing messages as different as
photons, organic odorants, nucleotides, nucleosides, peptides, lipids
and proteins. Indirect studies, as well as two-dimensional crystallization
of rhodopsin, have led to a useful model of a common 'central core',
composed of seven transmembrane helical domains, and its structural
modifications during activation. There are at least six families
of GPCRs showing no sequence similarity. They use an amazing number
of different domains both to bind their ligands and to activate G
proteins. The fine-tuning of their coupling to G proteins is regulated
by splicing, RNA editing and phosphorylation. Some GPCRs have been
found to form either homo- or heterodimers with a structurally different
GPCR, but also with membrane-bound proteins having one transmembrane
domain such as nina-A, odr-4 or RAMP, the latter being involved in
their targeting, function and pharmacology. Finally, some GPCRs are
unfaithful to G proteins and interact directly, via their C-terminal
domain, with proteins containing PDZ and Enabled/VASP homology (EVH)-like
domains.},
doi = {10.1093/emboj/18.7.1723},
keywords = {chemogenomics},
owner = {laurent},
pmid = {10202136},
timestamp = {2008.01.16},
url = {http://dx.doi.org/10.1093/emboj/18.7.1723}
}
@article{Boese2005Mechanistic,
author = {Boese, Q. and Leake, D. and Reynolds, A. and Read, S. and Scaringe,
S. A. and Marshall, W. S. and Khvorova, A.},
title = {Mechanistic insights aid computational short interfering {RNA} design.},
journal = {Methods {E}nzymol.},
year = {2005},
volume = {392},
pages = {73-96},
abstract = {R{NA} interference is widely recognized for its utility as a functional
genomics tool. {I}n the absence of reliable target site selection
tools, however, the impact of {RNA} interference ({RNA}i) may be
diminished. {T}he primary determinants of silencing are influenced
by highly coordinated {RNA}-protein interactions that occur throughout
the {RNA}i process, including short interfering {RNA} (si{RNA}) binding
and unwinding followed by target recognition, cleavage, and subsequent
product release. {R}ecently developed strategies for identification
of functional si{RNA}s reveal that thermodynamic and si{RNA} sequence-specific
properties are crucial to predict functional duplexes ({K}hvorova
et al., 2003; {R}eynolds et al., 2004; {S}chwarz et al., 2003). {A}dditional
assessments of si{RNA} specificity reveal that more sophisticated
sequence comparison tools are also required to minimize potential
off-target effects ({J}ackson et al., 2003; {S}emizarov et al., 2003).
{T}his chapter reviews the biological basis for current computational
design tools and how best to utilize and assess their predictive
capabilities for selecting functional and specific si{RNA}s.},
doi = {10.1016/S0076-6879(04)92005-8},
keywords = {sirna},
pii = {S0076687904920058},
url = {http://dx.doi.org/10.1016/S0076-6879(04)92005-8}
}
@article{Boeva2011Control-free,
author = {Boeva, V. and Zinovyev, A. and Bleakley, K. and Vert, J.-P. and Janoueix-Lerosey,
I. and Delattre, O. and Barillot, E.},
title = {Control-free calling of copy number alterations in deep-sequencing
data using {GC}-content normalization.},
journal = {Bioinformatics},
year = {2011},
volume = {27},
pages = {268--269},
number = {2},
month = {Jan},
abstract = {We present a tool for control-free copy number alteration (CNA) detection
using deep-sequencing data, particularly useful for cancer studies.
The tool deals with two frequent problems in the analysis of cancer
deep-sequencing data: absence of control sample and possible polyploidy
of cancer cells. FREEC (control-FREE Copy number caller) automatically
normalizes and segments copy number profiles (CNPs) and calls CNAs.
If ploidy is known, FREEC assigns absolute copy number to each predicted
CNA. To normalize raw CNPs, the user can provide a control dataset
if available; otherwise GC content is used. We demonstrate that for
Illumina single-end, mate-pair or paired-end sequencing, GC-contentr
normalization provides smooth profiles that can be further segmented
and analyzed in order to predict CNAs.Source code and sample data
are available at http://bioinfo-out.curie.fr/projects/freec/.freec@curie.frSupplementary
data are available at Bioinformatics online.},
doi = {10.1093/bioinformatics/btq635},
pdf = {../local/Boeva2011Control-free.pdf},
file = {Boeva2011Control-free.pdf:Boeva2011Control-free.pdf:PDF},
institution = {Institut Curie, INSERM, U900, Paris, F-75248, Mines ParisTech, Fontainebleau,
F-77300 and INSERM, U830, Paris, F-75248 France.},
keywords = {ngs},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {btq635},
pmid = {21081509},
timestamp = {2011.01.25},
url = {http://dx.doi.org/10.1093/bioinformatics/btq635}
}
@article{Bohnert2009Transcript,
author = {Bohnert, R. and Behr, J. and R\"atsch, G.},
title = {Transcript quantification with {RNA-Seq} data},
journal = {BMC Bioinformatics},
year = {2009},
volume = {10 (Suppl 13)},
pages = {P5},
doi = {10.1186/1471-2105-10-S13-P5},
pdf = {../local/Bohnert2009Transcript.pdf},
file = {Bohnert2009Transcript.pdf:Bohnert2009Transcript.pdf:PDF},
keywords = {ngs, rnaseq},
owner = {jp},
timestamp = {2012.03.06},
url = {http://dx.doi.org/10.1186/1471-2105-10-S13-P5}
}
@article{Bolstad2003comparison,
author = {Bolstad, B.M. and Irizarry, R.A. and {\AA}strand, M. and Speed, T.P.},
title = {A comparison of normalization methods for high density oligonucleotide
array data based on variance and bias},
journal = {Bioinformatics},
year = {2003},
volume = {19},
pages = {185--193},
number = {2},
publisher = {Oxford Univ Press}
}
@article{Bonachera2008Fuzzy,
author = {Bonach{\'e}ra, F. and Horvath, D.},
title = {Fuzzy tricentric pharmacophore fingerprints. 2. Application of topological
fuzzy pharmacophore triplets in quantitative structure-activity relationships.},
journal = {J. Chem. Inf. Model.},
year = {2008},
volume = {48},
pages = {409--425},
number = {2},
month = {Feb},
abstract = {Topological fuzzy pharmacophore triplets (2D-FPT), using the number
of interposed bonds to measure separation between the atoms representing
pharmacophore types, were employed to establish and validate quantitative
structure-activity relationships (QSAR). Thirteen data sets for which
state-of-the-art QSAR models were reported in literature were revisited
in order to benchmark 2D-FPT biological activity-explaining propensities.
Linear and nonlinear QSAR models were constructed for each compound
series (following the original author's splitting into training/validation
subsets) with three different 2D-FPT versions, using the genetic
algorithm-driven Stochastic QSAR sampler (SQS) to pick relevant triplets
and fit their coefficients. 2D-FPT QSARs are computationally cheap,
interpretable, and perform well in benchmarking. In a majority of
cases (10/13), default 2D-FPT models validated better than or as
well as the best among those reported, including 3D overlay-dependent
approaches. Most of the analogues series, either unaffected by protonation
equilibria or unambiguously adopting expected protonation states,
were equally well described by rule- or pKa-based pharmacophore flagging.
Thermolysin inhibitors represent a notable exception: pKa-based flagging
boosts model quality, although--surprisingly--not due to proteolytic
equilibrium effects. The optimal degree of 2D-FPT fuzziness is compound
set dependent. This work further confirmed the higher robustness
of nonlinear over linear SQS models. In spite of the wealth of studied
sets, benchmarking is nevertheless flawed by low intraset diversity:
a whole series of thereby caused artifacts were evidenced, implicitly
raising questions about the way QSAR studies are conducted nowadays.
An in-depth investigation of thrombin inhibition models revealed
that some of the selected triplets make sense (one of these stands
for a topological pharmacophore covering the P1 and P2 binding pockets).
Nevertheless, equations were either unable to predict the activity
of the structurally different ligands or tended to indiscriminately
predict any compound outside the training family to be active. 2D-FPT
QSARs do however not depend on any common scaffold required for molecule
superimposition and may in principle be trained on hand of diverse
sets, which is a must in order to obtain widely applicable models.
Adding (assumed) inactives of various families for training enabled
discovery of models that specifically recognize the structurally
different actives.},
doi = {10.1021/ci7003237},
pdf = {../local/Bonachera2008Fuzzy.pdf},
file = {Bonachera2008Fuzzy.pdf:Bonachera2008Fuzzy.pdf:PDF},
institution = { Fonctionnelle, Université des Sciences et Technologies de Lille,
Bât. C9-59655 Villeneuve d'Ascq Cedex, France.},
keywords = {chemoinformatics},
owner = {jp},
pmid = {18254617},
timestamp = {2009.03.12},
url = {http://dx.doi.org/10.1021/ci7003237}
}
@article{Bonachera2006Fuzzy,
author = {Bonach{\'e}ra, F. and Parent, B. and Barbosa, F. and Froloff, N.
and Horvath, D.},
title = {Fuzzy tricentric pharmacophore fingerprints. 1. Topological fuzzy
pharmacophore triplets and adapted molecular similarity scoring schemes.},
journal = {J. Chem. Inf. Model.},
year = {2006},
volume = {46},
pages = {2457--2477},
number = {6},
abstract = {This paper introduces a novel molecular description--topological (2D)
fuzzy pharmacophore triplets, 2D-FPT--using the number of interposed
bonds as the measure of separation between the atoms representing
pharmacophore types (hydrophobic, aromatic, hydrogen-bond donor and
acceptor, cation, and anion). 2D-FPT features three key improvements
with respect to the state-of-the-art pharmacophore fingerprints:
(1) The first key novelty is fuzzy mapping of molecular triplets
onto the basis set of pharmacophore triplets: unlike in the binary
scheme where an atom triplet is set to highlight the bit of a single,
best-matching basis triplet, the herein-defined fuzzy approach allows
for gradual mapping of each atom triplet onto several related basis
triplets, thus minimizing binary classification artifacts. (2) The
second innovation is proteolytic equilibrium dependence, by explicitly
considering all of the conjugated acids and bases (microspecies).
2D-FPTs are concentration-weighted (as predicted at pH=7.4) averages
of microspecies fingerprints. Therefore, small structural modifications,
not affecting the overall pharmacophore pattern (in the sense of
classical rule-based assignment), but nevertheless triggering a pKa
shift, will have a major impact on 2D-FPT. Pairs of almost identical
compounds with significantly differing activities ("activity cliffs"
in classical descriptor spaces) were in many cases predictable by
2D-FPT. (3) The third innovation is a new similarity scoring formula,
acknowledging that the simultaneous absence of a triplet in two molecules
is a less-constraining indicator of similarity than its simultaneous
presence. It displays excellent neighborhood behavior, outperforming
2D or 3D two-point pharmacophore descriptors or chemical fingerprints.
The 2D-FPT calculator was developed using the chemoinformatics toolkit
of ChemAxon (www.chemaxon.com).},
doi = {10.1021/ci6002416},
pdf = {../local/Bonachera2006Fuzzy.pdf},
file = {Bonachera2006Fuzzy.pdf:Bonachera2006Fuzzy.pdf:PDF},
institution = {ex, France.},
keywords = {chemoinformatics},
owner = {jp},
pmid = {17125187},
timestamp = {2009.03.12},
url = {http://dx.doi.org/10.1021/ci6002416}
}
@book{Bondy1976Graph,
title = {Graph theory with applications},
publisher = {Macmillan Press Ltd.},
year = {1976},
author = {J. A. Bondy and U. S. R. Murty}
}
@incollection{Bonilla2008Multi-task,
author = {Edwin Bonilla and Kian Ming Chai and Chris Williams},
title = {Multi-task Gaussian Process Prediction},
booktitle = {Advances in Neural Information Processing Systems 20},
publisher = {MIT Press},
year = {2008},
editor = {J.C. Platt and D. Koller and Y. Singer and S. Roweis},
address = {Cambridge, MA}
}
@inproceedings{Bonilla2007Kernel,
author = {Edwin V. Bonilla and Felix V. Agakov and Christopher K. I. Williams},
title = {Kernel Multi-task Learning using Task-specific Features},
booktitle = {Proceedings of the 11th International Conference on Artificial Intelligence
and Statistics},
year = {2007},
month = {March},
publisher = {Omnipress},
location = {San Juan, Puerto Rico}
}
@article{Boobis2002In,
author = {A. Boobis and U. Gundert-Remy and P. Kremers and P. Macheras and
O. Pelkonen},
title = {{I}n silico prediction of {ADME} and pharmacokinetics. {R}eport of
an expert meeting organised by {COST} {B}15.},
journal = {Eur. J. Pharm. Sci.},
year = {2002},
volume = {17},
pages = {183--193},
number = {4-5},
month = {Dec},
abstract = {The computational approach is one of the newest and fastest developing
techniques in pharmacokinetics, ADME (absorption, distribution, metabolism,
excretion) evaluation, drug discovery and toxicity. However, to date,
the software packages devoted to ADME prediction, especially of metabolism,
have not yet been adequately validated and still require improvements
to be effective. Most are 'open' systems, under constant evolution
and able to incorporate rapidly, and often easily, new information
from user or developer databases. Quantitative in silico predictions
are now possible for several pharmacokinetic (PK) parameters, particularly
absorption and distribution. The emerging consensus is that the predictions
are no worse than those made using in vitro tests, with the decisive
advantage that much less investment in technology, resources and
time is needed. In addition, and of critical importance, it is possible
to screen virtual compounds. Some packages are able to handle thousands
of molecules in a few hours. However, common experience shows that,
in part at least for essentially irrational reasons, there is currently
a lack of confidence in these approaches. An effort should be made
by the software producers towards more transparency, in order to
improve the confidence of their consumers. It seems highly probable
that in silico approaches will evolve rapidly, as did in vitro methods
during the last decade. Past experience with the latter should be
helpful in avoiding repetition of similar errors and in taking the
necessary steps to ensure effective implementation. A general concern
is the lack of access to the large amounts of data on compounds no
longer in development, but still kept secret by the pharmaceutical
industry. Controlled access to these data could be particularly helpful
in validating new in silico approaches.},
keywords = {Adsorption, Biological Availability, Chemical, Computer Simulation,
Models, Pharmaceutical, Pharmaceutical Preparations, Predictive Value
of Tests, Software, Technology, 12453607},
owner = {mahe},
pii = {S0928098702001859},
pmid = {12453607},
timestamp = {2006.08.16}
}
@article{Bookstein1989Principal,
author = {Bookstein, F. L. },
title = {Principal warps: thin-plate splines and the decomposition of deformations},
journal = {IEEE T. Pattern. Anal.},
year = {1989},
volume = {11},
pages = {567--585},
number = {6},
abstract = {The decomposition of deformations by principal warps is demonstrated.
The method is extended to deal with curving edges between landmarks.
This formulation is related to other applications of splines current
in computer vision. How they might aid in the extraction of features
for analysis, comparison, and diagnosis of biological and medical
images in indicated},
doi = {10.1109/34.24792},
pdf = {../local/Bookstein1989Principal.pdf},
file = {Bookstein1989Principal.pdf:Bookstein1989Principal.pdf:PDF},
owner = {jp},
timestamp = {2009.10.30},
url = {http://dx.doi.org/10.1109/34.24792}
}
@book{Border1985Fixed,
title = {Fixed point theorems with applications to economics and game theory},
publisher = {Cambridge University Press},
year = {1985},
author = {Border, K. C.},
address = {Cambridge, UK},
owner = {jp},
timestamp = {2011.04.30}
}
@article{Bordner2005Statistical,
author = {Andrew J Bordner and Ruben Abagyan},
title = {Statistical analysis and prediction of protein-protein interfaces.},
journal = {Proteins},
year = {2005},
volume = {60},
pages = {353-66},
number = {3},
month = {Aug},
abstract = {Predicting protein-protein interfaces from a three-dimensional structure
is a key task of computational structural proteomics. {I}n contrast
to geometrically distinct small molecule binding sites, protein-protein
interface are notoriously difficult to predict. {W}e generated a
large nonredundant data set of 1494 true protein-protein interfaces
using biological symmetry annotation where necessary. {T}he data
set was carefully analyzed and a {S}upport {V}ector {M}achine was
trained on a combination of a new robust evolutionary conservation
signal with the local surface properties to predict protein-protein
interfaces. {F}ivefold cross validation verifies the high sensitivity
and selectivity of the model. {A}s much as 97\% of the predicted
patches had an overlap with the true interface patch while only 22\%
of the surface residues were included in an average predicted patch.
{T}he model allowed the identification of potential new interfaces
and the correction of mislabeled oligomeric states.},
doi = {10.1002/prot.20433},
pdf = {../local/Bordner2005Statistical.pdf},
file = {Bordner2005Statistical.pdf:local/Bordner2005Statistical.pdf:PDF},
keywords = {biosvm},
url = {http://dx.doi.org/10.1002/prot.20433}
}
@article{Borgwardt2005Protein,
author = {Borgwardt, K.M. and Ong, C.S. and Sch{\"o}nauer, S. and Vishwanathan,
S.V.N. and Smola, A.J. and Kriegel, H.-P.},
title = {Protein function prediction via graph kernels.},
journal = {Bioinformatics},
year = {2005},
volume = {21},
pages = {i47-i56},
number = {Suppl. 1},
month = {Jun},
abstract = {M{OTIVATION}: {C}omputational approaches to protein function prediction
infer protein function by finding proteins with similar sequence,
structure, surface clefts, chemical properties, amino acid motifs,
interaction partners or phylogenetic profiles. {W}e present a new
approach that combines sequential, structural and chemical information
into one graph model of proteins. {W}e predict functional class membership
of enzymes and non-enzymes using graph kernels and support vector
machine classification on these protein graphs. {RESULTS}: {O}ur
graph model, derivable from protein sequence and structure only,
is competitive with vector models that require additional protein
information, such as the size of surface pockets. {I}f we include
this extra information into our graph model, our classifier yields
significantly higher accuracy levels than the vector models. {H}yperkernels
allow us to select and to optimally combine the most relevant node
attributes in our protein graphs. {W}e have laid the foundation for
a protein function prediction system that integrates protein information
from various sources efficiently and effectively. {AVAILABILITY}:
{M}ore information available via www.dbs.ifi.lmu.de/{M}itarbeiter/borgwardt.html.
{CONTACT}: borgwardt@dbs.ifi.lmu.de.},
doi = {10.1093/bioinformatics/bti1007},
pdf = {../local/Borgwardt2005Protein.pdf},
file = {Borgwardt2005Protein.pdf:local/Borgwardt2005Protein.pdf:PDF},
keywords = {biosvm},
pii = {21/suppl_1/i47},
url = {http://dx.doi.org/10.1093/bioinformatics/bti1007}
}
@inproceedings{Borgwardt2005Shortest-Path,
author = {Karsten M. Borgwardt and Hans-Peter Kriegel},
title = {Shortest-Path Kernels on Graphs},
booktitle = {{ICDM} '05: {P}roceedings of the {F}ifth {IEEE} {I}nternational {C}onference
on {D}ata {M}ining},
year = {2005},
pages = {74--81},
address = {Washington, DC, USA},
publisher = {IEEE Computer Society},
doi = {http://dx.doi.org/10.1109/ICDM.2005.132},
pdf = {../local/Borgwardt2005Shortest-Path.pdf},
file = {Borgwardt2005Shortest-Path.pdf:Borgwardt2005Shortest-Path.pdf:PDF},
isbn = {0-7695-2278-5},
keywords = {chemoinformatics kernel-theory}
}
@book{Borwein2000Convex,
title = {Convex Analysis and Nonlinear Optimization},
publisher = {Springer-Verlag, New York},
year = {2000},
author = {J. M. Borwein and A. S. Lewis}
}
@inproceedings{Boser1992training,
author = {Boser, B. E. and Guyon, I. M. and Vapnik, V. N.},
title = {A training algorithm for optimal margin classifiers},
booktitle = {Proceedings of the 5th annual {ACM} workshop on {C}omputational {L}earning
{T}heory},
year = {1992},
pages = {144--152},
address = {New York, NY, USA},
publisher = {ACM Press},
pdf = {../local/bose92.pdf},
file = {bose92.pdf:local/bose92.pdf:PDF},
location = {Pittsburgh, Pennsylvania, United States},
subject = {kernel},
url = {http://www.clopinet.com/isabelle/Papers/colt92.ps.Z}
}
@article{Bosshard2001Molecular,
author = {H. R. Bosshard},
title = {Molecular recognition by induced fit: how fit is the concept?},
journal = {News Physiol Sci},
year = {2001},
volume = {16},
pages = {171--173},
month = {Aug},
abstract = {Induced fit explains why biomolecules can bind together even if they
are not optimized for binding. However, induced fit can lead to a
kinetic bottleneck and does not describe every interaction in the
absence of prior complementarity. Preselection of a fitting conformer
is an alternative to induced fit.},
institution = {and.},
keywords = {Antigen-Antibody Complex, physiology; Biological Products, chemistry/metabolism;
Models, Biological; Molecular Conformation},
owner = {bricehoffmann},
pmid = {11479367},
timestamp = {2009.02.13}
}
@article{Bostroem2001Reproducing,
author = {J. Bostr\"om},
title = {Reproducing the conformations of protein-bound ligands: a critical
evaluation of several popular conformational searching tools.},
journal = {J Comput Aided Mol Des},
year = {2001},
volume = {15},
pages = {1137--1152},
number = {12},
month = {Dec},
abstract = {Several programs (Catalyst, Confort, Flo99, MacroModel, and Omega)
that are commonly used to generate conformational ensembles have
been tested for their ability to reproduce bioactive conformations.
The ligands from thirty-two different ligand-protein complexes determined
by high-resolution (< 2.0 A) X-ray crystallography have been analyzed.
The Low-Mode Conformational Search method (with AMBER* and the GB/SA
hydration model), as implemented in MacroModel, was found to perform
better than the other algorithms. The rule-based method Omega, which
is orders of magnitude faster than the other methods, also gave reasonable
results but were found to be dependent on the input structure. The
methods supporting diverse sampling (Catalyst, Confort) performed
least well. For the seven ligands in the set having eight or more
rotatable bonds, none of the bioactive conformations were ever found,
save for one exception (Flo99). These ligands do not bind in a local
minimum conformation according to AMBER*\GB/SA. Taking these last
two observations together, it is clear that geometrically similar
structures should be collected in order to increase the probability
of finding the bioactive conformation among the generated ensembles.
Factors influencing bioactive conformational retrieval have been
identified and are discussed.},
keywords = {Algorithms; Crystallography, X-Ray; Ligands; Models, Molecular; Molecular
Conformation; Protein Binding; Quantum Theory; Software},
owner = {laurent},
pmid = {12160095},
timestamp = {2008.01.16}
}
@article{Bostroem2003Assessing,
author = {Bostr{\"o}m, J. and Greenwood, J. R. and Gottfries, J.},
title = {Assessing the performance of {OMEGA} with respect to retrieving bioactive
conformations.},
journal = {J. Mol. Graph. Model.},
year = {2003},
volume = {21},
pages = {449--462},
number = {5},
month = {Mar},
abstract = {OMEGA is a rule-based program which rapidly generates conformational
ensembles of small molecules. We have varied the parameters which
control the nature of the ensembles generated by OMEGA in a statistical
fashion (D-optimal) with the aim of increasing the probability of
generating bioactive conformations. Thirty-six drug-like ligands
from different ligand-protein complexes determined by high-resolution
(< or =2.0A) X-ray crystallography have been analyzed. Statistically
significant models (Q(2)> or =0.75) confirm that one can increase
the performance of OMEGA by modifying the parameters. Twenty-eight
of the bioactive conformations were retrieved when using a low-energy
cut-off (5 kcal/mol), a low RMSD value (0.6A) for duplicate removal,
and a maximum of 1000 output conformations. All of those that were
not retrieved had eight or more rotatable bonds. The duplicate removal
parameter was found to have the largest impact on retrieval of bioactive
conformations, and the maximum number of conformations also affected
the results considerably. The input conformation was found to influence
the results largely because certain bond angles can prevent the bioactive
conformation from being generated as a low-energy conformation. Pre-optimizing
the input structures with MMFF94s improved the results significantly.
We also investigated the performance of OMEGA in connection with
database searching. The shape-matching program Rapid Overlay of Chemical
Structures (ROCS) was used as search tool. Two multi-conformational
databases were built from the MDDR database plus the 36 compounds;
one large (maximum 1000 conformations/mol) and one small (maximum
100 conformations/mol). Both databases provided satisfactory results
in terms of retrieval. ROCS was able to rank 35 out of 36 X-ray structures
among the top 500 hits from the large database.},
owner = {laurent},
pii = {S1093-3263(02)00204-8},
pmid = {12543140},
timestamp = {2008.01.16}
}
@inproceedings{Bouchard2008Efficient,
author = {Alexandre Bouchard-C{\^o}t{\'e} and Michael I. Jordan and Dan Klein},
title = {Efficient Inference in Phylogenetic InDel Trees.},
booktitle = {NIPS},
year = {2008},
editor = {Daphne Koller and Dale Schuurmans and Yoshua Bengio and L{\'e}on
Bottou},
pages = {177-184},
publisher = {MIT Press},
date = {2009-04-15},
ee = {http://books.nips.cc/papers/files/nips21/NIPS2008_0438.pdf},
url = {http://dblp.uni-trier.de/db/conf/nips/nips2008.html#Bouchard-CoteJK08}
}
@book{Bouchaud2003Theory,
title = {Theory of financial risk and derivative pricing},
publisher = {Cambridge University Press},
year = {2003},
author = {Bouchaud, J.-P. and Potters, M.},
owner = {jp},
timestamp = {2011.01.29}
}
@article{Boucheron2000sharp,
author = {Boucheron, S. and Lugosi, G. and Massart, P.},
title = {A sharp concentration inequality with applications},
journal = {Random {S}tructures and {A}lgorithms},
year = {2000},
volume = {16},
pages = {277--292},
pdf = {../local/bouc00.pdf},
file = {bouc00.pdf:local/bouc00.pdf:PDF},
subject = {stat},
url = {http://www.econ.upf.es/~lugosi/concentration.ps}
}
@article{Boulesteix2010Over-optimism,
author = {Boulesteix, A.L.},
title = {Over-optimism in bioinformatics research},
journal = {Bioinformatics},
year = {2010},
volume = {26},
pages = {437--439},
number = {3},
publisher = {Oxford Univ Press}
}
@article{Boulesteix2009Stability,
author = {Boulesteix, A.L. and Slawski, M.},
title = {Stability and aggregation of ranked gene lists},
journal = {Briefings in bioinformatics},
year = {2009},
volume = {10},
pages = {556--568},
number = {5},
publisher = {Oxford Univ Press}
}
@article{Bowd2002Comparing,
author = {Christopher Bowd and Kwokleung Chan and Linda M Zangwill and Michael
H Goldbaum and Te-Won Lee and Terrence J Sejnowski and Robert N Weinreb},
title = {Comparing neural networks and linear discriminant functions for glaucoma
detection using confocal scanning laser ophthalmoscopy of the optic
disc.},
journal = {Invest {O}phthalmol {V}is {S}ci},
year = {2002},
volume = {43},
pages = {3444-54},
number = {11},
month = {Nov},
abstract = {P{URPOSE}: {T}o determine whether neural network techniques can improve
differentiation between glaucomatous and nonglaucomatous eyes, using
the optic disc topography parameters of the {H}eidelberg {R}etina
{T}omograph ({HRT}; {H}eidelberg {E}ngineering, {H}eidelberg, {G}ermany).
{METHODS}: {W}ith the {HRT}, one eye was imaged from each of 108
patients with glaucoma (defined as having repeatable visual field
defects with standard automated perimetry) and 189 subjects without
glaucoma (no visual field defects with healthy-appearing optic disc
and retinal nerve fiber layer on clinical examination) and the optic
nerve topography was defined by 17 global and 66 regional {HRT} parameters.
{W}ith all the {HRT} parameters used as input, receiver operating
characteristic ({ROC}) curves were generated for the classification
of eyes, by three neural network techniques: linear and {G}aussian
support vector machines ({SVM} linear and {SVM} {G}aussian, respectively)
and a multilayer perceptron ({MLP}), as well as four previously proposed
linear discriminant functions ({LDF}s) and one {LDF} developed on
the current data with all {HRT} parameters used as input. {RESULTS}:
{T}he areas under the {ROC} curves for {SVM} linear and {SVM} {G}aussian
were 0.938 and 0.945, respectively; for {MLP}, 0.941; for the current
{LDF}, 0.906; and for the best previously proposed {LDF}, 0.890.
{W}ith the use of forward selection and backward elimination optimization
techniques, the areas under the {ROC} curves for {SVM} {G}aussian
and the current {LDF} were increased to approximately 0.96. {CONCLUSIONS}:
{T}rained neural networks, with global and regional {HRT} parameters
used as input, improve on previously proposed {HRT} parameter-based
{LDF}s for discriminating between glaucomatous and nonglaucomatous
eyes. {T}he performance of both neural networks and {LDF}s can be
improved with optimization of the features in the input. {N}eural
network analyses show promise for increasing diagnostic accuracy
of tests for glaucoma.},
pdf = {../local/Bowd2002Comparing.pdf},
file = {Bowd2002Comparing.pdf:local/Bowd2002Comparing.pdf:PDF},
keywords = {Acute, Algorithms, Animals, Anion Exchange Resins, Artificial Intelligence,
Automated, Base Pair Mismatch, Base Pairing, Base Sequence, Biological,
Biosensing Techniques, Carcinoma, Chemical, Chromatography, Citric
Acid Cycle, Classification, Cluster Analysis, Comparative Study,
Computational Biology, Computer-Assisted, Cystadenoma, DNA, Databases,
Decision Making, Diagnosis, Differential, Discriminant Analysis,
Drug, Drug Design, Electrostatics, Eukaryotic Cells, Factual, Feasibility
Studies, Female, Gene Expression, Gene Expression Profiling, Gene
Expression Regulation, Genes, Genetic, Genetic Heterogeneity, Genetic
Markers, Glaucoma, Hemolysins, Humans, Internet, Intraocular Pressure,
Ion Exchange, Lasers, Leukemia, Ligands, Likelihood Functions, Logistic
Models, Lung Neoplasms, Lymphocytic, Lymphoma, Markov Chains, Mathematics,
Messenger, Models, Molecular, Molecular Probe Techniques, Molecular
Sequence Data, Nanotechnology, Neoplasm, Neoplasms, Neoplastic, Neural
Networks (Computer), Non-P.H.S., Non-Small-Cell Lung, Non-U.S. Gov't,
Nucleic Acid Conformation, Nucleic Acid Hybridization, Observer Variation,
Oligonucleotide Array Sequence Analysis, Open-Angle, Ophthalmoscopy,
Optic Disk, Ovarian Neoplasms, P.H.S., Pattern Recognition, Probability,
Probability Learning, Protein Binding, Protein Conformation, Proteins,
Quality Control, Quantum Theory, RNA, RNA Splicing, ROC Curve, Receptors,
Reference Values, Regression Analysis, Reproducibility of Results,
Research Support, Robotics, Saccharomyces cerevisiae Proteins, Sensitivity
and Specificity, Sequence Analysis, Signal Processing, Software,
Statistical, Stomach Neoplasms, Structural, Structure-Activity Relationship,
Thermodynamics, Transcription, Tumor Markers, U.S. Gov't, 12407155},
url = {http://www.iovs.org/cgi/content/abstract/43/11/3444}
}
@article{Bowd2005Relevance,
author = {Christopher Bowd and Felipe A Medeiros and Zuohua Zhang and Linda
M Zangwill and Jiucang Hao and Te-Won Lee and Terrence J Sejnowski
and Robert N Weinreb and Michael H Goldbaum},
title = {Relevance vector machine and support vector machine classifier analysis
of scanning laser polarimetry retinal nerve fiber layer measurements.},
journal = {Invest {O}phthalmol {V}is {S}ci},
year = {2005},
volume = {46},
pages = {1322-9},
number = {4},
month = {Apr},
abstract = {P{URPOSE}: {T}o classify healthy and glaucomatous eyes using relevance
vector machine ({RVM}) and support vector machine ({SVM}) learning
classifiers trained on retinal nerve fiber layer ({RNFL}) thickness
measurements obtained by scanning laser polarimetry ({SLP}). {METHODS}:
{S}eventy-two eyes of 72 healthy control subjects (average age =
64.3 +/- 8.8 years, visual field mean deviation = -0.71 +/- 1.2 d{B})
and 92 eyes of 92 patients with glaucoma (average age = 66.9 +/-
8.9 years, visual field mean deviation = -5.32 +/- 4.0 d{B}) were
imaged with {SLP} with variable corneal compensation ({GD}x {VCC};
{L}aser {D}iagnostic {T}echnologies, {S}an {D}iego, {CA}). {RVM}
and {SVM} learning classifiers were trained and tested on {SLP}-determined
{RNFL} thickness measurements from 14 standard parameters and 64
sectors (approximately 5.6 degrees each) obtained in the circumpapillary
area under the instrument-defined measurement ellipse (total 78 parameters).
{T}en-fold cross-validation was used to train and test {RVM} and
{SVM} classifiers on unique subsets of the full 164-eye data set
and areas under the receiver operating characteristic ({AUROC}) curve
for the classification of eyes in the test set were generated. {AUROC}
curve results from {RVM} and {SVM} were compared to those for 14
{SLP} software-generated global and regional {RNFL} thickness parameters.
{A}lso reported was the {AUROC} curve for the {GD}x {VCC} software-generated
nerve fiber indicator ({NFI}). {RESULTS}: {T}he {AUROC} curves for
{RVM} and {SVM} were 0.90 and 0.91, respectively, and increased to
0.93 and 0.94 when the training sets were optimized with sequential
forward and backward selection (resulting in reduced dimensional
data sets). {AUROC} curves for optimized {RVM} and {SVM} were significantly
larger than those for all individual {SLP} parameters. {T}he {AUROC}
curve for the {NFI} was 0.87. {CONCLUSIONS}: {R}esults from {RVM}
and {SVM} trained on {SLP} {RNFL} thickness measurements are similar
and provide accurate classification of glaucomatous and healthy eyes.
{RVM} may be preferable to {SVM}, because it provides a {B}ayesian-derived
probability of glaucoma as an output. {T}hese results suggest that
these machine learning classifiers show good potential for glaucoma
diagnosis.},
doi = {10.1167/iovs.04-1122},
pdf = {../local/Bowd2005Relevance.pdf},
file = {Bowd2005Relevance.pdf:local/Bowd2005Relevance.pdf:PDF},
keywords = {80 and over, Aged, Algorithms, Area Under Curve, Cross-Sectional Studies,
Diagnostic Imaging, Diagnostic Techniques, Glaucoma, Humans, Lasers,
Middle Aged, Nerve Fibers, Non-U.S. Gov't, Ophthalmological, Optic
Nerve Diseases, P.H.S., ROC Curve, Research Support, Retinal Ganglion
Cells, Sensitivity and Specificity, U.S. Gov't, 15790898},
pii = {46/4/1322},
url = {http://dx.doi.org/10.1167/iovs.04-1122}
}
@article{Bowd2004Confocal,
author = {Christopher Bowd and Linda M Zangwill and Felipe A Medeiros and Jiucang
Hao and Kwokleung Chan and Te-Won Lee and Terrence J Sejnowski and
Michael H Goldbaum and Pamela A Sample and Jonathan G Crowston and
Robert N Weinreb},
title = {Confocal scanning laser ophthalmoscopy classifiers and stereophotograph
evaluation for prediction of visual field abnormalities in glaucoma-suspect
eyes.},
journal = {Invest {O}phthalmol {V}is {S}ci},
year = {2004},
volume = {45},
pages = {2255-62},
number = {7},
month = {Jul},
abstract = {P{URPOSE}: {T}o determine whether {H}eidelberg {R}etina {T}omograph
({HRT}; {H}eidelberg {E}ngineering, {D}ossenheim, {G}ermany) classification
techniques and investigational support vector machine ({SVM}) analyses
can detect optic disc abnormalities in glaucoma-suspect eyes before
the development of visual field abnormalities. {METHODS}: {G}laucoma-suspect
eyes (n = 226) were classified as converts or nonconverts based on
the development of repeatable (either two or three consecutive) standard
automated perimetry ({SAP})-detected abnormalities over the course
of the study (mean follow-up, approximately 4.5 years). {H}azard
ratios for development of {SAP} abnormalities were calculated based
on baseline classification results, follow-up time, and end point
status (convert, nonconvert). {C}lassification techniques applied
were {HRT} classification ({HRTC}), {M}oorfields {R}egression {A}nalysis,
forward-selection optimized {SVM} ({SVM} fwd) and backward elimination-optimized
{SVM} ({SVM} back) analysis of {HRT} data, and stereophotograph assessment.
{RESULTS}: {U}nivariate analyses indicated that all classification
techniques were predictors of the development of two repeatable abnormal
{SAP} results, with hazards ratios (95\% confidence interval [{CI}])
ranging from 1.32 (1.00-1.75) for {HRTC} to 2.0 (1.48-2.76) for stereophotograph
assessment (all {P} < or = 0.05). {O}nly {SVM} ({SVM} fwd and {SVM}
back) analysis of {HRT} data and stereophotograph assessment were
univariate predictors of the development of three repeatable abnormal
{SAP} results, with hazard ratios (95\% {CI}) ranging from 1.73 (1.16-2.82)
for {SVM} fwd to 1.82 (1.19-3.12) for {SVM} back (both {P} < 0.007).
{M}ultivariate analyses including each classification technique individually
in a model with age, baseline {SAP} pattern standard deviation [{PSD}],
and baseline {IOP} indicated that all classification techniques except
{HRTC} ({P} = 0.06) were predictors of the development of two repeatable
abnormal {SAP} results with hazards ratios ranging from 1.30 (0.99,
1.73) for {HRTC} to 1.90 (1.37, 2.69) for stereophotograph assessment.
{O}nly {SVM} ({SVM} fwd and {SVM} back) analysis of {HRT} data and
stereophotograph assessment were significant predictors of the development
of three repeatable abnormal {SAP} results in multivariate analyses;
hazard ratios of 1.57 (1.03, 2.59) and 1.70 (1.18, 2.51), respectively.
{SAP} {PSD} was a significant predictor of two repeatable abnormal
{SAP} results in multivariate models with all classification techniques,
with hazard ratios ranging from 3.31 (1.39, 7.89) to 4.70 (2.02,
10.93) per 1-d{B} increase. {CONCLUSIONS}: {HRT} classifications
techniques and stereophotograph assessment can detect optic disc
topography abnormalities in glaucoma-suspect eyes before the development
of {SAP} abnormalities. {T}hese data support strongly the importance
of optic disc examination for early glaucoma diagnosis.},
doi = {10.1167/iovs.03-1087},
pdf = {../local/Bowd2004Confocal.pdf},
file = {Bowd2004Confocal.pdf:local/Bowd2004Confocal.pdf:PDF},
keywords = {80 and over, Adolescent, Adult, Aged, Algorithms, Artificial Intelligence,
Auditory, Benchmarking, Binding Sites, Brain Stem, Breast Diseases,
Chemical, Child, Chromosomes, Comparative Study, Computational Biology,
Computer Simulation, Computer-Assisted, Data Interpretation, Databases,
Diagnosis, Diagnostic Errors, Differential, Drug Resistance, Electroencephalography,
Epilepsy, Evoked Potentials, Female, Forecasting, Gene Expression,
Gene Expression Profiling, Genetic, Genotype, Glaucoma, Greece, HIV
Protease Inhibitors, HIV-1, Human, Humans, Infant, Information Management,
Information Storage and Retrieval, Intraocular Pressure, Kinetics,
Language Development Disorders, Lasers, Least-Squares Analysis, Linear
Models, Male, Microbial Sensitivity Tests, Middle Aged, Models, Molecular,
Monitoring, Nephroblastoma, Non-U.S. Gov't, Nonlinear Dynamics, Ocular
Hypertension, Oligonucleotide Array Sequence Analysis, Ophthalmoscopy,
Optic Disk, Optic Nerve Diseases, P.H.S., Pair 1, Perimetry, Periodicals,
Phosphorylation, Phosphotransferases, Photography, Physiologic, Point
Mutation, Preschool, Prognosis, Protein, Proteins, Pyrimidinones,
Reaction Time, Recurrence, Reproducibility of Results, Research Support,
Reverse Transcriptase Inhibitors, Sensitivity and Specificity, Sequence
Alignment, Sequence Analysis, Signal Processing, Software, Sound
Localization, Statistical, Stochastic Processes, Structure-Activity
Relationship, Theoretical, Time Factors, U.S. Gov't, Viral, Vision
Disorders, Visual Fields, 15223803},
url = {http://dx.doi.org/10.1167/iovs.03-1087}
}
@book{Bower2001Computational,
title = {Computational modeling of genetic and biochemical networks},
publisher = {MIT Press},
year = {2001},
author = {Bower, J. M. and Bolouri, H.},
address = {Cambridge, MA}
}
@article{Boyd2011Distributed,
author = {Boyd, S. and Parikh, N. and Chu, E. and Peleato, B. and Eckstein,
J.},
title = {Distributed optimization and statistical learning via the alternating
direction method of multipliers},
journal = {Foundations and Trends{\textregistered} in Machine Learning},
year = {2011},
volume = {3},
pages = {1--122},
number = {1},
doi = {10.1561/2200000016},
pdf = {../local/Boyd2011Distributed.pdf},
file = {Boyd2011Distributed.pdf:Boyd2011Distributed.pdf:PDF},
publisher = {Now Publishers Inc.},
url = {http://dx.doi.org/10.1561/2200000016}
}
@book{Boyd2004Convex,
title = {Convex Optimization},
publisher = {Cambridge {U}niversity {P}ress},
year = {2004},
author = {S. Boyd and L. Vandenberghe},
address = {New York, NY, USA},
pdf = {../local/Boyd2004Convex.pdf},
file = {Boyd2004Convex.pdf:Boyd2004Convex.pdf:PDF},
isbn = {0521833787}
}
@article{Boyle2005Cancer,
author = {Boyle, P. and Ferlay, J.},
title = {Cancer incidence and mortality in Europe, 2004},
journal = {Ann. Oncol.},
year = {2005},
volume = {16},
pages = {481--488},
number = {3},
month = {Mar},
abstract = {BACKGROUND: There are no recent estimates of the incidence and mortality
from cancer at a European level. Those data that are available generally
refer to the mid-1990s and are of limited use for cancer control
planning. We present estimates of the cancer burden in Europe in
2004, including data for the (25 Member States) European Union. METHODS:
The most recent sources of incidence and mortality data available
in the Descriptive Epidemiology Group at IARC were applied to population
projections to derive the best estimates of the burden of cancer,
in terms of incidence and mortality, for Europe in 2004. RESULTS:
In 2004 in Europe, there were an estimated 2,886,800 incident cases
of cancer diagnosed and 1,711,000 cancer deaths. The most common
incident form of cancer was lung cancer (13.3\% of all incident cases),
followed by colorectal cancer (13.2\%) and breast cancer (13\%).
Lung cancer was also the most common cause of cancer death (341,800
deaths), followed by colorectal (203,700), stomach (137,900) and
breast (129,900). CONCLUSIONS: With an estimated 2.9 million new
cases (54\% occurring in men, 46\% in women) and 1.7 million deaths
(56\% in men, 44\% in women) each year, cancer remains an important
public health problem in Europe, and the ageing of the European population
will cause these numbers to continue to increase even if age-specific
rates remain constant. To make great progress quickly against cancer
in Europe, the need is evident to make a concerted attack on the
big killers: lung, colorectal, breast and stomach cancer. Stomach
cancer rates are falling everywhere in Europe and public health measures
are available to reduce the incidence and mortality of lung cancer,
colorectal cancer and breast cancer.},
doi = {10.1093/annonc/mdi098},
pdf = {../local/Boyle2005Cancer.pdf},
file = {Boyle2005Cancer.pdf:Boyle2005Cancer.pdf:PDF},
institution = {International Agency for Research on Cancer, 150 cours Albert Thomas,
69372 Lyon Cedex 08, France. director@iarc.fr},
keywords = {breastcancer},
owner = {jp},
pii = {mdi098},
pmid = {15718248},
timestamp = {2008.11.26},
url = {http://dx.doi.org/10.1093/annonc/mdi098}
}
@article{Boysen2009Consistencies,
author = {Boysen, L. and Kempe, A. and Liebscher, V. and Munk, A. and Wittich,
O.},
title = {Consistencies and rates of convergence of jump-penalized least squares
estimators},
journal = {Ann. Stat.},
year = {2009},
volume = {37},
pages = {157--183},
number = {1},
abstract = {We study the asymptotics for jump-penalized least squares regression
aiming at approximating a regression function by piecewise constant
functions. Besides conventional consistency and convergence rates
of the estimates in L2([0, 1)) our results cover other metrics like
Skorokhod metric on the space of cà dlà g functions and uniform metrics
on C([0, 1]). We will show that these estimators are in an adaptive
sense rate optimal over certain classes of "approximation spaces."
Special cases are the class of functions of bounded variation (piecewise)
Hölder continuous functions of order 0http://dx.doi.org/10.1214/07-AOS558}
}
@article{Bozdech2004Antioxidant,
author = {Bozdech, Z. and Ginsburg, H.},
title = {Antioxidant defense in {P}lasmodium falciparum - data mining of the
transcriptome},
journal = {Malaria {J}ournal},
year = {2004},
volume = {3},
pages = {23},
number = {1},
abstract = {The intraerythrocytic malaria parasite is under constant oxidative
stress originating both from endogenous and exogenous processes.
{T}he parasite is endowed with a complete network of enzymes and
proteins that protect it from those threats, but also uses redox
activities to regulate enzyme activities. {I}n the present analysis,
the transcription of the genes coding for the antioxidant defense
elements are viewed in the time-frame of the intraerythrocytic cycle.
{T}ime-dependent transcription data were taken from the transcriptome
of the human malaria parasite {P}lasmodium falciparum. {W}hereas
for several processes the transcription of the many participating
genes is coordinated, in the present case there are some outstanding
deviations where gene products that utilize glutathione or thioredoxin
are transcribed before the genes coding for elements that control
the levels of those substrates are transcribed. {S}uch insights may
hint to novel, non-classical pathways that necessitate further investigations.},
doi = {10.1186/1475-2875-3-23},
pdf = {../local/Bozdech2004Antioxidant.pdf},
file = {Bozdech2004Antioxidant.pdf:local/Bozdech2004Antioxidant.pdf:PDF},
keywords = {microarray plasmodium},
owner = {vert},
url = {http://www.malariajournal.com/content/3/1/23}
}
@article{Bozdech2003Transcriptome,
author = {Bozdech, Z. and Llinas, M. and Pulliam, B. L. and Wong, E. D. and
Zhu, J. and DeRisi, J. L.},
title = {The {T}ranscriptome of the {I}ntraerythrocytic {D}evelopmental {C}ycle
of {P}lasmodium falciparum },
journal = {P{L}o{S} {B}iology},
year = {2003},
volume = {1},
pages = {e5},
number = {1},
abstract = {Plasmodium falciparum is the causative agent of the most burdensome
form of human malaria, affecting 200-300 million individuals per
year worldwide. {T}he recently sequenced genome of {P}. falciparum
revealed over 5,400 genes, of which 60{percnt} encode proteins of
unknown function. {I}nsights into the biochemical function and regulation
of these genes will provide the foundation for future drug and vaccine
development efforts toward eradication of this disease. {B}y analyzing
the complete asexual intraerythrocytic developmental cycle ({IDC})
transcriptome of the {HB}3 strain of {P}. falciparum, we demonstrate
that at least 60{percnt} of the genome is transcriptionally active
during this stage. {O}ur data demonstrate that this parasite has
evolved an extremely specialized mode of transcriptional regulation
that produces a continuous cascade of gene expression, beginning
with genes corresponding to general cellular processes, such as protein
synthesis, and ending with {P}lasmodium-specific functionalities,
such as genes involved in erythrocyte invasion. {T}he data reveal
that genes contiguous along the chromosomes are rarely coregulated,
while transcription from the plastid genome is highly coregulated
and likely polycistronic. {C}omparative genomic hybridization between
{HB}3 and the reference genome strain (3{D}7) was used to distinguish
between genes not expressed during the {IDC} and genes not detected
because of possible sequence variations. {G}enomic differences between
these strains were found almost exclusively in the highly antigenic
subtelomeric regions of chromosomes. {T}he simple cascade of gene
regulation that directs the asexual development of {P}. falciparum
is unprecedented in eukaryotic biology. {T}he transcriptome of the
{IDC} resembles a "just-in-time" manufacturing process whereby induction
of any given gene occurs once per cycle and only at a time when it
is required. {T}hese data provide to our knowledge the first comprehensive
view of the timing of transcription throughout the intraerythrocytic
development of {P}. falciparum and provide a resource for the identification
of new chemotherapeutic and vaccine candidates.},
comment = {(JP Vert) The paper that monitors the 48h cell cycle of P. falciparum},
doi = {10.1371/journal.pbio.0000005},
pdf = {../local/Bozdech2003Transcriptome.pdf},
file = {Bozdech2003Transcriptome.pdf:local/Bozdech2003Transcriptome.pdf:PDF},
keywords = {microarray plasmodium},
owner = {vert},
url = {http://dx.doi.org/10.1371/journal.pbio.0000005 }
}
@article{Bozdech2003Expression,
author = {Bozdech, Z. and Zhu, J. and Joachimiak, M. and Cohen, F. and Pulliam,
B. and DeRisi, J.},
title = {Expression profiling of the schizont and trophozoite stages of {P}lasmodium
falciparum with a long-oligonucleotide microarray},
journal = {Genome {B}iology},
year = {2003},
volume = {4},
pages = {R9},
number = {2},
abstract = {B{ACKGROUND}:{T}he worldwide persistence of drug-resistant {P}lasmodium
falciparum, the most lethal variety of human malaria, is a global
health concern. {T}he {P}. falciparum sequencing project has brought
new opportunities for identifying molecular targets for antimalarial
drug and vaccine development.{RESULTS}:{W}e developed a software
package, {A}rray{O}ligo{S}elector, to design an open reading frame
({ORF})-specific {DNA} microarray using the publicly available {P}.
falciparum genome sequence. {E}ach gene was represented by one or
more long 70 mer oligonucleotides selected on the basis of uniqueness
within the genome, exclusion of low-complexity sequence, balanced
base composition and proximity to the 3' end. {A} first-generation
microarray representing approximately 6,000 {ORF}s of the {P}. falciparum
genome was constructed. {A}rray performance was evaluated through
the use of control oligonucleotide sets with increasing levels of
introduced mutations, as well as traditional northern blotting. {U}sing
this array, we extensively characterized the gene-expression profile
of the intraerythrocytic trophozoite and schizont stages of {P}.
falciparum. {T}he results revealed extensive transcriptional regulation
of genes specialized for processes specific to these two stages.{CONCLUSIONS}:{DNA}
microarrays based on long oligonucleotides are powerful tools for
the functional annotation and exploration of the {P}. falciparum
genome. {E}xpression profiling of trophozoites and schizonts revealed
genes associated with stage-specific processes and may serve as the
basis for future drug targets and vaccine development.},
doi = {10.1186/gb-2003-4-2-r9},
pdf = {../local/Bozdech2003Expression.pdf},
file = {Bozdech2003Expression.pdf:local/Bozdech2003Expression.pdf:PDF},
keywords = {microarray plasmodium},
owner = {vert},
url = {http://genomebiology.com/2003/4/2/R9}
}
@article{Bradford2005Improved,
author = {James R Bradford and David R Westhead},
title = {Improved prediction of protein-protein binding sites using a support
vector machines approach.},
journal = {Bioinformatics},
year = {2005},
volume = {21},
pages = {1487-94},
number = {8},
month = {Apr},
abstract = {M{OTIVATION}: {S}tructural genomics projects are beginning to produce
protein structures with unknown function, therefore, accurate, automated
predictors of protein function are required if all these structures
are to be properly annotated in reasonable time. {I}dentifying the
interface between two interacting proteins provides important clues
to the function of a protein and can reduce the search space required
by docking algorithms to predict the structures of complexes. {RESULTS}:
{W}e have combined a support vector machine ({SVM}) approach with
surface patch analysis to predict protein-protein binding sites.
{U}sing a leave-one-out cross-validation procedure, we were able
to successfully predict the location of the binding site on 76\%
of our dataset made up of proteins with both transient and obligate
interfaces. {W}ith heterogeneous cross-validation, where we trained
the {SVM} on transient complexes to predict on obligate complexes
(and vice versa), we still achieved comparable success rates to the
leave-one-out cross-validation suggesting that sufficient properties
are shared between transient and obligate interfaces. {AVAILABILITY}:
{A} web application based on the method can be found at http://www.bioinformatics.leeds.ac.uk/ppi_pred.
{T}he dataset of 180 proteins used in this study is also available
via the same web site. {CONTACT}: westhead@bmb.leeds.ac.uk {SUPPLEMENTARY}
{INFORMATION}: http://www.bioinformatics.leeds.ac.uk/ppi-pred/supp-material.},
doi = {10.1093/bioinformatics/bti242},
pdf = {../local/Bradford2005Improved.pdf},
file = {Bradford2005Improved.pdf:local/Bradford2005Improved.pdf:PDF},
keywords = {biosvm},
pii = {bti242},
url = {http://dx.doi.org/10.1093/bioinformatics/bti242}
}
@article{Brahnam2005Machine,
author = {Sheryl Brahnam and Chao-Fa Chuang and Frank Y Shih and Melinda R
Slack},
title = {Machine recognition and representation of neonatal facial displays
of acute pain.},
journal = {Artif. {I}ntell. {M}ed.},
year = {2005},
month = {Jun},
abstract = {O{BJECTIVE}:: {I}t has been reported in medical literature that health
care professionals have difficulty distinguishing a newborn's facial
expressions of pain from facial reactions to other stimuli. {A}lthough
a number of pain instruments have been developed to assist health
professionals, studies demonstrate that health professionals are
not entirely impartial in their assessment of pain and fail to capitalize
on all the information exhibited in a newborn's facial displays.
{T}his study tackles these problems by applying three different state-of-the-art
face classification techniques to the task of distinguishing a newborn's
facial expressions of pain. {METHODS}:: {T}he facial expressions
of 26 neonates between the ages of 18h and 3 days old were photographed
experiencing the pain of a heel lance and a variety of stressors,
including transport from one crib to another (a disturbance that
can provoke crying that is not in response to pain), an air stimulus
on the nose, and friction on the external lateral surface of the
heel. {T}hree face classification techniques, principal component
analysis ({PCA}), linear discriminant analysis ({LDA}), and support
vector machine ({SVM}), were used to classify the faces. {RESULTS}::
{I}n our experiments, the best recognition rates of pain versus nonpain
(88.00\%), pain versus rest (94.62\%), pain versus cry (80.00\%),
pain versus air puff (83.33\%), and pain versus friction (93.00\%)
were obtained from an {SVM} with a polynomial kernel of degree 3.
{T}he {SVM} outperformed two commonly used methods in face classification:
{PCA} and {LDA}, each using the {L}(1) distance metric. {CONCLUSION}::
{T}he results of this study indicate that the application of face
classification techniques in pain assessment and management is a
promising area of investigation.},
doi = {10.1016/j.artmed.2004.12.003},
pdf = {../local/Brahnam2005Machine.pdf},
file = {Brahnam2005Machine.pdf:local/Brahnam2005Machine.pdf:PDF},
keywords = {Artificial Intelligence, Conservation of Natural Resources, Decision
Support Techniques, Ecosystem, Environment, Forestry, Regression
Analysis, Spain, 15979291},
pii = {S0933-3657(05)00013-8},
url = {http://dx.doi.org/10.1016/j.artmed.2004.12.003}
}
@article{Brancotte2011Gene,
author = {Brancotte, B. and Biton, A. and Bernard-Pierrot, I. and Radvanyi,
F. and Reyal, F. and Cohen-Boulakia, S.},
title = {Gene List significance at-a-glance with {GeneValorization}.},
journal = {Bioinformatics},
year = {2011},
volume = {27},
pages = {1187--1189},
number = {8},
month = {Apr},
abstract = {High-throughput technologies provide fundamental informations concerning
thousands of genes. Many of the current research laboratories daily
use one or more of these technologies and end-up with lists of genes.
Assessing the originality of the results obtained includes being
aware of the number of publications available concerning individual
or multiple genes and accessing information about these publications.
Faced with the exponential growth of publications avaliable and number
of genes involved in a study, this task is becoming particularly
difficult to achieve.We introduce GeneValorization, a web-based tool
that gives a clear and handful overview of the bibliography available
corresponding to the user input formed by (i) a gene list (expressed
by gene names or ids from EntrezGene) and (ii) a context of study
(expressed by keywords). From this input, GeneValorization provides
a matrix containing the number of publications with co-occurrences
of gene names and keywords. Graphics are automatically generated
to assess the relative importance of genes within various contexts.
Links to publications and other databases offering information on
genes and keywords are also available. To illustrate how helpful
GeneValorization is, we will consider the gene list of the OncotypeDX
prognostic marker test.http://bioguide-project.net/gvcohen@lri.frSupplementary
data are available at Bioinformatics online.},
doi = {10.1093/bioinformatics/btr073},
institution = {Laboratoire de Recherche en Informatique, CNRS UMR 8623, Université
Paris-Sud, F-91405 Orsay Cedex, CNRS, UMR 144, Institut Curie, 26
rue d'Ulm, F-75248 Paris Cedex 05, Institut Curie, Centre de Recherche,
Paris, F-75248, INSERM, U900, Paris, F-75248, Mines ParisTech, Fontainebleau,
F-77300 and Institut Curie, Departement de Chirurgie, 6 rue d'Ulm,
F-75005 Paris, France.},
owner = {mordelet},
pii = {btr073},
pmid = {21349868},
timestamp = {2011.05.17},
url = {http://dx.doi.org/10.1093/bioinformatics/btr073}
}
@article{Brazma2001Minimum,
author = {Brazma, A. and Hingamp, P. and Quackenbush, J. and Sherlock, G. and
Spellman, P. and Stoeckert, C. and Aach, J. and Ansorge, W. and Ball,
C. A. and Causton, H. C. and Gaasterland, T. and Glenisson, P. and
Holstege, F. C. and Kim, I. F. and Markowitz, V. and Matese, J. C.
and Parkinson, H. and Robinson, A. and Sarkans, U. and Schulze-Kremer,
S. and Stewart, J. and Taylor, R. and Vilo, J. and Vingron, M.},
title = {Minimum information about a microarray experiment (MIAME)-toward
standards for microarray data.},
journal = {Nat. Genet.},
year = {2001},
volume = {29},
pages = {365--371},
number = {4},
month = {Dec},
abstract = {Microarray analysis has become a widely used tool for the generation
of gene expression data on a genomic scale. Although many significant
results have been derived from microarray studies, one limitation
has been the lack of standards for presenting and exchanging such
data. Here we present a proposal, the Minimum Information About a
Microarray Experiment (MIAME), that describes the minimum information
required to ensure that microarray data can be easily interpreted
and that results derived from its analysis can be independently verified.
The ultimate goal of this work is to establish a standard for recording
and reporting microarray-based gene expression data, which will in
turn facilitate the establishment of databases and public repositories
and enable the development of data analysis tools. With respect to
MIAME, we concentrate on defining the content and structure of the
necessary information rather than the technical format for capturing
it.},
doi = {10.1038/ng1201-365},
institution = {European Bioinformatics Institute, EMBL outstation, Wellcome Trust
Genome Campus, Hinxton, Cambridge CB10 1SD, UK. brazma@ebi.ac.uk},
keywords = {Computational Biology; Gene Expression Profiling, methods; Oligonucleotide
Array Sequence Analysis, standards},
language = {eng},
medline-pst = {ppublish},
owner = {phupe},
pii = {ng1201-365},
pmid = {11726920},
timestamp = {2011.04.08},
url = {http://dx.doi.org/10.1038/ng1201-365}
}
@article{Bredel2004Chemogenomics,
author = {Bredel, M. and Jacoby, E.},
title = {Chemogenomics: an emerging strategy for rapid target and drug discovery.},
journal = {Nat. Rev. Genet.},
year = {2004},
volume = {5},
pages = {262--275},
number = {4},
month = {Apr},
doi = {10.1038/nrg1317},
pdf = {../local/Bredel2004Chemogenomics.pdf},
file = {Bredel2004Chemogenomics.pdf:Bredel2004Chemogenomics.pdf:PDF},
keywords = {chemogenomics},
owner = {vert},
pii = {nrg1317},
pmid = {15131650},
timestamp = {2007.08.02},
url = {http://dx.doi.org/10.1038/nrg1317}
}
@inproceedings{Breese1998Empirical,
author = {Breese, J. S. and Heckerman, D. and Kadie, C.},
title = {Empirical analysis of predictive algorithms for collaborative filtering},
booktitle = {14th Conference on Uncertainty in Artificial Intelligence},
year = {1998},
pages = {43-52},
address = {Madison, W.I.},
publisher = {Morgan Kaufman}
}
@article{Breiman2001Random,
author = {Breiman, L.},
title = {Random forests},
journal = {Mach. Learn.},
year = {2001},
volume = {45},
pages = {5--32},
number = {1},
abstract = {Random forests are a combination of tree predictors such that each
tree depends on the values of a random vector sampled independently
and with the same distribution for all trees in the forest. The generalization
error for forests converges a.s. to a limit as the number of trees
in the forest becomes large. The generalization error of a forest
of tree classifiers depends on the strength of the individual trees
in the forest and the correlation between them. Using a random selection
of features to split each node yields error rates that compare favorably
to Adaboost (Y. Freund & R. Schapire, Machine Learning: Proceedings
of the Thirteenth International conference, ***, 148–156), but are
more robust with respect to noise. Internal estimates monitor error,
strength, and correlation and these are used to show the response
to increasing the number of features used in the splitting. Internal
estimates are also used to measure variable importance. These ideas
are also applicable to regression.},
doi = {10.1023/A:1010933404324},
pdf = {../local/Breiman2001Random.pdf},
file = {Breiman2001Random.pdf:Breiman2001Random.pdf:PDF},
keywords = {PUlearning},
owner = {jp},
timestamp = {2010.02.01},
url = {http://dx.doi.org/10.1023/A:1010933404324}
}
@article{Breiman2001Statistical,
author = {Breiman, L.},
title = {Statistical modeling: The two cultures (with comments and a rejoinder
by the author)},
journal = {Statistical Science},
year = {2001},
volume = {16},
pages = {199--231},
number = {3},
publisher = {Institute of Mathematical Statistics}
}
@article{Breiman1996Bagging,
author = {Breiman, L.},
title = {Bagging predictors},
journal = {Mach. Learn.},
year = {1996},
volume = {24},
pages = {123--140},
number = {2},
doi = {10.1023/A:1018054314350},
pdf = {../local/Breiman1996Bagging.pdf},
file = {Breiman1996Bagging.pdf:Breiman1996Bagging.pdf:PDF},
keywords = {PUlearning},
owner = {jp},
timestamp = {2010.01.29},
url = {http://dx.doi.org/10.1023/A:1018054314350}
}
@book{Breiman1984Classification,
title = {Classification and regression trees},
publisher = {Chapman \& Hall/CRC},
year = {1984},
author = {Breiman, L. and Friedman, J. and Stone, C.J. and Olshen, R.A.}
}
@article{Brein2005Inparanoid,
author = {K. Brein and M. Remm and E. Sonnhammer},
title = {Inparanoid: a comprehensive database of eukaryothic orthologs},
journal = {Nucleic Acids Res.},
year = {2005},
volume = {33},
owner = {michael},
timestamp = {2008.10.02}
}
@article{Brennecke2005Principles,
author = {Julius Brennecke and Alexander Stark and Robert B Russell and Stephen
M Cohen},
title = {Principles of microRNA-target recognition.},
journal = {PLoS Biol},
year = {2005},
volume = {3},
pages = {e85},
number = {3},
month = {Mar},
abstract = {MicroRNAs (miRNAs) are short non-coding RNAs that regulate gene expression
in plants and animals. Although their biological importance has become
clear, how they recognize and regulate target genes remains less
well understood. Here, we systematically evaluate the minimal requirements
for functional miRNA-target duplexes in vivo and distinguish classes
of target sites with different functional properties. Target sites
can be grouped into two broad categories. 5' dominant sites have
sufficient complementarity to the miRNA 5' end to function with little
or no support from pairing to the miRNA 3' end. Indeed, sites with
3' pairing below the random noise level are functional given a strong
5' end. In contrast, 3' compensatory sites have insufficient 5' pairing
and require strong 3' pairing for function. We present examples and
genome-wide statistical support to show that both classes of sites
are used in biologically relevant genes. We provide evidence that
an average miRNA has approximately 100 target sites, indicating that
miRNAs regulate a large fraction of protein-coding genes and that
miRNA 3' ends are key determinants of target specificity within miRNA
families.},
doi = {10.1371/journal.pbio.0030085},
institution = {European Molecular Biology Laboratory, Heidelberg, Germany.},
keywords = {sirna},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pmid = {15723116},
timestamp = {2009.10.28},
url = {http://dx.doi.org/10.1371/journal.pbio.0030085}
}
@article{Breslin2005Signal,
author = {Breslin, T. and Krogh, M. and Peterson, C. and Troein, C.},
title = {Signal transduction pathway profiling of individual tumor samples.},
journal = {BMC Bioinformatics},
year = {2005},
volume = {6},
pages = {163},
abstract = {Signal transduction pathways convey information from the outside of
the cell to transcription factors, which in turn regulate gene expression.
Our objective is to analyze tumor gene expression data from microarrays
in the context of such pathways.We use pathways compiled from the
TRANSPATH/TRANSFAC databases and the literature, and three publicly
available cancer microarray data sets. Variation in pathway activity,
across the samples, is gauged by the degree of correlation between
downstream targets of a pathway. Two correlation scores are applied;
one considers all pairs of downstream targets, and the other considers
only pairs without common transcription factors. Several pathways
are found to be differentially active in the data sets using these
scores. Moreover, we devise a score for pathway activity in individual
samples, based on the average expression value of the downstream
targets. Statistical significance is assigned to the scores using
permutation of genes as null model. Hence, for individual samples,
the status of a pathway is given as a sign, + or -, and a p-value.
This approach defines a projection of high-dimensional gene expression
data onto low-dimensional pathway activity scores. For each dataset
and many pathways we find a much larger number of significant samples
than expected by chance. Finally, we find that several sample-wise
pathway activities are significantly associated with clinical classifications
of the samples.This study shows that it is feasible to infer signal
transduction pathway activity, in individual samples, from gene expression
data. Furthermore, these pathway activities are biologically relevant
in the three cancer data sets.},
doi = {10.1186/1471-2105-6-163},
pdf = {../local/Breslin2005Signal.pdf},
file = {Breslin2005Signal.pdf:Breslin2005Signal.pdf:PDF},
institution = {Complex Systems Division, Department of Theoretical Physics, University
of Lund, Sölvegatan 14A, SE-223 62 Lund, Sweden. thomas@thep.lu.se},
keywords = {csbcbook-ch4},
language = {eng},
medline-pst = {epublish},
owner = {jp},
pii = {1471-2105-6-163},
pmid = {15987529},
timestamp = {2011.08.06},
url = {http://dx.doi.org/10.1186/1471-2105-6-163}
}
@article{Breslin2004Autofluorescence,
author = {Tara M Breslin and Fushen Xu and Gregory M Palmer and Changfang Zhu
and Kennedy W Gilchrist and Nirmala Ramanujam},
title = {Autofluorescence and diffuse reflectance properties of malignant
and benign breast tissues.},
journal = {Ann {S}urg {O}ncol},
year = {2004},
volume = {11},
pages = {65-70},
number = {1},
month = {Jan},
abstract = {B{ACKGROUND}: {F}luorescence spectroscopy is an evolving technology
that can rapidly differentiate between benign and malignant tissues.
{T}hese differences are thought to be due to endogenous fluorophores,
including nicotinamide adenine dinucleotide, flavin adenine dinucleotide,
and tryptophan, and absorbers such as beta-carotene and hemoglobin.
{W}e hypothesized that a statistically significant difference would
be demonstrated between benign and malignant breast tissues on the
basis of their unique fluorescence and reflectance properties. {METHODS}:
{O}ptical measurements were performed on 56 samples of tumor or benign
breast tissue. {A}utofluorescence spectra were measured at excitation
wavelengths ranging from 300 to 460 nm, and diffuse reflectance was
measured between 300 and 600 nm. {P}rincipal component analysis to
dimensionally reduce the spectral data and a {W}ilcoxon ranked sum
test were used to determine which wavelengths showed statistically
significant differences. {A} support vector machine algorithm compared
classification results with the histological diagnosis (gold standard).
{RESULTS}: {S}everal excitation wavelengths and diffuse reflectance
spectra showed significant differences between tumor and benign tissues.
{B}y using the support vector machine algorithm to incorporate relevant
spectral differences, a sensitivity of 70.0\% and specificity of
91.7\% were achieved. {CONCLUSIONS}: {A} statistically significant
difference was demonstrated in the diffuse reflectance and fluorescence
emission spectra of benign and malignant breast tissue. {T}hese differences
could be exploited in the development of adjuncts to diagnostic and
surgical procedures.},
doi = {10.1245/ASO.2004.03.031},
pdf = {../local/Breslin2004Autofluorescence.pdf},
file = {Breslin2004Autofluorescence.pdf:Breslin2004Autofluorescence.pdf:PDF},
keywords = {breastcancer},
url = {http://dx.doi.org/10.1245/ASO.2004.03.031}
}
@article{Briem2005Classifying,
author = {Hans Briem and Judith G{\"u}nther},
title = {Classifying "kinase inhibitor-likeness" by using machine-learning
methods.},
journal = {ChemBioChem},
year = {2005},
volume = {6},
pages = {558-66},
number = {3},
month = {Mar},
abstract = {By using an in-house data set of small-molecule structures, encoded
by {G}hose-{C}rippen parameters, several machine learning techniques
were applied to distinguish between kinase inhibitors and other molecules
with no reported activity on any protein kinase. {A}ll four approaches
pursued--support-vector machines ({SVM}), artificial neural networks
({ANN}), k nearest neighbor classification with {GA}-optimized feature
selection ({GA}/k{NN}), and recursive partitioning ({RP})--proved
capable of providing a reasonable discrimination. {N}evertheless,
substantial differences in performance among the methods were observed.
{F}or all techniques tested, the use of a consensus vote of the 13
different models derived improved the quality of the predictions
in terms of accuracy, precision, recall, and {F}1 value. {S}upport-vector
machines, followed by the {GA}/k{NN} combination, outperformed the
other techniques when comparing the average of individual models.
{B}y using the respective majority votes, the prediction of neural
networks yielded the highest {F}1 value, followed by {SVM}s.},
doi = {10.1002/cbic.200400109},
pdf = {../local/Briem2005Classifying.pdf},
file = {Briem2005Classifying.pdf:local/Briem2005Classifying.pdf:PDF},
keywords = {biosvm chemoinformatics},
url = {http://dx.doi.org/10.1002/cbic.200400109}
}
@article{Briggs2001Histone,
author = {S. D. Briggs and M. Bryk and B. D. Strahl and W. L. Cheung and J.
K. Davie and S. Y. Dent and F. Winston and C. D. Allis},
title = {Histone H3 lysine 4 methylation is mediated by Set1 and required
for cell growth and rDNA silencing in Saccharomyces cerevisiae.},
journal = {Genes Dev},
year = {2001},
volume = {15},
pages = {3286--3295},
number = {24},
month = {Dec},
abstract = {Histone methylation is known to be associated with both transcriptionally
active and repressive chromatin states. Recent studies have identified
SET domain-containing proteins such as SUV39H1 and Clr4 as mediators
of H3 lysine 9 (Lys9) methylation and heterochromatin formation.
Interestingly, H3 Lys9 methylation is not observed from bulk histones
isolated from asynchronous populations of Saccharomyces cerevisiae
or Tetrahymena thermophila. In contrast, H3 lysine 4 (Lys4) methylation
is a predominant modification in these smaller eukaryotes. To identify
the responsible methyltransferase(s) and to gain insight into the
function of H3 Lys4 methylation, we have developed a histone H3 Lys4
methyl-specific antiserum. With this antiserum, we show that deletion
of SET1, but not of other putative SET domain-containing genes, in
S. cerevisiae, results in the complete abolishment of H3 Lys4 methylation
in vivo. Furthermore, loss of H3 Lys4 methylation in a set1 Delta
strain can be rescued by SET1. Analysis of histone H3 mutations at
Lys4 revealed a slow-growth defect similar to a set1 Delta strain.
Chromatin immunoprecipitation assays show that H3 Lys4 methylation
is present at the rDNA locus and that Set1-mediated H3 Lys4 methylation
is required for repression of RNA polymerase II transcription within
rDNA. Taken together, these data suggest that Set1-mediated H3 Lys4
methylation is required for normal cell growth and transcriptional
silencing.},
doi = {10.1101/gad.940201},
institution = {Department of Biochemistry and Molecular Genetics, University of
Virginia Health System, Charlottesville, Virginia 22908, USA.},
keywords = {Animals; Antibody Formation; Blotting, Western; Cell Division; DNA
Primers, chemistry; DNA, Bacterial, genetics; DNA, Ribosomal, genetics;
DNA-Binding Proteins, metabolism; Fungal Proteins, metabolism; Gene
Silencing; Genetic Vectors; Heterochromatin, chemistry/metabolism;
Histone-Lysine N-Methyltransferase; Histones, metabolism; Lysine,
metabolism; Methylation; Methyltransferases, genetics/metabolism;
Mutation; Nucleosomes, chemistry/metabolism; Polymerase Chain Reaction;
Precipitin Tests; Protein Methyltransferases; RNA Polymerase III,
metabolism; Rabbits; Saccharomyces cerevisiae Proteins; Saccharomyces
cerevisiae, genetics; Transcription Factors, metabolism},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pmid = {11751634},
timestamp = {2010.11.23},
url = {http://dx.doi.org/10.1101/gad.940201}
}
@article{Briggs2002Gene,
author = {Scott D Briggs and Tiaojiang Xiao and Zu-Wen Sun and Jennifer A Caldwell
and Jeffrey Shabanowitz and Donald F Hunt and C. David Allis and
Brian D Strahl},
title = {Gene silencing: trans-histone regulatory pathway in chromatin.},
journal = {Nature},
year = {2002},
volume = {418},
pages = {498},
number = {6897},
month = {Aug},
abstract = {The fundamental unit of eukaryotic chromatin, the nucleosome, consists
of genomic DNA wrapped around the conserved histone proteins H3,
H2B, H2A and H4, all of which are variously modified at their amino-
and carboxy-terminal tails to influence the dynamics of chromatin
structure and function -- for example, conjugation of histone H2B
with ubiquitin controls the outcome of methylation at a specific
lysine residue (Lys 4) on histone H3, which regulates gene silencing
in the yeast Saccharomyces cerevisiae. Here we show that ubiquitination
of H2B is also necessary for the methylation of Lys 79 in H3, the
only modification known to occur away from the histone tails, but
that not all methylated lysines in H3 are regulated by this 'trans-histone'
pathway because the methylation of Lys 36 in H3 is unaffected. Given
that gene silencing is regulated by the methylation of Lys 4 and
Lys 79 in histone H3, we suggest that H2B ubiquitination acts as
a master switch that controls the site-selective histone methylation
patterns responsible for this silencing.},
doi = {10.1038/nature00970},
institution = {Department of Biochemistry and Molecular Genetics, University of
Virginia Health System, Charlottesville, Virginia 22908, USA.},
keywords = {Chromatin, chemistry/metabolism; Gene Expression Regulation, Fungal;
Gene Silencing; Histone-Lysine N-Methyltransferase; Histones, chemistry/metabolism;
Ligases, metabolism; Methylation; Models, Biological; Nuclear Proteins,
metabolism; Saccharomyces cerevisiae Proteins; Saccharomyces cerevisiae,
genetics/metabolism; Ubiquitin, metabolism; Ubiquitin-Conjugating
Enzymes},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {nature00970},
pmid = {12152067},
timestamp = {2010.11.23},
url = {http://dx.doi.org/10.1038/nature00970}
}
@article{Brodersen2009Revisiting,
author = {Peter Brodersen and Olivier Voinnet},
title = {Revisiting the principles of microRNA target recognition and mode
of action.},
journal = {Nat Rev Mol Cell Biol},
year = {2009},
volume = {10},
pages = {141--148},
number = {2},
month = {Feb},
abstract = {MicroRNAs (miRNAs) are fundamental regulatory elements of animal and
plant gene expression. Although rapid progress in our understanding
of miRNA biogenesis has been achieved by experimentation, computational
approaches have also been influential in determining the general
principles that are thought to govern miRNA target recognition and
mode of action. We discuss how these principles are being progressively
challenged by genetic and biochemical studies. In addition, we discuss
the role of target-site-specific endonucleolytic cleavage, which
is the hallmark of experimental RNA interference and a mechanism
that is used by plant miRNAs and a few animal miRNAs. Generally thought
to be merely a degradation mechanism, we propose that this might
also be a biogenesis mechanism for biologically functional, non-coding
RNA fragments.},
doi = {10.1038/nrm2619},
pdf = {../local/Brodersen2009Revisiting.pdf},
file = {Brodersen2009Revisiting.pdf:Brodersen2009Revisiting.pdf:PDF},
institution = {Institut de Biologie Moléculaire des Plantes, Centre National de
la Recherche Scientifique, 12 rue du Général Zimmer, 67084 Strasbourg
Cedex, France. peter.brodersen@ibmp-ulp.u-strasbg.fr},
keywords = {sirna},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {nrm2619},
pmid = {19145236},
timestamp = {2009.10.28},
url = {http://dx.doi.org/10.1038/nrm2619}
}
@book{Brodsky1993Nonparametric,
title = {Nonparametric Methods in Change-Point Problems},
publisher = {Kluwer Academic Publishers},
year = {1993},
author = {Brodsky, B. and Darkhovsky, B.},
owner = {jp},
timestamp = {2010.06.02}
}
@article{Brown1998Chemoinformatics,
author = {F.K. Brown},
title = {Chemoinformatics : {W}hat is it and {H}ow does it {I}mpact {D}rug
{D}iscovery},
journal = {Annual Reports in Med. Chem.},
year = {1998},
volume = {33},
pages = {375-384},
keywords = {chemoinformatics},
owner = {mahe},
timestamp = {2006.08.16}
}
@inproceedings{Brown1993Using,
author = {Brown, M.P. and Hughey, R. and Krogh, A. and Mian, I.S. and Sjolander,
K. and Haussler, D.},
title = {Using {D}irichlet mixture priors to derive hidden {M}arkov models
for protein families},
booktitle = {Proc. {F}irst {I}nternational {C}onference on {I}ntelligent {S}ystems
for {M}olecular {B}iology ({ISMB} 1993)},
year = {1993},
owner = {vert}
}
@article{Brown2000Knowledge-based,
author = {Brown, M. P. and Grundy, W. N. and Lin, D. and Cristianini, N. and
Sugnet, C. W. and Furey, T. S. and Ares, M. and Haussler, D.},
title = {Knowledge-based analysis of microarray gene expression data by using
support vector machines.},
journal = {Proc. {N}atl. {A}cad. {S}ci. {USA}},
year = {2000},
volume = {97},
pages = {262-7},
number = {1},
month = {Jan},
abstract = {We introduce a method of functionally classifying genes by using gene
expression data from {DNA} microarray hybridization experiments.
{T}he method is based on the theory of support vector machines ({SVM}s).
{SVM}s are considered a supervised computer learning method because
they exploit prior knowledge of gene function to identify unknown
genes of similar function from expression data. {SVM}s avoid several
problems associated with unsupervised clustering methods, such as
hierarchical clustering and self-organizing maps. {SVM}s have many
mathematical features that make them attractive for gene expression
analysis, including their flexibility in choosing a similarity function,
sparseness of solution when dealing with large data sets, the ability
to handle large feature spaces, and the ability to identify outliers.
{W}e test several {SVM}s that use different similarity metrics, as
well as some other supervised learning methods, and find that the
{SVM}s best identify sets of genes with a common function using expression
data. {F}inally, we use {SVM}s to predict functional roles for uncharacterized
yeast {ORF}s based on their expression data.},
pdf = {../local/Brown2000Knowledge-based.pdf},
file = {Brown2000Knowledge-based.pdf:local/Brown2000Knowledge-based.pdf:PDF},
keywords = {biosvm microarray},
url = {http://www.pnas.org/cgi/content/abstract/97/1/262}
}
@article{Brown2000Exploring,
author = {P.O. Brown and D. Botstein},
title = {Exploring the new world of the genome with {DNA} microarrays},
journal = {Nat. {G}enet.},
year = {2000},
volume = {21},
pages = {33--37},
pdf = {../local/brow00b.pdf},
file = {brow00b.pdf:local/brow00b.pdf:PDF},
subject = {microarray},
url = {http://www.nature.com/ng/journal/v21/n1s/abs/ng0199supp_33.html}
}
@article{Brown1980Adaptive,
author = {P. J. Brown and J. V. Zidek},
title = {Adaptive Multivariate Ridge Regression},
journal = {Ann. Statist.},
year = {1980},
volume = {8},
pages = {64--74},
number = {1}
}
@article{Brown1997information,
author = {Brown, R. D. and Martin, Y. C.},
title = {The information content of 2{D} and 3{D} structural descriptors relevant
to ligand-receptor binding},
journal = {J {C}hem {I}nf {C}omput {S}ci},
year = {1997},
volume = {37},
pages = {1-9},
keywords = {chemoinformatics}
}
@article{Brown1996Use,
author = {Robert D. Brown and Yvonne C. Martin},
title = {Use of {S}tructure-{A}ctivity {D}ata {T}o {C}ompare {S}tructure-{B}ased
{C}lustering {M}ethods and {D}escriptors for {U}se in {C}ompound
{S}election},
journal = {J Chem Inf Comput Sci},
year = {1996},
volume = {36},
pages = {572-584},
keywords = {chemoinformatics},
owner = {mahe},
timestamp = {2006.08.22}
}
@article{Brunet2004Metagenes,
author = {Brunet, J. P. and Tamayo, P. and Golub, T. R. and Mesirov, J. P.},
title = {Metagenes and molecular pattern discovery using matrix factorization},
journal = {Proc {N}atl {A}cad {S}ci {U} {S} {A}},
year = {2004},
volume = {101},
pages = {4164-9},
number = {12},
abstract = {We describe here the use of nonnegative matrix factorization ({NMF}),
an algorithm based on decomposition by parts that can reduce the
dimension of expression data from thousands of genes to a handful
of metagenes. {C}oupled with a model selection mechanism, adapted
to work for any stochastic clustering algorithm, {NMF} is an efficient
method for identification of distinct molecular patterns and provides
a powerful method for class discovery. {W}e demonstrate the ability
of {NMF} to recover meaningful biological information from cancer-related
microarray data. {NMF} appears to have advantages over other methods
such as hierarchical clustering or self-organizing maps. {W}e found
it less sensitive to a priori selection of genes or initial conditions
and able to detect alternative or context-dependent patterns of gene
expression in complex biological systems. {T}his ability, similar
to semantic polysemy in text, provides a general method for robust
molecular pattern discovery.},
doi = {10.1073/pnas.0308531101},
pdf = {../local/Brunet2004Metagenes.pdf},
file = {Brunet2004Metagenes.pdf:Brunet2004Metagenes.pdf:PDF},
url = {http://dx.doi.org/10.1073/pnas.0308531101}
}
@article{Brusic2002Prediction,
author = {Brusic, V. and Petrovsky, N. and Zhang, G. and Bajic, V. B.},
title = {{P}rediction of promiscuous peptides that bind {HLA} class {I} molecules.},
journal = {Immunol. Cell Biol.},
year = {2002},
volume = {80},
pages = {280--285},
number = {3},
month = {Jun},
abstract = {Promiscuous T-cell epitopes make ideal targets for vaccine development.
We report here a computational system, MULTIPRED, for the prediction
of peptide binding to the HLA-A2 supertype. It combines a novel representation
of peptide/MHC interactions with a hidden Markov model as the prediction
algorithm. MULTIPREDis both sensitive and specific, and demonstrates
high accuracy of peptide-binding predictions for HLA-A*0201, *0204,
and *0205 alleles, good accuracy for *0206 allele, and marginal accuracy
for *0203 allele. MULTIPREDreplaces earlier requirements for individual
prediction models for each HLA allelic variant and simplifies computational
aspects of peptide-binding prediction. Preliminary testing indicates
that MULTIPRED can predict peptide binding to HLA-A2 supertype molecules
with high accuracy, including those allelic variants for which no
experimental binding data are currently available.},
keywords = {Algorithms, Amino Acid Motifs, Amino Acid Sequence, Antigen-Antibody
Complex, Automated, Binding Sites, Computational Biology, Drug Delivery
Systems, Drug Design, Epitopes, Forecasting, Genes, HLA Antigens,
HLA-A Antigens, HLA-A2 Antigen, HLA-DR Antigens, Humans, Internet,
MHC Class I, Markov Chains, Molecular Sequence Data, Neural Networks
(Computer), Pattern Recognition, Peptide Fragments, Peptides, Protein,
Protein Binding, Protein Interaction Mapping, Sensitivity and Specificity,
Sequence Analysis, Software, T-Lymphocyte, User-Computer Interface,
Viral Vaccines, 12067415},
pii = {1088},
pmid = {12067415},
timestamp = {2007.01.25}
}
@article{Bryk2002Evidence,
author = {Mary Bryk and Scott D Briggs and Brian D Strahl and M. Joan Curcio
and C. David Allis and Fred Winston},
title = {Evidence that Set1, a factor required for methylation of histone
H3, regulates rDNA silencing in S. cerevisiae by a Sir2-independent
mechanism.},
journal = {Curr Biol},
year = {2002},
volume = {12},
pages = {165--170},
number = {2},
month = {Jan},
abstract = {Several types of histone modifications have been shown to control
transcription. Recent evidence suggests that specific combinations
of these modifications determine particular transcription patterns.
The histone modifications most recently shown to play critical roles
in transcription are arginine-specific and lysine-specific methylation.
Lysine-specific histone methyltransferases all contain a SET domain,
a conserved 130 amino acid motif originally identified in polycomb-
and trithorax-group proteins from Drosophila. Members of the SU(VAR)3-9
family of SET-domain proteins methylate K9 of histone H3. Methylation
of H3 has also been shown to occur at K4. Several studies have suggested
a correlation between K4-methylated H3 and active transcription.
In this paper, we provide evidence that K4-methylated H3 is required
in a negative role, rDNA silencing in Saccharomyces cerevisiae. In
a screen for rDNA silencing mutants, we identified a mutation in
SET1, previously shown to regulate silencing at telomeres and HML.
Recent work has shown that Set1 is a member of a complex and is required
for methylation of K4 of H3 at several genomic locations. In addition,
we demonstrate that a K4R change in H3, which prevents K4 methylation,
impairs rDNA silencing, indicating that Set1 regulates rDNA silencing,
directly or indirectly, via H3 methylation. Furthermore, we present
several lines of evidence that the role of Set1 in rDNA silencing
is distinct from that of the histone deacetylase Sir2. Together,
these results suggest that Set1-dependent H3 methylation is required
for rDNA silencing in a Sir2-independent fashion.},
institution = {Department of Genetics, Harvard Medical School, 200 Longwood Avenue,
Boston, MA 02115, USA.},
keywords = {Acetylation; DNA Methylation; DNA, Ribosomal, genetics; DNA-Binding
Proteins, metabolism; Drosophila Proteins; Fungal Proteins, metabolism;
Gene Silencing; Histone Deacetylases, metabolism; Histone-Lysine
N-Methyltransferase; Histones, metabolism; Mutation; Saccharomyces
cerevisiae Proteins; Saccharomyces cerevisiae, metabolism; Silent
Information Regulator Proteins, Saccharomyces cerevisiae; Sirtuin
2; Sirtuins; Trans-Activators, metabolism; Transcription Factors,
metabolism},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {S0960982201006522},
pmid = {11818070},
timestamp = {2010.11.23}
}
@article{Buchala2005role,
author = {Samarasena Buchala and Neil Davey and Ray J Frank and Martin Loomes
and Tim M Gale},
title = {The role of global and feature based information in gender classification
of faces: a comparison of human performance and computational models.},
journal = {Int {J} {N}eural {S}yst},
year = {2005},
volume = {15},
pages = {121-8},
number = {1-2},
abstract = {Most computational models for gender classification use global information
(the full face image) giving equal weight to the whole face area
irrespective of the importance of the internal features. {H}ere,
we use a global and feature based representation of face images that
includes both global and featural information. {W}e use dimensionality
reduction techniques and a support vector machine classifier and
show that this method performs better than either global or feature
based representations alone. {W}e also present results of human subjects
performance on gender classification task and evaluate how the different
dimensionality reduction techniques compare with human subjects performance.
{T}he results support the psychological plausibility of the global
and feature based representation.},
pii = {S0129065705000074}
}
@article{Buck2004ChIP,
author = {Michael J Buck and Jason D Lieb},
title = {ChIP-chip: considerations for the design, analysis, and application
of genome-wide chromatin immunoprecipitation experiments.},
journal = {Genomics},
year = {2004},
volume = {83},
pages = {349--360},
number = {3},
month = {Mar},
abstract = {Chromatin immunoprecipitation (ChIP) is a well-established procedure
to investigate interactions between proteins and DNA. Coupled with
whole-genome DNA microarrays, ChIPS allow one to determine the entire
spectrum of in vivo DNA binding sites for any given protein. The
design and analysis of ChIP-microarray (also called ChIP-chip) experiments
differ significantly from the conventions used for locus ChIP approaches
and ChIP-chip experiments, and these differences require new methods
of analysis. In this light, we review the design of DNA microarrays,
the selection of controls, the level of repetition required, and
other critical parameters for success in the design and analysis
of ChIP-chip experiments, especially those conducted in the context
of mammalian or other relatively large genomes.},
institution = {Department of Biology and Carolina Center for Genome Sciences, University
of North Carolina at Chapel Hill, Chapel Hill, NC 27599-3280, USA.},
keywords = {Animals; Chromatin, metabolism; Genome; Humans; Immunoprecipitation,
methods; Models, Theoretical; Oligonucleotide Array Sequence Analysis,
methods; Research Design},
language = {eng},
medline-pst = {ppublish},
owner = {philippe},
pmid = {14986705},
timestamp = {2010.08.05}
}
@article{Bui2006Structural,
author = {Bui, H.-H. and Schiewe, A. J. and von Grafenstein, H. and Haworth,
I. S.},
title = {{S}tructural prediction of peptides binding to {MHC} class {I} molecules.},
journal = {Proteins},
year = {2006},
volume = {63},
pages = {43--52},
number = {1},
month = {Apr},
abstract = {Peptide binding to class I major histocompatibility complex (MHCI)
molecules is a key step in the immune response and the structural
details of this interaction are of importance in the design of peptide
vaccines. Algorithms based on primary sequence have had success in
predicting potential antigenic peptides for MHCI, but such algorithms
have limited accuracy and provide no structural information. Here,
we present an algorithm, PePSSI (peptide-MHC prediction of structure
through solvated interfaces), for the prediction of peptide structure
when bound to the MHCI molecule, HLA-A2. The algorithm combines sampling
of peptide backbone conformations and flexible movement of MHC side
chains and is unique among other prediction algorithms in its incorporation
of explicit water molecules at the peptide-MHC interface. In an initial
test of the algorithm, PePSSI was used to predict the conformation
of eight peptides bound to HLA-A2, for which X-ray data are available.
Comparison of the predicted and X-ray conformations of these peptides
gave RMSD values between 1.301 and 2.475 A. Binding conformations
of 266 peptides with known binding affinities for HLA-A2 were then
predicted using PePSSI. Structural analyses of these peptide-HLA-A2
conformations showed that peptide binding affinity is positively
correlated with the number of peptide-MHC contacts and negatively
correlated with the number of interfacial water molecules. These
results are consistent with the relatively hydrophobic binding nature
of the HLA-A2 peptide binding interface. In summary, PePSSI is capable
of rapid and accurate prediction of peptide-MHC binding conformations,
which may in turn allow estimation of MHCI-peptide binding affinity.},
doi = {10.1002/prot.20870},
keywords = {Algorithms, Amino Acid Sequence, Antigens, Artificial Intelligence,
Automated, Binding Sites, Chemical, Computational Biology, Computer
Simulation, Crystallog, Crystallography, Electrostatics, Genes, Genetic,
HLA Antigens, Histocompatibility Antigens Class I, Humans, Hydrogen
Bonding, Ligands, MHC Class I, Major Histocompatibility Complex,
Models, Molecular, Molecular Conformation, Molecular Sequence Data,
Pattern Recognition, Peptides, Protein, Protein Binding, Protein
Conformation, Proteomics, Quantitative Structure-Activity Relationship,
Sequence Alignment, Sequence Analysis, Software, Structural Homology,
Structure-Activity Relationship, Thermodynamics, Water, X-Ray, X-Rays,
raphy, 16447245},
pmid = {16447245},
timestamp = {2007.01.25},
url = {http://dx.doi.org/10.1002/prot.20870}
}
@article{Bui2005Automated,
author = {Huynh-Hoa Bui and John Sidney and Bjoern Peters and Muthuraman Sathiamurthy
and Asabe Sinichi and Kelly-Anne Purton and Bianca R Moth\'e and
Francis V Chisari and David I Watkins and Alessandro Sette},
title = {Automated generation and evaluation of specific MHC binding predictive
tools: ARB matrix applications.},
journal = {Immunogenetics},
year = {2005},
volume = {57},
pages = {304--314},
number = {5},
month = {Jun},
abstract = {Prediction of which peptides can bind major histocompatibility complex
(MHC) molecules is commonly used to assist in the identification
of T cell epitopes. However, because of the large numbers of different
MHC molecules of interest, each associated with different predictive
tools, tool generation and evaluation can be a very resource intensive
task. A methodology commonly used to predict MHC binding affinity
is the matrix or linear coefficients method. Herein, we described
Average Relative Binding (ARB) matrix methods that directly predict
IC(50) values allowing combination of searches involving different
peptide sizes and alleles into a single global prediction. A computer
program was developed to automate the generation and evaluation of
ARB predictive tools. Using an in-house MHC binding database, we
generated a total of 85 and 13 MHC class I and class II matrices,
respectively. Results from the automated evaluation of tool efficiency
are presented. We anticipate that this automation framework will
be generally applicable to the generation and evaluation of large
numbers of MHC predictive methods and tools, and will be of value
to centralize and rationalize the process of evaluation of MHC predictions.
MHC binding predictions based on ARB matrices were made available
at http://epitope.liai.org:8080/matrix web server.},
doi = {10.1007/s00251-005-0798-y},
keywords = {Animals; Binding Sites; Computer Simulation; Databases, Protein; Epitopes;
Histocompatibility Antigens; Humans; Major Histocompatibility Complex;
Models, Biological; Protein Binding},
owner = {laurent},
pmid = {15868141},
timestamp = {2007.07.12},
url = {http://dx.doi.org/10.1007/s00251-005-0798-y}
}
@incollection{Buijsman2005Structural,
author = {Rogier Buijsman},
title = {Structural Aspects of Kinases and Their Inhibitors},
booktitle = {Chemogenomics in Drug Discovery},
publisher = {Wiley-VCH},
year = {2005},
chapter = {7},
pages = {191-219}
}
@article{Bullard2010Polygenic,
author = {J. H. Bullard and Y. Mostovoy and S. Dudoit and R. B. Brem},
title = {Polygenic and directional regulatory evolution across pathways in
{{\it Saccharomyces}}},
journal = {PNAS},
year = {2010},
volume = {107},
pages = {5058-5063},
number = {11},
url = {http://www.pnas.org/content/107/11/5058.abstract}
}
@article{Bullard2010Evaluation,
author = {Bullard, J. H. and Purdom, E. and Hansen, K. D. and Dudoit, S.},
title = {Evaluation of statistical methods for normalization and differential
expression in mRNA-Seq experiments.},
journal = {BMC Bioinformatics},
year = {2010},
volume = {11},
pages = {94},
abstract = {High-throughput sequencing technologies, such as the Illumina Genome
Analyzer, are powerful new tools for investigating a wide range of
biological and medical questions. Statistical and computational methods
are key for drawing meaningful and accurate conclusions from the
massive and complex datasets generated by the sequencers. We provide
a detailed evaluation of statistical methods for normalization and
differential expression (DE) analysis of Illumina transcriptome sequencing
(mRNA-Seq) data.We compare statistical methods for detecting genes
that are significantly DE between two types of biological samples
and find that there are substantial differences in how the test statistics
handle low-count genes. We evaluate how DE results are affected by
features of the sequencing platform, such as, varying gene lengths,
base-calling calibration method (with and without phi X control lane),
and flow-cell/library preparation effects. We investigate the impact
of the read count normalization method on DE results and show that
the standard approach of scaling by total lane counts (e.g., RPKM)
can bias estimates of DE. We propose more general quantile-based
normalization procedures and demonstrate an improvement in DE detection.Our
results have significant practical and methodological implications
for the design and analysis of mRNA-Seq experiments. They highlight
the importance of appropriate statistical methods for normalization
and DE inference, to account for features of the sequencing platform
that could impact the accuracy of results. They also reveal the need
for further research in the development of statistical and computational
methods for mRNA-Seq.},
doi = {10.1186/1471-2105-11-94},
institution = {Division of Biostatistics, University of California, Berkeley, Berkeley,
CA, USA. bullard@berkeley.edu},
keywords = {Computational Biology; Databases, Genetic; RNA, Messenger; Sequence
Analysis, RNA},
owner = {laurent},
pii = {1471-2105-11-94},
pmid = {20167110},
timestamp = {2012.04.11},
url = {http://dx.doi.org/10.1186/1471-2105-11-94}
}
@article{Bultinck2003Quantum,
author = {P. Bultinck and T. Kuppens and X. Giron{\`e}s and R. Carb{\'o}-Dorca},
title = {{Q}uantum similarity superposition algorithm ({QSSA}): a consistent
scheme for molecular alignment and molecular similarity based on
quantum chemistry.},
journal = {J Chem Inf Comput Sci},
year = {2003},
volume = {43},
pages = {1143--1150},
number = {4},
abstract = {The use of the molecular quantum similarity overlap measure for molecular
alignment is investigated. A new algorithm is presented, the quantum
similarity superposition algorithm (QSSA), expressing the relative
positions of two molecules in terms of mutual translation in three
Cartesian directions and three Euler angles. The quantum similarity
overlap is then used to optimize the mutual positions of the molecules.
A comparison is made with TGSA, a topogeometrical approach, and the
influence of differences on molecular clustering is discussed.},
doi = {10.1021/ci0340153},
keywords = {Aldosterone, Algorithms, Chemical, Comparative Study, Estrone, Isomerism,
Models, Molecular, Molecular Structure, Non-U.S. Gov't, Quantitative
Structure-Activity Relationship, Quantum Theory, Research Support,
12870905},
owner = {mahe},
pmid = {12870905},
timestamp = {2006.08.22},
url = {http://dx.doi.org/10.1021/ci0340153}
}
@article{Bulyk2006DNA,
author = {Martha L Bulyk},
title = {{DNA} microarray technologies for measuring protein-{DNA} interactions.},
journal = {Curr Opin Biotechnol},
year = {2006},
volume = {17},
pages = {422--430},
number = {4},
month = {Aug},
abstract = {DNA-binding proteins have key roles in many cellular processes, including
transcriptional regulation and replication. Microarray-based technologies
permit the high-throughput identification of binding sites and enable
the functional roles of these binding proteins to be elucidated.
In particular, microarray readout either of chromatin immunoprecipitated
DNA-bound proteins (ChIP-chip) or of DNA adenine methyltransferase
fusion proteins (DamID) enables the identification of in vivo genomic
target sites of proteins. A complementary approach to analyse the
in vitro binding of proteins directly to double-stranded DNA microarrays
(protein binding microarrays; PBMs), permits rapid characterization
of their DNA binding site sequence specificities. Recent advances
in DNA microarray synthesis technologies have facilitated the definition
of DNA-binding sites at much higher resolution and coverage, and
advances in these and emerging technologies will further increase
the efficiencies of these exciting new approaches.},
doi = {10.1016/j.copbio.2006.06.015},
institution = {Division of Genetics, Department of Medicine, Harvard/MIT Division
of Health Sciences and Technology (HST), Brigham and Women's Hospital
and Harvard Medical School, Boston, MA 02115, USA. mlbulyk@receptor.med.harvard.edu},
keywords = {Animals; Chromatin Immunoprecipitation, methods; Cross-Linking Reagents,
chemistry; DNA, analysis/chemistry/metabolism; DNA-Binding Proteins,
analysis/genetics/metabolism; Humans; Oligonucleotide Array Sequence
Analysis, methods; Protein Binding},
language = {eng},
medline-pst = {ppublish},
owner = {philippe},
pii = {S0958-1669(06)00099-1},
pmid = {16839757},
timestamp = {2010.08.05},
url = {http://dx.doi.org/10.1016/j.copbio.2006.06.015}
}
@inproceedings{Bunea2006Aggregation,
author = {Bunea, F. and Tsybakov, A. and Wegkamp, M.},
title = {Aggregation and sparsity via $l_1$ penalized least squares},
booktitle = {Proceedings of the 19th Annual Conference on Learning Theory, COLT
2006.},
year = {2006},
editor = {Lugosi, G. and Simon, H. U.},
number = {4005},
series = {LNAI},
pages = {379--391},
address = {Berlin Heidelberg},
publisher = {Springer-Verlag},
pdf = {../local/Bunea2006Aggregation.pdf},
file = {Bunea2006Aggregation.pdf:Bunea2006Aggregation.pdf:PDF},
keywords = {lasso},
owner = {jp},
timestamp = {2009.05.07}
}
@article{Bunea2007Sparsity,
author = {Bunea, F. and Tsybakov, A. and Wegkamp, M.},
title = {Sparsity oracle inequalities for the Lasso},
journal = {Electron. J. Statist.},
year = {2007},
volume = {1},
pages = {169--194},
doi = {10.1214/07-EJS008},
pdf = {../local/Bunea2007Sparsity.pdf},
file = {Bunea2007Sparsity.pdf:Bunea2007Sparsity.pdf:PDF},
keywords = {lasso},
owner = {jp},
timestamp = {2009.05.02},
url = {http://dx.doi.org/10.1214/07-EJS008}
}
@article{Bunescu2005Comparative,
author = {Bunescu, R. and Ge, R. and Kate, R. J. and Marcotte, E. M. and Mooney,
R. J. and Ramani, A. K. and Wong, Y. W.},
title = {Comparative experiments on learning information extractors for proteins
and their interactions.},
journal = {Artif. {I}ntell. {M}ed.},
year = {2005},
volume = {33},
pages = {139-55},
number = {2},
month = {Feb},
abstract = {O{BJECTIVE}: {A}utomatically extracting information from biomedical
text holds the promise of easily consolidating large amounts of biological
knowledge in computer-accessible form. {T}his strategy is particularly
attractive for extracting data relevant to genes of the human genome
from the 11 million abstracts in {M}edline. {H}owever, extraction
efforts have been frustrated by the lack of conventions for describing
human genes and proteins. {W}e have developed and evaluated a variety
of learned information extraction systems for identifying human protein
names in {M}edline abstracts and subsequently extracting information
on interactions between the proteins. {METHODS} {AND} {MATERIAL}:
{W}e used a variety of machine learning methods to automatically
develop information extraction systems for extracting information
on gene/protein name, function and interactions from {M}edline abstracts.
{W}e present cross-validated results on identifying human proteins
and their interactions by training and testing on a set of approximately
1000 manually-annotated {M}edline abstracts that discuss human genes/proteins.
{RESULTS}: {W}e demonstrate that machine learning approaches using
support vector machines and maximum entropy are able to identify
human proteins with higher accuracy than several previous approaches.
{W}e also demonstrate that various rule induction methods are able
to identify protein interactions with higher precision than manually-developed
rules. {CONCLUSION}: {O}ur results show that it is promising to use
machine learning to automatically build systems for extracting information
from biomedical text. {T}he results also give a broad picture of
the relative strengths of a wide variety of methods when tested on
a reasonably large human-annotated corpus.},
doi = {10.1016/j.artmed.2004.07.016},
pdf = {../local/Bunescu2005Comparative.pdf},
file = {Bunescu2005Comparative.pdf:local/Bunescu2005Comparative.pdf:PDF},
keywords = {biosvm},
pii = {S0933-3657(04)00131-9},
url = {http://dx.doi.org/10.1016/j.artmed.2004.07.016}
}
@article{Bungaro2009Integration,
author = {Bungaro, Silvia and Dell'Orto, Marta Campo and Zangrando, Andrea
and Basso, Dario and Gorletta, Tatiana and {Lo Nigro}, Luca and Leszl,
Anna and Young, Bryan D. and Basso, Giuseppe and Bicciato, Silvio
and Biondi, Andrea and {te Kronnie}, Gertruy and Cazzaniga, Giovanni},
title = {Integration of genomic and gene expression data of childhood ALL
without known aberrations identifies subgroups with specific genetic
hallmarks.},
journal = {Genes Chromosomes Cancer},
year = {2009},
volume = {48},
pages = {22--38},
number = {1},
month = {Jan},
abstract = {Pediatric acute lymphoblastic leukemia (ALL) comprises genetically
distinct subtypes. However, 25\% of cases still lack defined genetic
hallmarks. To identify genomic aberrancies in childhood ALL patients
nonclassifiable by conventional methods, we performed a single nucleotide
polymorphisms (SNP) array-based genomic analysis of leukemic cells
from 29 cases. The vast majority of cases analyzed (19/24, 79\%)
showed genomic abnormalities; at least one of them affected either
genes involved in cell cycle regulation or in B-cell development.
The most relevant abnormalities were CDKN2A/9p21 deletions (7/24,
29\%), ETV6 (TEL)/12p13 deletions (3/24, 12\%), and intrachromosomal
amplifications of chromosome 21 (iAMP21) (3/24, 12\%). To identify
variation in expression of genes directly or indirectly affected
by recurrent genomic alterations, we integrated genomic and gene
expression data generated by microarray analyses of the same samples.
SMAD1 emerged as a down-regulated gene in CDKN2A homozygous deleted
cases compared with nondeleted. The JAG1 gene, encoding the Jagged
1 ligand of the Notch receptor, was among a list of differentially
expressed (up-regulated) genes in ETV6-deleted cases. Our findings
demonstrate that integration of genomic analysis and gene expression
profiling can identify genetic lesions undetected by routine methods
and potential novel pathways involved in B-progenitor ALL pathogenesis.},
doi = {10.1002/gcc.20616},
institution = {Centro Ricerca Tettamanti, Clinica Pediatrica Università Milano-Bicocca,
Ospedale San Gerardo, Monza, Italy.},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pmid = {18803328},
timestamp = {2012.02.29},
url = {http://dx.doi.org/10.1002/gcc.20616}
}
@article{Bunke1983Inexact,
author = {Bunke, H.},
title = {Inexact graph matching for structural pattern recognition},
journal = {Pattern Recogn. Lett.},
year = {1983},
volume = {1},
pages = {245--253},
number = {4},
month = {May},
abstract = {This paper is concerned with the inexact matching of attributed, relational
graphs for structural pattern recognition. The matching procedure
is based on a state space search utilizing heuristic information.
Some experimental results are reported.},
doi = {10.1016/0167-8655(83)90033-8},
issn = {01678655},
keywords = {graph, matching},
owner = {misha},
posted-at = {2009-07-13 05:03:03},
priority = {0},
url = {http://dx.doi.org/10.1016/0167-8655(83)90033-8}
}
@article{Bunke1998Graph,
author = {H. Bunke and K. Shearer},
title = {A {G}raph {D}istance {M}etric based on the {M}aximal {C}ommon {S}ubgraph},
journal = {Pattern Recogn. Lett.},
year = {1998},
volume = {19},
pages = {255-259},
doi = {doi:10.1016/S0167-8655(97)00179-7},
owner = {mahe},
timestamp = {2006.08.22}
}
@article{Burbidge2001Drug,
author = {Burbidge, R. and Trotter, M. and Buxton, B. and Holden, S.},
title = {Drug design by machine learning: support vector machines for pharmaceutical
data analysis},
journal = {Comput. {C}hem.},
year = {2001},
volume = {26},
pages = {4--15},
number = {1},
month = {December},
pdf = {../local/burb01.pdf},
file = {burb01.pdf:local/burb01.pdf:PDF},
keywords = {biosvm chemoinformatics},
subject = {qsar},
url = {http://stats.ma.ic.ac.uk/~rdb/pubs/candc-aisb00-rbmt-final.pdf}
}
@article{Burckin2005Exploring,
author = {Burckin, T. and Nagel, R. and Mandel-Gutfreund, Y. and Shiue, L.
and Clark, T. A. and Chong, J.-L. and Chang, T.-H. and Squazzo, S.
and Hartzog, G. and Ares, M.},
title = {Exploring functional relationships between components of the gene
expression machinery.},
journal = {Nat. {S}truct. {M}ol. {B}iol.},
year = {2005},
volume = {12},
pages = {175-82},
number = {2},
month = {Feb},
abstract = {Eukaryotic gene expression requires the coordinated activity of many
macromolecular machines including transcription factors and {RNA}
polymerase, the spliceosome, m{RNA} export factors, the nuclear pore,
the ribosome and decay machineries. {Y}east carrying mutations in
genes encoding components of these machineries were examined using
microarrays to measure changes in both pre-m{RNA} and m{RNA} levels.
{W}e used these measurements as a quantitative phenotype to ask how
steps in the gene expression pathway are functionally connected.
{A} multiclass support vector machine was trained to recognize the
gene expression phenotypes caused by these mutations. {I}n several
cases, unexpected phenotype assignments by the computer revealed
functional roles for specific factors at multiple steps in the gene
expression pathway. {T}he ability to resolve gene expression pathway
phenotypes provides insight into how the major machineries of gene
expression communicate with each other.},
doi = {10.1038/nsmb891},
pdf = {../local/Burckin2005Exploring.pdf},
file = {Burckin2005Exploring.pdf:local/Burckin2005Exploring.pdf:PDF},
keywords = {biosvm microarray},
pii = {nsmb891},
url = {http://dx.doi.org/10.1038/nsmb891}
}
@article{Burges1998Tutorial,
author = {Burges, C. J. C.},
title = {A {T}utorial on {S}upport {V}ector {M}achines for {P}attern {R}ecognition},
journal = {Data {M}in. {K}nowl. {D}iscov.},
year = {1998},
volume = {2},
pages = {121-167},
number = {2},
pdf = {../local/burg98.pdf},
file = {burg98.pdf:local/burg98.pdf:PDF},
subject = {kernel},
url = {http://www.kernel-machines.org/papers/Burges98.ps.gz}
}
@article{Buriol04New,
author = {Buriol,, L. and Fran\c{c}a,, P. M. and Moscato,, P.},
title = {A New Memetic Algorithm for the Asymmetric Traveling Salesman Problem},
journal = {Journal of Heuristics},
year = {2004},
volume = {10},
pages = {483--506},
number = {5},
address = {Hingham, MA, USA},
doi = {http://dx.doi.org/10.1023/B:HEUR.0000045321.59202.52},
issn = {1381-1231},
publisher = {Kluwer Academic Publishers}
}
@article{Bussemaker2001Regulatory,
author = {Bussemaker, H. J. and Li, H. and Siggia, E. D.},
title = {Regulatory element detection using correlation with expression},
journal = {Nat. {G}enet.},
year = {2001},
volume = {27},
pages = {167--174},
pdf = {../local/buss01.pdf},
file = {buss01.pdf:local/buss01.pdf:PDF},
subject = {microarray},
url = {http://www.nature.com/cgi-taf/DynaPage.taf?file=/ng/journal/v27/n2/full/ng0201_167.html&filetype=pdf}
}
@article{Busuttil2004Support,
author = {Busuttil, S. and Abela, J. and Pace, G. J.},
title = {Support vector machines with profile-based kernels for remote protein
homology detection.},
journal = {Genome {I}nform {S}er {W}orkshop {G}enome {I}nform},
year = {2004},
volume = {15},
pages = {191-200},
number = {2},
abstract = {Two new techniques for remote protein homology detection particulary
suited for sparse data are introduced. {T}hese methods are based
on position specific scoring matrices or profiles and use a support
vector machine ({SVM}) for discrimination. {T}he performance on standard
benchmarks outperforms previous non-discriminative techniques and
is comparable to that of other {SVM}-based methods while giving distinct
advantages.},
pdf = {../local/Busuttil2004Support.pdf},
file = {Busuttil2004Support.pdf:local/Busuttil2004Support.pdf:PDF},
keywords = {biosvm},
url = {http://www.jsbi.org/journal/GIW04/GIW04F020.html}
}
@article{Butina2002Predicting,
author = {D. Butina and M. D. Segall and K. Frankcombe},
title = {Predicting {ADME} properties in silico: methods and models.},
journal = {Drug {D}iscov {T}oday},
year = {2002},
volume = {7},
pages = {S83--S88},
number = {11 Suppl},
month = {Jun},
abstract = {Unfavourable absorption, distribution, metabolism and elimination
({ADME}) properties have been identified as a major cause of failure
for candidate molecules in drug development. {C}onsequently, there
is increasing interest in the early prediction of {ADME} properties,
with the objective of increasing the success rate of compounds reaching
development. {T}his review explores in silico approaches and selected
published models for predicting {ADME} properties from chemical structure
alone. {I}n particular, we provide a comparison of methods based
on pattern recognition to identify correlations between molecular
descriptors and {ADME} properties, structural models based on classical
molecular mechanics and quantum mechanical techniques for modelling
chemical reactions.},
keywords = {chemoinformatics},
owner = {mahe},
pii = {S1359644602022882},
pmid = {12047885},
timestamp = {2006.02.03}
}
@article{Butte2000Mutual,
author = {A. J. Butte and I. S. Kohane},
title = {Mutual information relevance networks: functional genomic clustering
using pairwise entropy measurements.},
journal = {Pac Symp Biocomput},
year = {2000},
pages = {418--429},
abstract = {Increasing numbers of methodologies are available to find functional
genomic clusters in RNA expression data. We describe a technique
that computes comprehensive pair-wise mutual information for all
genes in such a data set. An association with a high mutual information
means that one gene is non-randomly associated with another; we hypothesize
this means the two are related biologically. By picking a threshold
mutual information and using only associations at or above the threshold,
we show how this technique was used on a public data set of 79 RNA
expression measurements of 2,467 genes to construct 22 clusters,
or Relevance Networks. The biological significance of each Relevance
Network is explained.},
institution = {Children's Hospital Informatics Program, Boston, MA 02115, USA.},
keywords = {Computer Simulation; Gene Expression; Genome; Genome, Fungal; Genome,
Human; Humans; Models, Genetic; Multigene Family; RNA; Saccharomyces
cerevisiae},
owner = {fantine},
pmid = {10902190},
timestamp = {2010.10.21}
}
@article{Butte2000Discovering,
author = {Butte, A. J. and Tamayo, P. and Slonim, D. and Golub, T. R. and Kohane,
I. S.},
title = {{D}iscovering functional relationships between {RNA} expression and
chemotherapeutic susceptibility using relevance networks.},
journal = {Proc. Natl. Acad. Sci. USA},
year = {2000},
volume = {97},
pages = {12182--12186},
number = {22},
month = {Oct},
abstract = {In an effort to find gene regulatory networks and clusters of genes
that affect cancer susceptibility to anticancer agents, we joined
a database with baseline expression levels of 7,245 genes measured
by using microarrays in 60 cancer cell lines, to a database with
the amounts of 5,084 anticancer agents needed to inhibit growth of
those same cell lines. Comprehensive pair-wise correlations were
calculated between gene expression and measures of agent susceptibility.
Associations weaker than a threshold strength were removed, leaving
networks of highly correlated genes and agents called relevance networks.
Hypotheses for potential single-gene determinants of anticancer agent
susceptibility were constructed. The effect of random chance in the
large number of calculations performed was empirically determined
by repeated random permutation testing; only associations stronger
than those seen in multiply permuted data were used in clustering.
We discuss the advantages of this methodology over alternative approaches,
such as phylogenetic-type tree clustering and self-organizing maps.},
doi = {10.1073/pnas.220392197},
pdf = {../local/Butte2000Discovering.pdf},
file = {Butte2000Discovering.pdf:Butte2000Discovering.pdf:PDF},
pii = {220392197},
pmid = {11027309},
timestamp = {2008.02.04},
url = {http://dx.doi.org/10.1073/pnas.220392197}
}
@article{Buus2003Sensitive,
author = {S. Buus and S. L. Lauem{\o}ller and P. Worning and C. Kesmir and
T. Frimurer and S. Corbet and A. Fomsgaard and J. Hilden and A. Holm
and S. Brunak},
title = {Sensitive quantitative predictions of peptide-{MHC} binding by a
'Query by Committee' artificial neural network approach.},
journal = {Tissue Antigens},
year = {2003},
volume = {62},
pages = {378--384},
number = {5},
month = {Nov},
abstract = {We have generated Artificial Neural Networks (ANN) capable of performing
sensitive, quantitative predictions of peptide binding to the MHC
class I molecule, HLA-A*0204. We have shown that such quantitative
ANN are superior to conventional classification ANN, that have been
trained to predict binding vs non-binding peptides. Furthermore,
quantitative ANN allowed a straightforward application of a 'Query
by Committee' (QBC) principle whereby particularly information-rich
peptides could be identified and subsequently tested experimentally.
Iterative training based on QBC-selected peptides considerably increased
the sensitivity without compromising the efficiency of the prediction.
This suggests a general, rational and unbiased approach to the development
of high quality predictions of epitopes restricted to this and other
HLA molecules. Due to their quantitative nature, such predictions
will cover a wide range of MHC-binding affinities of immunological
interest, and they can be readily integrated with predictions of
other events involved in generating immunogenic epitopes. These predictions
have the capacity to perform rapid proteome-wide searches for epitopes.
Finally, it is an example of an iterative feedback loop whereby advanced,
computational bioinformatics optimize experimental strategy, and
vice versa.},
keywords = {HLA-A Antigens; Humans; Neural Networks (Computer); Peptides; Protein
Binding; Proteome; Research Support, Non-U.S. Gov't; Research Support,
U.S. Gov't, P.H.S.},
owner = {jacob},
pii = {112},
pmid = {14617044},
timestamp = {2006.08.30}
}
@article{Buyse2006Validation,
author = {Buyse, M. and Loi, S. and van't Veer, S. and Viale, G. and Delorenzi,
M. and Glas, A. M. and Saghatchian d'Assignies, M. and Bergh, J.
and Lidereau, R. and Ellis, P. and Harris, A. and Bogaerts, J. and
Therasse, P. and Floore, A. and Amakrane, M. and Piette, F. and Rutgers,
E. and Sotiriou, C. and Cardoso, F. and Piccart, M. J. and T. R.
A. N. S. B. I. G. Consortium},
title = {Validation and clinical utility of a 70-gene prognostic signature
for women with node-negative breast cancer.},
journal = {J. Natl. Canc. Inst.},
year = {2006},
volume = {98},
pages = {1183--1192},
number = {17},
month = {Sep},
abstract = {BACKGROUND: A 70-gene signature was previously shown to have prognostic
value in patients with node-negative breast cancer. Our goal was
to validate the signature in an independent group of patients. METHODS:
Patients (n = 307, with 137 events after a median follow-up of 13.6
years) from five European centers were divided into high- and low-risk
groups based on the gene signature classification and on clinical
risk classifications. Patients were assigned to the gene signature
low-risk group if their 5-year distant metastasis-free survival probability
as estimated by the gene signature was greater than 90\%. Patients
were assigned to the clinicopathologic low-risk group if their 10-year
survival probability, as estimated by Adjuvant! software, was greater
than 88\% (for estrogen receptor [ER]-positive patients) or 92\%
(for ER-negative patients). Hazard ratios (HRs) were estimated to
compare time to distant metastases, disease-free survival, and overall
survival in high- versus low-risk groups. RESULTS: The 70-gene signature
outperformed the clinicopathologic risk assessment in predicting
all endpoints. For time to distant metastases, the gene signature
yielded HR = 2.32 (95\% confidence interval [CI] = 1.35 to 4.00)
without adjustment for clinical risk and hazard ratios ranging from
2.13 to 2.15 after adjustment for various estimates of clinical risk;
clinicopathologic risk using Adjuvant! software yielded an unadjusted
HR = 1.68 (95\% CI = 0.92 to 3.07). For overall survival, the gene
signature yielded an unadjusted HR = 2.79 (95\% CI = 1.60 to 4.87)
and adjusted hazard ratios ranging from 2.63 to 2.89; clinicopathologic
risk yielded an unadjusted HR = 1.67 (95\% CI = 0.93 to 2.98). For
patients in the gene signature high-risk group, 10-year overall survival
was 0.69 for patients in both the low- and high-clinical risk groups;
for patients in the gene signature low-risk group, the 10-year survival
rates were 0.88 and 0.89, respectively. CONCLUSIONS: The 70-gene
signature adds independent prognostic information to clinicopathologic
risk assessment for patients with early breast cancer.},
doi = {10.1093/jnci/djj329},
pdf = {../local/Buyse2006Validation.pdf},
file = {Buyse2006Validation.pdf:Buyse2006Validation.pdf:PDF},
institution = {International Drug Development Institute, Brussels, Belgium.},
keywords = {csbcbook, csbcbook-ch3},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pmid = {16954471},
timestamp = {2009.10.17},
url = {http://dx.doi.org/10.1093/jnci/djj329}
}
@article{Byerly2009Effects,
author = {Byerly, S. and Sundin, K. and Raja, R. and Stanchfield, J. and Bejjani,
B. A. and Shaffer, L. G.},
title = {Effects of ozone exposure during microarray posthybridization washes
and scanning},
journal = {J. Mol. Diagn.},
year = {2009},
volume = {11},
pages = {590--597},
number = {6},
month = {Nov},
abstract = {The increasing prevalence of array-based comparative genomic hybridization
in the clinical laboratory necessitates the implementation of quality
control measures to attain accurate results with a high level of
confidence. Environmental ozone is present in all industrialized
cities and has been found to be detrimental to array data even at
levels considered acceptable by US Environmental Protection Agency
standards. In this study, we characterized the effect of ozone on
microarray data on three different labeling platforms that use different
fluorescent dyes (Cy3 and Cy5, Alexa Fluor 555 and Alexa Fluor 647,
and Alexa Fluor 3 and Alexa Fluor 5) that are commonly used in array-based
comparative genomic hybridization. We investigated the effects of
ozone on microarray data by washing the array in variable ozone environments.
In addition, we observed the effects of prolonged exposure to ozone
on the microarray after washing in an ozone-free environment. Our
results demonstrate the necessity of minimizing ozone exposure when
washing and drying the microarray. We also found that washed microarrays
produce the best results when immediately scanned; however, if a
low-ozone environment is maintained, there will be little compromise
in the data collected.},
doi = {10.2353/jmoldx.2009.090009},
institution = {Signature Genomic Laboratories, 2820 N. Astor St., Spokane, WA 99207,
USA.},
keywords = {Humans; Oligonucleotide Array Sequence Analysis, methods; Ozone},
language = {eng},
medline-pst = {ppublish},
owner = {phupe},
pii = {S1525-1578(10)60283-8},
pmid = {19767590},
timestamp = {2011.04.08},
url = {http://dx.doi.org/10.2353/jmoldx.2009.090009}
}
@article{Byvatov2003Comparison,
author = {Byvatov, E. and Fechner, U. and Sadowski, J. and Schneider, G.},
title = {Comparison of support vector machine and artificial neural network
systems for drug/nondrug classification.},
journal = {J {C}hem {I}nf {C}omput {S}ci},
year = {2003},
volume = {43},
pages = {1882-9},
number = {6},
abstract = {Support vector machine ({SVM}) and artificial neural network ({ANN})
systems were applied to a drug/nondrug classification problem as
an example of binary decision problems in early-phase virtual compound
filtering and screening. {T}he results indicate that solutions obtained
by {SVM} training seem to be more robust with a smaller standard
error compared to {ANN} training. {G}enerally, the {SVM} classifier
yielded slightly higher prediction accuracy than {ANN}, irrespective
of the type of descriptors used for molecule encoding, the size of
the training data sets, and the algorithm employed for neural network
training. {T}he performance was compared using various different
descriptor sets and descriptor combinations based on the 120 standard
{G}hose-{C}rippen fragment descriptors, a wide range of 180 different
properties and physicochemical descriptors from the {M}olecular {O}perating
{E}nvironment ({MOE}) package, and 225 topological pharmacophore
({CATS}) descriptors. {F}or the complete set of 525 descriptors cross-validated
classification by {SVM} yielded 82\% correct predictions ({M}atthews
cc = 0.63), whereas {ANN} reached 80\% correct predictions ({M}atthews
cc = 0.58). {A}lthough {SVM} outperformed the {ANN} classifiers with
regard to overall prediction accuracy, both methods were shown to
complement each other, as the sets of true positives, false positives
(overprediction), true negatives, and false negatives (underprediction)
produced by the two classifiers were not identical. {T}he theory
of {SVM} and {ANN} training is briefly reviewed.},
doi = {10.1021/ci0341161},
pdf = {../local/Byvatov2003Comparison.pdf},
file = {Byvatov2003Comparison.pdf:local/Byvatov2003Comparison.pdf:PDF},
keywords = {biosvm chemoinformatics},
url = {http://dx.doi.org/10.1021/ci0341161}
}
@article{Byvatov2004SVM-based,
author = {Evgeny Byvatov and Gisbert Schneider},
title = {S{VM}-based feature selection for characterization of focused compound
collections.},
journal = {J {C}hem {I}nf {C}omput {S}ci},
year = {2004},
volume = {44},
pages = {993-9},
number = {3},
abstract = {Artificial neural networks, the support vector machine ({SVM}), and
other machine learning methods for the classification of molecules
are often considered as a "black box", since the molecular features
that are most relevant for a given classifier are usually not presented
in a human-interpretable form. {W}e report on an {SVM}-based algorithm
for the selection of relevant molecular features from a trained classifier
that might be important for an understanding of ligand-receptor interactions.
{T}he original {SVM} approach was extended to allow for feature selection.
{T}he method was applied to characterize focused libraries of enzyme
inhibitors. {A} comparison with classical {K}olmogorov-{S}mirnov
({KS})-based feature selection was performed. {I}n most of the applications
the {SVM} method showed sustained classification accuracy, thereby
relying on a smaller number of molecular features than {KS}-based
classifiers. {I}n one case both methods produced comparable results.
{L}imiting the calculation of descriptors to only the most relevant
ones for a certain biological activity can also be used to speed
up high-throughput virtual screening.},
doi = {10.1021/ci0342876},
pdf = {../local/Byvatov2004SVM-based.pdf},
file = {Byvatov2004SVM-based.pdf:local/Byvatov2004SVM-based.pdf:PDF},
keywords = {biosvm chemoinformatics featureselection},
url = {http://dx.doi.org/10.1021/ci0342876}
}
@article{Byvatov2003Support,
author = {E. Byvatov and G. Schneider},
title = {Support vector machine applications in bioinformatics.},
journal = {Appl {B}ioinformatics},
year = {2003},
volume = {2},
pages = {67-77},
number = {2},
abstract = {The support vector machine ({SVM}) approach represents a data-driven
method for solving classification tasks. {I}t has been shown to produce
lower prediction error compared to classifiers based on other methods
like artificial neural networks, especially when large numbers of
features are considered for sample description. {I}n this review,
the theory and main principles of the {SVM} approach are outlined,
and successful applications in traditional areas of bioinformatics
research are described. {C}urrent developments in techniques related
to the {SVM} approach are reviewed which might become relevant for
future functional genomics and chemogenomics projects. {I}n a comparative
study, we developed neural network and {SVM} models to identify small
organic molecules that potentially modulate the function of {G}-protein
coupled receptors. {T}he {SVM} system was able to correctly classify
approximately 90\% of the compounds in a cross-validation study yielding
a {M}atthews correlation coefficient of 0.78. {T}his classifier can
be used for fast filtering of compound libraries in virtual screening
applications.},
keywords = {biosvm}
}
@book{International1992Enzyme,
title = {Enzyme Nomenclature 1992},
publisher = {Academic Press},
year = {1992},
author = {{I}nternational {U}nion of {B}iochemistry and {M}olecular {B}iology},
address = {San Diego, California, United States},
month = {August},
abstract = {Recommendations of the Nomenclature Committee of the International
Union of Biochemistry and Molecular Biology on the Nomenclature and
Classification of Enzymes.},
citeulike-article-id = {1096963},
howpublished = {Hardcover},
isbn = {0122271645},
keywords = {gene-ontology, ontology},
posted-at = {2007-10-29 12:38:57},
priority = {2},
url = {http://www.amazon.ca/exec/obidos/redirect?tag=citeulike09-20\&path=ASIN/0122271645}
}
@article{Buerckstuemmer2006efficient,
author = {Tilmann Bürckstümmer and Keiryn L Bennett and Adrijana Preradovic
and Gregor Schütze and Oliver Hantschel and Giulio Superti-Furga
and Angela Bauch},
title = {An efficient tandem affinity purification procedure for interaction
proteomics in mammalian cells.},
journal = {Nat Methods},
year = {2006},
volume = {3},
pages = {1013--1019},
number = {12},
month = {Dec},
abstract = {Tandem affinity purification (TAP) is a generic two-step affinity
purification protocol that enables the isolation of protein complexes
under close-to-physiological conditions for subsequent analysis by
mass spectrometry. Although TAP was instrumental in elucidating the
yeast cellular machinery, in mammalian cells the method suffers from
a low overall yield. We designed several dual-affinity tags optimized
for use in mammalian cells and compared the efficiency of each tag
to the conventional TAP tag. A tag based on protein G and the streptavidin-binding
peptide (GS-TAP) resulted in a tenfold increase in protein-complex
yield and improved the specificity of the procedure. This allows
purification of protein complexes that were hitherto not amenable
to TAP and use of less starting material, leading to higher success
rates and enabling systematic interaction proteomics projects. Using
the well-characterized Ku70-Ku80 protein complex as an example, we
identified both core elements as well as new candidate effectors.},
doi = {10.1038/nmeth968},
pdf = {../local/Buerckstuemmer2006efficient.pdf},
file = {Buerckstuemmer2006efficient.pdf:Buerckstuemmer2006efficient.pdf:PDF},
institution = {Research Center for Molecular Medicine (CeMM), Lazarettgasse 19/3,
1090 Vienna, Austria.},
owner = {phupe},
pii = {nmeth968},
pmid = {17060908},
timestamp = {2010.09.01},
url = {http://dx.doi.org/10.1038/nmeth968}
}
@article{Cabusora2005Differential,
author = {Cabusora, L. and Sutton, E. and Fulmer, A. and Forst, C. V.},
title = {Differential network expression during drug and stress response.},
journal = {Bioinformatics},
year = {2005},
volume = {21},
pages = {2898--2905},
number = {12},
month = {Jun},
abstract = {The application of microarray chip technology has led to an explosion
of data concerning the expression levels of the genes in an organism
under a plethora of conditions. One of the major challenges of systems
biology today is to devise generally applicable methods of interpreting
this data in a way that will shed light on the complex relationships
between multiple genes and their products. The importance of such
information is clear, not only as an aid to areas of research like
drug design, but also as a contribution to our understanding of the
mechanisms behind an organism's ability to react to its environment.We
detail one computational approach for using gene expression data
to identify response networks in an organism. The method is based
on the construction of biological networks given different sets of
interaction information and the reduction of the said networks to
important response sub-networks via the integration of the gene expression
data. As an application, the expression data of known stress responders
and DNA repair genes in Mycobacterium tuberculosis is used to construct
a generic stress response sub-network. This is compared to similar
networks constructed from data obtained from subjecting M.tuberculosis
to various drugs; we are thus able to distinguish between generic
stress response and specific drug response. We anticipate that this
approach will be able to accelerate target identification and drug
development for tuberculosis in the future.chris@lanl.govSupplementary
Figures 1 through 6 on drug response networks and differential network
analyses on cerulenin, chlorpromazine, ethionamide, ofloxacin, thiolactomycin
and triclosan. Supplementary Tables 1 to 3 on predicted protein interactions.
http://www.santafe.edu/~chris/DifferentialNW.},
doi = {10.1093/bioinformatics/bti440},
pdf = {../local/Cabusora2005Differential.pdf},
file = {Cabusora2005Differential.pdf:Cabusora2005Differential.pdf:PDF},
institution = {Los Alamos National Laboratory, PO Box 1663, Mailstop M888, Los Alamos,
NM 87545, USA.},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {bti440},
pmid = {15840709},
timestamp = {2011.10.08},
url = {http://dx.doi.org/10.1093/bioinformatics/bti440}
}
@article{Caelli2004eigenspace,
author = {Caelli, T. and Kosinov, S.},
title = {An eigenspace projection clustering method for inexact graph matching},
journal = {IEEE Trans. Pattern Anal. Mach. Intell.},
year = {2004},
volume = {26},
pages = {515--519},
number = {4},
month = {April },
doi = {10.1109/TPAMI.2004.1265866},
owner = {jp},
timestamp = {2008.10.05},
url = {http://dx.doi.org/10.1109/TPAMI.2004.1265866}
}
@article{Cai2004Enzyme,
author = {Cai, C.Z. and Han, L.Y. and Ji, Z.L. and Chen, Y.Z.},
title = {Enzyme family classification by support vector machines.},
journal = {Proteins},
year = {2004},
volume = {55},
pages = {66-76},
number = {1},
abstract = {One approach for facilitating protein function prediction is to classify
proteins into functional families. {R}ecent studies on the classification
of {G}-protein coupled receptors and other proteins suggest that
a statistical learning method, {S}upport vector machines ({SVM}),
may be potentially useful for protein classification into functional
families. {I}n this work, {SVM} is applied and tested on the classification
of enzymes into functional families defined by the {E}nzyme {N}omenclature
{C}ommittee of {IUBMB}. {SVM} classification system for each family
is trained from representative enzymes of that family and seed proteins
of {P}fam curated protein families. {T}he classification accuracy
for enzymes from 46 families and for non-enzymes is in the range
of 50.0% to 95.7% and 79.0% to 100% respectively. {T}he corresponding
{M}atthews correlation coefficient is in the range of 54.1% to 96.1%.
{M}oreover, 80.3% of the 8,291 correctly classified enzymes are uniquely
classified into a specific enzyme family by using a scoring function,
indicating that {SVM} may have certain level of unique prediction
capability. {T}esting results also suggest that {SVM} in some cases
is capable of classification of distantly related enzymes and homologous
enzymes of different functions. {E}ffort is being made to use a more
comprehensive set of enzymes as training sets and to incorporate
multi-class {SVM} classification systems to further enhance the unique
prediction accuracy. {O}ur results suggest the potential of {SVM}
for enzyme family classification and for facilitating protein function
prediction. {O}ur software is accessible at http://jing.cz3.nus.edu.sg/cgi-bin/svmprot.cgi.},
doi = {10.1002/prot.20045},
pdf = {../local/Cai2004Enzyme.pdf},
file = {Cai2004Enzyme.pdf:local/Cai2004Enzyme.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://dx.doi.org/10.1002/prot.20045}
}
@article{Cai2003Protein,
author = {Cai, C.Z. and Wang, W.L. and Sun, L.Z. and Chen, Y.Z.},
title = {Protein function classification via support vector machine approach.},
journal = {Math. {B}iosci.},
year = {2003},
volume = {185},
pages = {111-122},
number = {2},
abstract = {Support vector machine ({SVM}) is introduced as a method for the classification
of proteins into functionally distinguished classes. {S}tudies are
conducted on a number of protein classes including {RNA}-binding
proteins; protein homodimers, proteins responsible for drug absorption,
proteins involved in drug distribution and excretion, and drug metabolizing
enzymes. {T}esting accuracy for the classification of these protein
classes is found to be in the range of 84-96%. {T}his suggests the
usefulness of {SVM} in the classification of protein functional classes
and its potential application in protein function prediction.},
doi = {10.1016/S0025-5564(03)00096-8},
pdf = {../local/Cai2003Protein.pdf},
file = {Cai2003Protein.pdf:local/Cai2003Protein.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert}
}
@article{Cai2003SVM-Prot,
author = {C. Z. Cai and L. Y. Han and Z. L. Ji and X. Chen and Y. Z. Chen},
title = {S{VM}-{P}rot: {W}eb-based support vector machine software for functional
classification of a protein from its primary sequence.},
journal = {Nucleic {A}cids {R}es},
year = {2003},
volume = {31},
pages = {3692-7},
number = {13},
month = {Jul},
abstract = {Prediction of protein function is of significance in studying biological
processes. {O}ne approach for function prediction is to classify
a protein into functional family. {S}upport vector machine ({SVM})
is a useful method for such classification, which may involve proteins
with diverse sequence distribution. {W}e have developed a web-based
software, {SVMP}rot, for {SVM} classification of a protein into functional
family from its primary sequence. {SVMP}rot classification system
is trained from representative proteins of a number of functional
families and seed proteins of {P}fam curated protein families. {I}t
currently covers 54 functional families and additional families will
be added in the near future. {T}he computed accuracy for protein
family classification is found to be in the range of 69.1-99.6\%.
{SVMP}rot shows a certain degree of capability for the classification
of distantly related proteins and homologous proteins of different
function and thus may be used as a protein function prediction tool
that complements sequence alignment methods. {SVMP}rot can be accessed
at http://jing.cz3.nus.edu.sg/cgi-bin/svmprot.cgi.},
pdf = {../local/Cai2003SVM-Prot.pdf},
file = {Cai2003SVM-Prot.pdf:local/Cai2003SVM-Prot.pdf:PDF},
keywords = {biosvm},
url = {http://nar.oxfordjournals.org/cgi/content/abstract/31/13/3692}
}
@article{Cai2003Supportc,
author = {Cai, Y.D. and Feng, K.Y. and Li, Y.X. and Chou, K.C.},
title = {Support vector machine for predicting alpha-turn types.},
journal = {Peptides},
year = {2003},
volume = {24},
pages = {629-630},
number = {4},
abstract = {Tight turns play an important role in globular proteins from both
the structural and functional points of view. {O}f tight turns, beta-turns
and gamma-turns have been extensively studied, but alpha-turns were
little investigated. {R}ecently, a systematic search for alpha-turns
classified alpha-turns into nine different types according to their
backbone trajectory features. {I}n this paper, {S}upport {V}ector
{M}achines ({SVM}s), a new machine learning method, is proposed for
predicting the alpha-turn types in proteins. {T}he high rates of
correct prediction imply that that the formation of different alpha-turn
types is evidently correlated with the sequence of a pentapeptide,
and hence can be approximately predicted based on the sequence information
of the pentapeptide alone, although the incorporation of its interaction
with the other part of a protein, the so-called "long distance interaction",
will further improve the prediction quality.},
doi = {10.1016/S0196-9781(03)00100-1},
pdf = {../local/Cai2003Supportc.pdf},
file = {Cai2003Supportc.pdf:local/Cai2003Supportc.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://dx.doi.org/10.1016/S0196-9781(03)00100-1}
}
@article{Cai2003Supportd,
author = {Cai, Y.D. and Lin, S.L.},
title = {Support vector machines for predicting r{RNA}-, {RNA}-, and {DNA}-binding
proteins from amino acid sequence.},
journal = {Biochim. {B}iophys. {A}cta},
year = {2003},
volume = {1648},
pages = {127-133},
number = {1-2},
abstract = {Classification of gene function remains one of the most important
and demanding tasks in the post-genome era. {M}ost of the current
predictive computer methods rely on comparing features that are essentially
linear to the protein sequence. {H}owever, features of a protein
nonlinear to the sequence may also be predictive to its function.
{M}achine learning methods, for instance the {S}upport {V}ector {M}achines
({SVM}s), are particularly suitable for exploiting such features.
{I}n this work we introduce {SVM} and the pseudo-amino acid composition,
a collection of nonlinear features extractable from protein sequence,
to the field of protein function prediction. {W}e have developed
prototype {SVM}s for binary classification of r{RNA}-, {RNA}-, and
{DNA}-binding proteins. {U}sing a protein's amino acid composition
and limited range correlation of hydrophobicity and solvent accessible
surface area as input, each of the {SVM}s predicts whether the protein
belongs to one of the three classes. {I}n self-consistency and cross-validation
tests, which measures the success of learning and prediction, respectively,
the r{RNA}-binding {SVM} has consistently achieved >95% accuracy.
{T}he {RNA}- and {DNA}-binding {SVM}s demonstrate more diverse accuracy,
ranging from approximately 76% to approximately 97%. {A}nalysis of
the test results suggests the directions of improving the {SVM}s.},
doi = {10.1016/S1570-9639(03)00112-2},
pdf = {../local/Cai2003Supportd.pdf},
file = {Cai2003Supportd.pdf:local/Cai2003Supportd.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://dx.doi.org/10.1016/S1570-9639(03)00112-2}
}
@article{Cai2003Supporta,
author = {Cai, Y.D. and Lin, S.L. and Chou, K.C.},
title = {Support vector machines for prediction of protein signal sequences
and their cleavage sites},
journal = {Peptides},
year = {2003},
volume = {24},
pages = {159-161},
number = {1},
abstract = {Given a nascent protein sequence, how can one predict its signal peptide
or "{Z}ipcode" sequence? {T}his is an important problem for scientists
to use signal peptides as a vehicle to find new drugs or to reprogram
cells for gene therapy (see, e.g. [7] {K}.{C}. {C}hou, {C}urrent
{P}rotein and {P}eptide {S}cience 2002;3:615?22). {I}n this paper,
support vector machines ({SVM}s), a new machine learning method,
is applied to approach this problem. {T}he overall rate of correct
prediction for 1939 secretary proteins and 1440 nonsecretary proteins
was over 91%. {I}t has not escaped our attention that the new method
may also serve as a useful tool for further investigating many unclear
details regarding the molecular mechanism of the {ZIP} code protein-sorting
system in cells.},
doi = {10.1016/S0196-9781(02)00289-9},
pdf = {../local/Cai2003Supporta.pdf},
file = {Cai2003Supporta.pdf:local/Cai2003Supporta.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert}
}
@article{Cai2003Prediction,
author = {Cai, Y.D. and Liu, X.J. and Li, Y.X. and Xu, X.B. and Chou, K.C.},
title = {Prediction of beta-turns with learning machines.},
journal = {Peptides},
year = {2003},
volume = {24},
pages = {665-669},
number = {5},
abstract = {The support vector machine approach was introduced to predict the
beta-turns in proteins. {T}he overall self-consistency rate by the
re-substitution test for the training or learning dataset reached
100%. {B}oth the training dataset and independent testing dataset
were taken from {C}hou [{J}. {P}ept. {R}es. 49 (1997) 120]. {T}he
success prediction rates by the jackknife test for the beta-turn
subset of 455 tetrapeptides and non-beta-turn subset of 3807 tetrapeptides
in the training dataset were 58.1 and 98.4%, respectively. {T}he
success rates with the independent dataset test for the beta-turn
subset of 110 tetrapeptides and non-beta-turn subset of 30,231 tetrapeptides
were 69.1 and 97.3%, respectively. {T}he results obtained from this
study support the conclusion that the residue-coupled effect along
a tetrapeptide is important for the formation of a beta-turn.},
doi = {10.1016/S0196-9781(03)00133-5},
pdf = {../local/Cai2003Prediction.pdf},
file = {Cai2003Prediction.pdf:local/Cai2003Prediction.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://dx.doi.org/10.1016/S0196-9781(03)00133-5}
}
@article{Cai2003Supportb,
author = {Cai, Y.D. and Liu, X.J. and Xu, X.B. and Chou, K.C.},
title = {Support vector machines for prediction of protein domain structural
class.},
journal = {J. {T}heor. {B}iol.},
year = {2003},
volume = {221},
pages = {115-120},
number = {1},
abstract = {The support vector machines ({SVM}s) method was introduced for predicting
the structural class of protein domains. {T}he results obtained through
the self-consistency test, jack-knife test, and independent dataset
test have indicated that the current method and the elegant component-coupled
algorithm developed by {C}hou and co-workers, if effectively complemented
with each other, may become a powerful tool for predicting the structural
class of protein domains.},
doi = {10.1006/jtbi.2003.3179},
pdf = {../local/Cai2003Supportb.pdf},
file = {Cai2003Supportb.pdf:local/Cai2003Supportb.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://dx.doi.org/10.1006/jtbi.2003.3179}
}
@article{Cai2002Supporta,
author = {Cai, Y.D. and Liu, X.J. and Xu, X.B. and Chou, K.C.},
title = {Support {V}ector {M}achines for predicting {HIV} protease cleavage
sites in protein.},
journal = {J. {C}omput. {C}hem.},
year = {2002},
volume = {23},
pages = {267-274},
number = {2},
abstract = {Knowledge of the polyprotein cleavage sites by {HIV} protease will
refine our understanding of its specificity, and the information
thus acquired is useful for designing specific and efficient {HIV}
protease inhibitors. {T}he pace in searching for the proper inhibitors
of {HIV} protease will be greatly expedited if one can find an accurate,
robust, and rapid method for predicting the cleavage sites in proteins
by {HIV} protease. {I}n this article, a {S}upport {V}ector {M}achine
is applied to predict the cleavability of oligopeptides by proteases
with multiple and extended specificity subsites. {W}e selected {HIV}-1
protease as the subject of the study. {T}wo hundred ninety-nine oligopeptides
were chosen for the training set, while the other 63 oligopeptides
were taken as a test set. {B}ecause of its high rate of self-consistency
(299/299 = 100%), a good result in the jackknife test (286/299 95%)
and correct prediction rate (55/63 = 87%), it is expected that the
{S}upport {V}ector {M}achine method can be referred to as a useful
assistant technique for finding effective inhibitors of {HIV} protease,
which is one of the targets in designing potential drugs against
{AIDS}. {T}he principle of the {S}upport {V}ector {M}achine method
can also be applied to analyzing the specificity of other multisubsite
enzymes.},
doi = {10.1002/jcc.10017},
pdf = {../local/local},
file = {local:local/:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://dx.doi.org/10.1002/jcc.10017}
}
@article{Cai2002Supportb,
author = {Cai, Y.D. and Liu, X.J. and Xu, X.B. and Chou, K.C.},
title = {Support vector machines for predicting the specificity of {{G}al{NA}c}-transferase},
journal = {Peptides},
year = {2002},
volume = {23},
pages = {205-208},
abstract = {Support {V}ector {M}achines ({SVM}s) which is one kind of learning
machines, was applied to predict the specificity of {G}al{NA}c-transferase.
{T}he examination for the self-consistency and the jackknife test
of the {SVM}s method were tested for the training dataset (305 oligopeptides),
the correct rate of self-consistency and jackknife test reaches 100%
and 84.9%, respectively. {F}urthermore, the prediction of the independent
testing dataset (30 oligopeptides) was tested, the rate reaches 76.67%.},
doi = {10.1016/S0196-9781(01)00597-6},
pdf = {../local/Cai2002Supportb.pdf},
file = {Cai2002Supportb.pdf:local/Cai2002Supportb.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://dx.doi.org/10.1016/S0196-9781(01)00597-6}
}
@article{Cai2002Supportc,
author = {Cai, Y.D. and Liu, X.J. and Xu, X.B. and Chou, K.C.},
title = {Support vector machines for the classification and prediction of
beta-turn types},
journal = {J. {P}ept. {S}ci.},
year = {2002},
volume = {8},
pages = {297-301},
number = {7},
abstract = {The support vector machines ({SVM}s) method is proposed because it
can reflect the sequence-coupling effect for a tetrapeptide in not
only a beta-turn or non-beta-turn, but also in different types of
beta-turn. {T}he results of the model for 6022 tetrapeptides indicate
that the rates of self-consistency for beta-turn types {I}, {I}',
{II}, {II}', {VI} and {VIII} and non-beta-turns are 99.92%, 96.8%,
98.02%, 97.75%, 100%, 97.19% and 100%, respectively. {U}sing these
training data, the rate of correct prediction by the {SVM}s for a
given protein: rubredoxin (54 residues. 51 tetrapeptides) which includes
12 beta-turn type {I} tetrapeptides, 1 beta-turn type {II} tetrapeptide
and 38 non-beta-turns reached 82.4%. {T}he high quality of prediction
of the {SVM}s implies that the formation of different beta-turn types
or non-beta-turns is considerably correlated with the sequence of
a tetrapeptide. {T}he {SVM}s can save {CPU} time and avoid the overfitting
problem compared with the neural network method.},
doi = {10.1002/psc.401},
pdf = {../local/local},
file = {local:local/:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://dx.doi.org/10.1002/psc.401}
}
@article{Cai2000Support,
author = {Cai, Y.D. and Liu, X.J. and Xu, X.B. and Chou, K.C.},
title = {Support vector machines for prediction of protein subcellular location},
journal = {Mol. {C}ell {B}iol. {R}es. {C}ommun.},
year = {2000},
volume = {4},
pages = {230-234},
number = {4},
abstract = {Support {V}ector {M}achine ({SVM}), which is one kind of learning
machines, was applied to predict the subcellular location of proteins
from their amino acid composition. {I}n this research, the proteins
are classified into the following 12 groups: (1) chloroplast, (2)
cytoplasm, (3) cytoskeleton, (4) endoplasmic reticulum, (5) extracall,
(6) {G}olgi apparatus, (7) lysosome, (8) mitochondria, (9) nucleus,
(10) peroxisome, (11) plasma membrane, and (12) vacuole, which have
covered almost all the organelles and subcellular compartments in
an animal or plant cell. {T}he examination for the self-consistency
and the jackknife test of the {SVM}s method was tested for the three
sets: 2022 proteins, 2161 proteins, and 2319 proteins. {A}s a result,
the correct rate of self-consistency and jackknife test reaches 91
and 82% for 2022 proteins, 89 and 75% for 2161 proteins, and 85 and
73% for 2319 proteins, respectively. {F}urthermore, the predicting
rate was tested by the three independent testing datasets containing
2240 proteins, 2513 proteins, and 2591 proteins. {T}he correct prediction
rates reach 82, 75, and 73% for 2240 proteins, 2513 proteins, and
2591 proteins, respectively.},
doi = {10.1006/mcbr.2001.0285},
pdf = {../local/local},
file = {local:local/:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://dx.doi.org/10.1006/mcbr.2001.0285}
}
@article{Cai2004Application,
author = {Cai, Y.D. and Ricardo, P.W. and Jen, C.H. and Chou, K.C.},
title = {Application of {SVM} to predict membrane protein types.},
journal = {J. {T}heor. {B}iol.},
year = {2004},
volume = {226},
pages = {373-376},
number = {4},
abstract = {As a continuous effort to develop automated methods for predicting
membrane protein types that was initiated by {C}hou and {E}lrod ({PROTEINS}:
{S}tructure, {F}unction, and {G}enetics, 1999, 34, 137-153), the
support vector machine ({SVM}) is introduced. {R}esults obtained
through re-substitution, jackknife, and independent data set tests,
respectively, have indicated that the {SVM} approach is quite a promising
one, suggesting that the covariant discriminant algorithm ({C}hou
and {E}lrod, {P}rotein {E}ng. 12 (1999) 107) and {SVM}, if effectively
complemented with each other, will become a powerful tool for predicting
membrane protein types and the other protein attributes as well.},
doi = {10.1016/j.jtbi.2003.08.015},
pdf = {../local/Cai2004Application.pdf},
file = {Cai2004Application.pdf:local/Cai2004Application.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://dx.doi.org/10.1016/j.jtbi.2003.08.015}
}
@article{Cai2004Identify,
author = {Cai, Y.D. and Zhou, G.P. and Jen, C.H. and Lin, S.L. and Chou, K.C.},
title = {Identify catalytic triads of serine hydrolases by support vector
machines.},
journal = {J. {T}heor. {B}iol.},
year = {2004},
volume = {228},
pages = {551-557},
number = {4},
abstract = {The core of an enzyme molecule is its active site from the viewpoints
of both academic research and industrial application. {T}o reveal
the structural and functional mechanism of an enzyme, one needs to
know its active site; to conduct structure-based drug design by regulating
the function of an enzyme, one needs to know the active site and
its microenvironment as well. {G}iven the atomic coordinates of an
enzyme molecule, how can we predict its active site? {T}o tackle
such a problem, a distance group approach was proposed and the support
vector machine algorithm applied to predict the catalytic triad of
serine hydrolase family. {T}he success rate by jackknife test for
the 139 serine hydrolases was 85%, implying that the method is quite
promising and may become a useful tool in structural bioinformatics.},
doi = {10.1016/j.jtbi.2004.02.019},
pdf = {../local/Cai2004Identify.pdf},
file = {Cai2004Identify.pdf:local/Cai2004Identify.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://dx.doi.org/10.1016/j.jtbi.2004.02.019}
}
@article{Cai2004Prediction,
author = {Yu-Dong Cai and Andrew J Doig},
title = {Prediction of {S}accharomyces cerevisiae protein functional class
from functional domain composition.},
journal = {Bioinformatics},
year = {2004},
volume = {20},
pages = {1292-300},
number = {8},
month = {May},
abstract = {M{OTIVATION}: {A} key goal of genomics is to assign function to genes,
especially for orphan sequences. {RESULTS}: {W}e compared the clustered
functional domains in the {SBASE} database to each protein sequence
using {BLASTP}. {T}his representation for a protein is a vector,
where each of the non-zero entries in the vector indicates a significant
match between the sequence of interest and the {SBASE} domain. {T}he
machine learning methods nearest neighbour algorithm ({NNA}) and
support vector machines are used for predicting protein functional
classes from this information. {W}e find that the best results are
found using the {SBASE}-{A} database and the {NNA}, namely 72\% accuracy
for 79\% coverage. {W}e tested an assigning function based on searching
for {I}nter{P}ro sequence motifs and by taking the most significant
{BLAST} match within the dataset. {W}e applied the functional domain
composition method to predict the functional class of 2018 currently
unclassified yeast open reading frames. {AVAILABILITY}: {A} program
for the prediction method, that uses {NNA} called {F}unctional {C}lass
{P}rediction based on {F}unctional {D}omains ({FCPFD}) is available
and can be obtained by contacting {Y}.{D}.{C}ai at y.cai@umist.ac.uk},
doi = {10.1093/bioinformatics/bth085},
pdf = {../local/Cai2004Prediction.pdf},
file = {Cai2004Prediction.pdf:local/Cai2004Prediction.pdf:PDF},
keywords = {biosvm},
pii = {bth085},
url = {http://dx.doi.org/10.1093/bioinformatics/bth085}
}
@article{Cai2005Using,
author = {Yu-Dong Cai and Kai-Yan Feng and Wen-Cong Lu and Kuo-Chen Chou},
title = {Using {L}ogit{B}oost classifier to predict protein structural classes.},
journal = {J {T}heor {B}iol},
year = {2005},
month = {Jul},
abstract = {Prediction of protein classification is an important topic in molecular
biology. {T}his is because it is able to not only provide useful
information from the viewpoint of structure itself, but also greatly
stimulate the characterization of many other features of proteins
that may be closely correlated with their biological functions. {I}n
this paper, the {L}ogit{B}oost, one of the boosting algorithms developed
recently, is introduced for predicting protein structural classes.
{I}t performs classification using a regression scheme as the base
learner, which can handle multi-class problems and is particularly
superior in coping with noisy data. {I}t was demonstrated that the
{L}ogit{B}oost outperformed the support vector machines in predicting
the structural classes for a given dataset, indicating that the new
classifier is very promising. {I}t is anticipated that the power
in predicting protein structural classes as well as many other bio-macromolecular
attributes will be further strengthened if the {L}ogit{B}oost and
some other existing algorithms can be effectively complemented with
each other.},
doi = {10.1016/j.jtbi.2005.05.034},
pdf = {../local/Cai2005Using.pdf},
file = {Cai2005Using.pdf:local/Cai2005Using.pdf:PDF},
pii = {S0022-5193(05)00252-3},
url = {http://dx.doi.org/10.1016/j.jtbi.2005.05.034}
}
@article{Cai2002Support,
author = {Cai, Y.-D. and Liu, X.-J. and Xu, X.-B. and Chou, K.-C.},
title = {Support vector machines for prediction of protein subcellular location
by incorporating quasi-sequence-order effect},
journal = {J. {C}ell. {B}iochem.},
year = {2002},
volume = {84},
pages = {343-348},
number = {2},
abstract = {Support {V}ector {M}achine ({SVM}), which is one class of learning
machines, was applied to predict the subcellular location of proteins
by incorporating the quasi-sequence-order effect ({C}hou [2000] {B}iochem.
{B}iophys. {R}es. {C}ommun. 278:477-483). {I}n this study, the proteins
are classified into the following 12 groups: (1) chloroplast, (2)
cytoplasm, (3) cytoskeleton, (4) endoplasmic reticulum, (5) extracellular,
(6) {G}olgi apparatus, (7) lysosome, (8) mitochondria, (9) nucleus,
(10) peroxisome, (11) plasma membrane, and (12) vacuole, which account
for most organelles and subcellular compartments in an animal or
plant cell. {E}xaminations for self-consistency and jackknife testing
of the {SVM}s method were conducted for three sets consisting of
1,911, 2,044, and 2,191 proteins. {T}he correct rates for self-consistency
and the jackknife test values achieved with these protein sets were
94 and 83% for 1,911 proteins, 92 and 78% for 2,044 proteins, and
89 and 75% for 2,191 proteins, respectively. {F}urthermore, tests
for correct prediction rates were undertaken with three independent
testing datasets containing 2,148 proteins, 2,417 proteins, and 2,494
proteins producing values of 84, 77, and 74%, respectively.},
doi = {10.1002/jcb.10030},
pdf = {../local/Cai2002Support.pdf},
file = {Cai2002Support.pdf:local/Cai2002Support.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://dx.doi.org/10.1002/jcb.10030}
}
@article{Cai2002Prediction,
author = {Cai, Y.-D. and Liu, X.-J. and Xu, X.-B. and Zhou, G.-P.},
title = {Prediction of protein structural classes by support vector machines.},
journal = {Comput. {C}hem.},
year = {2002},
volume = {26},
pages = {293-296},
number = {3},
abstract = {In this paper, we apply a new machine learning method which is called
support vector machine to approach the prediction of protein structural
class. {T}he support vector machine method is performed based on
the database derived from {SCOP} which is based upon domains of known
structure and the evolutionary relationships and the principles that
govern their 3{D} structure. {A}s a result, high rates of both self-consistency
and jackknife test are obtained. {T}his indicates that the structural
class of a protein inconsiderably correlated with its amino and composition,
and the support vector machine can be referred as a powerful computational
tool for predicting the structural classes of proteins.},
doi = {10.1016/S0097-8485(01)00113-9},
pdf = {../local/Cai2002Prediction.pdf},
file = {Cai2002Prediction.pdf:local/Cai2002Prediction.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://dx.doi.org/10.1016/S0097-8485(01)00113-9}
}
@article{Cai2001Support,
author = {Cai, Y.-D. and Liu, X.-J. and Xu, X.-B. and Zhou, G.-P.},
title = {Support {V}ector {M}achines for predicting protein structural class},
journal = {B{MC} {B}ioinformatics},
year = {2001},
volume = {2},
pages = {3},
number = {3},
abstract = {Background {W}e apply a new machine learning method, the so-called
{S}upport {V}ector {M}achine method, to predict the protein structural
class. {S}upport {V}ector {M}achine method is performed based on
the database derived from {SCOP}, in which protein domains are classified
based on known structures and the evolutionary relationships and
the principles that govern their 3-{D} structure. {R}esults {H}igh
rates of both self-consistency and jackknife tests are obtained.
{T}he good results indicate that the structural class of a protein
is considerably correlated with its amino acid composition. {C}onclusions
{I}t is expected that the {S}upport {V}ector {M}achine method and
the elegant component-coupled method, also named as the covariant
discrimination algorithm, if complemented with each other, can provide
a powerful computational tool for predicting the structural classes
of proteins.},
doi = {10.1186/1471-2105-2-3},
pdf = {../local/Cai2001Support.pdf},
file = {Cai2001Support.pdf:local/Cai2001Support.pdf:PDF},
keywords = {biosvm},
owner = {vert},
url = {http://www.biomedcentral.com/1471-2105/2/3/abstract}
}
@article{Cai2003Support,
author = {Cai, Y.-D. and Zhou, G.-P. and Chou, K.-C.},
title = {Support {V}ector {M}achines for {P}redicting {M}embrane {P}rotein
{T}ypes by {U}sing {F}unctional {D}omain {C}omposition},
journal = {Biophys. {J}.},
year = {2003},
volume = {84},
pages = {3257-3263},
number = {5},
abstract = {Membrane proteins are generally classified into the following five
types: 1), type {I} membrane protein; 2), type {II} membrane protein;
3), multipass transmembrane proteins; 4), lipid chain-anchored membrane
proteins; and 5), {GPI}-anchored membrane proteins. {I}n this article,
based on the concept of using the functional domain composition to
define a protein, the {S}upport {V}ector {M}achine algorithm is developed
for predicting the membrane protein type. {H}igh success rates are
obtained by both the self-consistency and jackknife tests. {T}he
current approach, complemented with the powerful covariant discriminant
algorithm based on the pseudo-amino acid composition that has incorporated
quasi-sequence-order effect as recently proposed by {K}. {C}. {C}hou
(2001), may become a very useful high-throughput tool in the area
of bioinformatics and proteomics.},
pdf = {../local/Cai2003Support.pdf},
file = {Cai2003Support.pdf:local/Cai2003Support.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://www.biophysj.org/cgi/content/abstract/84/5/3257}
}
@article{Caie2010High,
author = {Peter D Caie and Rebecca E Walls and Alexandra Ingleston-Orme and
Sandeep Daya and Tom Houslay and Rob Eagle and Mark E Roberts and
Neil O Carragher},
title = {High-content phenotypic profiling of drug response signatures across
distinct cancer cells.},
journal = {Mol Cancer Ther},
year = {2010},
volume = {9},
pages = {1913--1926},
number = {6},
month = {Jun},
abstract = {The application of high-content imaging in conjunction with multivariate
clustering techniques has recently shown value in the confirmation
of cellular activity and further characterization of drug mode of
action following pharmacologic perturbation. However, such practical
examples of phenotypic profiling of drug response published to date
have largely been restricted to cell lines and phenotypic response
markers that are amenable to basic cellular imaging. As such, these
approaches preclude the analysis of both complex heterogeneous phenotypic
responses and subtle changes in cell morphology across physiologically
relevant cell panels. Here, we describe the application of a cell-based
assay and custom designed image analysis algorithms designed to monitor
morphologic phenotypic response in detail across distinct cancer
cell types. We further describe the integration of these methods
with automated data analysis workflows incorporating principal component
analysis, Kohonen neural networking, and kNN classification to enable
rapid and robust interrogation of such data sets. We show the utility
of these approaches by providing novel insight into pharmacologic
response across four cancer cell types, Ovcar3, MiaPaCa2, and MCF7
cells wild-type and mutant for p53. These methods have the potential
to drive the development of a new generation of novel therapeutic
classes encompassing pharmacologic compositions or polypharmacology
in appropriate disease context.},
doi = {10.1158/1535-7163.MCT-09-1148},
institution = {D Charnwood, Loughborough, United Kingdom.},
language = {eng},
medline-pst = {ppublish},
owner = {philippe},
pii = {1535-7163.MCT-09-1148},
pmid = {20530715},
timestamp = {2010.07.26},
url = {http://dx.doi.org/10.1158/1535-7163.MCT-09-1148}
}
@article{Caldwell1995introduction,
author = {Caldwell, J. and Gardner, I. and Swales, N.},
title = {An introduction to drug disposition: the basic principles of absorption,
distribution, metabolism, and excretion.},
journal = {Toxicol. Pathol.},
year = {1995},
volume = {23},
pages = {102--114},
number = {2},
abstract = {A knowledge of the fate of a drug, its disposition (absorption, distribution,
metabolism, and excretion, known by the acronym ADME) and pharmacokinetics
(the mathematical description of the rates of these processes and
of concentration-time relationships), plays a central role throughout
pharmaceutical research and development. These studies aid in the
discovery and selection of new chemical entities, support safety
assessment, and are critical in defining conditions for safe and
effective use in patients. ADME studies provide the only basis for
critical judgments from situations where the behavior of the drug
is understood to those where it is unknown: this is most important
in bridging from animal studies to the human situation. This presentation
is intended to provide an introductory overview of the life cycle
of a drug in the animal body and indicates the significance of such
information for a full understanding of mechanisms of action and
toxicity.},
keywords = {chemogenomics},
owner = {laurent},
pmid = {7569663},
timestamp = {2008.07.16}
}
@article{Calin2006MicroRNA,
author = {Calin, G. A. and Croce, C. M.},
title = {Micro{RNA} signatures in human cancers},
journal = {Nat. Rev. Cancer},
year = {2006},
volume = {6},
pages = {857--866},
number = {11},
month = {Nov},
abstract = {MicroRNA (miRNA) alterations are involved in the initiation and progression
of human cancer. The causes of the widespread differential expression
of miRNA genes in malignant compared with normal cells can be explained
by the location of these genes in cancer-associated genomic regions,
by epigenetic mechanisms and by alterations in the miRNA processing
machinery. MiRNA-expression profiling of human tumours has identified
signatures associated with diagnosis, staging, progression, prognosis
and response to treatment. In addition, profiling has been exploited
to identify miRNA genes that might represent downstream targets of
activated oncogenic pathways, or that target protein-coding genes
involved in cancer.},
doi = {10.1038/nrc1997},
pdf = {../local/Calin2006MicroRNA.pdf},
file = {Calin2006MicroRNA.pdf:Calin2006MicroRNA.pdf:PDF},
institution = {Department of Molecular Virology, Immunology and Medical Genetics
and Comprehensive Cancer Center, Ohio State University, Columbus,
Ohio 43210, USA.},
keywords = {csbcbook, csbcbook-ch3},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {nrc1997},
pmid = {17060945},
timestamp = {2009.10.17},
url = {http://dx.doi.org/10.1038/nrc1997}
}
@article{Calin2006MicroRNA-cancer,
author = {Calin, G.A. and Croce, C. M.},
title = {{MicroRNA}-cancer connection: the beginning of a new tale},
journal = {Cancer Res.},
year = {2006},
volume = {66},
pages = {7390--7394},
number = {15},
month = {Aug},
abstract = {Cancer initiation and progression can involve microRNAs (miRNA), which
are small noncoding RNAs that can regulate gene expression. Their
expression profiles can be used for the classification, diagnosis,
and prognosis of human malignancies. Loss or amplification of miRNA
genes has been reported in a variety of cancers, and altered patterns
of miRNA expression may affect cell cycle and survival programs.
Germ-line and somatic mutations in miRNAs or polymorphisms in the
mRNAs targeted by miRNAs may also contribute to cancer predisposition
and progression. We propose that alterations in miRNA genes play
a critical role in the pathophysiology of many, perhaps all, human
cancers.},
doi = {10.1158/0008-5472.CAN-06-0800},
pdf = {../local/Calin2006MicroRNA-cancer.pdf},
file = {Calin2006MicroRNA-cancer.pdf:Calin2006MicroRNA-cancer.pdf:PDF},
institution = {Department of Molecular Virology, Immunology, and Medical Genetics,
Ohio State University, 400 12th Avenue, Columbus, OH 43210, USA.},
keywords = {csbcbook},
owner = {jp},
pii = {66/15/7390},
pmid = {16885332},
timestamp = {2009.10.09},
url = {http://dx.doi.org/10.1158/0008-5472.CAN-06-0800}
}
@article{Calinski1974dendrite,
author = {Calinski, R. B. and Harabasz, J.},
title = {A dendrite method for cluster analysis},
journal = {Communs Statist.},
year = {1974},
volume = {3},
pages = {1--27},
owner = {jp},
timestamp = {2011.12.29}
}
@article{Calvo2007partially,
author = {Calvo, B. and L{\'o}pez-Bigas, N. and Furney, S.J. and Larra{\~n}aga,
P. and Lozano, J.A.},
title = {A partially supervised classification approach to dominant and recessive
human disease gene prediction.},
journal = {Comput. Methods Programs Biomed.},
year = {2007},
volume = {85},
pages = {229--237},
number = {3},
month = {Mar},
abstract = {The discovery of the genes involved in genetic diseases is a very
important step towards the understanding of the nature of these diseases.
In-lab identification is a difficult, time-consuming task, where
computational methods can be very useful. In silico identification
algorithms can be used as a guide in future studies. Previous works
in this topic have not taken into account that no reliable sets of
negative examples are available, as it is not possible to ensure
that a given gene is not related to any genetic disease. In this
paper, this feature of the nature of the problem is considered, and
identification is approached as a partially supervised classification
problem. In addition, we have performed a more specific method to
identify disease genes by classifying, for the first time, genes
causing dominant and recessive diseases independently. We base this
separation on previous results that show that these two types of
genes present differences in their sequence properties. In this paper,
we have applied a new model averaging algorithm to the identification
of human genes associated with both dominant and recessive Mendelian
diseases.},
doi = {10.1016/j.cmpb.2006.12.003},
institution = {Intelligent Systems Group, Department of Computer Science and Artificial
Intelligence, University of the Basque Country UPV-EHU, Paseo Manuel
de Lardizabal 1, E-20018 San Sebastián, Spain. borxa@si.ehu.es},
keywords = {Algorithms; Genes, Dominant; Genes, Recessive; Genetic Predisposition
to Disease; Humans; Sequence Analysis, DNA; Spain},
owner = {mordelet},
pii = {S0169-2607(06)00293-8},
pmid = {17258838},
timestamp = {2010.09.27},
url = {http://dx.doi.org/10.1016/j.cmpb.2006.12.003}
}
@article{Calzone2006BIOCHAM,
author = {Calzone, L. and Fages, F. and Soliman, S.},
title = {{BIOCHAM: an environment for modeling biological systems and formalizing
experimental knowledge}},
journal = {Bioinformatics},
year = {2006},
volume = {22},
pages = {1805-1807},
number = {14},
abstract = {Summary: BIOCHAM (the BIOCHemical Abstract Machine) is a software
environment for modeling biochemical systems. It is based on two
aspects: (1) the analysis and simulation of boolean, kinetic and
stochastic models and (2) the formalization of biological properties
in temporal logic. BIOCHAM provides tools and languages for describing
protein networks with a simple and straightforward syntax, and for
integrating biological properties into the model. It then becomes
possible to analyze, query, verify and maintain the model with respect
to those properties. For kinetic models, BIOCHAM can search for appropriate
parameter values in order to reproduce a specific behavior observed
in experiments and formalized in temporal logic. Coupled with other
methods such as bifurcation diagrams, this search assists the modeler/biologist
in the modeling process. Availability: BIOCHAM (v. 2.5) is a free
software available for download, with example models, at http://contraintes.inria.fr/BIOCHAM/
Contact: Sylvain.Soliman@inria.fr},
doi = {10.1093/bioinformatics/btl172},
eprint = {http://bioinformatics.oxfordjournals.org/cgi/reprint/22/14/1805.pdf},
pdf = {../local/Calzone2006BIOCHAM.pdf},
file = {Calzone2006BIOCHAM.pdf:Calzone2006BIOCHAM.pdf:PDF},
keywords = {csbcbook},
url = {http://bioinformatics.oxfordjournals.org/cgi/content/abstract/22/14/1805}
}
@article{Calzone2008comprehensive,
author = {Calzone, L. and Gelay, A. and Zinovyev, A. and Radvanyi, F. and Barillot,
E.},
title = {A comprehensive modular map of molecular interactions in {RB/E2F}
pathway.},
journal = {Mol. Syst. Biol.},
year = {2008},
volume = {4},
pages = {173},
abstract = {We present, here, a detailed and curated map of molecular interactions
taking place in the regulation of the cell cycle by the retinoblastoma
protein (RB/RB1). Deregulations and/or mutations in this pathway
are observed in most human cancers. The map was created using Systems
Biology Graphical Notation language with the help of CellDesigner
3.5 software and converted into BioPAX 2.0 pathway description format.
In the current state the map contains 78 proteins, 176 genes, 99
protein complexes, 208 distinct chemical species and 165 chemical
reactions. Overall, the map recapitulates biological facts from approximately
350 publications annotated in the diagram. The network contains more
details about RB/E2F interaction network than existing large-scale
pathway databases. Structural analysis of the interaction network
revealed a modular organization of the network, which was used to
elaborate a more summarized, higher-level representation of RB/E2F
network. The simplification of complex networks opens the road for
creating realistic computational models of this regulatory pathway.},
doi = {10.1038/msb.2008.7},
pdf = {../local/Calzone2008comprehensive.pdf},
file = {Calzone2008comprehensive.pdf:Calzone2008comprehensive.pdf:PDF},
institution = {Institut Curie, Service Blolnforrnatique, Paris, France.},
keywords = {csbcbook},
owner = {jp},
pii = {msb20087},
pmid = {18319725},
timestamp = {2009.10.09},
url = {http://dx.doi.org/10.1038/msb.2008.7}
}
@article{Camastra2005novel,
author = {Francesco Camastra and Alessandro Verri},
title = {A novel kernel method for clustering.},
journal = {IEEE Trans. Pattern Anal. Mach. Intell.},
year = {2005},
volume = {27},
pages = {801-5},
number = {5},
month = {May},
abstract = {Kernel {M}ethods are algorithms that, by replacing the inner product
with an appropriate positive definite function, implicitly perform
a nonlinear mapping of the input data into a high-dimensional feature
space. {I}n this paper, we present a kernel method for clustering
inspired by the classical {K}-{M}eans algorithm in which each cluster
is iteratively refined using a one-class {S}upport {V}ector {M}achine.
{O}ur method, which can be easily implemented, compares favorably
with respect to popular clustering algorithms, like {K}-{M}eans,
{N}eural {G}as, and {S}elf-{O}rganizing {M}aps, on a synthetic data
set and three {UCI} real data benchmarks ({IRIS} data, {W}isconsin
breast cancer database, {S}pam database).},
doi = {10.1109/TPAMI.2005.88},
pdf = {../local/Camastra2005novel.pdf},
file = {Camastra2005novel.pdf:local/Camastra2005novel.pdf:PDF},
url = {http://dx.doi.org/10.1109/TPAMI.2005.88}
}
@article{Campanini2004novel,
author = {Renato Campanini and Danilo Dongiovanni and Emiro Iampieri and Nico
Lanconelli and Matteo Masotti and Giuseppe Palermo and Alessandro
Riccardi and Matteo Roffilli},
title = {A novel featureless approach to mass detection in digital mammograms
based on support vector machines.},
journal = {Phys {M}ed {B}iol},
year = {2004},
volume = {49},
pages = {961-75},
number = {6},
month = {Mar},
abstract = {In this work, we present a novel approach to mass detection in digital
mammograms. {T}he great variability of the appearance of masses is
the main obstacle to building a mass detection method. {I}t is indeed
demanding to characterize all the varieties of masses with a reduced
set of features. {H}ence, in our approach we have chosen not to extract
any feature, for the detection of the region of interest; in contrast,
we exploit all the information available on the image. {A} multiresolution
overcomplete wavelet representation is performed, in order to codify
the image with redundancy of information. {T}he vectors of the very-large
space obtained are then provided to a first support vector machine
({SVM}) classifier. {T}he detection task is considered here as a
two-class pattern recognition problem: crops are classified as suspect
or not, by using this {SVM} classifier. {F}alse candidates are eliminated
with a second cascaded {SVM}. {T}o further reduce the number of false
positives, an ensemble of experts is applied: the final suspect regions
are achieved by using a voting strategy. {T}he sensitivity of the
presented system is nearly 80\% with a false-positive rate of 1.1
marks per image, estimated on images coming from the {USF} {DDSM}
database.},
doi = {doi:10.1088/0031-9155/49/6/007},
pdf = {../local/Campanini2004novel.pdf},
file = {Campanini2004novel.pdf:local/Campanini2004novel.pdf:PDF},
url = {http://dx.doi.org/doi:10.1088/0031-9155/49/6/007}
}
@article{Campbell2008Identification,
author = {Peter J Campbell and Philip J Stephens and Erin D Pleasance and Sarah
O'Meara and Heng Li and Thomas Santarius and Lucy A Stebbings and
Catherine Leroy and Sarah Edkins and Claire Hardy and Jon W Teague
and Andrew Menzies and Ian Goodhead and Daniel J Turner and Christopher
M Clee and Michael A Quail and Antony Cox and Clive Brown and Richard
Durbin and Matthew E Hurles and Paul A W Edwards and Graham R Bignell
and Michael R Stratton and P. Andrew Futreal},
title = {Identification of somatically acquired rearrangements in cancer using
genome-wide massively parallel paired-end sequencing.},
journal = {Nat. Genet.},
year = {2008},
volume = {40},
pages = {722--729},
number = {6},
month = {Jun},
abstract = {Human cancers often carry many somatically acquired genomic rearrangements,
some of which may be implicated in cancer development. However, conventional
strategies for characterizing rearrangements are laborious and low-throughput
and have low sensitivity or poor resolution. We used massively parallel
sequencing to generate sequence reads from both ends of short DNA
fragments derived from the genomes of two individuals with lung cancer.
By investigating read pairs that did not align correctly with respect
to each other on the reference human genome, we characterized 306
germline structural variants and 103 somatic rearrangements to the
base-pair level of resolution. The patterns of germline and somatic
rearrangement were markedly different. Many somatic rearrangements
were from amplicons, although rearrangements outside these regions,
notably including tandem duplications, were also observed. Some somatic
rearrangements led to abnormal transcripts, including two from internal
tandem duplications and two fusion transcripts created by interchromosomal
rearrangements. Germline variants were predominantly mediated by
retrotransposition, often involving AluY and LINE elements. The results
demonstrate the feasibility of systematic, genome-wide characterization
of rearrangements in complex human cancer genomes, raising the prospect
of a new harvest of genes associated with cancer using this strategy.},
doi = {10.1038/ng.128},
pdf = {../local/Campbell2008Identification.pdf},
file = {Campbell2008Identification.pdf:Campbell2008Identification.pdf:PDF},
institution = {Wellcome Trust Sanger Institute, Hinxton CB10 1SA, UK.},
keywords = {ngs},
owner = {jp},
pii = {ng.128},
pmid = {18438408},
timestamp = {2009.10.09},
url = {http://dx.doi.org/10.1038/ng.128}
}
@article{Camps-Valls2004Profiled,
author = {Camps-Valls, G. and Chalk, A.M. and Serrano-Lopez, A.J. and Martin-Guerrero,
J.D. and Sonnhammer, E.L.},
title = {Profiled support vector machines for antisense oligonucleotide efficacy
prediction.},
journal = {B{MC} {B}ioinformatics},
year = {2004},
volume = {5},
pages = {135},
number = {135},
abstract = {Background {T}his paper presents the use of {S}upport {V}ector {M}achines
({SVM}s) for prediction and analysis of antisense oligonucleotide
({AO}) efficacy. {T}he collected database comprises 315 {AO} molecules
including 68 features each, inducing a problem well-suited to {SVM}s.
{T}he task of feature selection is crucial given the presence of
noisy or redundant features, and the well-known problem of the curse
of dimensionality. {W}e propose a two-stage strategy to develop an
optimal model: (1) feature selection using correlation analysis,
mutual information, and {SVM}-based recursive feature elimination
({SVM}-{RFE}), and (2) {AO} prediction using standard and profiled
{SVM} formulations. {A} profiled {SVM} gives different weights to
different parts of the training data to focus the training on the
most important regions. {R}esults {I}n the first stage, the {SVM}-{RFE}
technique was most efficient and robust in the presence of low number
of samples and high input space dimension. {T}his method yielded
an optimal subset of 14 representative features, which were all related
to energy and sequence motifs. {T}he second stage evaluated the performance
of the predictors (overall correlation coefficient between observed
and predicted efficacy, r; mean error, {ME}; and root-mean-square-error,
{RMSE}) using 8-fold and minus-one-{RNA} cross-validation methods.
{T}he profiled {SVM} produced the best results (r = 0.44, {ME} =
0.022, and {RMSE}= 0.278) and predicted high (>75% inhibition of
gene expression) and low efficacy (<25%) {AO}s with a success rate
of 83.3% and 82.9%, respectively, which is better than by previous
approaches. {A} web server for {AO} prediction is available online
at http://aosvm.cgb.ki.se/. {C}onclusions {T}he {SVM} approach is
well suited to the {AO} prediction problem, and yields a prediction
accuracy superior to previous methods. {T}he profiled {SVM} was found
to perform better than the standard {SVM}, suggesting that it could
lead to improvements in other prediction problems as well.},
doi = {10.1186/1471-2105-5-135},
pdf = {../local/Camps-Valls2004Profiled.pdf},
file = {Camps-Valls2004Profiled.pdf:local/Camps-Valls2004Profiled.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://www.biomedcentral.com/1471-2105/5/135}
}
@article{Candes2008The,
author = {Cand{\`e}s, E.},
title = {The restricted isometry property},
journal = {Compte {R}endus de l'{A}cad\'emie des {S}ciences, {P}aris},
year = {2008},
volume = {1},
pages = {589--592},
number = {346}
}
@article{Candes2006Stable,
author = {Cand{\`e}s, E. and Romberk, J. K. and Tao, T.},
title = {Stable signal recovery from incomplete and inaccurate measurements},
journal = {Comm. {P}ure {A}ppl. {M}ath.},
year = {2006},
volume = {59},
pages = {1207--1223},
number = {8}
}
@article{Candes2007Dantzig,
author = {Cand{\`e}s, E. and Tao, T.},
title = {The {D}antzig selector: Statistical estimation when $p$ is much larger
than $n$},
journal = {Ann. Stat.},
year = {2007},
volume = {35},
pages = {2313--2351},
number = {6},
doi = {10.1214/009053606000001523},
pdf = {../local/Candes2007Dantzig.pdf},
file = {Candes2007Dantzig.pdf:Candes2007Dantzig.pdf:PDF},
keywords = {lasso},
owner = {jp},
timestamp = {2009.05.08},
url = {http://dx.doi.org/10.1214/009053606000001523}
}
@article{Candes2005Decoding,
author = {Cand{\`e}s, E. and Tao, T.},
title = {Decoding by linear programming},
journal = {{IEEE} {T}ransactions on {I}nformation {T}heory},
year = {2005},
volume = {51},
pages = {4203--4215},
number = {12}
}
@article{Candes2011Robust,
author = {Cand{\`e}s, E. J. and Li, X. and Ma, Y. and Wright, J.},
title = {Robust principal component analysis?},
journal = {J. ACM},
year = {2011},
volume = {58},
pages = {11:1--11:37},
number = {3},
month = {jun},
doi = {10.1145/1970392.1970395},
pdf = {../local/Candes2011Robust.pdf},
file = {Candes2011Robust.pdf:Candes2011Robust.pdf:PDF},
owner = {jp},
timestamp = {2013.02.19},
url = {http://doi.acm.org/10.1145/1970392.1970395}
}
@article{Candes2009Exact,
author = {Cand{\`e}s, E. J. and Recht, B.},
title = {Exact Matrix Completion via Convex Optimization},
journal = {Found. Comput. Math.},
year = {2009},
volume = {9},
pages = {717--772},
number = {6},
doi = {10.1007/s10208-009-9045-5},
pdf = {../local/Candes2009Exact.pdf},
file = {Candes2009Exact.pdf:Candes2009Exact.pdf:PDF},
owner = {jp},
timestamp = {2012.12.22},
url = {http://dx.doi.org/10.1007/s10208-009-9045-5}
}
@article{Caplen2001Specific,
author = {Caplen, N. J. and Parrish, S. and Imani, F. and Fire, A. and Morgan,
R. A.},
title = {{S}pecific inhibition of gene expression by small double-stranded
{RNA}s in invertebrate and vertebrate systems.},
journal = {Proc. Natl. Acad. Sci. USA},
year = {2001},
volume = {98},
pages = {9742--9747},
number = {17},
month = {Aug},
abstract = {Short interfering RNAs (siRNAs) are double-stranded RNAs of approximately
21-25 nucleotides that have been shown to function as key intermediaries
in triggering sequence-specific RNA degradation during posttranscriptional
gene silencing in plants and RNA interference in invertebrates. siRNAs
have a characteristic structure, with 5'-phosphate/3'-hydroxyl ends
and a 2-base 3' overhang on each strand of the duplex. In this study,
we present data that synthetic siRNAs can induce gene-specific inhibition
of expression in Caenorhabditis elegans and in cell lines from humans
and mice. In each case, the interference by siRNAs was superior to
the inhibition of gene expression mediated by single-stranded antisense
oligonucleotides. The siRNAs seem to avoid the well documented nonspecific
effects triggered by longer double-stranded RNAs in mammalian cells.
These observations may open a path toward the use of siRNAs as a
reverse genetic and therapeutic tool in mammalian cells.},
doi = {10.1073/pnas.171251798},
pdf = {../local/Caplen2001Specific.pdf},
file = {Caplen2001Specific.pdf:Caplen2001Specific.pdf:PDF},
keywords = {sirna},
owner = {vert},
pii = {171251798},
pmid = {11481446},
timestamp = {2006.03.28},
url = {http://dx.doi.org/10.1073/pnas.171251798}
}
@article{Capriotti2005I-Mutant,
author = {Capriotti, E. and Fariselli, P. and Casadio, R.},
title = {I-{M}utant2.0: predicting stability changes upon mutation from the
protein sequence or structure.},
journal = {Nucleic {A}cids {R}es.},
year = {2005},
volume = {33},
pages = {W306-10},
number = {Web Server issue},
month = {Jul},
abstract = {I-{M}utant2.0 is a support vector machine ({SVM})-based tool for the
automatic prediction of protein stability changes upon single point
mutations. {I}-{M}utant2.0 predictions are performed starting either
from the protein structure or, more importantly, from the protein
sequence. {T}his latter task, to the best of our knowledge, is exploited
for the first time. {T}he method was trained and tested on a data
set derived from {P}ro{T}herm, which is presently the most comprehensive
available database of thermodynamic experimental data of free energy
changes of protein stability upon mutation under different conditions.
{I}-{M}utant2.0 can be used both as a classifier for predicting the
sign of the protein stability change upon mutation and as a regression
estimator for predicting the related {D}elta{D}elta{G} values. {A}cting
as a classifier, {I}-{M}utant2.0 correctly predicts (with a cross-validation
procedure) 80\% or 77\% of the data set, depending on the usage of
structural or sequence information, respectively. {W}hen predicting
{D}elta{D}elta{G} values associated with mutations, the correlation
of predicted with expected/experimental values is 0.71 (with a standard
error of 1.30 kcal/mol) and 0.62 (with a standard error of 1.45 kcal/mol)
when structural or sequence information are respectively adopted.
{O}ur web interface allows the selection of a predictive mode that
depends on the availability of the protein structure and/or sequence.
{I}n this latter case, the web server requires only pasting of a
protein sequence in a raw format. {W}e therefore introduce {I}-{M}utant2.0
as a unique and valuable helper for protein design, even when the
protein structure is not yet known with atomic resolution. {A}vailability:
http://gpcr.biocomp.unibo.it/cgi/predictors/{I}-{M}utant2.0/{I}-{M}utant2.0.cgi.},
doi = {10.1093/nar/gki375},
pdf = {../local/local},
file = {local:local/:PDF},
keywords = {biosvm},
pii = {33/suppl_2/W306},
url = {http://dx.doi.org/10.1093/nar/gki375}
}
@article{Carbo1980How,
author = {R. Carb{\'o} and L. Leyda and M. Arnau},
title = {How similar is a molecule to another - an electron-density measure
of similarity between 2 molecular structures},
journal = {Int. J. Qantum Chem.},
year = {1980},
volume = {17},
pages = {1185-1189},
keywords = {chemoinformatics},
owner = {mahe},
timestamp = {2006.09.01}
}
@article{Carcassoni2002Spectral,
author = {Carcassoni, M. and Hancock, E.},
title = {Spectral correspondence for point pattern matching},
journal = {Pattern Recogn.},
year = {2003},
volume = {36},
pages = {193--204},
number = {1},
month = {January},
abstract = {This paper investigates the correspondence matching of point-sets
using spectral graph analysis. In particular, we are interested in
the problem of how the modal analysis of point-sets can be rendered
robust to contamination and drop-out. We make three contributions.
First, we show how the modal structure of point-sets can be embedded
within the framework of the EM algorithm. Second, we present several
methods for computing the probabilities of point correspondences
from the modes of the point proximity matrix. Third, we consider
alternatives to the Gaussian proximity matrix. We evaluate the new
method on both synthetic and real-world data. Here we show that the
method can be used to compute useful correspondences even when the
level of point contamination is as large as 50\%. We also provide
some examples on deformed point-set tracking.},
citeulike-article-id = {4071215},
citeulike-linkout-0 = {http://dx.doi.org/10.1016/S0031-3203(02)00054-7},
citeulike-linkout-1 = {http://linkinghub.elsevier.com/retrieve/pii/S0031320302000547},
doi = {10.1016/S0031-3203(02)00054-7},
issn = {00313203},
keywords = {correspondences, matching},
posted-at = {2009-02-19 14:46:34},
priority = {2},
url = {http://dx.doi.org/10.1016/S0031-3203(02)00054-7}
}
@article{Carhart1985Atom,
author = {R.E. Carhart and D.H. Smith and R. Venkataraghavan},
title = {Atom {P}airs as {M}olecular {F}eatures in {S}tructure-{A}ctivity
{S}tudies: {D}efinitions and {A}pplications},
journal = {J Chem Inf Comput Sci},
year = {1985},
volume = {25},
pages = {64-73},
owner = {mahe},
timestamp = {2006.08.22}
}
@article{Carlo1999Phylogenetic,
author = {Monte Carlo and Shuying Li and Dennis K. Pearl and Hani Doss},
title = {Phylogenetic Tree Construction Using Markov Chain Monte Carlo},
journal = {Journal of the {A}merican {S}tatistical {A}ssociation},
year = {1999},
volume = {95},
pages = {493--508}
}
@article{Carter2001computational,
author = {Carter, R. J. and Dubchak, I. and Holbrook, S. R.},
title = {A computational approach to identify genes for functional {{RNA}s}
in genomic sequences},
journal = {Nucl. {A}cids {R}es.},
year = {2001},
volume = {29},
pages = {3928-3938},
number = {19},
abstract = {Currently there is no successful computational approach for identification
of genes encoding novel functional {RNA}s (f{RNA}s) in genomic sequences.
{W}e have developed a machine learning approach using neural networks
and support vector machines to extract common features among known
{RNA}s for prediction of new {RNA} genes in the unannotated regions
of prokaryotic and archaeal genomes. {T}he {E}scherichia coli genome
was used for development, but we have applied this method to several
other bacterial and archaeal genomes. {N}etworks based on nucleotide
composition were 80-90% accurate in jackknife testing experiments
for bacteria and 90-99% for hyperthermophilic archaea. {W}e also
achieved a significant improvement in accuracy by combining these
predictions with those obtained using a second set of parameters
consisting of known {RNA} sequence motifs and the calculated free
energy of folding. {S}everal known f{RNA}s not included in the training
datasets were identified as well as several hundred predicted novel
{RNA}s. {T}hese studies indicate that there are many unidentified
{RNA}s in simple genomes that can be predicted computationally as
a precursor to experimental study. {P}ublic access to our {RNA} gene
predictions and an interface for user predictions is available via
the web.},
pdf = {../local/Carter2001computational.pdf},
file = {Carter2001computational.pdf:local/Carter2001computational.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://nar.oupjournals.org/cgi/content/abstract/29/19/3928}
}
@article{Caruana1997Multitask,
author = {Rich Caruana},
title = {Multitask Learning},
journal = {Machine Learning},
year = {1997},
volume = {28},
pages = {41-75},
number = {1}
}
@inproceedings{Caruana1993Multitask,
author = {Richard Caruana},
title = {Multitask Learning: A Knowledge-Based Source of Inductive Bias},
booktitle = {Proceedings of the Tenth International Conference on Machine Learning},
year = {1993},
pages = {41--48},
publisher = {Morgan Kaufmann}
}
@article{Catapano2007G,
author = {Catapano, L. A. and Manji, H. K.},
title = {{G} protein-coupled receptors in major psychiatric disorders.},
journal = {Biochim. Biophys. Acta},
year = {2007},
volume = {1768},
pages = {976--993},
number = {4},
month = {Apr},
doi = {10.1016/j.bbamem.2006.09.025},
keywords = {chemogenomics},
owner = {laurent},
pii = {S0005-2736(06)00384-1},
pmid = {17078926},
timestamp = {2008.01.16},
url = {http://dx.doi.org/10.1016/j.bbamem.2006.09.025}
}
@inproceedings{Catoni2002Data,
author = {Catoni, O.},
title = {Data {C}ompression and {A}daptive {H}istograms},
booktitle = {Foundations of {C}omputational {M}athematics, {P}roceedings of {S}malefest
2000},
year = {2002},
editor = {Felipe Cucker and J. Maurice Rojas},
publisher = {World Scientific},
pdf = {../local/cato02.pdf},
file = {cato02.pdf:local/cato02.pdf:PDF},
subject = {stat},
url = {http://www.proba.jussieu.fr/users/catoni/gibbsHist_doc/}
}
@unpublished{CatoniGibbs,
author = {Catoni, O.},
title = {Gibbs estimators},
note = {Revised version},
pdf = {../local/cato02.ps},
file = {cato02.ps:local/cato02.ps:PostScript},
subject = {stat},
url = {http://www.proba.jussieu.fr/users/catoni/homepage/gibbs5.dvi}
}
@article{Causier2004Studying,
author = {Barry Causier},
title = {Studying the interactome with the yeast two-hybrid system and mass
spectrometry.},
journal = {Mass Spectrom Rev},
year = {2004},
volume = {23},
pages = {350--367},
number = {5},
abstract = {Protein interactions are crucial to the life of a cell. The analysis
of such interactions is allowing biologists to determine the function
of uncharacterized proteins and the genes that encode them. The yeast
two-hybrid system has become one of the most popular and powerful
tools to study protein-protein interactions. With the advent of proteomics,
the two-hybrid system has found a niche in interactome mapping. However,
it is clear that only by combining two-hybrid data with that from
complementary approaches such as mass spectrometry (MS) can the interactome
be analyzed in full. This review introduces the yeast two-hybrid
system to those unfamiliar with the technique, and discusses how
it can be used in combination with MS to unravel the network of protein
interactions that occur in a cell.},
doi = {10.1002/mas.10080},
institution = {School of Biology, University of Leeds, Leeds LS2 9JT, United Kingdom.
b.e.causier@leeds.ac.uk},
keywords = {Genes, Fungal; Genome; Mass Spectrometry; Proteins; Proteomics; Yeasts},
owner = {phupe},
pmid = {15264234},
timestamp = {2010.08.31},
url = {http://dx.doi.org/10.1002/mas.10080}
}
@article{Cavalieri2005,
author = {Cavalieri, D. and De Filippo, C.},
title = {Bioinformatic methods for integrating whole-genome expression results
into cellular networks},
journal = {Drug {D}iscov {T}oday},
year = {2005},
volume = {10},
pages = {727-34},
number = {10},
abstract = {Extracting a comprehensive overview from the huge amount of information
arising from whole-genome analyses is a significant challenge. {T}his
review critically surveys the state of the art methods that are used
to connect information from functional genomic studies to biological
function. {C}luster analysis methods for inferring the correlation
between genes are discussed, as are the methods for integrating gene
expression information with existing information on biological pathways
and the methods that combine cluster analysis with biological information
to reconstruct novel biological networks.},
keywords = {Cluster Analysis *Computational Biology/methods/organization & administration/trends
*Genomics/methods/organization & administration/trends Humans Oligonucleotide
Array Sequence Analysis/methods}
}
@article{Cavalli2002Toward,
author = {Cavalli, A. and Poluzzi, E. and De Ponti, F. and Recanatini, M.},
title = {{T}oward a pharmacophore for drugs inducing the long {QT} syndrome:
insights from a {C}o{MFA} study of {HERG} {K}(+) channel blockers.},
journal = {J. Med. Chem.},
year = {2002},
volume = {45},
pages = {3844--3853},
number = {18},
month = {Aug},
abstract = {In this paper, we present a pharmacophore for QT-prolonging drugs,
along with a 3D QSAR (CoMFA) study for a series of very structurally
variegate HERG K(+) channel blockers. The blockade of HERG K(+) channels
is one of the most important molecular mechanisms through which QT-prolonging
drugs increase cardiac action potential duration. Since QT prolongation
is one of the most undesirable side effects of drugs, we first tried
to identify the minimum set of molecular features responsible for
this action and then we attempted to develop a quantitative model
correlating the 3D stereoelectronic characteristics of the molecules
with their HERG blocking potency. Having considered an initial set
of 31 QT-prolonging drugs for which the HERG K(+) channel blocking
activity was measured on mammalian transfected cells, we started
the construction of a theoretical screening tool able to predict
whether a new molecule can interact with the HERG channel and eventually
induce the long QT syndrome. This in silico tool might be useful
in the design of new drug candidates devoid of the physicochemical
features likely to cause the above-mentioned side effect.},
keywords = {chemoinformatics herg},
pii = {jm0208875},
pmid = {12190308},
timestamp = {2006.10.06}
}
@article{Cavasotto2003Structure-based,
author = {Cavasotto, C. N. and Orry, A. J. W. and Abagyan, R. A.},
title = {Structure-based identification of binding sites, native ligands and
potential inhibitors for {G}-protein coupled receptors.},
journal = {Proteins},
year = {2003},
volume = {51},
pages = {423--433},
number = {3},
month = {May},
abstract = {G-protein coupled receptors (GPCRs) are the largest family of cell-surface
receptors involved in signal transmission. Drugs associated with
GPCRs represent more than one fourth of the 100 top-selling drugs
and are the targets of more than half of the current therapeutic
agents on the market. Our methodology based on the internal coordinate
mechanics (ICM) program can accurately identify the ligand-binding
pocket in the currently available crystal structures of seven transmembrane
(7TM) proteins [bacteriorhodopsin (BR) and bovine rhodopsin (bRho)].
The binding geometry of the ligand can be accurately predicted by
ICM flexible docking with and without the loop regions, a useful
finding for GPCR docking because the transmembrane regions are easier
to model. We also demonstrate that the native ligand can be identified
by flexible docking and scoring in 1.5\% and 0.2\% (for bRho and
BR, respectively) of the best scoring compounds from two different
types of compound database. The same procedure can be applied to
the database of available chemicals to identify specific GPCR binders.
Finally, we demonstrate that even if the sidechain positions in the
bRho binding pocket are entirely wrong, their correct conformation
can be fully restored with high accuracy (0.28 A) through the ICM
global optimization with and without the ligand present. These binding
site adjustments are critical for flexible docking of new ligands
to known structures or for docking to GPCR homology models. The ICM
docking method has the potential to be used to "de-orphanize" orphan
GPCRs (oGPCRs) and to identify antagonists-agonists for GPCRs if
an accurate model (experimentally and computationally validated)
of the structure has been constructed or when future crystal structures
are determined.},
doi = {10.1002/prot.10362},
keywords = {chemogenomics},
owner = {laurent},
pmid = {12696053},
timestamp = {2008.01.16},
url = {http://dx.doi.org/10.1002/prot.10362}
}
@article{Cavasotto2008Discovery,
author = {Cavasotto, C. N. and Orry, A. J. W. and Murgolo, N. J. and Czarniecki,
M. F. and Kocsi, S. A. and Hawes, B. E. and O'Neill, K. A. and Hine,
H. and Burton, M. S. and Voigt, J. H. and Abagyan, R. A. and Bayne,
M. L. and Monsma, F. J.},
title = {Discovery of novel chemotypes to a {G}-protein-coupled receptor through
ligand-steered homology modeling and structure-based virtual screening},
journal = {J. Med. Chem.},
year = {2008},
volume = {51},
pages = {581--588},
number = {3},
month = {Feb},
abstract = {Melanin-concentrating hormone receptor 1 (MCH-R1) is a G-protein-coupled
receptor (GPCR) and a target for the development of therapeutics
for obesity. The structure-based development of MCH-R1 and other
GPCR antagonists is hampered by the lack of an available experimentally
determined atomic structure. A ligand-steered homology modeling approach
has been developed (where information about existing ligands is used
explicitly to shape and optimize the binding site) followed by docking-based
virtual screening. Top scoring compounds identified virtually were
tested experimentally in an MCH-R1 competitive binding assay, and
six novel chemotypes as low micromolar affinity antagonist "hits"
were identified. This success rate is more than a 10-fold improvement
over random high-throughput screening, which supports our ligand-steered
method. Clearly, the ligand-steered homology modeling method reduces
the uncertainty of structure modeling for difficult targets like
GPCRs.},
doi = {10.1021/jm070759m},
keywords = {chemogenomics},
owner = {laurent},
pmid = {18198821},
timestamp = {2008.07.16},
url = {http://dx.doi.org/10.1021/jm070759m}
}
@article{Cawley2004Fast,
author = {Gavin C Cawley and Nicola L C Talbot},
title = {Fast exact leave-one-out cross-validation of sparse least-squares
support vector machines.},
journal = {Neural {N}etw},
year = {2004},
volume = {17},
pages = {1467-75},
number = {10},
month = {Dec},
abstract = {Leave-one-out cross-validation has been shown to give an almost unbiased
estimator of the generalisation properties of statistical models,
and therefore provides a sensible criterion for model selection and
comparison. {I}n this paper we show that exact leave-one-out cross-validation
of sparse {L}east-{S}quares {S}upport {V}ector {M}achines ({LS}-{SVM}s)
can be implemented with a computational complexity of only {O}(ln2)
floating point operations, rather than the {O}(l2n2) operations of
a naïve implementation, where l is the number of training patterns
and n is the number of basis vectors. {A}s a result, leave-one-out
cross-validation becomes a practical proposition for model selection
in large scale applications. {F}or clarity the exposition concentrates
on sparse least-squares support vector machines in the context of
non-linear regression, but is equally applicable in a pattern recognition
setting.},
doi = {10.1016/j.neunet.2004.07.002},
pdf = {../local/Cawley2004Fast.pdf},
file = {Cawley2004Fast.pdf:local/Cawley2004Fast.pdf:PDF},
pii = {S0893-6080(04)00143-1},
url = {http://dx.doi.org/10.1016/j.neunet.2004.07.002}
}
@article{Cayley1877Number,
author = {A. Cayley},
title = {On the number of univalent radicals $C_nH_{2n+1}$},
journal = {Philos. Mag.},
year = {1877},
volume = {18},
pages = {34-35},
number = {3}
}
@article{Cayley1875Theorychemical,
author = {A. Cayley},
title = {On the theory of the analytical forms called threes, with application
to the theory of chemical combinations},
journal = {Rep. Brit. Assoc. Sci.},
year = {1875},
volume = {4},
pages = {257-305},
number = {45}
}
@article{Cayley1874Mathematical,
author = {A. Cayley},
title = {On the mathematical theory of isomers},
journal = {Philos. Mag.},
year = {1874},
volume = {10},
pages = {444-446},
number = {47}
}
@article{Cayley1859Theory,
author = {A. Cayley},
title = {On the theory of the analytical forms called threes},
journal = {Philos. Mag.},
year = {1859},
volume = {37},
pages = {374-378},
number = {18}
}
@misc{Cela2007Quadratic,
author = {E. Cela},
title = {Qaudratuc assignment problem library},
year = {2007},
url = {http://www.opt.math.tu-graz.ac.at/qaplib/}
}
@article{Chalk2004Improved,
author = {Chalk, A. M. and Wahlestedt, C. and Sonnhammer, E. L. L.},
title = {Improved and automated prediction of effective si{RNA}.},
journal = {Biochem. {B}iophys. {R}es. {C}ommun.},
year = {2004},
volume = {319},
pages = {264-74},
number = {1},
month = {Jun},
abstract = {Short interfering {RNA}s are used in functional genomics studies to
knockdown a single gene in a reversible manner. {T}he results of
si{RNA} experiments are highly dependent on the choice of si{RNA}
sequence. {I}n order to evaluate si{RNA} design rules, we collected
a database of 398 si{RNA}s of known efficacy from 92 genes. {W}e
used this database to evaluate previously proposed rules from smaller
datasets, and to find a new set of rules that are optimal for the
entire database. {W}e also trained a regression tree with full cross-validation.
{I}t was however difficult to obtain the same precision as methods
previously tested on small datasets from one or two genes. {W}e show
that those methods are overfitting as they work poorly on independent
validation datasets from multiple genes. {O}ur new design rules can
predict si{RNA}s with efficacy >/= 50\% in 91\% of cases, and with
efficacy >/=90\% in 52\% of cases, which is more than a twofold improvement
over random selection. {S}oftware for designing si{RNA}s is available
online via a web server at or as a standalone version for high-throughput
applications.},
doi = {10.1016/j.bbrc.2004.04.181},
pdf = {../local/Chalk2004Improved.pdf},
file = {Chalk2004Improved.pdf:local/Chalk2004Improved.pdf:PDF},
keywords = {sirna},
pii = {S0006291X04009374},
url = {http://dx.doi.org/10.1016/j.bbrc.2004.04.181}
}
@article{Chalk2005siRNAdb,
author = {Chalk, A. M. and Warfinge, R. E. and Georgii-Hemming, P. and Sonnhammer,
E. L. L.},
title = {si{RNA}db: a database of si{RNA} sequences.},
journal = {Nucleic Acids Res.},
year = {2005},
volume = {33},
pages = {D131--D134},
number = {Database issue},
month = {Jan},
abstract = {Short interfering RNAs (siRNAs) are a popular method for gene-knockdown,
acting by degrading the target mRNA. Before performing experiments
it is invaluable to locate and evaluate previous knockdown experiments
for the gene of interest. The siRNA database provides a gene-centric
view of siRNA experimental data, including siRNAs of known efficacy
and siRNAs predicted to be of high efficacy by a combination of methods.
Linked to these sequences is information such as siRNA thermodynamic
properties and the potential for sequence-specific off-target effects.
The database enables the user to evaluate an siRNA's potential for
inhibition and non-specific effects. The database is available at
http://siRNA.cgb.ki.se.},
doi = {10.1093/nar/gki136},
pdf = {../local/Chalk2005siRNAdb.pdf},
file = {Chalk2005siRNAdb.pdf:Chalk2005siRNAdb.pdf:PDF},
keywords = {sirna},
owner = {vert},
pii = {33/suppl_1/D131},
pmid = {15608162},
timestamp = {2006.03.28},
url = {http://dx.doi.org/10.1093/nar/gki136}
}
@article{Chan2003Detection,
author = {Ian Chan and William Wells and Robert V Mulkern and Steven Haker
and Jianqing Zhang and Kelly H Zou and Stephan E Maier and Clare
M C Tempany},
title = {Detection of prostate cancer by integration of line-scan diffusion,
{T}2-mapping and {T}2-weighted magnetic resonance imaging; a multichannel
statistical classifier.},
journal = {Med {P}hys},
year = {2003},
volume = {30},
pages = {2390-8},
number = {9},
month = {Sep},
abstract = {A multichannel statistical classifier for detecting prostate cancer
was developed and validated by combining information from three different
magnetic resonance ({MR}) methodologies: {T}2-weighted, {T}2-mapping,
and line scan diffusion imaging ({LSDI}). {F}rom these {MR} sequences,
four different sets of image intensities were obtained: {T}2-weighted
({T}2{W}) from {T}2-weighted imaging, {A}pparent {D}iffusion {C}oefficient
({ADC}) from {LSDI}, and proton density ({PD}) and {T}2 ({T}2 {M}ap)
from {T}2-mapping imaging. {M}anually segmented tumor labels from
a radiologist, which were validated by biopsy results, served as
tumor "ground truth." {T}extural features were extracted from the
images using co-occurrence matrix ({CM}) and discrete cosine transform
({DCT}). {A}natomical location of voxels was described by a cylindrical
coordinate system. {A} statistical jack-knife approach was used to
evaluate our classifiers. {S}ingle-channel maximum likelihood ({ML})
classifiers were based on 1 of the 4 basic image intensities. {O}ur
multichannel classifiers: support vector machine ({SVM}) and {F}isher
linear discriminant ({FLD}), utilized five different sets of derived
features. {E}ach classifier generated a summary statistical map that
indicated tumor likelihood in the peripheral zone ({PZ}) of the prostate
gland. {T}o assess classifier accuracy, the average areas under the
receiver operator characteristic ({ROC}) curves over all subjects
were compared. {O}ur best {FLD} classifier achieved an average {ROC}
area of 0.839(+/-0.064), and our best {SVM} classifier achieved an
average {ROC} area of 0.761(+/-0.043). {T}he {T}2{W} {ML} classifier,
our best single-channel classifier, only achieved an average {ROC}
area of 0.599(+/-0.146). {C}ompared to the best single-channel {ML}
classifier, our best multichannel {FLD} and {SVM} classifiers have
statistically superior {ROC} performance ({P}=0.0003 and 0.0017,
respectively) from pairwise two-sided t-test. {B}y integrating the
information from multiple images and capturing the textural and anatomical
features in tumor areas, summary statistical maps can potentially
aid in image-guided prostate biopsy and assist in guiding and controlling
delivery of localized therapy under image guidance.},
pdf = {../local/Chan2003Detection.pdf},
file = {Chan2003Detection.pdf:local/Chan2003Detection.pdf:PDF},
keywords = {Algorithms, Anion Exchange Resins, Antigen-Antibody Complex, Artificial
Intelligence, Automated, Automatic Data Processing, Biological, Blood
Cells, Chemical, Chromatography, Cluster Analysis, Comparative Study,
Computational Biology, Computer Simulation, Computer-Assisted, Data
Interpretation, Databases, Decision Making, Decision Trees, Diffusion
Magnetic Resonance Imaging, English Abstract, Epitopes, Expert Systems,
Factual, Fuzzy Logic, Gene Expression Profiling, Gene Expression
Regulation, Gene Targeting, Genome, Histocompatibility Antigens Class
I, Humans, Image Interpretation, Image Processing, In Vitro, Indicators
and Reagents, Information Storage and Retrieval, Ion Exchange, Least-Squares
Analysis, Liver Cirrhosis, Magnetic Resonance Imaging, Male, Models,
Neural Networks (Computer), Non-P.H.S., Non-U.S. Gov't, Nonl, Nucleic
Acid Conformation, P.H.S., Pattern Recognition, Pro, Prostatic Neoplasms,
Protein, Protein Binding, Protein Interaction Mapping, Proteins,
Quantitative Structure-Activity Relationship, RNA, ROC Curve, Reproducibility
of Results, Research Support, Sensitivity and Specificity, Sequence
Analysis, Severity of Illness Index, Statistical, Structure-Activity
Relationship, Subtraction Technique, T-Lymphocyte, Transcription
Factors, Transfer, Treatment Outcome, U.S. Gov't, User-Computer Interface,
inear Dynamics, teome, 14528961}
}
@article{Chan2002Comparison,
author = {Kwokleung Chan and Te-Won Lee and Pamela A Sample and Michael H Goldbaum
and Robert N Weinreb and Terrence J Sejnowski},
title = {Comparison of machine learning and traditional classifiers in glaucoma
diagnosis.},
journal = {I{EEE} {T}rans {B}iomed {E}ng},
year = {2002},
volume = {49},
pages = {963-74},
number = {9},
month = {Sep},
abstract = {Glaucoma is a progressive optic neuropathy with characteristic structural
changes in the optic nerve head reflected in the visual field. {T}he
visual-field sensitivity test is commonly used in a clinical setting
to evaluate glaucoma. {S}tandard automated perimetry ({SAP}) is a
common computerized visual-field test whose output is amenable to
machine learning. {W}e compared the performance of a number of machine
learning algorithms with {STATPAC} indexes mean deviation, pattern
standard deviation, and corrected pattern standard deviation. {T}he
machine learning algorithms studied included multilayer perceptron
({MLP}), support vector machine ({SVM}), and linear ({LDA}) and quadratic
discriminant analysis ({QDA}), {P}arzen window, mixture of {G}aussian
({MOG}), and mixture of generalized {G}aussian ({MGG}). {MLP} and
{SVM} are classifiers that work directly on the decision boundary
and fall under the discriminative paradigm. {G}enerative classifiers,
which first model the data probability density and then perform classification
via {B}ayes' rule, usually give deeper insight into the structure
of the data space. {W}e have applied {MOG}, {MGG}, {LDA}, {QDA},
and {P}arzen window to the classification of glaucoma from {SAP}.
{P}erformance of the various classifiers was compared by the areas
under their receiver operating characteristic curves and by sensitivities
(true-positive rates) at chosen specificities (true-negative rates).
{T}he machine-learning-type classifiers showed improved performance
over the best indexes from {STATPAC}. {F}orward-selection and backward-elimination
methodology further improved the classification rate and also has
the potential to reduce testing time by diminishing the number of
visual-field location measurements.},
doi = {10.1109/TBME.2002.802012},
pdf = {../local/Chan2002Comparison.pdf},
file = {Chan2002Comparison.pdf:local/Chan2002Comparison.pdf:PDF},
keywords = {Acute, Algorithms, Animals, Anion Exchange Resins, Artificial Intelligence,
Automated, Base Pair Mismatch, Base Pairing, Base Sequence, Biological,
Biosensing Techniques, Carcinoma, Chemical, Chromatography, Citric
Acid Cycle, Classification, Cluster Analysis, Comparative Study,
Computational Biology, Computer-Assisted, Cystadenoma, DNA, Databases,
Decision Making, Diagnosis, Differential, Discriminant Analysis,
Drug, Drug Design, Electrostatics, Epitopes, Eukaryotic Cells, Factual,
False Negative Reactions, False Positive Reactions, Feasibility Studies,
Female, Gene Expression, Gene Expression Profiling, Gene Expression
Regulation, Genes, Genetic, Genetic Heterogeneity, Genetic Markers,
Glaucoma, HLA Antigens, Hemolysins, Histocompatibility Antigens Class
I, Humans, Internet, Intraocular Pressure, Ion Exchange, Lasers,
Leukemia, Ligands, Likelihood Functions, Logistic Models, Lung Neoplasms,
Lymphocytic, Lymphoma, Markov Chains, Mathematics, Messenger, Models,
Molecular, Molecular Probe Techniques, Molecular Sequence Data, Nanotechnology,
Neoplasm, Neoplasms, Neoplastic, Neural Networks (Computer), Neurological,
Non-P.H.S., Non-Small-Cell Lung, Non-U.S. Gov't, Nucleic Acid Conformation,
Nucleic Acid Hybridization, Observer Variation, Oligonucleotide Array
Sequence Analysis, Open-Angle, Ophthalmoscopy, Optic Disk, Optic
Nerve Diseases, Ovarian Neoplasms, P.H.S., Pattern Recognition, Peptides,
Perimetry, Predictive Value of Tests, Probability, Probability Learning,
Protein, Protein Binding, Protein Conformation, Proteins, Quality
Control, Quantum Theory, RNA, RNA Splicing, ROC Curve, Receptors,
Reference Values, Regression Analysis, Reproducibility of Results,
Research Support, Robotics, Saccharomyces cerevisiae Proteins, Sensitivity
and Specificity, Sequence Analysis, Signal Processing, Software,
Statistical, Stomach Neoplasms, Structural, Structure-Activity Relationship,
T-Lymphocyte, Thermodynamics, Transcription, Tumor Markers, U.S.
Gov't, 12214886},
url = {http://dx.doi.org/10.1109/TBME.2002.802012}
}
@article{Chanda2003Fulfilling,
author = {S. K. Chanda and J. S. Caldwell},
title = {{F}ulfilling the promise: drug discovery in the post-genomic era.},
journal = {Drug Discov Today},
year = {2003},
volume = {8},
pages = {168--174},
number = {4},
month = {Feb},
abstract = {The genomic era has brought with it a basic change in experimentation,
enabling researchers to look more comprehensively at biological systems.
The sequencing of the human genome coupled with advances in automation
and parallelization technologies have afforded a fundamental transformation
in the drug target discovery paradigm, towards systematic whole genome
and proteome analyses. In conjunction with novel proteomic techniques,
genome-wide annotation of function in cellular models is possible.
Overlaying data derived from whole genome sequence, expression and
functional analysis will facilitate the identification of causal
genes in disease and significantly streamline the target validation
process. Moreover, several parallel technological advances in small
molecule screening have resulted in the development of expeditious
and powerful platforms for elucidating inhibitors of protein or pathway
function. Conversely, high-throughput and automated systems are currently
being used to identify targets of orphan small molecules. The consolidation
of these emerging functional genomics and drug discovery technologies
promises to reap the fruits of the genomic revolution.},
owner = {mahe},
pii = {S1359644602025953},
pmid = {12581711},
timestamp = {2006.08.11}
}
@article{Chang2008Fast,
author = {Chang, C. Q. and Ding, Z. and Hung, Y. S. and Fung, P. C.},
title = {Fast network component analysis (FastNCA) for gene regulatory network
reconstruction from microarray data},
journal = {Bioinformatics},
year = {2008},
volume = {24},
pages = {1349--1358},
number = {11},
owner = {jp},
timestamp = {2011.07.23}
}
@manual{Chang2001LIBSVM,
title = {{LIBSVM}: a library for support vector machines},
author = {Chang, C.-C. and Lin, C.-J.},
year = {2001},
note = {Software available at \url{http://www.csie.ntu.edu.tw/~cjlin/libsvm}},
owner = {jp},
timestamp = {2010.11.02}
}
@article{Chang2004Analysis,
author = {Ming-Wei Chang and Chih-Jen Lin and Ruby Chiu-Hsing Weng},
title = {Analysis of switching dynamics with competing support vector machines.},
journal = {I{EEE} {T}rans {N}eural {N}etw},
year = {2004},
volume = {15},
pages = {720-7},
number = {3},
month = {May},
abstract = {We present a framework for the unsupervised segmentation of switching
dynamics using support vector machines. {F}ollowing the architecture
by {P}awelzik et al., where annealed competing neural networks were
used to segment a nonstationary time series, in this paper, we exploit
the use of support vector machines, a well-known learning technique.
{F}irst, a new formulation of support vector regression is proposed.
{S}econd, an expectation-maximization step is suggested to adaptively
adjust the annealing parameter. {R}esults indicate that the proposed
approach is promising.},
doi = {10.1109/TNN.2004.824270},
pdf = {../local/Chang2004Analysis.pdf},
file = {Chang2004Analysis.pdf:local/Chang2004Analysis.pdf:PDF},
keywords = {Algorithms, Artificial Intelligence, Bayes Theorem, Computing Methodologies,
Models, Neural Networks (Computer), Non-U.S. Gov't, Normal Distribution,
Regression Analysis, Research Design, Research Support, Theoretical,
15384558},
url = {http://dx.doi.org/10.1109/TNN.2004.824270}
}
@article{Chang2005Automatic,
author = {Ruey-Feng Chang and Wen-Jie Wu and Woo Kyung Moon and Dar-Ren Chen},
title = {Automatic ultrasound segmentation and morphology based diagnosis
of solid breast tumors.},
journal = {Breast {C}ancer {R}es {T}reat},
year = {2005},
volume = {89},
pages = {179-85},
number = {2},
month = {Jan},
abstract = {Ultrasound ({US}) is a useful diagnostic tool to distinguish benign
from malignant masses of the breast. {I}t is a very convenient and
safe diagnostic method. {H}owever, there is a considerable overlap
benignancy and malignancy in ultrasonic images and interpretation
is subjective. {A} high performance breast tumors computer-aided
diagnosis ({CAD}) system can provide an accurate and reliable diagnostic
second opinion for physicians to distinguish benign breast lesions
from malignant ones. {T}he potential of sonographic texture analysis
to improve breast tumor classifications has been demonstrated. {H}owever,
the texture analysis is system-dependent. {T}he disadvantages of
these systems which use texture analysis to classify tumors are they
usually perform well only in one specific ultrasound system. {W}hile
{M}orphological based {US} diagnosis of breast tumor will take the
advantage of nearly independent to either the setting of {US} system
and different {US} machines. {I}n this study, the tumors are segmented
using the newly developed level set method at first and then six
morphologic features are used to distinguish the benign and malignant
cases. {T}he support vector machine ({SVM}) is used to classify the
tumors. {T}here are 210 ultrasonic images of pathologically proven
benign breast tumors from 120 patients and carcinomas from 90 patients
in the ultrasonic image database. {T}he database contains only one
image from each patient. {T}he ultrasonic images are captured at
the largest diameter of the tumor. {T}he images are collected consecutively
from {A}ugust 1, 1999 to {M}ay 31, 2000; the patients' ages ranged
from 18 to 64 years. {S}onography is performed using an {ATL} {HDI}
3000 system with a {L}10-5 small part transducer. {I}n the experiment,
the accuracy of {SVM} with shape information for classifying malignancies
is 90.95\% (191/210), the sensitivity is 88.89\% (80/90), the specificity
is 92.5\% (111/120), the positive predictive value is 89.89\% (80/89),
and the negative predictive value is 91.74\% (111/121).},
doi = {10.1007/s10549-004-2043-z},
pdf = {../local/Chang2005Automatic.pdf},
file = {Chang2005Automatic.pdf:local/Chang2005Automatic.pdf:PDF},
keywords = {breastcancer},
url = {http://dx.doi.org/10.1007/s10549-004-2043-z}
}
@article{Chang2003Improvement,
author = {Ruey-Feng Chang and Wen-Jie Wu and Woo Kyung Moon and Dar-Ren Chen},
title = {Improvement in breast tumor discrimination by support vector machines
and speckle-emphasis texture analysis.},
journal = {Ultrasound {M}ed {B}iol},
year = {2003},
volume = {29},
pages = {679-86},
number = {5},
month = {May},
abstract = {Recent statistics show that breast cancer is a major cause of death
among women in developed countries. {H}ence, finding an accurate
and effective diagnostic method is very important. {I}n this paper,
we propose a high precision computer-aided diagnosis ({CAD}) system
for sonography. {W}e utilize a support vector machine ({SVM}) to
classify breast tumors according to their texture information surrounding
speckle pixels. {W}e test our system with 250 pathologically-proven
breast tumors including 140 benign and 110 malignant ones. {A}lso
we compare the diagnostic performances of three texture features,
i.e., speckle-emphasis texture feature, nonspeckle-emphasis texture
feature and conventional all pixels texture feature, applied to breast
sonography using {SVM}. {I}n our experiment, the accuracy of {SVM}
with speckle information for classifying malignancies is 93.2\% (233/250),
the sensitivity is 95.45\% (105/110), the specificity is 91.43\%
(128/140), the positive predictive value is 89.74\% (105/117) and
the negative predictive value is 96.24\% (128/133). {B}ased on the
experimental results, speckle phenomenon is a useful tool to be used
in computer-aided diagnosis; its performance is better than those
of the other two features. {S}peckle phenomenon, which is considered
as noise in sonography, can intrude into judgments of a physician
using naked eyes but it is another story for application in a computer-aided
diagnosis algorithm.},
doi = {10.1016/S0301-5629(02)00788-3},
pdf = {../local/Chang2003Improvement.pdf},
file = {Chang2003Improvement.pdf:local/Chang2003Improvement.pdf:PDF},
keywords = {breastcancer},
pii = {S0301562902007883},
url = {http://dx.doi.org/10.1016/S0301-5629(02)00788-3}
}
@article{Chang2003Support,
author = {Ruey-Feng Chang and Wen-Jie Wu and Woo Kyung Moon and Yi-Hong Chou
and Dar-Ren Chen},
title = {Support vector machines for diagnosis of breast tumors on {US} images.},
journal = {Acad {R}adiol},
year = {2003},
volume = {10},
pages = {189-97},
number = {2},
month = {Feb},
abstract = {R{ATIONALE} {AND} {OBJECTIVES}: {B}reast cancer has become the leading
cause of cancer deaths among women in developed countries. {T}o decrease
the related mortality, disease must be treated as early as possible,
but it is hard to detect and diagnose tumors at an early stage. {A}
well-designed computer-aided diagnostic system can help physicians
avoid misdiagnosis and avoid unnecessary biopsy without missing cancers.
{I}n this study, the authors tested one such system to determine
its effectiveness. {MATERIALS} {AND} {METHODS}: {M}any computer-aided
diagnostic systems for ultrasonography are based on the neural network
model and classify breast tumors according to texture features. {T}he
authors tested a refinement of this model, an advanced support vector
machine ({SVM}), in 250 cases of pathologically proved breast tumors
(140 benign and 110 malignant), and compared its performance with
that of a multilayer propagation neural network. {RESULTS}: {T}he
accuracy of the {SVM} for classifying malignancies was 85.6\% (214
of 250); the sensitivity, 95.45\% (105 of 110); the specificity,
77.86\% (109 of 140); the positive predictive value, 77.21\% (105
of 136); and the negative predictive value, 95.61\% (109 of 114).
{CONCLUSION}: {T}he {SVM} proved helpful in the imaging diagnosis
of breast cancer. {T}he classification ability of the {SVM} is nearly
equal to that of the neural network model, and the {SVM} has a much
shorter training time (1 vs 189 seconds). {G}iven the increasing
size and complexity of data sets, the {SVM} is therefore preferable
for computer-aided diagnosis.},
doi = {10.1016/S1076-6332(03)80044-2},
url = {http://dx.doi.org/10.1016/S1076-6332(03)80044-2}
}
@book{Chapelle2006Semi-Supervised,
title = {Semi-Supervised Learning},
publisher = {MIT Press},
year = {2006},
author = {Chapelle, O. and Sch{\"o}lkopf, B. and Zien, A.},
address = {Cambridge, MA},
owner = {fantine},
timestamp = {2009.07.09},
url = {http://www.kyb.tuebingen.mpg.de/ssl-book}
}
@incollection{Chapelle2005A,
author = {Olivier {Chapelle} and Zaid {Harchaoui}},
title = {A Machine Learning Approach to Conjoint Analysis},
booktitle = {Advances in Neural Information Processing Systems 17},
publisher = {MIT Press},
year = {2005},
editor = {Lawrence K. Saul and Yair Weiss and {L\'{e}on} Bottou},
pages = {257-264},
address = {Cambridge, MA},
original = {0257_852.PDF}
}
@book{Chavel1984Eigenvalues,
title = {Eigenvalues in {R}iemannian geometry},
publisher = {Academic Press},
year = {1984},
author = {I. Chavel},
address = {Orlando, Fl.},
subject = {net}
}
@article{Chen2007BiophysJ,
author = {Chen, C. and Cui, J. and Lu, H. and Wang, R. and Zhang, S. and Shen,
P.},
title = {Modeling of the role of a Bax-activation switch in the mitochondrial
apoptosis decision},
journal = {Biophys J},
year = {2007},
volume = {92},
pages = {4304-15},
number = {12},
abstract = {We performed in silico modeling of the regulatory network of mitochondrial
apoptosis through which we examined the role of a Bax-activation
switch in governing the mitochondrial apoptosis decision. Two distinct
modeling methods were used in this article. One is continuous and
deterministic, comprised of a set of ordinary differential equations.
The other, carried out in a discrete manner, is based on a cellular
automaton, which takes stochastic fluctuations into consideration.
We focused on dynamic properties of the mitochondrial apoptosis regulatory
network. The roles of Bcl-2 family proteins in cellular responses
to apoptotic stimuli were examined. In our simulations, a self-amplification
process of Bax-activation is indicated. Further analysis suggests
that the core module of Bax-activation is bistable in both deterministic
and stochastic models, and this feature is robust to noise and wide
ranges of parameter variation. When coupling with Bax-polymerization,
it forms a one-way-switch, which governs irreversible behaviors of
Bax-activation even with attenuation of apoptotic stimulus. Together
with the growing biochemical evidence, we propose a novel molecular
switch mechanism embedded in the mitochondrial apoptosis regulatory
network and give a plausible explanation for the all-or-none, irreversible
character of mitochondrial apoptosis.},
keywords = {csbcbook}
}
@article{Chen2007FEBS,
author = {Chen, C. and Cui, J. and Zhang, W. and Shen, P.},
title = {Robustness analysis identifies the plausible model of the Bcl-2 apoptotic
switch},
journal = {FEBS Lett},
year = {2007},
volume = {581},
pages = {5143-50},
number = {26},
abstract = {In this paper two competing models of the B-cell lymphoma 2 (Bcl-2)
apoptotic switch were contrasted by mathematical modeling and robustness
analysis. Since switch-like behaviors are required for models that
attempt to explain the all-or-none decisions of apoptosis, ultrasensitivity
was employed as a criterion for comparison. Our results successfully
exhibit that the direct activation model operates more reliably to
achieve a robust switch in cellular conditions. Moreover, by investigating
the robustness of other important features of the Bcl-2 apoptotic
switch (including low Bax basal activation, inhibitory role of anti-apoptotic
proteins and insensitivity to small perturbations) the direct activation
model was further supported. In all, we identified the direct activation
model as a more plausible explanation for the Bcl-2 apoptotic switch.},
keywords = {csbcbook}
}
@article{Chen2011Removing,
author = {Chao Chen and Kay Grennan and Judith Badner and Dandan Zhang and
Elliot Gershon and Li Jin and Chunyu Liu},
title = {Removing batch effects in analysis of expression microarray data:
an evaluation of six batch adjustment methods.},
journal = {PLoS One},
year = {2011},
volume = {6},
pages = {e17238},
number = {2},
abstract = {The expression microarray is a frequently used approach to study gene
expression on a genome-wide scale. However, the data produced by
the thousands of microarray studies published annually are confounded
by "batch effects," the systematic error introduced when samples
are processed in multiple batches. Although batch effects can be
reduced by careful experimental design, they cannot be eliminated
unless the whole study is done in a single batch. A number of programs
are now available to adjust microarray data for batch effects prior
to analysis. We systematically evaluated six of these programs using
multiple measures of precision, accuracy and overall performance.
ComBat, an Empirical Bayes method, outperformed the other five programs
by most metrics. We also showed that it is essential to standardize
expression data at the probe level when testing for correlation of
expression profiles, due to a sizeable probe effect in microarray
data that can inflate the correlation among replicates and unrelated
samples.},
doi = {10.1371/journal.pone.0017238},
institution = {National Ministry of Education Key Laboratory of Contemporary Anthropology,
Fudan University, Shanghai, People's Republic of China.},
keywords = {Bayes Theorem; Case-Control Studies; Data Interpretation, Statistical;
Gene Expression Profiling, standards/statistics /&/ numerical data;
Humans; Microarray Analysis, standards/statistics /&/ numerical data;
ROC Curve; Reference Standards; Research Design; Sample Size; Selection
Bias; Validation Studies as Topic},
language = {eng},
medline-pst = {epublish},
owner = {jp},
pmid = {21386892},
timestamp = {2012.02.29},
url = {http://dx.doi.org/10.1371/journal.pone.0017238}
}
@article{Chen2005ChemDB,
author = {Chen, J. and Swamidass, S. J. and Dou, Y. and Bruand, J. and Baldi,
P.},
title = {Chem{DB}: a public database of small molecules and related chemoinformatics
resources},
journal = {Bioinformatics},
year = {2005},
volume = {21},
pages = {4133--4139},
number = {22},
month = {Sep},
abstract = {M{OTIVATION}: {T}he development of chemoinformatics has been hampered
by the lack of large, publicly available, comprehensive repositories
of molecules, in particular of small molecules. {S}mall molecules
play a fundamental role in organic chemistry and biology. {T}hey
can be used as combinatorial building blocks for chemical synthesis,
as molecular probes in chemical genomics and systems biology, and
for the screening and discovery of new drugs and other useful compounds.
{RESULTS}: {W}e describe {C}hem{DB}, a public database of small molecules
available over the {W}eb. {C}hem{DB} is built using the digital catalogs
of over a hundred vendors and other public sources and is annotated
with information derived from these sources as well as from computational
methods, such as predicted solubility and 3{D} structure. {I}t supports
multiple molecular formats and is periodically updated, automatically
whenever possible. {T}he current version of the database contains
approximately 4.1 {M} commercially available compounds, 8.2 {M} counting
isomers. {T}he database includes a user-friendly graphical interface,
chemical reactions capabilities, as well as unique search capabilities.
{AVAILABILITY}: {D}atabase, datasets, and supplementary materials
available through: http://cdb.ics.uci.edu.},
doi = {10.1093/bioinformatics/bti683},
pii = {bti683},
url = {http://dx.doi.org/10.1093/bioinformatics/bti683}
}
@inproceedings{Chen2009A,
author = {Chen, Jianhui and Tang, Lei and Liu, Jun and Ye, Jieping},
title = {A convex formulation for learning shared structures from multiple
tasks},
booktitle = {ICML '09: Proceedings of the 26th Annual International Conference
on Machine Learning},
year = {2009},
pages = {137--144},
address = {New York, NY, USA},
publisher = {ACM},
doi = {http://doi.acm.org/10.1145/1553374.1553392},
isbn = {978-1-60558-516-1},
location = {Montreal, Quebec, Canada}
}
@article{Chen2004Reducing,
author = {Jiun-Hung Chen and Chu-Song Chen},
title = {Reducing {SVM} classification time using multiple mirror classifiers.},
journal = {I{EEE} {T}rans {S}yst {M}an {C}ybern {B} {C}ybern},
year = {2004},
volume = {34},
pages = {1173-83},
number = {2},
month = {Apr},
abstract = {We propose an approach that uses mirror point pairs and a multiple
classifier system to reduce the classification time of a support
vector machine ({SVM}). {D}ecisions made with multiple simple classifiers
formed from mirror pairs are integrated to approximate the classification
rule of a single {SVM}. {A} coarse-to-fine approach is developed
for selecting a given number of member classifiers. {A} clustering
method, derived from the similarities between classifiers, is used
for a coarse selection. {A} greedy strategy is then used for fine
selection of member classifiers. {S}elected member classifiers are
further refined by finding a weighted combination with a perceptron.
{E}xperiment results show that our approach can successfully speed
up {SVM} decisions while maintaining comparable classification accuracy.},
doi = {10.1109/TSMCB.2003.821867},
pdf = {../local/Chen2004Reducing.pdf},
file = {Chen2004Reducing.pdf:local/Chen2004Reducing.pdf:PDF},
url = {http://dx.doi.org/10.1109/TSMCB.2003.821867}
}
@article{Chen2007GPCR,
author = {Chen, J.-Z. and Wang, J. and Xie, X.-Q.},
title = {GPCR structure-based virtual screening approach for CB2 antagonist
search.},
journal = {J. Chem. Inf. Model.},
year = {2007},
volume = {47},
pages = {1626--1637},
number = {4},
abstract = {The potential for therapeutic specificity in regulating diseases has
made cannabinoid (CB) receptors one of the most important G-protein-coupled
receptor (GPCR) targets in search for new drugs. Considering the
lack of related 3D experimental structures, we have established a
structure-based virtual screening protocol to search for CB2 bioactive
antagonists based on the 3D CB2 homology structure model. However,
the existing homology-predicted 3D models often deviate from the
native structure and therefore may incorrectly bias the in silico
design. To overcome this problem, we have developed a 3D testing
database query algorithm to examine the constructed 3D CB2 receptor
structure model as well as the predicted binding pocket. In the present
study, an antagonist-bound CB2 receptor complex model was initially
generated using flexible docking simulation and then further optimized
by molecular dynamic and mechanical (MD/MM) calculations. The refined
3D structural model of the CB2-ligand complex was then inspected
by exploring the interactions between the receptor and ligands in
order to predict the potential CB2 binding pocket for its antagonist.
The ligand-receptor complex model and the predicted antagonist binding
pockets were further processed and validated by FlexX-Pharm docking
against a testing compound database that contains known antagonists.
Furthermore, a consensus scoring (CScore) function algorithm was
established to rank the binding interaction modes of a ligand on
the CB2 receptor. Our results indicated that the known antagonists
seeded in the testing database can be distinguished from a significant
amount of randomly chosen molecules. Our studies demonstrated that
the established GPCR structure-based virtual screening approach provided
a new strategy with a high potential for in silico identifying novel
CB2 antagonist leads based on the homology-generated 3D CB2 structure
model.},
doi = {10.1021/ci7000814},
pdf = {../local/Chen2007GPCR.pdf},
file = {Chen2007GPCR.pdf:Chen2007GPCR.pdf:PDF},
keywords = {chemogenomics},
owner = {laurent},
pmid = {17580929},
timestamp = {2008.07.21},
url = {http://dx.doi.org/10.1021/ci7000814}
}
@article{Chen2005stochastic,
author = {Chen, K.-C. and Wang, T.-Y. and Tseng, H.-H. and Huang, C.-Y. F.
and Kao, C.-Y.},
title = {{A} stochastic differential equation model for quantifying transcriptional
regulatory network in {S}accharomyces cerevisiae},
journal = {Bioinformatics},
year = {2005},
volume = {21},
pages = {2883--2890},
number = {12},
month = {Jun},
abstract = {MOTIVATION: The explosion of microarray studies has promised to shed
light on the temporal expression patterns of thousands of genes simultaneously.
However, available methods are far from adequate in efficiently extracting
useful information to aid in a greater understanding of transcriptional
regulatory network. Biological systems have been modeled as dynamic
systems for a long history, such as genetic networks and cell regulatory
network. This study evaluated if the stochastic differential equation
(SDE), which is prominent for modeling dynamic diffusion process
originating from the irregular Brownian motion, can be applied in
modeling the transcriptional regulatory network in Saccharomyces
cerevisiae. RESULTS: To model the time-continuous gene-expression
datasets, a model of SDE is applied to depict irregular patterns.
Our goal is to fit a generalized linear model by combining putative
regulators to estimate the transcriptional pattern of a target gene.
Goodness-of-fit is evaluated by log-likelihood and Akaike Information
Criterion. Moreover, estimations of the contribution of regulators
and inference of transcriptional pattern are implemented by statistical
approaches. Our SDE model is basic but the test results agree well
with the observed dynamic expression patterns. It implies that advanced
SDE model might be perfectly suited to portray transcriptional regulatory
networks. AVAILABILITY: The R code is available on request. CONTACT:
cykao@csie.ntu.edu.tw SUPPLEMENTARY INFORMATION: http://www.csie.ntu.edu.tw/~b89x035/yeast/},
doi = {10.1093/bioinformatics/bti415},
pii = {bti415},
pmid = {15802287},
timestamp = {2008.02.04},
url = {http://dx.doi.org/10.1093/bioinformatics/bti415}
}
@article{Chen2004Level,
author = {Q. Chen and Z. M. Zhou and Y. G. Qu and P. A. Heng and D. S. Xia},
title = {Level set based auto segmentation of the tagged left ventricle {MR}
images.},
journal = {Stud {H}ealth {T}echnol {I}nform},
year = {2004},
volume = {98},
pages = {63-5},
abstract = {To facilitate automatic segmentation, we adopt {SVM} ({S}upport {V}ector
{M}achine) to localize the left ventricle, and the segmentation is
then carried out with narrow band level set. {T}he method of generating
the narrow band is improved such that the time used is reduced. {B}ased
on the imaging characteristics of the tagged left ventricle {MR}
images, {BPV} (block-pixel variation) and intensity comparability
are introduced to improve the speed term of level set and to increase
the precision of segmentation. {O}ur method can perform the segmentation
of the tagged left ventricle {MR} images accurately and automatically.}
}
@article{Chen2004Sparse,
author = {Sheng Chen and Xia Hong and Chris J Harris},
title = {Sparse kernel density construction using orthogonal forward regression
with leave-one-out test score and local regularization.},
journal = {I{EEE} {T}rans {S}yst {M}an {C}ybern {B} {C}ybern},
year = {2004},
volume = {34},
pages = {1708-17},
number = {4},
month = {Aug},
abstract = {This paper presents an efficient construction algorithm for obtaining
sparse kernel density estimates based on a regression approach that
directly optimizes model generalization capability. {C}omputational
efficiency of the density construction is ensured using an orthogonal
forward regression, and the algorithm incrementally minimizes the
leave-one-out test score. {A} local regularization method is incorporated
naturally into the density construction process to further enforce
sparsity. {A}n additional advantage of the proposed algorithm is
that it is fully automatic and the user is not required to specify
any criterion to terminate the density construction procedure. {T}his
is in contrast to an existing state-of-art kernel density estimation
method using the support vector machine ({SVM}), where the user is
required to specify some critical algorithm parameter. {S}everal
examples are included to demonstrate the ability of the proposed
algorithm to effectively construct a very sparse kernel density estimate
with comparable accuracy to that of the full sample optimized {P}arzen
window density estimate. {O}ur experimental results also demonstrate
that the proposed algorithm compares favorably with the {SVM} method,
in terms of both test accuracy and sparsity, for constructing kernel
density estimates.},
doi = {10.1109/TSMCB.2003.817107},
pdf = {../local/Chen2004Sparse.pdf},
file = {Chen2004Sparse.pdf:Chen2004Sparse.pdf:PDF},
url = {http://dx.doi.org/10.1109/TSMCB.2003.817107}
}
@article{Chen1998Atomic,
author = {Chen, S. S. and Donoho, D. L. and Saunders, M.},
title = {Atomic decomposition by basis pursuit},
journal = {SIAM J. Sci. Comput.},
year = {1998},
volume = {20},
pages = {33--61},
number = {1},
doi = {10.1137/S1064827596304010},
timestamp = {2007.12.31},
url = {http://dx.doi.org/10.1137/S1064827596304010}
}
@article{Chen1999Modeling,
author = {Chen, T. and He, H. L. and Church, G. M.},
title = {Modeling gene expression with differential equations},
journal = {Pac. Symp. Biocomput.},
year = {1999},
volume = {4},
pages = {29--40},
abstract = {We propose a differential equation model for gene expression and provide
two methods to construct the model from a set of temporal data. We
model both transcription and translation by kinetic equations with
feedback loops from translation products to transcription. Degradation
of proteins and mRNAs is also incorporated. We study two methods
to construct the model from experimental data: Minimum Weight Solutions
to Linear Equations (MWSLE), which determines the regulation by solving
under-determined linear equations, and Fourier Transform for Stable
Systems (FTSS), which refines the model with cell cycle constraints.
The results suggest that a minor set of temporal data may be sufficient
to construct the model at the genome level. We also give a comprehensive
discussion of other extended models: the RNA Model, the Protein Model,
and the Time Delay Model.},
pmid = {10380183},
timestamp = {2008.02.04}
}
@article{Chen2008Mapping,
author = {Wei Chen and Vera Kalscheuer and Andreas Tzschach and Corinna Menzel
and Reinhard Ullmann and Marcel Holger Schulz and Fikret Erdogan
and Na Li and Zofia Kijas and Ger Arkesteijn and Isidora Lopez Pajares
and Margret Goetz-Sothmann and Uwe Heinrich and Imma Rost and Andreas
Dufke and Ute Grasshoff and Birgitta Glaeser and Martin Vingron and
H. Hilger Ropers},
title = {Mapping translocation breakpoints by next-generation sequencing.},
journal = {Genome Res.},
year = {2008},
volume = {18},
pages = {1143--1149},
number = {7},
month = {Jul},
abstract = {Balanced chromosome rearrangements (BCRs) can cause genetic diseases
by disrupting or inactivating specific genes, and the characterization
of breakpoints in disease-associated BCRs has been instrumental in
the molecular elucidation of a wide variety of genetic disorders.
However, mapping chromosome breakpoints using traditional methods,
such as in situ hybridization with fluorescent dye-labeled bacterial
artificial chromosome clones (BAC-FISH), is rather laborious and
time-consuming. In addition, the resolution of BAC-FISH is often
insufficient to unequivocally identify the disrupted gene. To overcome
these limitations, we have performed shotgun sequencing of flow-sorted
derivative chromosomes using "next-generation" (Illumina/Solexa)
multiplex sequencing-by-synthesis technology. As shown here for three
different disease-associated BCRs, the coverage attained by this
platform is sufficient to bridge the breakpoints by PCR amplification,
and this procedure allows the determination of their exact nucleotide
positions within a few weeks. Its implementation will greatly facilitate
large-scale breakpoint mapping and gene finding in patients with
disease-associated balanced translocations.},
doi = {10.1101/gr.076166.108},
pdf = {../local/Chen2008Mapping.pdf},
file = {Chen2008Mapping.pdf:Chen2008Mapping.pdf:PDF},
institution = {Max Planck Institute for Molecular Genetics, 14195 Berlin, Germany.
wei@molgen.mpg.de},
keywords = {ngs, csbcbook, csbcbook-ch2},
owner = {jp},
pii = {gr.076166.108},
pmid = {18326688},
timestamp = {2009.10.09},
url = {http://dx.doi.org/10.1101/gr.076166.108}
}
@article{Chen2002Gene,
author = {Chen, X. and Cheung, S. T. and So, S. and Fan, S. T. and Barry, C.
and Higgins, J. and Lai, K.-M. and Ji, J. and Dudoit, S. and Ng,
I. O L. and {Van De Rijn}, M. and Botstein, D. and Brown, P. O.},
title = {Gene expression patterns in human liver cancers.},
journal = {Mol. Biol. Cell},
year = {2002},
volume = {13},
pages = {1929--1939},
number = {6},
month = {Jun},
abstract = {Hepatocellular carcinoma (HCC) is a leading cause of death worldwide.
Using cDNA microarrays to characterize patterns of gene expression
in HCC, we found consistent differences between the expression patterns
in HCC compared with those seen in nontumor liver tissues. The expression
patterns in HCC were also readily distinguished from those associated
with tumors metastatic to liver. The global gene expression patterns
intrinsic to each tumor were sufficiently distinctive that multiple
tumor nodules from the same patient could usually be recognized and
distinguished from all the others in the large sample set on the
basis of their gene expression patterns alone. The distinctive gene
expression patterns are characteristic of the tumors and not the
patient; the expression programs seen in clonally independent tumor
nodules in the same patient were no more similar than those in tumors
from different patients. Moreover, clonally related tumor masses
that showed distinct expression profiles were also distinguished
by genotypic differences. Some features of the gene expression patterns
were associated with specific phenotypic and genotypic characteristics
of the tumors, including growth rate, vascular invasion, and p53
overexpression.},
doi = {10.1091/mbc.02-02-0023},
pdf = {../local/Chen2002Gene.pdf},
file = {Chen2002Gene.pdf:Chen2002Gene.pdf:PDF},
institution = {Department of Biochemistry, Stanford University School of Medicine,
Stanford, California 94305, USA.},
keywords = {csbcbook-ch3, csbcbook},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pmid = {12058060},
timestamp = {2011.11.30},
url = {http://dx.doi.org/10.1091/mbc.02-02-0023}
}
@article{Chen1998Recursive,
author = {X. Chen and A. Russinko III and S. S. Young},
title = {Recursive {P}artitioning {A}nalysis of a {L}arge {S}tructure-{A}ctivity
{D}ata {S}et {U}sing {T}hree-{D}imensional {D}escriptors},
journal = {J Chem Inf Comput Sci},
year = {1998},
volume = {38},
pages = {1054-1062},
keywords = {chemoinformatics},
owner = {mahe},
timestamp = {2006.08.22}
}
@article{Chen2004Prediction,
author = {Chen, Y.C. and Lin, Y.S. and Lin, C.J. and Hwang, J.K.},
title = {Prediction of the bonding states of cysteines using the support vector
machines based on multiple feature vectors and cysteine state sequences},
journal = {Proteins},
year = {2004},
volume = {55},
pages = {1036-1042},
number = {4},
abstract = {The support vector machine ({SVM}) method is used to predict the bonding
states of cysteines. {B}esides using local descriptors such as the
local sequences, we include global information, such as amino acid
compositions and the patterns of the states of cysteines (bonded
or nonbonded), or cysteine state sequences, of the proteins. {W}e
found that {SVM} based on local sequences or global amino acid compositions
yielded similar prediction accuracies for the data set comprising
4136 cysteine-containing segments extracted from 969 nonhomologous
proteins. {H}owever, the {SVM} method based on multiple feature vectors
(combining local sequences and global amino acid compositions) significantly
improves the prediction accuracy, from 80% to 86%. {I}f coupled with
cysteine state sequences, {SVM} based on multiple feature vectors
yields 90% in overall prediction accuracy and a 0.77 {M}atthews correlation
coefficient, around 10% and 22% higher than the corresponding values
obtained by {SVM} based on local sequence information.},
doi = {10.1002/prot.20079},
pdf = {../local/Chen2004Prediction.pdf},
file = {Chen2004Prediction.pdf:local/Chen2004Prediction.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://dx.doi.org/Chen2004Prediction.pdf}
}
@article{Chen2005Understanding,
author = {Chen, Y. and Xu, D.},
title = {Understanding protein dispensability through machine-learning analysis
of high-throughput data},
journal = {Bioinformatics},
year = {2005},
volume = {21},
pages = {575-581},
month = {Mar},
abstract = {Motivation: {P}rotein dispensability is fundamental to understanding
of gene function and evolution. {R}ecent advances in generating high-throughput
data such as genomic sequence data, protein-protein interaction data,
gene-expression data, and growth-rate data of mutants allow us to
investigate protein dispensability systematically at the genome scale.{R}esults:
{I}n our studies, protein dispensability is represented as a fitness
score that is measured by the growth rate of gene-deletion mutants.
{T}hrough analyses of high-throughput data in yeast {S}accharomyces
cerevisia, we found that a protein's dispensability had significant
correlations with its evolutionary rate and duplication rate, as
well as its connectivity in protein-protein interaction network and
gene-expression correlation network. {N}eural network and support
vector machine were applied to predict protein dispensability through
high-throughput data. {O}ur studies shed some lights on global characteristics
of protein dispensability and evolution.{A}vailability: {T}he original
datasets for protein dispensability analysis and prediction, together
with related scripts, are available at http://digbio.missouri.edu/~ychen/{P}ro{D}ispen/.},
doi = {10.1093/bioinformatics/bti058},
pdf = {../local/Chen2005Understanding.pdf},
file = {Chen2005Understanding.pdf:local/Chen2005Understanding.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://dx.doi.org/10.1093/bioinformatics/bti058}
}
@article{Cheng2005Protein,
author = {Betty Yee Man Cheng and Jaime G Carbonell and Judith Klein-Seetharaman},
title = {Protein classification based on text document classification techniques.},
journal = {Proteins},
year = {2005},
volume = {58},
pages = {955-70},
number = {4},
month = {Mar},
abstract = {The need for accurate, automated protein classification methods continues
to increase as advances in biotechnology uncover new proteins. {G}-protein
coupled receptors ({GPCR}s) are a particularly difficult superfamily
of proteins to classify due to extreme diversity among its members.
{P}revious comparisons of {BLAST}, k-nearest neighbor (k-{NN}), hidden
markov model ({HMM}) and support vector machine ({SVM}) using alignment-based
features have suggested that classifiers at the complexity of {SVM}
are needed to attain high accuracy. {H}ere, analogous to document
classification, we applied {D}ecision {T}ree and {N}aive {B}ayes
classifiers with chi-square feature selection on counts of n-grams
(i.e. short peptide sequences of length n) to this classification
task. {U}sing the {GPCR} dataset and evaluation protocol from the
previous study, the {N}aive {B}ayes classifier attained an accuracy
of 93.0 and 92.4\% in level {I} and level {II} subfamily classification
respectively, while {SVM} has a reported accuracy of 88.4 and 86.3\%.
{T}his is a 39.7 and 44.5\% reduction in residual error for level
{I} and level {II} subfamily classification, respectively. {T}he
{D}ecision {T}ree, while inferior to {SVM}, outperforms {HMM} in
both level {I} and level {II} subfamily classification. {F}or those
{GPCR} families whose profiles are stored in the {P}rotein {FAM}ilies
database of alignments and {HMM}s ({PFAM}), our method performs comparably
to a search against those profiles. {F}inally, our method can be
generalized to other protein families by applying it to the superfamily
of nuclear receptors with 94.5, 97.8 and 93.6\% accuracy in family,
level {I} and level {II} subfamily classification respectively.},
doi = {10.1002/prot.20373},
pdf = {../local/Cheng2005Protein.pdf},
file = {Cheng2005Protein.pdf:local/Cheng2005Protein.pdf:PDF},
url = {http://dx.doi.org/10.1002/prot.20373}
}
@article{Cheng2000Biclustering,
author = {Y. Cheng and G. M. Church},
title = {Biclustering of expression data.},
journal = {Proc Int Conf Intell Syst Mol Biol},
year = {2000},
volume = {8},
pages = {93--103},
abstract = {An efficient node-deletion algorithm is introduced to find submatrices
in expression data that have low mean squared residue scores and
it is shown to perform well in finding co-regulation patterns in
yeast and human. This introduces "biclustering", or simultaneous
clustering of both genes and conditions, to knowledge discovery from
expression data. This approach overcomes some problems associated
with traditional clustering methods, by allowing automatic discovery
of similarity based on a subset of attributes, simultaneous clustering
of genes and conditions, and overlapped grouping that provides a
better representation for genes with multiple functions or regulated
by many factors.},
institution = {Department of Genetics, Harvard Medical School, Boston, MA 02115,
USA. yizong.cheng@uc.edu},
keywords = {Algorithms; Animals; Gene Expression Profiling, methods; Humans; Multigene
Family; Oligonucleotide Array Sequence Analysis, methods},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pmid = {10977070},
timestamp = {2012.02.27}
}
@article{Chenna2003Multiple,
author = {Chenna, R. and Sugawara, H. and Koike, T. and Lopez, R. and Gibson,
T. J. and Higgins, D. G. and Thompson, J. D.},
title = {Multiple sequence alignment with the {Clustal} series of programs.},
journal = {Nucleic Acids Res.},
year = {2003},
volume = {31},
pages = {3497--3500},
number = {13},
month = {Jul},
abstract = {The Clustal series of programs are widely used in molecular biology
for the multiple alignment of both nucleic acid and protein sequences
and for preparing phylogenetic trees. The popularity of the programs
depends on a number of factors, including not only the accuracy of
the results, but also the robustness, portability and user-friendliness
of the programs. New features include NEXUS and FASTA format output,
printing range numbers and faster tree calculation. Although, Clustal
was originally developed to run on a local computer, numerous Web
servers have been set up, notably at the EBI (European Bioinformatics
Institute) (http://www.ebi.ac.uk/clustalw/).},
keywords = {Algorithms; Amino Acid Sequence; Internet; Nucleic Acids; Phylogeny;
Sequence Alignment; Sequence Analysis; Sequence Analysis, Protein;
Software},
owner = {laurent},
pmid = {12824352},
timestamp = {2008.01.15}
}
@article{Cherezov2007High-resolution,
author = {Vadim Cherezov and Daniel M Rosenbaum and Michael A Hanson and Søren
G F Rasmussen and Foon Sun Thian and Tong Sun Kobilka and Hee-Jung
Choi and Peter Kuhn and William I Weis and Brian K Kobilka and Raymond
C Stevens},
title = {High-resolution crystal structure of an engineered human beta2-adrenergic
G protein-coupled receptor.},
journal = {Science},
year = {2007},
volume = {318},
pages = {1258--1265},
number = {5854},
month = {Nov},
abstract = {Heterotrimeric guanine nucleotide-binding protein (G protein)-coupled
receptors constitute the largest family of eukaryotic signal transduction
proteins that communicate across the membrane. We report the crystal
structure of a human beta2-adrenergic receptor-T4 lysozyme fusion
protein bound to the partial inverse agonist carazolol at 2.4 angstrom
resolution. The structure provides a high-resolution view of a human
G protein-coupled receptor bound to a diffusible ligand. Ligand-binding
site accessibility is enabled by the second extracellular loop, which
is held out of the binding cavity by a pair of closely spaced disulfide
bridges and a short helical segment within the loop. Cholesterol,
a necessary component for crystallization, mediates an intriguing
parallel association of receptor molecules in the crystal lattice.
Although the location of carazolol in the beta2-adrenergic receptor
is very similar to that of retinal in rhodopsin, structural differences
in the ligand-binding site and other regions highlight the challenges
in using rhodopsin as a template model for this large receptor family.},
doi = {10.1126/science.1150577},
pdf = {../local/Cherezov2007High-resolution.pdf},
file = {Cherezov2007High-resolution.pdf:Cherezov2007High-resolution.pdf:PDF},
owner = {laurent},
pii = {1150577},
pmid = {17962520},
timestamp = {2008.04.01},
url = {http://dx.doi.org/10.1126/science.1150577}
}
@article{Cherkassky2004Practical,
author = {Vladimir Cherkassky and Yunqian Ma},
title = {Practical selection of {SVM} parameters and noise estimation for
{SVM} regression.},
journal = {Neural {N}etw},
year = {2004},
volume = {17},
pages = {113-26},
number = {1},
month = {Jan},
abstract = {We investigate practical selection of hyper-parameters for support
vector machines ({SVM}) regression (that is, epsilon-insensitive
zone and regularization parameter {C}). {T}he proposed methodology
advocates analytic parameter selection directly from the training
data, rather than re-sampling approaches commonly used in {SVM} applications.
{I}n particular, we describe a new analytical prescription for setting
the value of insensitive zone epsilon, as a function of training
sample size. {G}ood generalization performance of the proposed parameter
selection is demonstrated empirically using several low- and high-dimensional
regression problems. {F}urther, we point out the importance of {V}apnik's
epsilon-insensitive loss for regression problems with finite samples.
{T}o this end, we compare generalization performance of {SVM} regression
(using proposed selection of epsilon-values) with regression using
'least-modulus' loss (epsilon=0) and standard squared loss. {T}hese
comparisons indicate superior generalization performance of {SVM}
regression under sparse sample settings, for various types of additive
noise.},
doi = {10.1016/S0893-6080(03)00169-2},
pdf = {../local/Cherkassky2004Practical.pdf},
file = {Cherkassky2004Practical.pdf:Cherkassky2004Practical.pdf:PDF},
pii = {S0893608003001692},
url = {http://dx.doi.org/10.1016/S0893-6080(03)00169-2}
}
@article{Chi2008year,
author = {Chi, K. R.},
title = {The year of sequencing},
journal = {Nat. Methods},
year = {2008},
volume = {5},
pages = {11--14},
number = {1},
month = {Jan},
abstract = {In 2007, the next-generation sequencing technologies have come into
their own with an impressive array of successful applications. Kelly
Rae Chi reports.},
doi = {10.1038/nmeth1154},
pdf = {../local/Chi2008year.pdf},
file = {Chi2008year.pdf:Chi2008year.pdf:PDF},
keywords = {csbcbook, csbcbook-ch2},
owner = {jp},
pmid = {18175410},
timestamp = {2009.10.13},
url = {http://dx.doi.org/10.1038/nmeth1154}
}
@article{Chiang2001Visualizing,
author = {Chiang, D. Y. and Brown, P. O. and Eisen, M. B.},
title = {Visualizing associations between genome sequences and gene expression
data using genome-mean expression profiles},
journal = {Bioinformatics},
year = {2001},
volume = {17},
pages = {49S--55S},
pdf = {../local/chia01.pdf},
file = {chia01.pdf:local/chia01.pdf:PDF},
subject = {microarray},
url = {http://bioinformatics.oupjournals.org/cgi/reprint/17/suppl_1/S49.pdf}
}
@article{Chiang2009High-resolution,
author = {Derek Y Chiang and Gad Getz and David B Jaffe and Michael J T O'Kelly
and Xiaojun Zhao and Scott L Carter and Carsten Russ and Chad Nusbaum
and Matthew Meyerson and Eric S Lander},
title = {High-resolution mapping of copy-number alterations with massively
parallel sequencing.},
journal = {Nat. Methods},
year = {2009},
volume = {6},
pages = {99--103},
number = {1},
month = {Jan},
abstract = {Cancer results from somatic alterations in key genes, including point
mutations, copy-number alterations and structural rearrangements.
A powerful way to discover cancer-causing genes is to identify genomic
regions that show recurrent copy-number alterations (gains and losses)
in tumor genomes. Recent advances in sequencing technologies suggest
that massively parallel sequencing may provide a feasible alternative
to DNA microarrays for detecting copy-number alterations. Here we
present: (i) a statistical analysis of the power to detect copy-number
alterations of a given size; (ii) SegSeq, an algorithm to segment
equal copy numbers from massively parallel sequence data; and (iii)
analysis of experimental data from three matched pairs of tumor and
normal cell lines. We show that a collection of approximately 14
million aligned sequence reads from human cell lines has comparable
power to detect events as the current generation of DNA microarrays
and has over twofold better precision for localizing breakpoints
(typically, to within approximately 1 kilobase).},
doi = {10.1038/nmeth.1276},
pdf = {../local/Chiang2009High-resolution.pdf},
file = {Chiang2009High-resolution.pdf:Chiang2009High-resolution.pdf:PDF},
institution = {Broad Institute, Massachusetts Institute of Technology, 7 Cambridge
Center, Cambridge, MA 02142, USA.},
keywords = {ngs},
owner = {jp},
pii = {nmeth.1276},
pmid = {19043412},
timestamp = {2009.10.09},
url = {http://dx.doi.org/10.1038/nmeth.1276}
}
@article{Chin2008Translating,
author = {Chin, L. and Gray, J. W.},
title = {Translating insights from the cancer genome into clinical practice.},
journal = {Nature},
year = {2008},
volume = {452},
pages = {553--563},
number = {7187},
month = {Apr},
abstract = {Cancer cells have diverse biological capabilities that are conferred
by numerous genetic aberrations and epigenetic modifications. Today's
powerful technologies are enabling these changes to the genome to
be catalogued in detail. Tomorrow is likely to bring a complete atlas
of the reversible and irreversible alterations that occur in individual
cancers. The challenge now is to work out which molecular abnormalities
contribute to cancer and which are simply 'noise' at the genomic
and epigenomic levels. Distinguishing between these will aid in understanding
how the aberrations in a cancer cell collaborate to drive pathophysiology.
Past successes in converting information from genomic discoveries
into clinical tools provide valuable lessons to guide the translation
of emerging insights from the genome into clinical end points that
can affect the practice of cancer medicine.},
doi = {10.1038/nature06914},
pdf = {../local/Chin2008Translating.pdf},
file = {Chin2008Translating.pdf:Chin2008Translating.pdf:PDF},
institution = {Dana-Farber Cancer Institute and Harvard Medical School, 44 Binney
Street, Boston, Massachusetts 02115, USA. lynda_chin@dfci.harvard.edu},
keywords = {csbcbook-ch3},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {nature06914},
pmid = {18385729},
timestamp = {2011.11.30},
url = {http://dx.doi.org/10.1038/nature06914}
}
@article{Chin2007High-resolution,
author = {Chin, S. F. and Teschendorff, A. E. and Marioni, J. C. and Wang,
Y. and Barbosa-Morais, N. L. and Thorne, N. P. and Costa, J. L. and
Pinder, S. E. and van de Wiel, M. A. and Green, A. R. and Ellis,
I. O. and Porter, P. L. and Tavar{\'e}, S. and Brenton, J. D. and
Ylstra, B. and Caldas, C.},
title = {High-resolution {aCGH} and expression profiling identifies a novel
genomic subtype of {ER} negative breast cancer.},
journal = {Genome Biol.},
year = {2007},
volume = {8},
pages = {R215},
number = {10},
abstract = {BACKGROUND: The characterization of copy number alteration patterns
in breast cancer requires high-resolution genome-wide profiling of
a large panel of tumor specimens. To date, most genome-wide array
comparative genomic hybridization studies have used tumor panels
of relatively large tumor size and high Nottingham Prognostic Index
(NPI) that are not as representative of breast cancer demographics.
RESULTS: We performed an oligo-array-based high-resolution analysis
of copy number alterations in 171 primary breast tumors of relatively
small size and low NPI, which was therefore more representative of
breast cancer demographics. Hierarchical clustering over the common
regions of alteration identified a novel subtype of high-grade estrogen
receptor (ER)-negative breast cancer, characterized by a low genomic
instability index. We were able to validate the existence of this
genomic subtype in one external breast cancer cohort. Using matched
array expression data we also identified the genomic regions showing
the strongest coordinate expression changes ('hotspots'). We show
that several of these hotspots are located in the phosphatome, kinome
and chromatinome, and harbor members of the 122-breast cancer CAN-list.
Furthermore, we identify frequently amplified hotspots on 8q22.3
(EDD1, WDSOF1), 8q24.11-13 (THRAP6, DCC1, SQLE, SPG8) and 11q14.1
(NDUFC2, ALG8, USP35) associated with significantly worse prognosis.
Amplification of any of these regions identified 37 samples with
significantly worse overall survival (hazard ratio (HR) = 2.3 (1.3-1.4)
p = 0.003) and time to distant metastasis (HR = 2.6 (1.4-5.1) p =
0.004) independently of NPI. CONCLUSION: We present strong evidence
for the existence of a novel subtype of high-grade ER-negative tumors
that is characterized by a low genomic instability index. We also
provide a genome-wide list of common copy number alteration regions
in breast cancer that show strong coordinate aberrant expression,
and further identify novel frequently amplified regions that correlate
with poor prognosis. Many of the genes associated with these regions
represent likely novel oncogenes or tumor suppressors.},
doi = {10.1186/gb-2007-8-10-r215},
pdf = {../local/Chin2007High-resolution.pdf},
file = {Chin2007High-resolution.pdf:Chin2007High-resolution.pdf:PDF},
institution = {Breast Cancer Functional Genomics, Cancer Research UK Cambridge Research
Institute and Department of Oncology University of Cambridge, Li
Ka-Shing Centre, Robinson Way, Cambridge CB2 0RE, UK. sc10021@cam.ac.uk},
keywords = {breastcancer, cgh},
owner = {jp},
pii = {gb-2007-8-10-r215},
pmid = {17925008},
timestamp = {2008.12.09},
url = {http://dx.doi.org/10.1186/gb-2007-8-10-r215}
}
@article{Chin2006Using,
author = {Chin, S.-F. and Wang, Y. and Thorne, N. P. and Teschendorff, A. E.
and Pinder, S. E. and Vias, M. and Naderi, A. and Roberts, I. and
Barbosa-Morais, N. L. and Garcia, M. J. and Iyer, N. G. and Kranjac,
T. and Robertson, J. F. R. and Aparicio, S. and Tavare, S. and Ellis,
I. and Brenton, J. D. and Caldas, C.},
title = {Using array-comparative genomic hybridization to define molecular
portraits of primary breast cancers},
journal = {Oncogene},
year = {2006},
volume = {26},
pages = {1959--1970},
number = {13},
month = sep,
doi = {10.1038/sj.onc.1209985},
pdf = {../local/Chin2006Using.pdf},
file = {Chin2006Using.pdf:Chin2006Using.pdf:PDF},
issn = {0950-9232},
keywords = {breastcancer},
owner = {franck},
timestamp = {2007.11.23},
url = {http://dx.doi.org/10.1038/sj.onc.1209985}
}
@inproceedings{Chipman2003Clustering,
author = {Chipman, H. and Hastie, T. and Tibshirani, R.},
title = {Clustering Microarray Data},
booktitle = {Statistical Analysis of Gene Expression Microarray Data},
year = {2003},
editor = {Speed, T.},
pages = {159--200},
publisher = {Chapman and Hall, CRC press.},
owner = {jp},
timestamp = {2011.12.29}
}
@inbook{Chipman2003Statistical,
chapter = {Clustering Microarray Data},
pages = {159--200},
title = {Statistical Analysis of Gene Expression Microarray Data},
publisher = {Chapman and Hall, CRC press.},
year = {2003},
editor = {Speed, T.},
author = {Chipman, H. and Hastie, T. and Tibshirani, R.},
owner = {jp},
timestamp = {2011.12.29}
}
@article{Choi2007Coupled,
author = {Choi, H.-S. and Han, S. and Yokota, H. and Cho, K.-H.},
title = {Coupled positive feedbacks provoke slow induction plus fast switching
in apoptosis},
journal = {FEBS Letters},
year = {2007},
volume = {581},
pages = {2684 - 2690},
number = {14},
abstract = {Apoptosis is a form of a programmed cell death for multicellular organisms
to remove unwanted or damaged cells. This critical choice of cellular
fate is an all-or-none process, but its dynamics remains unraveled.
The switch-like apoptotic decision has to be reliable, and once a
pro-apoptotic fate is determined it requires fast and irreversible
execution. One of the key regulators in apoptosis is caspase-3. Interestingly,
activated caspase-3 quickly executes apoptosis, but it takes considerable
time to activate it. Here, we have analyzed this slow induction plus
fast switching mechanism of caspase-3 through mathematical modeling
and computational simulation. First, we have shown that two positive
feedbacks, composed of caspase-8 and XIAP, are essential for the
slow induction plus fast switching behavior of caspase-3. Second,
we have found that XIAP in the feedback loops primarily regulates
induction time of caspase-3. In many cancer cells activation of caspase-3
is suppressed. Our results suggest that reinforcement of the positive
feedback by XIAP, which relieves XIAP-mediated caspase-3 inhibition,
might favor a pro-apoptotic cellular fate.},
doi = {DOI: 10.1016/j.febslet.2007.05.016},
pdf = {../local/Choi2007Coupled.pdf},
file = {Choi2007Coupled.pdf:Choi2007Coupled.pdf:PDF},
issn = {0014-5793},
keywords = {csbcbook},
url = {http://www.sciencedirect.com/science/article/B6T36-4NSR1M2-F/2/1d13173e1580459d4c4430a96116dc3d}
}
@article{Chou2001Prediction,
author = {Chou, K.-C.},
title = {Prediction of protein signal sequences and their cleavage sites},
journal = {Protein. {S}truct. {F}unct. {G}enet.},
year = {2001},
volume = {42},
pages = {136--139},
pdf = {../local/chou01.pdf},
file = {chou01.pdf:local/chou01.pdf:PDF},
subject = {bioprot},
url = {http://www3.interscience.wiley.com/cgi-bin/abstract/75504759/START}
}
@article{Chou2001Using,
author = {Chou, K.-C.},
title = {Using subsite coupling to predict signal peptides},
journal = {Protein {E}ng.},
year = {2001},
volume = {14},
pages = {75--79},
number = {2},
pdf = {../local/chou01b.pdf},
file = {chou01b.pdf:local/chou01b.pdf:PDF},
subject = {bioprot},
url = {http://protein.oupjournals.org/cgi/content/abstract/14/2/75}
}
@article{Chou2002Using,
author = {Chou, K.-C. and Cai, Y.-D.},
title = {Using {F}unctional {D}omain {C}omposition and {S}upport {V}ector
{M}achines for {P}rediction of {P}rotein {S}ubcellular {L}ocation},
journal = {J. {B}iol. {C}hem.},
year = {2002},
volume = {277},
pages = {45765-45769},
number = {48},
abstract = {Proteins are generally classified into the following 12 subcellular
locations: 1) chloroplast, 2) cytoplasm, 3) cytoskeleton, 4) endoplasmic
reticulum, 5) extracellular, 6) {G}olgi apparatus, 7) lysosome, 8)
mitochondria, 9) nucleus, 10) peroxisome, 11) plasma membrane, and
12) vacuole. {B}ecause the function of a protein is closely correlated
with its subcellular location, with the rapid increase in new protein
sequences entering into databanks, it is vitally important for both
basic research and pharmaceutical industry to establish a high throughput
tool for predicting protein subcellular location. {I}n this paper,
a new concept, the so-called "functional domain composition" is introduced.
{B}ased on the novel concept, the representation for a protein can
be defined as a vector in a high-dimensional space, where each of
the clustered functional domains derived from the protein universe
serves as a vector base. {W}ith such a novel representation for a
protein, the support vector machine ({SVM}) algorithm is introduced
for predicting protein subcellular location. {H}igh success rates
are obtained by the self-consistency test, jackknife test, and independent
dataset test, respectively. {T}he current approach not only can play
an important complementary role to the powerful covariant discriminant
algorithm based on the pseudo amino acid composition representation
({C}hou, {K}. {C}. (2001) {P}roteins {S}truct. {F}unct. {G}enet.
43, 246-255; {C}orrection (2001) {P}roteins {S}truct. {F}unct. {G}enet.
44, 60), but also may greatly stimulate the development of this area.},
pdf = {../local/Chou2002Using.pdf},
file = {Chou2002Using.pdf:local/Chou2002Using.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://www.jbc.org/cgi/content/abstract/277/48/45765}
}
@article{Choudhary2010Decoding,
author = {Chunaram Choudhary and Matthias Mann},
title = {Decoding signalling networks by mass spectrometry-based proteomics.},
journal = {Nat Rev Mol Cell Biol},
year = {2010},
volume = {11},
pages = {427--439},
number = {6},
month = {Jun},
abstract = {Signalling networks regulate essentially all of the biology of cells
and organisms in normal and disease states. Signalling is often studied
using antibody-based techniques such as western blots. Large-scale
'precision proteomics' based on mass spectrometry now enables the
system-wide characterization of signalling events at the levels of
post-translational modifications, protein-protein interactions and
changes in protein expression. This technology delivers accurate
and unbiased information about the quantitative changes of thousands
of proteins and their modifications in response to any perturbation.
Current studies focus on phosphorylation, but acetylation, methylation,
glycosylation and ubiquitylation are also becoming amenable to investigation.
Large-scale proteomics-based signalling research will fundamentally
change our understanding of signalling networks.},
doi = {10.1038/nrm2900},
institution = {The Novo Nordisk Foundation Center for Protein Research, Faculty
of Health Sciences, University of Copenhagen, 2200 Copenhagen, Denmark.
chuna.choudhary@cpr.ku.dk},
keywords = {Animals; Humans; Mass Spectrometry; Protein Processing, Post-Translational;
Proteome; Proteomics; Signal Transduction},
owner = {phupe},
pii = {nrm2900},
pmid = {20461098},
timestamp = {2010.08.13},
url = {http://dx.doi.org/10.1038/nrm2900}
}
@article{Chow2001Identifying,
author = {M. L. Chow and E. J. Moler and I. S. Mian},
title = {Identifying marker genes in transcription profiling data using a
mixture of feature relevance experts.},
journal = {Physiol. {G}enomics},
year = {2001},
volume = {5},
pages = {99-111},
number = {2},
month = {Mar},
abstract = {Transcription profiling experiments permit the expression levels of
many genes to be measured simultaneously. {G}iven profiling data
from two types of samples, genes that most distinguish the samples
(marker genes) are good candidates for subsequent in-depth experimental
studies and developing decision support systems for diagnosis, prognosis,
and monitoring. {T}his work proposes a mixture of feature relevance
experts as a method for identifying marker genes and illustrates
the idea using published data from samples labeled as acute lymphoblastic
and myeloid leukemia ({ALL}, {AML}). {A} feature relevance expert
implements an algorithm that calculates how well a gene distinguishes
samples, reorders genes according to this relevance measure, and
uses a supervised learning method [here, support vector machines
({SVM}s)] to determine the generalization performances of different
nested gene subsets. {T}he mixture of three feature relevance experts
examined implement two existing and one novel feature relevance measures.
{F}or each expert, a gene subset consisting of the top 50 genes distinguished
{ALL} from {AML} samples as completely as all 7,070 genes. {T}he
125 genes at the union of the top 50s are plausible markers for a
prototype decision support system. {C}hromosomal aberration and other
data support the prediction that the three genes at the intersection
of the top 50s, cystatin {C}, azurocidin, and adipsin, are good targets
for investigating the basic biology of {ALL}/{AML}. {T}he same data
were employed to identify markers that distinguish samples based
on their labels of {T} cell/{B} cell, peripheral blood/bone marrow,
and male/female. {S}elenoprotein {W} may discriminate {T} cells from
{B} cells. {R}esults from analysis of transcription profiling data
from tumor/nontumor colon adenocarcinoma samples support the general
utility of the aforementioned approach. {T}heoretical issues such
as choosing {SVM} kernels and their parameters, training and evaluating
feature relevance experts, and the impact of potentially mislabeled
samples on marker identification (feature selection) are discussed.},
pdf = {../local/Chow2001Identifying.pdf},
file = {Chow2001Identifying.pdf:local/Chow2001Identifying.pdf:PDF},
keywords = {biosvm},
pii = {5/2/99},
url = {http://physiolgenomics.physiology.org/cgi/content/abstract/5/2/99}
}
@article{Chowdhury2010Identification,
author = {Chowdhury, S. A. and Koyut\"urk, M.},
title = {Identification of coordinately dysregulated subnetworks in complex
phenotypes.},
journal = {Pac. Symp. Biocomput.},
year = {2010},
pages = {133--144},
abstract = {In the study of complex phenotypes, single gene markers can only provide
limited insights into the manifestation of phenotype. To this end,
protein-protein interaction (PPI) networks prove useful in the identification
of multiple interacting markers. Recent studies show that, when considered
together, many proteins that are connected via physical and functional
interactions exhibit significant differential expression with respect
to various complex phenotypes, including cancers. As compared to
single gene markers, these "coordinately dysregulated subnetworks"
improve diagnosis and prognosis of cancer significantly and offer
novel insights into the network dynamics of phenotype. However, the
problem of identifying coordinately dysregulated subnetworks presents
significant algorithmic challenges. Existing approaches utilize heuristics
that aim to greedily maximize information-theoretic class separability
measures, however, by definition of "coordinate" dysregulation, such
greedy algorithms do not suit well to this problem. In this paper,
we formulate coordinate dysregulation in the context of the well-known
set-cover problem, with a view to capturing the coordination between
multiple genes at a sample-specific resolution. Based on this formulation,
we adapt state-of-the-art approximation algorithms for set-cover
to the identification of coordinately dysregulated subnetworks. Comprehensive
experimental results on human colorectal cancer (CRC) show that,
when compared to existing algorithms, the proposed algorithm, NETCOVER,
improves diagnosis of cancer and prediction of metastasis significantly.
Our results also demonstrate that subnetworks in the neighborhood
of known CRC driver genes exhibit significant coordinate dysregulation,
indicating that the notion of coordinate dysregulation may indeed
be useful in understanding the network dynamics of complex phenotypes.},
pdf = {../local/Chowdhury2010Identification.pdf},
file = {Chowdhury2010Identification.pdf:Chowdhury2010Identification.pdf:PDF},
institution = {Department of Electrical Engineering and Computer Science, Case Western
Reserve University, Cleveland, OH, USA. sxc426@eecs.case.edu.},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {9789814295291_0016},
pmid = {19908366},
timestamp = {2011.10.08}
}
@article{Chowdhury2011Subnetwork,
author = {Chowdhury, S. A. and Nibbe, R. K. and Chance, M. R. and Koyut\"urk,
M.},
title = {Subnetwork state functions define dysregulated subnetworks in cancer.},
journal = {J. Comput. Biol.},
year = {2011},
volume = {18},
pages = {263--281},
number = {3},
month = {Mar},
abstract = {Emerging research demonstrates the potential of protein-protein interaction
(PPI) networks in uncovering the mechanistic bases of cancers, through
identification of interacting proteins that are coordinately dysregulated
in tumorigenic and metastatic samples. When used as features for
classification, such coordinately dysregulated subnetworks improve
diagnosis and prognosis of cancer considerably over single-gene markers.
However, existing methods formulate coordination between multiple
genes through additive representation of their expression profiles
and utilize fast heuristics to identify dysregulated subnetworks,
which may not be well suited to the potentially combinatorial nature
of coordinate dysregulation. Here, we propose a combinatorial formulation
of coordinate dysregulation and decompose the resulting objective
function to cast the problem as one of identifying subnetwork state
functions that are indicative of phenotype. Based on this formulation,
we show that coordinate dysregulation of larger subnetworks can be
bounded using simple statistics on smaller subnetworks. We then use
these bounds to devise an efficient algorithm, Crane, that can search
the subnetwork space more effectively than existing algorithms. Comprehensive
cross-classification experiments show that subnetworks identified
by Crane outperform those identified by additive algorithms in predicting
metastasis of colorectal cancer (CRC).},
doi = {10.1089/cmb.2010.0269},
pdf = {../local/Chowdhury2011Subnetwork.pdf},
file = {Chowdhury2011Subnetwork.pdf:Chowdhury2011Subnetwork.pdf:PDF},
institution = {Department of Electrical Engineering and Computer Science, Case Western
Reserve University, Cleveland, Ohio, USA.},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pmid = {21385033},
timestamp = {2011.10.08},
url = {http://dx.doi.org/10.1089/cmb.2010.0269}
}
@article{Chu1998Transcriptional,
author = {S. Chu and J. DeRisi and M. Eisen and J. Mulholland and D. Botstein
and P.O. Brown and I. Herskowitz},
title = {The {T}ranscriptional {P}rogram of {S}porulation in {B}udding {Y}east},
journal = {Science},
year = {1998},
volume = {282},
pages = {699--705},
pdf = {../local/chu98.pdf},
file = {chu98.pdf:local/chu98.pdf:PDF},
owner = {phupe},
subject = {microarray},
timestamp = {2009.10.15},
url = {http://www.sciencemag.org/cgi/reprint/282/5389/699.pdf}
}
@article{Chu2005improved,
author = {Wei Chu and Chong Jin Ong and S. Sathiya Keerthi},
title = {An improved conjugate gradient scheme to the solution of least squares
{SVM}.},
journal = {I{EEE} {T}rans {N}eural {N}etw},
year = {2005},
volume = {16},
pages = {498-501},
number = {2},
month = {Mar},
abstract = {The least square support vector machines ({LS}-{SVM}) formulation
corresponds to the solution of a linear system of equations. {S}everal
approaches to its numerical solutions have been proposed in the literature.
{I}n this letter, we propose an improved method to the numerical
solution of {LS}-{SVM} and show that the problem can be solved using
one reduced system of linear equations. {C}ompared with the existing
algorithm for {LS}-{SVM}, the approach used in this letter is about
twice as efficient. {N}umerical results using the proposed method
are provided for comparisons with other existing algorithms.},
doi = {10.1109/TNN.2004.841785},
pdf = {../local/Chu2005improved.pdf},
file = {Chu2005improved.pdf:local/Chu2005improved.pdf:PDF},
keywords = {80 and over, Aged, Algorithms, Area Under Curve, Cross-Sectional Studies,
Diagnostic Imaging, Diagnostic Techniques, Glaucoma, Humans, Lasers,
Least-Squares Analysis, Middle Aged, Nerve Fibers, Non-U.S. Gov't,
Ophthalmological, Optic Nerve Diseases, P.H.S., ROC Curve, Research
Support, Retinal Ganglion Cells, Sensitivity and Specificity, U.S.
Gov't, 15787157},
url = {http://dx.doi.org/10.1109/TNN.2004.841785}
}
@article{Chuang2007Network-based,
author = {Chuang, H.-Y. and Lee, E. and Liu, Y.-T. and Lee, D. and Ideker,
T.},
title = {Network-based classification of breast cancer metastasis.},
journal = {Mol. Syst. Biol.},
year = {2007},
volume = {3},
pages = {140},
abstract = {Mapping the pathways that give rise to metastasis is one of the key
challenges of breast cancer research. Recently, several large-scale
studies have shed light on this problem through analysis of gene
expression profiles to identify markers correlated with metastasis.
Here, we apply a protein-network-based approach that identifies markers
not as individual genes but as subnetworks extracted from protein
interaction databases. The resulting subnetworks provide novel hypotheses
for pathways involved in tumor progression. Although genes with known
breast cancer mutations are typically not detected through analysis
of differential expression, they play a central role in the protein
network by interconnecting many differentially expressed genes. We
find that the subnetwork markers are more reproducible than individual
marker genes selected without network information, and that they
achieve higher accuracy in the classification of metastatic versus
non-metastatic tumors.},
doi = {10.1038/msb4100180},
pdf = {../local/Chuang2007Network-based.pdf},
file = {Chuang2007Network-based.pdf:Chuang2007Network-based.pdf:PDF},
institution = {Bioinformatics Program, University of California San Diego, La Jolla,
CA 92093, USA.},
keywords = {breastcancer},
owner = {jp},
pii = {msb4100180},
pmid = {17940530},
timestamp = {2008.12.09},
url = {http://dx.doi.org/10.1038/msb4100180}
}
@article{Chung2003Spectra,
author = {Fan Chung and Linyuan Lu and Van Vu},
title = {{S}pectra of random graphs with given expected degrees.},
journal = {Proc. Natl. Acad. Sci. USA},
year = {2003},
volume = {100},
pages = {6313--6318},
number = {11},
month = {May},
abstract = {In the study of the spectra of power-law graphs, there are basically
two competing approaches. One is to prove analogues of Wigner's semicircle
law, whereas the other predicts that the eigenvalues follow a power-law
distribution. Although the semicircle law and the power law have
nothing in common, we will show that both approaches are essentially
correct if one considers the appropriate matrices. We will prove
that (under certain mild conditions) the eigenvalues of the (normalized)
Laplacian of a random power-law graph follow the semicircle law,
whereas the spectrum of the adjacency matrix of a power-law graph
obeys the power law. Our results are based on the analysis of random
graphs with given expected degrees and their relations to several
key invariants. Of interest are a number of (new) values for the
exponent beta, where phase transitions for eigenvalue distributions
occur. The spectrum distributions have direct implications to numerous
graph algorithms such as, for example, randomized algorithms that
involve rapidly mixing Markov chains.},
doi = {10.1073/pnas.0937490100},
keywords = {12743375},
pii = {0937490100},
pmid = {12743375},
timestamp = {2006.02.28},
url = {http://dx.doi.org/10.1073/pnas.0937490100}
}
@book{Chung1997Spectral,
title = {Spectral graph theory},
publisher = {American Mathematical Society},
year = {1997},
author = {Chung, F. R. K.},
volume = {92},
series = {CBMS Regional Conference Series},
address = {Providence},
subject = {net}
}
@article{Chung2003Radius,
author = {Kai-Min Chung and Wei-Chun Kao and Chia-Liang Sun and Li-Lun Wang
and Chih-Jen Lin},
title = {Radius margin bounds for support vector machines with the {RBF} kernel.},
journal = {Neural {C}omput},
year = {2003},
volume = {15},
pages = {2643-81},
number = {11},
month = {Nov},
abstract = {An important approach for efficient support vector machine ({SVM})
model selection is to use differentiable bounds of the leave-one-out
(loo) error. {P}ast efforts focused on finding tight bounds of loo
(e.g., radius margin bounds, span bounds). {H}owever, their practical
viability is still not very satisfactory. {D}uan, {K}eerthi, and
{P}oo (2003) showed that radius margin bound gives good prediction
for {L}2-{SVM}, one of the cases we look at. {I}n this letter, through
analyses about why this bound performs well for {L}2-{SVM}, we show
that finding a bound whose minima are in a region with small loo
values may be more important than its tightness. {B}ased on this
principle, we propose modified radius margin bounds for {L}1-{SVM}
(the other case) where the original bound is applicable only to the
hard-margin case. {O}ur modification for {L}1-{SVM} achieves comparable
performance to {L}2-{SVM}. {T}o study whether {L}1- or {L}2-{SVM}
should be used, we analyze other properties, such as their differentiability,
number of support vectors, and number of free support vectors. {I}n
this aspect, {L}1-{SVM} possesses the advantage of having fewer support
vectors. {T}heir implementations are also different, so we discuss
related issues in detail.},
doi = {10.1162/089976603322385108},
url = {http://dx.doi.org/10.1162/089976603322385108}
}
@article{Churchill2002Fundamentals,
author = {Churchill, G. A.},
title = {Fundamentals of experimental design for cDNA microarrays},
journal = {Nat. Genet.},
year = {2002},
volume = {32 Suppl},
pages = {490--495},
month = {Dec},
abstract = {Microarray technology is now widely available and is being applied
to address increasingly complex scientific questions. Consequently,
there is a greater demand for statistical assessment of the conclusions
drawn from microarray experiments. This review discusses fundamental
issues of how to design an experiment to ensure that the resulting
data are amenable to statistical analysis. The discussion focuses
on two-color spotted cDNA microarrays, but many of the same issues
apply to single-color gene-expression assays as well.},
doi = {10.1038/ng1031},
institution = {The Jackson Laboratory, 600 Main Street, Bar Harbor, ME 04609, USA.
garyc@jax.org},
keywords = {Animals; DNA, Complementary, analysis; Gene Expression; Gene Expression
Profiling, methods; Mice; Models, Biological; Oligonucleotide Array
Sequence Analysis, methods; Reference Standards; Reproducibility
of Results; Research Design; Statistics as Topic},
language = {eng},
medline-pst = {ppublish},
owner = {phupe},
pii = {ng1031},
pmid = {12454643},
timestamp = {2011.04.08},
url = {http://dx.doi.org/10.1038/ng1031}
}
@article{Cianchetta2005Predictive,
author = {Cianchetta, G. and Li, Y. and Kang, J. and Rampe, D. and Fravolini,
A. and Cruciani, G. and Vaz, R. J.},
title = {{P}redictive models for h{ERG} potassium channel blockers.},
journal = {Bioorg. Med. Chem. Lett.},
year = {2005},
volume = {15},
pages = {3637--3642},
number = {15},
month = {Aug},
abstract = {We report here a general method for the prediction of hERG potassium
channel blockers using computational models generated from correlation
analyses of a large dataset and pharmacophore-based GRIND descriptors.
These 3D-QSAR models are compared favorably with other traditional
and chemometric based HQSAR methods.},
doi = {03.062},
keywords = {chemoinformatics herg},
pii = {S0960-894X(05)00368-9},
pmid = {15978804},
timestamp = {2006.10.06},
url = {http://dx.doi.org/03.062}
}
@article{Cianfrocca2004Prognostic,
author = {Cianfrocca, M. and Goldstein, L. J.},
title = {Prognostic and predictive factors in early-stage breast cancer},
journal = {Oncologist},
year = {2004},
volume = {9},
pages = {606--616},
number = {6},
abstract = {Breast cancer is the most common malignancy among American women.
Due to increased screening, the majority of patients present with
early-stage breast cancer. The Oxford Overview Analysis demonstrates
that adjuvant hormonal therapy and polychemotherapy reduce the risk
of recurrence and death from breast cancer. Adjuvant systemic therapy,
however, has associated risks and it would be useful to be able to
optimally select patients most likely to benefit. The purpose of
adjuvant systemic therapy is to eradicate distant micrometastatic
deposits. It is essential therefore to be able to estimate an individual
patient's risk of harboring clinically silent micrometastatic disease
using established prognostic factors. It is also beneficial to be
able to select the optimal adjuvant therapy for an individual patient
based on established predictive factors. It is standard practice
to administer systemic therapy to all patients with lymph node-positive
disease. However, there are clearly differences among node-positive
women that may warrant a more aggressive therapeutic approach. Furthermore,
there are many node-negative women who would also benefit from adjuvant
systemic therapy. Prognostic factors therefore must be differentiated
from predictive factors. A prognostic factor is any measurement available
at the time of surgery that correlates with disease-free or overall
survival in the absence of systemic adjuvant therapy and, as a result,
is able to correlate with the natural history of the disease. In
contrast, a predictive factor is any measurement associated with
response to a given therapy. Some factors, such as hormone receptors
and HER2/neu overexpression, are both prognostic and predictive.},
doi = {10.1634/theoncologist.9-6-606},
pdf = {../local/Cianfrocca2004Prognostic.pdf},
file = {Cianfrocca2004Prognostic.pdf:Cianfrocca2004Prognostic.pdf:PDF},
institution = {D.O., Fox Chase Cancer Center, 333 Cottman Ave, Philadelphia, Pennsylvania
19111, USA. M_Cianfrocca@fccc.edu},
keywords = {csbcbook, csbcbook-ch3},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {9/6/606},
pmid = {15561805},
timestamp = {2009.10.18},
url = {http://dx.doi.org/10.1634/theoncologist.9-6-606}
}
@article{Cianfrocca2009New,
author = {Cianfrocca, M. and Gradishar, W.},
title = {New molecular classifications of breast cancer},
journal = {CA Cancer J. Clin.},
year = {2009},
volume = {59},
pages = {303--313},
number = {5},
abstract = {Traditionally, pathologic determinations of tumor size, lymph node
status, endocrine receptor status, and human epidermal growth factor
receptor 2 (HER2) status have driven prognostic predictions and adjuvant
therapy recommendations for patients with early stage breast cancer.
However, these prognostic and predictive factors are relatively crude
measures, resulting in many patients being overtreated or undertreated.
As a result of gene expression assays, there is growing recognition
that breast cancer is a molecularly heterogeneous disease. Evidence
from gene expression microarrays suggests the presence of multiple
molecular subtypes of breast cancer. The recent commercial availability
of gene expression profiling techniques that predict risk of disease
recurrence as well as potential chemotherapy benefit have shown promise
in refining clinical decision making. These techniques will be reviewed
in this article.},
doi = {10.3322/caac.20029},
pdf = {../local/Cianfrocca2009New.pdf},
file = {Cianfrocca2009New.pdf:Cianfrocca2009New.pdf:PDF},
institution = {Division of Hematology/Oncology, Northwestern University, Feinberg
School of Medicine, Robert H. Lurie Comprehensive Cancer Center,
Chicago, IL 60611, USA. m-cianfrocca@northwestern.edu},
keywords = {csbcbook, breastcancer},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {59/5/303},
pmid = {19729680},
timestamp = {2009.10.18},
url = {http://dx.doi.org/10.3322/caac.20029}
}
@article{Ciliberto2005CellCycle,
author = {Ciliberto, A. and Novak, B. and Tyson, J. J.},
title = {Steady states and oscillations in the p53/Mdm2 network},
journal = {Cell Cycle},
year = {2005},
volume = {4},
pages = {488-93},
number = {3},
abstract = {p53 is activated in response to events compromising the genetic integrity
of a cell. Recent data show that p53 activity does not increase steadily
with genetic damage but rather fluctuates in an oscillatory fashion.
Theoretical studies suggest that oscillations can arise from a combination
of positive and negative feedbacks or from a long negative feedback
loop alone. Both negative and positive feedbacks are present in the
p53/Mdm2 network, but it is not known what roles they play in the
oscillatory response to DNA damage. We developed a mathematical model
of p53 oscillations based on positive and negative feedbacks in the
p53/Mdm2 network. According to the model, the system reacts to DNA
damage by moving from a stable steady state into a region of stable
limit cycles. Oscillations in the model are born with large amplitude,
which guarantees an all-or-none response to damage. As p53 oscillates,
damage is repaired and the system moves back to a stable steady state
with low p53 activity. The model reproduces experimental data in
quantitative detail. We suggest new experiments for dissecting the
contributions of negative and positive feedbacks to the generation
of oscillations.},
keywords = {csbcbook}
}
@article{Citri2006MolCelBiol,
author = {Ami Citri and Yosef Yarden},
title = {EGF-ERBB signalling: towards the systems level.},
journal = {Nat Rev Mol Cell Biol},
year = {2006},
volume = {7},
pages = {505--516},
number = {7},
month = {Jul},
abstract = {Signalling through the ERBB/HER receptors is intricately involved
in human cancer and already serves as a target for several cancer
drugs. Because of its inherent complexity, it is useful to envision
ERBB signalling as a bow-tie-configured, evolvable network, which
shares modularity, redundancy and control circuits with robust biological
and engineered systems. Because network fragility is an inevitable
trade-off of robustness, systems-level understanding is expected
to generate therapeutic opportunities to intercept aberrant network
activation.},
doi = {10.1038/nrm1962},
institution = {Department of Biological Regulation, the Weizmann Institute of Science,
1 Hertzl Street, Rehovot 76100, Israel.},
keywords = {Animals; Endocytosis, physiology; Epidermal Growth Factor, metabolism;
Feedback, Physiological; Humans; Ligands; Models, Molecular; Oncogene
Proteins v-erbB, genetics/metabolism; Phosphatidylinositol 3-Kinases,
metabolism; Protein Conformation; Receptor, Epidermal Growth Factor,
chemistry/genetics/metabolism; Signal Transduction, physiology},
language = {eng},
medline-pst = {ppublish},
owner = {Andrei Zinovyev},
pii = {nrm1962},
pmid = {16829981},
timestamp = {2011.04.08},
url = {http://dx.doi.org/10.1038/nrm1962}
}
@article{Clark1994Do,
author = {Clark, G. M.},
title = {Do we really need prognostic factors for breast cancer?},
journal = {Breast Cancer Res Treat},
year = {1994},
volume = {30},
pages = {117--126},
doi = {10.1007/BF00666054},
owner = {jp},
timestamp = {2012.03.01},
url = {http://dx.doi.org/10.1007/BF00666054}
}
@article{Clarke1990Information-theoretic,
author = {Clarke, B.S. and Barron, A.R.},
title = {Information-theoretic asymptotics of {B}ayes methods},
journal = {I{EEE} {T}rans. {I}nform. {T}heory},
year = {1990},
volume = {36},
pages = {453-471},
number = {3},
month = {May},
abstract = {In the absence of knowledge of the true density function, {B}ayesian
models take the joint density function for a sequence of n random
variables to be an average of densities with respect to a prior.
{T}he authors examine the relative entropy distance {D}n between
the true density and the {B}ayesian density and show that the asymptotic
distance is (d/2)(log n)+c, where d is the dimension of the parameter
vector. {T}herefore, the relative entropy rate {D}n/n converges to
zero at rate (log n)/n. {T}he constant c, which the authors explicitly
identify, depends only on the prior density function and the {F}isher
information matrix evaluated at the true parameter value. {C}onsequences
are given for density estimation, universal data compression, composite
hypothesis testing, and stock-market portfolio selection },
pdf = {../local/Clarke1990Information-theoretic.pdf},
file = {Clarke1990Information-theoretic.pdf:local/Clarke1990Information-theoretic.pdf:PDF},
keywords = {information-theory},
owner = {vert}
}
@article{Clarke1994Jeffreys,
author = {Clarke, B. S. and Barron, A. R.},
title = {Jeffreys' prior is asymptotically least favorable under entropy risk},
journal = {J. {S}tat. {P}lann. {I}nfer.},
year = {1994},
volume = {31},
pages = {37-60},
number = {1},
abstract = {We provide a rigorous proof that {J}effreys' prior asymptotically
maximizes {S}hannon's mutual information between a sample of size
n and the parameter. {T}his was conjectured by {B}ernardo (1979)
and, despite the absence of a proof, forms the basis of the reference
prior method in {B}ayesian statistical analysis. {O}ur proof rests
on an examination of large sample decision theoretic properties associated
with the relative entropy or the {K}ullback?{L}eibler distance between
probability density functions for independent and identically distributed
random variables. {F}or smooth finite-dimensional parametric families
we derive an asymptotic expression for the minimax risk and for the
related maximin risk. {A}s a result, we show that, among continuous
positive priors, {J}effreys' prior uniquely achieves the asymptotic
maximin value. {I}n the discrete parameter case we show that, asymptotically,
the {B}ayes risk reduces to the entropy of the prior so that the
reference prior is seen to be the maximum entropy prior. {W}e identify
the physical significance of the risks by giving two information-theoretic
interpretations in terms of probabilistic coding.},
doi = {10.1016/0378-3758(94)90153-8},
pdf = {../local/Clarke1994Jeffreys.pdf},
file = {Clarke1994Jeffreys.pdf:local/Clarke1994Jeffreys.pdf:PDF},
keywords = {information-theory},
owner = {vert},
url = {http://dx.doi.org/10.1016/0378-3758(94)90153-8}
}
@inbook{Cleveland1992Statistical,
chapter = {Local regression models},
title = {Statistical Models in {S}},
publisher = {Wasworth \& Brooks/Cole},
year = {1992},
editor = {Chambers, J. M. and Hastie, T. J.},
author = {Cleveland, W. S. and Grosse, E. and Shyu, W. M.},
timestamp = {2007.09.19}
}
@article{Clifford1878Binary,
author = {W. K. Clifford},
title = {Binary forms of alternate variables},
journal = {Proc. London Math. Soc.},
year = {1878},
volume = {10},
pages = {277--286},
number = {9}
}
@article{Clifford1878Note,
author = {W. K. Clifford},
title = {Note on quantics of alternate numbers, used as a means for determining
the invariants and covariants of quantics in general},
journal = {Proc. London Math. Soc.},
year = {1878},
volume = {10},
pages = {258--265},
number = {9}
}
@article{Coe2006Differential,
author = {Coe, B. P. and Lockwood, W. W. and Girard, L. and Chari, R. and MacAulay,
C. and Lam, S. and Gazdar, A. F. and Minna, J. D. and Lam, W. L.},
title = {Differential disruption of cell cycle pathways in small cell and
non-small cell lung cancer},
journal = {Br. J. Cancer},
year = {2006},
volume = {94},
pages = {1927--1935},
owner = {kb},
timestamp = {2011.05.17}
}
@article{Coe2007Resolving,
author = {Coe, B. P. and Ylstra, B. and Carvalho, B. and Meijer, G. A. and
Macaulay, C. and Lam, W. L.},
title = {Resolving the resolution of array {CGH}},
journal = {Genomics},
year = {2007},
volume = {89},
pages = {647--653},
number = {5},
month = {May},
abstract = {Many recent technologies have been designed to supplant conventional
metaphase CGH technology with the goal of refining the description
of segmental copy number status throughout the genome. However, the
emergence of new technologies has led to confusion as to how to describe
adequately the capabilities of each array platform. The design of
a CGH array can incorporate a uniform or a highly variable element
distribution. This can lead to bias in the reporting of average or
median resolutions, making it difficult to provide a fair comparison
of platforms. In this report, we propose a new definition of resolution
for array CGH technology, termed "functional resolution," that incorporates
the uniformity of element spacing on the array, as well as the sensitivity
of each platform to single-copy alterations. Calculation of these
metrics is automated through the development of a Java-based application,
"ResCalc," which is applicable to any array CGH platform.},
doi = {10.1016/j.ygeno.2006.12.012},
pdf = {../local/Coe2007Resolving.pdf},
file = {Coe2007Resolving.pdf:Coe2007Resolving.pdf:PDF},
institution = {British Columbia Cancer Research Centre, 675 West 10th Avenue, Vancouver,
BC, Canada V5Z 1L3. bcoe@bccrc.ca},
keywords = {csbcbook, csbcbook-ch2},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {S0888-7543(07)00004-3},
pmid = {17276656},
timestamp = {2009.10.18},
url = {http://dx.doi.org/10.1016/j.ygeno.2006.12.012}
}
@article{Coelho2000Genome-wide,
author = {Coelho, P.S. and Kumar, A. and Snyder, M.},
title = {Genome-wide mutant collections: toolboxes for functional genomics},
journal = {Curr. {O}pin. {M}icrobiol.},
year = {2000},
volume = {3},
pages = {309-315},
pdf = {../local/Coelho2000Genome-wide.pdf},
file = {Coelho2000Genome-wide.pdf:local/Coelho2000Genome-wide.pdf:PDF},
owner = {vert}
}
@article{Cogoni1996Transgene,
author = {Cogoni, C. and Irelan, J. T. and Schumacher, M. and Schmidhauser,
T. J. and Selker, E. U. and Macino, G.},
title = {{T}ransgene silencing of the al-1 gene in vegetative cells of {N}eurospora
is mediated by a cytoplasmic effector and does not depend on {DNA}-{DNA}
interactions or {DNA} methylation.},
journal = {EMBO J.},
year = {1996},
volume = {15},
pages = {3153--3163},
number = {12},
month = {Jun},
abstract = {The molecular mechanisms involved in transgene-induced gene silencing
('quelling') in Neurospora crassa were investigated using the carotenoid
biosynthetic gene albino-1 (al-1) as a visual marker. Deletion derivatives
of the al-1 gene showed that a transgene must contain at least approximately
132 bp of sequences homologous to the transcribed region of the native
gene in order to induce quelling. Transgenes containing only al-1
promoter sequences do not cause quelling. Specific sequences are
not required for gene silencing, as different regions of the al-1
gene produced quelling. A mutant defective in cytosine methylation
(dim-2) exhibited normal frequencies and degrees of silencing, indicating
that cytosine methylation is not responsible for quelling, despite
the fact that methylation of transgene sequences frequently is correlated
with silencing. Silencing was shown to be a dominant trait, operative
in heterokaryotic strains containing a mixture of transgenic and
non-transgenic nuclei. This result indicates that a diffusable, trans-acting
molecule is involved in quelling. A transgene-derived, sense RNA
was detected in quelled strains and was found to be absent in their
revertants. These data are consistent with a model in which an RNA-DNA
or RNA-RNA interaction is involved in transgene-induced gene silencing
in Neurospora.},
pdf = {../local/Cogoni1996Transgene.pdf},
file = {Cogoni1996Transgene.pdf:Cogoni1996Transgene.pdf:PDF},
keywords = {sirna},
owner = {vert},
pmid = {8670816},
timestamp = {2006.03.28},
url = {http://www.ncbi.nlm.nih.gov/pmc/articles/PMC450258}
}
@article{Cohen2004application,
author = {Gilles Cohen and M\'elanie Hilario and Hugo Sax and Stéphane Hugonnet
and Christian Pellegrini and Antoine Geissbuhler},
title = {An application of one-class support vector machine to nosocomial
infection detection.},
journal = {Medinfo},
year = {2004},
volume = {11},
pages = {716-20},
number = {Pt 1},
abstract = {Nosocomial infections ({NI}s)---those acquired in health care settings---are
among the major causes of increased mortality among hospitalized
patients. {T}hey are a significant burden for patients and health
authorities alike; it is thus important to monitor and detect them
through an effective surveillance system. {T}his paper describes
a retrospective analysis of a prevalence survey of {NI}s done in
the {G}eneva {U}niversity {H}ospital. {O}ur goal is to identify patients
with one or more {NI}s on the basis of clinical and other data collected
during the survey. {I}n this two-class classification task, the main
difficulty lies in the significant imbalance between positive or
infected (11\%) and negative (89\%) cases. {T}o cope with class imbalance,
we investigate one-class {SVM}s which can be trained to distinguish
two classes on the basis of examples from a single class (in this
case, only "normal" or non infected patients). {T}he infected ones
are then identified as "abnormal" cases or outliers that deviate
significantly from the normal profile. {E}xperimental results are
encouraging: whereas standard 2-class {SVM}s scored a baseline sensitivity
of 50.6\% on this problem, the one-class approach increased sensitivity
to as much as 92.6\%. {T}hese results are comparable to those obtained
by the authors in a previous study on asymmetrical soft margin {SVM}s;
they suggest that one-class {SVM}s can provide an effective and efficient
way of overcoming data imbalance in classification problems.},
keywords = {Aged, Air, Algorithms, Amino Acids, Animals, Area Under Curve, Artifacts,
Artificial Intelligence, Atrial, Automated, Canada, Carotid Stenosis,
Cerebrovascular Accident, Cerebrovascular Circulation, Comparative
Study, Computer-Assisted, Cross Infection, Cysteine, Data Collection,
Decision Trees, Dementia, Diagnosis, Disulfides, Doppler, Embolism,
Expert Systems, Extramural, Factor Analysis, Female, Gene Expression,
Gene Expression Profiling, Health Status, Heart Septal Defects, Hospitals,
Humans, Infection Control, Intracranial Embolism, Male, Models, Molecular,
Myocardial Infarction, N.I.H., Neoplasms, Neural Networks (Computer),
Non-U.S. Gov't, Oligonucleotide Array Sequence Analysis, Oxidation-Reduction,
P.H.S., Pattern Recognition, Population Surveillance, Prevalence,
Prognosis, Protein Binding, Protein Folding, Proteins, ROC Curve,
Research Support, Retrospective Studies, Sensitivity and Specificity,
Software, Statistical, Switzerland, Transcranial, Treatment Outcome,
U.S. Gov't, Ultrasonography, University, 15360906},
pii = {D040004219}
}
@article{Coi2006Prediction,
author = {Coi, A. and Massarelli, I. and Murgia, L. and Saraceno, M. and Calderone,
V. and Bianucci, A. M.},
title = {{P}rediction of h{ERG} potassium channel affinity by the {CODESSA}
approach.},
journal = {Bioorg. Med. Chem.},
year = {2006},
volume = {14},
pages = {3153--3159},
number = {9},
month = {May},
abstract = {The problem of predicting torsadogenic cardiotoxicity of drugs is
afforded in this work. QSAR studies on a series of molecules, acting
as hERG K+ channel blockers, were carried out for this purpose by
using the CODESSA program. Molecules belonging to the analyzed dataset
are characterized by different therapeutic targets and by high molecular
diversity. The predictive power of the obtained models was estimated
by means of rigorous validation criteria implying the use of highly
diagnostic statistical parameters on the test set, other than the
training set. Validation results obtained for a blind set, disjoined
from the whole dataset initially considered, confirmed the predictive
potency of the models proposed here, so suggesting that they are
worth to be considered as a valuable tool for practical applications
in predicting the blockade of hERG K+ channels.},
doi = {10.1016/j.bmc.2005.12.030},
keywords = {chemoinformatics herg},
pii = {S0968-0896(05)01212-5},
pmid = {16426850},
timestamp = {2006.10.06},
url = {http://dx.doi.org/10.1016/j.bmc.2005.12.030}
}
@article{Cole2001Polychemotherapy,
author = {Cole, B. F. and Gelber, R. D. and Gelber, S. and Coates, A. S. and
Goldhirsch, A.},
title = {Polychemotherapy for early breast cancer: an overview of the randomised
clinical trials with quality-adjusted survival analysis.},
journal = {Lancet},
year = {2001},
volume = {358},
pages = {277--286},
number = {9278},
month = {Jul},
doi = {10.1016/S0140-6736(01)05483-6},
keywords = {Aged; Antineoplastic Agents, therapeutic use; Breast Neoplasms, drug
therapy/mortality; Chemotherapy, Adjuvant; Drug Therapy, Combination;
Female; Follow-Up Studies; Humans; Middle Aged; Prognosis; Quality-Adjusted
Life Years; Randomized Controlled Trials as Topic; Survival Analysis;
Tamoxifen, therapeutic use},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {S0140-6736(01)05483-6},
pmid = {11498214},
timestamp = {2012.03.01},
url = {http://dx.doi.org/10.1016/S0140-6736(01)05483-6}
}
@article{Cole2005Comparing,
author = {Jason C Cole and Christopher W Murray and J. Willem M Nissink and
Richard D Taylor and Robin Taylor},
title = {Comparing protein-ligand docking programs is difficult.},
journal = {Proteins},
year = {2005},
volume = {60},
pages = {325--332},
number = {3},
month = {Aug},
abstract = {There is currently great interest in comparing protein-ligand docking
programs. A review of recent comparisons shows that it is difficult
to draw conclusions of general applicability. Statistical hypothesis
testing is required to ensure that differences in pose-prediction
success rates and enrichment rates are significant. Numerical measures
such as root-mean-square deviation need careful interpretation and
may profitably be supplemented by interaction-based measures and
visual inspection of dockings. Test sets must be of appropriate diversity
and of good experimental reliability. The effects of crystal-packing
interactions may be important. The method used for generating starting
ligand geometries and positions may have an appreciable effect on
docking results. For fair comparison, programs must be given search
problems of equal complexity (e.g. binding-site regions of the same
size) and approximately equal time in which to solve them. Comparisons
based on rescoring require local optimization of the ligand in the
space of the new objective function. Re-implementations of published
scoring functions may give significantly different results from the
originals. Ostensibly minor details in methodology may have a profound
influence on headline success rates.},
doi = {10.1002/prot.20497},
institution = {Cambridge Crystallographic Data Centre, Cambridge, United Kingdom.},
keywords = {Algorithms; Artificial Intelligence; Binding Sites; Computational
Biology, methods; Computer Simulation; Crystallization; Crystallography,
X-Ray; Databases, Protein; Ligands; Models, Molecular; Molecular
Structure; Programming Languages; Protein Binding; Proteins, chemistry;
Proteomics, methods; Reproducibility of Results; Software},
owner = {bricehoffmann},
pmid = {15937897},
timestamp = {2009.02.13},
url = {http://dx.doi.org/10.1002/prot.20497}
}
@article{Coleman2004Noninvasive,
author = {D. Jackson Coleman and Ronald H Silverman and Mark J Rondeau and
H. Culver Boldt and Harriet O Lloyd and Frederic L Lizzi and Thomas
A Weingeist and Xue Chen and Sumalee Vangveeravong and Robert Folberg},
title = {Noninvasive in vivo detection of prognostic indicators for high-risk
uveal melanoma: ultrasound parameter imaging.},
journal = {Ophthalmology},
year = {2004},
volume = {111},
pages = {558-64},
number = {3},
month = {Mar},
abstract = {P{URPOSE}: {P}rimary malignant melanoma of the choroid and ciliary
body has traditionally been treated without histologic staging, using
purely clinical indicators. {T}he presence of extravascular matrix
patterns ({EMP}) in histologic sections of uveal melanoma has been
shown to be an independent indicator of metastatic risk. {T}hese
patterns are of a dimension and physical composition that are likely
to be detected with ultrasound backscatter analysis. {O}ur aim was
to determine whether ultrasound parameter imaging could detect the
presence of {EMP} at a diagnostically significant level for treatment
staging and for planning investigational studies of therapeutic modalities.
{DESIGN}: {P}rospective, masked ultrasound-pathologic correlative
study. {PARTICIPANTS}: {O}ne hundred seventeen patients diagnosed
with previously untreated choroidal melanoma were scanned within
2 weeks before enucleation. {METHODS}: {T}umors were evaluated histologically
and divided into high-risk and low-risk groups on the basis of the
presence of 2\% or more histologic cross-sectional area composed
of {EMP} patterns. {D}igital ultrasound data were processed to generate
parameter images representing the size and concentration of ultrasound
scatterers. {H}istologic and ultrasound images and data were correlated,
and linear and nonlinear statistical methods were used to create
multivariate models for noninvasive differentiation of high-risk
and low-risk tumors. {MAIN} {OUTCOME} {MEASURES}: {P}resence or absence
of high-risk {EMP} and associated ultrasound parameter classification
models. {RESULTS}: {O}f the 117 tumors, 69 were classified as low
risk, and 48 were classified as high-risk with histologic analysis.
{A} classification that used ultrasound parameter image features
with linear discriminant analysis could correctly identify 79.5\%
of cases retrospectively and 75.2\% of cases by use of cross-validation,
an estimate of prospective classification ability. {B}y use of a
more powerful classification technique (support vector machine),
93.1\% of cases were correctly classified retrospectively. {W}ith
a cross-validation procedure, 80.10\% of cases were correctly classified.
{CONCLUSIONS}: {U}ltrasound can be used noninvasively to classify
tumors into high-risk and low-risk groups by detecting the presence
of {EMP} patterns. {B}y the use of previous studies that compared
the histologic presence of {EMP} patterns with patient survival,
estimates of hazard rates associated with ultrasound risk groups
can be made. {T}he noninvasive ultrasound classification is potentially
useful as a prognostic variable and as a tool for stratification
of patient populations for tumor treatment evaluation.},
doi = {10.1016/j.ophtha.2003.06.021},
pdf = {../local/Coleman2004Noninvasive.pdf},
file = {Coleman2004Noninvasive.pdf:local/Coleman2004Noninvasive.pdf:PDF},
keywords = {Algorithms, Ambergris, Combinatorial Chemistry Techniques, Eye Enucleation,
Humans, Melanoma, Models, Molecular, Molecular Conformation, Non-U.S.
Gov't, Odors, P.H.S., Perfume, Predictive Value of Tests, Prognosis,
Prospective Studies, Quantitative Structure-Activity Relationship,
Research Support, U.S. Gov't, Uveal Neoplasms, 15019336},
pii = {S0161642003014982},
url = {http://dx.doi.org/10.1016/j.ophtha.2003.06.021}
}
@article{Collier2004Comparison,
author = {Nigel Collier and Koichi Takeuchi},
title = {Comparison of character-level and part of speech features for name
recognition in biomedical texts.},
journal = {J {B}iomed {I}nform},
year = {2004},
volume = {37},
pages = {423-35},
number = {6},
month = {Dec},
abstract = {The immense volume of data which is now available from experiments
in molecular biology has led to an explosion in reported results
most of which are available only in unstructured text format. {F}or
this reason there has been great interest in the task of text mining
to aid in fact extraction, document screening, citation analysis,
and linkage with large gene and gene-product databases. {I}n particular
there has been an intensive investigation into the named entity ({NE})
task as a core technology in all of these tasks which has been driven
by the availability of high volume training sets such as the {GENIA}
v3.02 corpus. {D}espite such large training sets accuracy for biology
{NE} has proven to be consistently far below the high levels of performance
in the news domain where {F} scores above 90 are commonly reported
which can be considered near to human performance. {W}e argue that
it is crucial that more rigorous analysis of the factors that contribute
to the model's performance be applied to discover where the underlying
limitations are and what our future research direction should be.
{O}ur investigation in this paper reports on variations of two widely
used feature types, part of speech ({POS}) tags and character-level
orthographic features, and makes a comparison of how these variations
influence performance. {W}e base our experiments on a proven state-of-the-art
model, support vector machines using a high quality subset of 100
annotated {MEDLINE} abstracts. {E}xperiments reveal that the best
performing features are orthographic features with {F} score of 72.6.
{A}lthough the {B}rill tagger trained in-domain on the {GENIA} v3.02p
{POS} corpus gives the best overall performance of any {POS} tagger,
at an {F} score of 68.6, this is still significantly below the orthographic
features. {I}n combination these two features types appear to interfere
with each other and degrade performance slightly to an {F} score
of 72.3.},
doi = {10.1016/j.jbi.2004.08.008},
pdf = {../local/Collier2004Comparison.pdf},
file = {Collier2004Comparison.pdf:local/Collier2004Comparison.pdf:PDF},
keywords = {biosvm nlp},
pii = {S1532-0464(04)00088-7},
url = {http://dx.doi.org/10.1016/j.jbi.2004.08.008}
}
@article{Collins2003Vision,
author = {Francis S Collins and Eric D Green and Alan E Guttmacher and Mark
S Guyer and U. S. National Human Genome Research Institute},
title = {A vision for the future of genomics research.},
journal = {Nature},
year = {2003},
volume = {422},
pages = {835--847},
number = {6934},
month = {Apr},
institution = {National Human Genome Research Institute, National Institutes of
Health, Bethesda, Maryland 20892, USA.},
language = {eng},
medline-pst = {ppublish},
owner = {philippe},
pmid = {12695777},
timestamp = {2010.07.28}
}
@inproceedings{Collins2001Convolution,
author = {Collins, M. and Duffy, N.},
title = {Convolution {K}ernels for {N}atural {L}anguage},
booktitle = {Adv. {N}eural. {I}nform. {P}rocess {S}yst.},
year = {2001},
editor = {Dietterich, T. G. and Becker, S. and Ghahramani, Z.},
volume = {14},
pages = {625-632},
publisher = {MIT Press}
}
@article{Collobert2002parallel,
author = {Ronan Collobert and Samy Bengio and Yoshua Bengio},
title = {A parallel mixture of {SVM}s for very large scale problems.},
journal = {Neural {C}omput},
year = {2002},
volume = {14},
pages = {1105-14},
number = {5},
month = {May},
abstract = {Support vector machines ({SVM}s) are the state-of-the-art models for
many classification problems, but they suffer from the complexity
of their training algorithm, which is at least quadratic with respect
to the number of examples. {H}ence, it is hopeless to try to solve
real-life problems having more than a few hundred thousand examples
with {SVM}s. {T}his article proposes a new mixture of {SVM}s that
can be easily implemented in parallel and where each {SVM} is trained
on a small subset of the whole data set. {E}xperiments on a large
benchmark data set ({F}orest) yielded significant time improvement
(time complexity appears empirically to locally grow linearly with
the number of examples). {I}n addition, and surprisingly, a significant
improvement in generalization was observed.},
doi = {10.1162/089976602753633402},
url = {http://dx.doi.org/10.1162/089976602753633402}
}
@article{Comon1994Independent,
author = {Pierre Comon},
title = {Independent Component Analysis: a new concept?},
journal = {Signal {P}rocessing},
year = {1994},
volume = {36},
pages = {287--314},
number = {3}
}
@article{Consortium2006MicroArray,
author = {M. A. Q. C. Consortium and Leming Shi and Laura H Reid and Wendell
D Jones and Richard Shippy and Janet A Warrington and Shawn C Baker
and Patrick J Collins and Francoise de Longueville and Ernest S Kawasaki
and Kathleen Y Lee and Yuling Luo and Yongming Andrew Sun and James
C Willey and Robert A Setterquist and Gavin M Fischer and Weida Tong
and Yvonne P Dragan and David J Dix and Felix W Frueh and Frederico
M Goodsaid and Damir Herman and Roderick V Jensen and Charles D Johnson
and Edward K Lobenhofer and Raj K Puri and Uwe Schrf and Jean Thierry-Mieg
and Charles Wang and Mike Wilson and Paul K Wolber and Lu Zhang and
Shashi Amur and Wenjun Bao and Catalin C Barbacioru and Anne Bergstrom
Lucas and Vincent Bertholet and Cecilie Boysen and Bud Bromley and
Donna Brown and Alan Brunner and Roger Canales and Xiaoxi Megan Cao
and Thomas A Cebula and James J Chen and Jing Cheng and Tzu-Ming
Chu and Eugene Chudin and John Corson and J. Christopher Corton and
Lisa J Croner and Christopher Davies and Timothy S Davison and Glenda
Delenstarr and Xutao Deng and David Dorris and Aron C Eklund and
Xiao-hui Fan and Hong Fang and Stephanie Fulmer-Smentek and James
C Fuscoe and Kathryn Gallagher and Weigong Ge and Lei Guo and Xu
Guo and Janet Hager and Paul K Haje and Jing Han and Tao Han and
Heather C Harbottle and Stephen C Harris and Eli Hatchwell and Craig
A Hauser and Susan Hester and Huixiao Hong and Patrick Hurban and
Scott A Jackson and Hanlee Ji and Charles R Knight and Winston P
Kuo and J. Eugene LeClerc and Shawn Levy and Quan-Zhen Li and Chunmei
Liu and Ying Liu and Michael J Lombardi and Yunqing Ma and Scott
R Magnuson and Botoul Maqsodi and Tim McDaniel and Nan Mei and Ola
Myklebost and Baitang Ning and Natalia Novoradovskaya and Michael
S Orr and Terry W Osborn and Adam Papallo and Tucker A Patterson
and Roger G Perkins and Elizabeth H Peters and Ron Peterson and Kenneth
L Philips and P. Scott Pine and Lajos Pusztai and Feng Qian and Hongzu
Ren and Mitch Rosen and Barry A Rosenzweig and Raymond R Samaha and
Mark Schena and Gary P Schroth and Svetlana Shchegrova and Dave D
Smith and Frank Staedtler and Zhenqiang Su and Hongmei Sun and Zoltan
Szallasi and Zivana Tezak and Danielle Thierry-Mieg and Karol L Thompson
and Irina Tikhonova and Yaron Turpaz and Beena Vallanat and Christophe
Van and Stephen J Walker and Sue Jane Wang and Yonghong Wang and
Russ Wolfinger and Alex Wong and Jie Wu and Chunlin Xiao and Qian
Xie and Jun Xu and Wen Yang and Liang Zhang and Sheng Zhong and Yaping
Zong and William Slikker},
title = {The {M}icro{A}rray {Q}uality {C}ontrol ({MAQC}) project shows inter-
and intraplatform reproducibility of gene expression measurements},
journal = {Nat. Biotechnol.},
year = {2006},
volume = {24},
pages = {1151--1161},
number = {9},
month = {Sep},
abstract = {Over the last decade, the introduction of microarray technology has
had a profound impact on gene expression research. The publication
of studies with dissimilar or altogether contradictory results, obtained
using different microarray platforms to analyze identical RNA samples,
has raised concerns about the reliability of this technology. The
MicroArray Quality Control (MAQC) project was initiated to address
these concerns, as well as other performance and data analysis issues.
Expression data on four titration pools from two distinct reference
RNA samples were generated at multiple test sites using a variety
of microarray-based and alternative technology platforms. Here we
describe the experimental design and probe mapping efforts behind
the MAQC project. We show intraplatform consistency across test sites
as well as a high level of interplatform concordance in terms of
genes identified as differentially expressed. This study provides
a resource that represents an important first step toward establishing
a framework for the use of microarrays in clinical and regulatory
settings.},
doi = {10.1038/nbt1239},
institution = {National Center for Toxicological Research, US Food and Drug Administration,
Jefferson, Arkansas 72079, USA.},
keywords = {Equipment Design; Equipment Failure Analysis; Gene Expression Profiling,
instrumentation/methods; Oligonucleotide Array Sequence Analysis,
instrumentation; Quality Assurance, Health Care, methods; Quality
Control; Reproducibility of Results; Sensitivity and Specificity;
United States},
language = {eng},
medline-pst = {ppublish},
owner = {phupe},
pii = {nbt1239},
pmid = {16964229},
timestamp = {2011.04.08},
url = {http://dx.doi.org/10.1038/nbt1239}
}
@article{Conte2004Thirty,
author = {Conte, D. and Foggia, P. and Sansone, C. and Vento, M.},
title = {Thirty years of graph matching in pattern recognition},
journal = {Int. J. Pattern. Recogn. Artif. Intell.},
year = {2004},
volume = {18},
pages = {265--298},
number = {3},
abstract = {A recent paper posed the question: "Graph Matching: What are we really
talking about?". Far from providing a definite answer to that question,
in this paper we will try to characterize the role that graphs play
within the Pattern Recognition field. To this aim two taxonomies
are presented and discussed. The first includes almost all the graph
matching algorithms proposed from the late seventies, and describes
the different classes of algorithms. The second taxonomy considers
the types of common applications of graph-based techniques in the
Pattern Recognition and Machine Vision field.},
doi = {10.1142/S0218001404003228},
owner = {michael},
timestamp = {2008.10.02},
url = {http://dx.doi.org/10.1142/S0218001404003228}
}
@article{Cook2010model,
author = {Peter R Cook},
title = {A model for all genomes: the role of transcription factories.},
journal = {J Mol Biol},
year = {2010},
volume = {395},
pages = {1--10},
number = {1},
month = {Jan},
abstract = {A model for all genomes involving one major architectural motif is
presented: DNA or chromatin loops are tethered to "factories" through
the transcription machinery-a polymerase (active or inactive) or
its transcription factors (activators or repressors). These loops
appear and disappear as polymerases initiate and terminate (and as
factors bind and dissociate), so the structure is ever-changing and
self-organizing. This model is parsimonious, detailed (and so easily
tested), and incorporates elements found in various other models.},
doi = {10.1016/j.jmb.2009.10.031},
institution = {Sir William Dunn School of Pathology, University of Oxford, South
Parks Road, Oxford OX1 3RE, UK. peter.cook@path.ox.ac.uk},
keywords = {Animals; Bacteria; Eukaryota; Gene Expression Regulation; Genome;
Humans; Models, Genetic; Transcription, Genetic},
owner = {phupe},
pii = {S0022-2836(09)01271-6},
pmid = {19852969},
timestamp = {2010.08.27},
url = {http://dx.doi.org/10.1016/j.jmb.2009.10.031}
}
@article{Cooper2001GlycoSuiteDB,
author = {Cooper, C. and Harrison, M. and Wilkins, M. and Packer, N.},
title = {Glyco{S}uite{DB}: a new curated relational database of glycoprotein
glycan structures and their biological sources},
journal = {Nucleic {A}cids {R}es.},
year = {2001},
volume = {29},
pages = {332-335},
pdf = {../local/Cooper2001GlycoSuiteDB.pdf},
file = {Cooper2001GlycoSuiteDB.pdf:local/Cooper2001GlycoSuiteDB.pdf:PDF},
owner = {vert},
url = {http://nar.oxfordjournals.org/cgi/content/full/29/1/332}
}
@inproceedings{Cordella01improved,
author = {L. P. Cordella and P. Foggia and C. Sansone and M. Vento},
title = {An improved algorithm for matching large graphs},
booktitle = {In: 3rd IAPR-TC15 Workshop on Graph-based Representations in Pattern
Recognition, Cuen},
year = {2001},
pages = {149--159}
}
@inproceedings{Cordella1999Performance,
author = {Cordella, L. P. and Foggia, P. and Sansone, C. and Vento, M.},
title = {Performance Evaluation of the VF Graph Matching Algorithm},
booktitle = {ICIAP '99: Proceedings of the 10th International Conference on Image
Analysis and Processing},
year = {1999},
pages = {1172},
address = {Washington, DC, USA},
publisher = {IEEE Computer Society},
isbn = {0-7695-0040-4}
}
@article{Cordella1996Efficient,
author = {L. P. Cordella and P. Foggia and C. Sansone and M. Vento},
title = {An Efficient Algorithm for the Inexact Matching of ARG Graphs Using
a Contextual Transformational Model},
journal = {Pattern Recognition, International Conference on},
year = {1996},
volume = {3},
pages = {180},
address = {Los Alamitos, CA, USA},
doi = {http://doi.ieeecomputersociety.org/10.1109/ICPR.1996.546934},
issn = {1051-4651},
publisher = {IEEE Computer Society}
}
@inproceedings{Cortes2003Rational,
author = {Cortes, C. and Haffner, P. and Mohri, M.},
title = {Rational {K}ernels},
booktitle = {Advances in {N}eural {I}nformation {P}rocessing {S}ystems 15},
year = {2003},
editor = {Suzanna Becker and Sebastian Thrun and Klaus Obermayer},
publisher = {MIT Press}
}
@article{Cortes1995Support-Vector,
author = {Cortes, C. and Vapnik, V.},
title = {Support-Vector Networks},
journal = {Machine Learning},
year = {1995},
volume = {20},
pages = {273-297},
note = {10.1023/A:1022627411411},
abstract = {The support-vector network is a new learning machine for two-group
classification problems. The machine conceptually implements the
following idea: input vectors are non-linearly mapped to a very high-dimension
feature space. In this feature space a linear decision surface is
constructed. Special properties of the decision surface ensures high
generalization ability of the learning machine. The idea behind the
support-vector network was previously implemented for the restricted
case where the training data can be separated without errors. We
here extend this result to non-separable training data.},
issn = {0885-6125},
issue = {3},
keyword = {Computer Science},
owner = {fantine},
publisher = {Springer Netherlands},
timestamp = {2010.10.24},
url = {http://dx.doi.org/10.1023/A:1022627411411}
}
@article{Coupez2006Docking,
author = {B. Coupez and R. A. Lewis},
title = {Docking and scoring--theoretically easy, practically impossible?},
journal = {Curr. Med. Chem.},
year = {2006},
volume = {13},
pages = {2995--3003},
number = {25},
abstract = {Structure-based Drug Design (SBDD) is an essential part of the modern
medicinal chemistry, and has led to the acceleration of many projects,
and even to drugs on the market. Programs that perform docking and
scoring of ligands to receptors are powerful tools in the drug designer's
armoury that enhance the process of SBDD. They are even deployed
on the desktop of many bench chemists. It is timely to review the
state of the art, to understand how good our docking programs are,
and what are the issues. In this review we would like to provide
a guide around the reliable aspects of docking and scoring and the
associated pitfalls aiming at an audience of medicinal chemists rather
than modellers. For convenience, we will divide the review into two
parts: docking and scoring. Docking concerns the preparation of the
receptor and the ligand(s), the sampling of conformational space
and stereochemistry (if appropriate). Scoring concerns the evaluation
of all of the ligand-receptor poses generated by docking. The two
processes are not truly independent, and this will be discussed here
in detail. The preparation of the receptor and ligand(s) before docking
requires great care. For the receptor, issues of protonation, tautomerisation
and hydration are key, and we will discuss current approaches to
these issues. Even more important is the degree of sampling: can
the algorithms reproduce what is observed experimentally? If they
can, are the scoring algorithms good enough to recognise this pose
as the best? Do the scores correlate with observed binding affinity?
How does local knowledge of the target (for example hinge-binding
to a kinase) affect the accuracy of the predictions? We will review
the key findings from several evaluation studies and present conclusions
about when and how to interpret and trust the results of docking
and scoring. Finally, we will present an outline of some of the latest
developments in the area of scoring functions.},
institution = {Computer-Aided Drug Discovery, Novartis Institute for Biomedical
Research, Switzerland.},
keywords = {Cluster Analysis; Computational Biology, methods; Computer Simulation;
Computer-Aided Design; Databases, Factual; Drug Design; Ligands;
Models, Chemical; Software; Structure-Activity Relationship},
owner = {bricehoffmann},
pmid = {17073642},
timestamp = {2009.02.13}
}
@inproceedings{Cour2007Recognizing,
author = {Cour, T. and Shi, J.},
title = {Recognizing objects by piecing together the Segmentation Puzzle},
booktitle = {Proc. IEEE Conference on Computer Vision and Pattern Recognition
CVPR '07},
year = {2007},
pages = {1--8},
month = {17--22 June },
doi = {10.1109/CVPR.2007.383051},
owner = {michael},
timestamp = {2009.11.10}
}
@inproceedings{Cour2006Balanced,
author = {Cour, T. and Srinivasan, P. and Shi, J. },
title = {Balanced Graph Matching},
booktitle = {Advanced in Neural Information Processing Systems},
year = {2006},
citeulike-article-id = {1072329},
keywords = {graph-matching, spectral, spectral-matching},
posted-at = {2007-01-28 07:49:33},
priority = {5}
}
@article{Cover1996Universal,
author = {Cover, T.M. and Ordentlich, E. },
title = {Universal portfolios with side information},
journal = {I{EEE} {T}rans. {I}nform. {T}heory},
year = {1996},
volume = {42},
pages = {348-363},
number = {2},
month = {Mar},
abstract = {We present a sequential investment algorithm, the ?-weighted universal
portfolio with side information, which achieves, to first order in
the exponent, the same wealth as the best side-information dependent
investment strategy (the best state-constant rebalanced portfolio)
determined in hindsight from observed market and side-information
outcomes. {T}his is an individual sequence result which shows the
difference between the exponential growth wealth of the best state-constant
rebalanced portfolio and the universal portfolio with side information
is uniformly less than (d/(2n))log (n+1)+(k/n)log 2 for every stock
market and side-information sequence and for all time n. {H}ere d=k(m-1)
is the number of degrees of freedom in the state-constant rebalanced
portfolio with k states of side information and m stocks. {T}he proof
of this result establishes a close connection between universal investment
and universal data compression },
pdf = {../local/Cover1996Universal.pdf},
file = {Cover1996Universal.pdf:local/Cover1996Universal.pdf:PDF},
keywords = {information-theory},
owner = {vert}
}
@book{Cover1990Elements,
title = {Elements of {I}nformation {T}heory},
publisher = {John Wiley},
year = {1990},
author = {Cover, T.M. and Thomas, J.A.},
address = {New-York},
owner = {vert}
}
@article{Cowell2007application,
author = {Cowell, J.K. and Hawthorn, L.},
title = {The application of microarray technology to the analysis of the cancer
genome},
journal = {Curr. Mol. Med.},
year = {2007},
volume = {7},
pages = {103--120},
number = {1},
month = {Feb},
abstract = {The identification of genetic events that are involved in the development
of human cancer has been facilitated through the development and
application of a diverse series of high resolution, high throughput
microarray platforms. Essentially there are two types of array; those
that carry PCR products from cloned nucleic acids (e.g. cDNA, BACs,
cosmids) and those that use oligonucleotides. Each has advantages
and disadvantages but it is now possible to survey genome wide DNA
copy number abnormalities and expression levels to allow correlations
between losses, gains and amplifications in tumor cells with genes
that are over- and under-expressed in the same samples. The gene
expression arrays that provide estimates of mRNA levels in tumors
have given rise to exon-specific arrays that can identify both gene
expression levels, alternative splicing events and mRNA processing
alterations. Oligonucleotide arrays are also being used to interrogate
single nucleotide polymorphisms (SNPs) throughout the genome for
linkage and association studies and these have been adapted to quantify
copy number abnormalities and loss of heterozygosity events. To identify
as yet unknown transcripts tiling arrays across the genome have been
developed which can also identify DNA methylation changes and be
used to identify DNA-protein interactions using ChIP on Chip protocols.
Ultimately DNA sequencing arrays will allow resequencing of chromosome
regions and whole genomes. With all of these capabilities becoming
routine in genomics laboratories, the idea of a systematic characterization
of the sum genetic events that give rise to a cancer cell is rapidly
becoming a reality.},
institution = {Department of Cancer Genetics, Roswell Park Cancer Institute, Buffalo,
New York 14263, USA. john.cowell@roswellpark.org},
keywords = {csbcbook, csbcbook-ch2},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pmid = {17311536},
timestamp = {2009.10.18}
}
@article{Cox1990Asymptotic,
author = {Cox, D. and O'Sullivan, F.},
title = {Asymptotic analysis of penalized likelihood and related estimators},
journal = {Ann. {S}tat.},
year = {1990},
volume = {18},
pages = {1676-1695},
pdf = {../local/Cox1990Asymptotic.pdf},
file = {Cox1990Asymptotic.pdf:local/Cox1990Asymptotic.pdf:PDF},
owner = {jeanphilippevert}
}
@article{Cox2003Functional,
author = {David D Cox and Robert L Savoy},
title = {Functional magnetic resonance imaging (f{MRI}) "brain reading": detecting
and classifying distributed patterns of f{MRI} activity in human
visual cortex.},
journal = {Neuroimage},
year = {2003},
volume = {19},
pages = {261-70},
number = {2 Pt 1},
month = {Jun},
abstract = {Traditional (univariate) analysis of functional {MRI} (f{MRI}) data
relies exclusively on the information contained in the time course
of individual voxels. {M}ultivariate analyses can take advantage
of the information contained in activity patterns across space, from
multiple voxels. {S}uch analyses have the potential to greatly expand
the amount of information extracted from f{MRI} data sets. {I}n the
present study, multivariate statistical pattern recognition methods,
including linear discriminant analysis and support vector machines,
were used to classify patterns of f{MRI} activation evoked by the
visual presentation of various categories of objects. {C}lassifiers
were trained using data from voxels in predefined regions of interest
during a subset of trials for each subject individually. {C}lassification
of subsequently collected f{MRI} data was attempted according to
the similarity of activation patterns to prior training examples.
{C}lassification was done using only small amounts of data (20 s
worth) at a time, so such a technique could, in principle, be used
to extract information about a subject's percept on a near real-time
basis. {C}lassifiers trained on data acquired during one session
were equally accurate in classifying data collected within the same
session and across sessions separated by more than a week, in the
same subject. {A}lthough the highest classification accuracies were
obtained using patterns of activity including lower visual areas
as input, classification accuracies well above chance were achieved
using regions of interest restricted to higher-order object-selective
visual areas. {I}n contrast to typical f{MRI} data analysis, in which
hours of data across many subjects are averaged to reveal slight
differences in activation, the use of pattern recognition methods
allows a subtle 10-way discrimination to be performed on an essentially
trial-by-trial basis within individuals, demonstrating that f{MRI}
data contain far more information than is typically appreciated.},
pii = {S1053811903000491}
}
@article{Crick1970Central,
author = {Crick, F.},
title = {Central Dogma of Molecular Biology},
journal = {Nature},
year = {1970},
volume = {227},
pages = {561--563},
abstract = {The central dogma of molecular biology deals with the detailed
residue-by-residue transfer of sequential information. It states
that such informatfon cannot be transferred from protein to either
proteln or nucleic acid.},
pdf = {../local/Crick1970Central.pdf},
file = {Crick1970Central.pdf:Crick1970Central.pdf:PDF},
keywords = {csbcbook},
owner = {jp},
url = {http://profiles.nlm.nih.gov/SC/B/C/C/H/_/scbcch.pdf}
}
@book{Cristianini2000introduction,
title = {An introduction to {S}upport {V}ector {M}achines and other kernel-based
learning methods},
publisher = {Cambridge University Press},
year = {2000},
author = {Cristianini, N. and Shawe-Taylor, J.},
subject = {kernel},
url = {http://www.support-vector.net}
}
@article{Cristianini2002Latent,
author = {Cristianini, N. and Shawe-Taylor, J. and Lodhi, H.},
title = {Latent semantic kernels},
journal = {J. {I}ntell. {I}nform. {S}yst.},
year = {2002},
volume = {18},
pages = {127--152},
number = {2-3},
doi = {10.1023/A:1013625426931},
pdf = {../local/Cristianini2002Latent.pdf},
file = {Cristianini2002Latent.pdf:local/Cristianini2002Latent.pdf:PDF},
url = {http://dx.doi.org/10.1023/A:1013625426931}
}
@article{Croce2009Causes,
author = {Carlo M. Croce},
title = {Causes and consequences of {microRNA} dysregulation in cancer},
journal = {Nat Rev Genet},
year = {2009},
volume = {10},
pages = {704--714},
number = {10},
month = oct,
doi = {10.1038/nrg2634},
pdf = {../local/Croce2009Causes.pdf},
file = {Croce2009Causes.pdf:Croce2009Causes.pdf:PDF},
issn = {1471-0056},
keywords = {csbcbook},
owner = {phupe},
timestamp = {2009.10.15},
url = {http://dx.doi.org/10.1038/nrg2634}
}
@article{Croce2008Oncogenes,
author = {Croce, C. M.},
title = {Oncogenes and cancer.},
journal = {N. Engl. J. Med.},
year = {2008},
volume = {358},
pages = {502--511},
number = {5},
month = {Jan},
doi = {10.1056/NEJMra072367},
pdf = {../local/Croce2008Oncogenes.pdf},
file = {Croce2008Oncogenes.pdf:Croce2008Oncogenes.pdf:PDF},
institution = {Department of Molecular Virology, Immunology, and Medical Genetics
and the Human Cancer Genetics Program, Ohio State University Medical
Center, Columbus, OH 43210, USA. carlo.croce@osumc.edu},
keywords = {csbcbook},
owner = {jp},
pii = {358/5/502},
pmid = {18234754},
timestamp = {2009.10.09},
url = {http://dx.doi.org/10.1056/NEJMra072367}
}
@book{Csorgo1998Limit,
title = {Limit theorems in change-point analysis},
publisher = {John Wiley},
year = {1998},
author = {Cs\"org\"o, M. and Horvath, L.},
address = {New York},
owner = {jp},
timestamp = {2012.10.02}
}
@article{Csorgo1978Strong,
author = {Csorgo, M. and Revesz, P.},
title = {Strong {A}pproximations of the {Q}uantile {P}rocess},
journal = {Ann. {S}tat.},
year = {1978},
volume = {6},
pages = {882-894},
number = {4},
month = {July},
booktitle = {Annals of {S}tatistics},
pdf = {../local/Csorgo1978Strong.pdf},
file = {Csorgo1978Strong.pdf:local/Csorgo1978Strong.pdf:PDF},
url = {http://links.jstor.org/sici?sici=0090-5364%28197807%296%3A4%3C882%3ASAOTQP%3E2.0.CO%3B2-H}
}
@article{Cucker2002Best,
author = {Cucker, F. and Smale, S.},
title = {Best choices for regularization parameters in learning theory: on
the bias-variance problem},
journal = {Foundations of {C}omputational {M}athematics},
year = {2002},
volume = {2},
pages = {413-428},
number = {4},
doi = {10.1007/s102080010030},
pdf = {../local/Cucker2002Best.pdf},
file = {Cucker2002Best.pdf:local/Cucker2002Best.pdf:PDF},
owner = {jeanphilippevert},
url = {http://dx.doi.org/10.1007/s102080010030}
}
@article{Cucker2002On,
author = {Cucker, F. and Smale, S.},
title = {On the mathematical foundations of learning},
journal = {Bull. {A}mer. {M}ath. {S}oc},
year = {2002},
volume = {39},
pages = {1-49},
pdf = {../local/Cucker2002On.pdf},
file = {Cucker2002On.pdf:local/Cucker2002On.pdf:PDF},
owner = {jeanphilippevert}
}
@article{Cuff1999Evaluation,
author = {Cuff, J. A. and Barton, G. J.},
title = {Evaluation and improvement of multiple sequence methods for protein
secondary structure prediction},
journal = {Protein. {S}truct. {F}unct. {G}enet.},
year = {1999},
volume = {34},
pages = {508-519},
pdf = {../local/cuff99.pdf},
file = {cuff99.pdf:local/cuff99.pdf:PDF},
subject = {biocasp},
url = {http://www3.interscience.wiley.com/cgi-bin/fulltext/65000270/FILE?TPL=ft_start}
}
@article{Cui2008Two,
author = {Cui, J. and Chen, C. and Lu, H. and Sun, T. and Shen, P.},
title = {Two Independent Positive Feedbacks and Bistability in the Bcl-2 Apoptotic
Switch},
journal = {PLoS ONE},
year = {2008},
volume = {3},
pages = {e1469},
number = {1},
month = {01},
abstract = {Background - The complex interplay between B-cell lymphoma 2 (Bcl-2)
family proteins constitutes a crucial checkpoint in apoptosis. Its
detailed molecular mechanism remains controversial. Our former modeling
studies have selected the ‘Direct Activation Model’ as a better explanation
for experimental observations. In this paper, we continue to extend
this model by adding interactions according to updating experimental
findings. Methodology/Principal Findings - Through mathematical simulation
we found bistability, a kind of switch, can arise from a positive
(double negative) feedback in the Bcl-2 interaction network established
by anti-apoptotic group of Bcl-2 family proteins. Moreover, Bax/Bak
auto-activation as an independent positive feedback can enforce the
bistability, and make it more robust to parameter variations. By
ensemble stochastic modeling, we also elucidated how intrinsic noise
can change ultrasensitive switches into gradual responses. Our modeling
result agrees well with recent experimental data where bimodal Bax
activation distributions in cell population were found. Conclusions/Significance
- Along with the growing experimental evidences, our studies successfully
elucidate the switch mechanism embedded in the Bcl-2 interaction
network and provide insights into pharmacological manipulation of
Bcl-2 apoptotic switch as further cancer therapies.},
doi = {10.1371/journal.pone.0001469},
pdf = {../local/Cui2008Two.pdf},
file = {Cui2008Two.pdf:Cui2008Two.pdf:PDF},
keywords = {csbcbook},
publisher = {Public Library of Science},
url = {http://dx.plos.org/10.1371/journal.pone.0001469}
}
@article{Cui2004Esub8,
author = {Cui, Q. and Jiang, T. and Liu, B. and Ma, S.},
title = {Esub8: {A} novel tool to predict protein subcellular localizations
in eukaryotic organisms},
journal = {B{MC} {B}ioinformatics},
year = {2004},
volume = {5},
pages = {66},
number = {66},
abstract = {Background {S}ubcellular localization of a new protein sequence is
very important and fruitful for understanding its function. {A}s
the number of new genomes has dramatically increased over recent
years, a reliable and efficient system to predict protein subcellular
location is urgently needed. {R}esults {E}sub8 was developed to predict
protein subcellular localizations for eukaryotic proteins based on
amino acid composition. {I}n this research, the proteins are classified
into the following eight groups: chloroplast, cytoplasm, extracellular,
{G}olgi apparatus, lysosome, mitochondria, nucleus and peroxisome.
{W}e know subcellular localization is a typical classification problem;
consequently, a one-against-one (1-v-1) multi-class support vector
machine was introduced to construct the classifier. {U}nlike previous
methods, ours considers the order information of protein sequences
by a different method. {O}ur method is tested in three subcellular
localization predictions for prokaryotic proteins and four subcellular
localization predictions for eukaryotic proteins on {R}einhardt's
dataset. {T}he results are then compared to several other methods.
{T}he total prediction accuracies of two tests are both 100% by a
self-consistency test, and are 92.9% and 84.14% by the jackknife
test, respectively. {E}sub8 also provides excellent results: the
total prediction accuracies are 100% by a self-consistency test and
87% by the jackknife test. {C}onclusions {O}ur method represents
a different approach for predicting protein subcellular localization
and achieved a satisfactory result; furthermore, we believe {E}sub8
will be a useful tool for predicting protein subcellular localizations
in eukaryotic organisms.},
doi = {10.1186/1471-2105-5-66},
pdf = {../local/Cui2004Esub8.pdf},
file = {Cui2004Esub8.pdf:local/Cui2004Esub8.pdf:PDF},
keywords = {biosvm},
owner = {vert},
url = {http://www.biomedcentral.com/1471-2105/5/66}
}
@article{Curran1995molecular,
author = {Curran, M. E. and Splawski, I. and Timothy, K. W. and Vincent, G.
M. and Green, E. D. and Keating, M. T.},
title = {{A} molecular basis for cardiac arrhythmia: {HERG} mutations cause
long {QT} syndrome.},
journal = {Cell},
year = {1995},
volume = {80},
pages = {795--803},
number = {5},
month = {Mar},
abstract = {To identify genes involved in cardiac arrhythmia, we investigated
patients with long QT syndrome (LQT), an inherited disorder causing
sudden death from a ventricular tachyarrythmia, torsade de pointes.
We previously mapped LQT loci on chromosomes 11 (LQT1), 7 (LQT2),
and 3 (LQT3). Here, linkage and physical mapping place LQT2 and a
putative potassium channel gene, HERG, on chromosome 7q35-36. Single
strand conformation polymorphism and DNA sequence analyses reveal
HERG mutations in six LQT families, including two intragenic deletions,
one splice-donor mutation, and three missense mutations. In one kindred,
the mutation arose de novo. Northern blot analyses show that HERG
is strongly expressed in the heart. These data indicate that HERG
is LQT2 and suggest a likely cellular mechanism for torsade de pointes.},
keywords = {herg},
pmid = {7889573},
timestamp = {2006.10.05}
}
@article{Curtis2012genomic,
author = {Curtis, Christina and Shah, Sohrab P. and Chin, Suet-Feung and Turashvili,
Gulisa and Rueda, Oscar M. and Dunning, Mark J. and Speed, Doug and
Lynch, Andy G. and Samarajiwa, Shamith and Yuan, Yinyin and Gräf,
Stefan and Ha, Gavin and Haffari, Gholamreza and Bashashati, Ali
and Russell, Roslin and McKinney, Steven and , M. E. T. A. B. R.
I. C Group and Langerød, Anita and Green, Andrew and Provenzano,
Elena and Wishart, Gordon and Pinder, Sarah and Watson, Peter and
Markowetz, Florian and Murphy, Leigh and Ellis, Ian and Purushotham,
Arnie and Børresen-Dale, Anne-Lise and Brenton, James D. and Tavaré,
Simon and Caldas, Carlos and Aparicio, Samuel},
title = {The genomic and transcriptomic architecture of 2,000 breast tumours
reveals novel subgroups.},
journal = {Nature},
year = {2012},
volume = {486},
pages = {346--352},
number = {7403},
month = {Jun},
abstract = {The elucidation of breast cancer subgroups and their molecular drivers
requires integrated views of the genome and transcriptome from representative
numbers of patients. We present an integrated analysis of copy number
and gene expression in a discovery and validation set of 997 and
995 primary breast tumours, respectively, with long-term clinical
follow-up. Inherited variants (copy number variants and single nucleotide
polymorphisms) and acquired somatic copy number aberrations (CNAs)
were associated with expression in ~40\% of genes, with the landscape
dominated by cis- and trans-acting CNAs. By delineating expression
outlier genes driven in cis by CNAs, we identified putative cancer
genes, including deletions in PPP2R2A, MTAP and MAP2K4. Unsupervised
analysis of paired DNA–RNA profiles revealed novel subgroups with
distinct clinical outcomes, which reproduced in the validation cohort.
These include a high-risk, oestrogen-receptor-positive 11q13/14 cis-acting
subgroup and a favourable prognosis subgroup devoid of CNAs. Trans-acting
aberration hotspots were found to modulate subgroup-specific gene
networks, including a TCR deletion-mediated adaptive immune response
in the ‘CNA-devoid’ subgroup and a basal-specific chromosome 5 deletion-associated
mitotic network. Our results provide a novel molecular stratification
of the breast cancer population, derived from the impact of somatic
CNAs on the transcriptome.},
doi = {10.1038/nature10983},
pdf = {../local/Curtis2012genomic.pdf},
file = {Curtis2012genomic.pdf:Curtis2012genomic.pdf:PDF},
institution = {Department of Oncology, University of Cambridge, Hills Road, Cambridge
CB2 2XZ, UK.},
language = {eng},
medline-pst = {epublish},
owner = {jp},
pii = {nature10983},
pmid = {22522925},
timestamp = {2012.09.03},
url = {http://dx.doi.org/10.1038/nature10983}
}
@article{Curtis2005,
author = {Curtis, Keira R. and Oresic, Matej and Vidal-Puig, Antonio },
title = {Pathways to the analysis of microarray data},
journal = {Trends in {B}iotechnology},
year = {2005},
volume = {23},
pages = {429--435},
number = {8},
abstract = {The development of microarray technology allows the simultaneous measurement
of the expression of many thousands of genes. {T}he information gained
offers an unprecedented opportunity to fully characterize biological
processes. {H}owever, this challenge will only be successful if new
tools for the efficient integration and interpretation of large datasets
are available. {O}ne of these tools, pathway analysis, involves looking
for consistent but subtle changes in gene expression by incorporating
either pathway or functional annotations. {W}e review several methods
of pathway analysis and compare the performance of three, the binomial
distribution, z scores, and gene set enrichment analysis, on two
microarray datasets. {P}athway analysis is a promising tool to identify
the mechanisms that underlie diseases, adaptive physiological compensatory
responses and new avenues for investigation.},
citeulike-article-id = {270629},
doi = {10.1016/j.tibtech.2005.05.011},
keywords = {networks pathways},
priority = {4},
url = {http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve\&db=pubmed\&dopt=Abstract\&list_uids=15950303}
}
@article{Cuturi2005Semigroupa,
author = {Cuturi, M. and Fukumizu, K. and Vert, J.-P.},
title = {Semigroup kernels on measures},
journal = {J. Mach. Learn. Res.},
year = {2005},
volume = {6},
pages = {1169-1198},
pdf = {../local/Cuturi2005Semigroupa.pdf},
file = {Cuturi2005Semigroupa.pdf:Cuturi2005Semigroupa.pdf:PDF},
keywords = {kernel-theory},
owner = {mahe},
timestamp = {2006.08.09},
url = {http://jmlr.csail.mit.edu/papers/v6/cuturi05a.html}
}
@inproceedings{Cuturi2007kernel,
author = {Cuturi, M. and Vert, J. P. and Birkenes, O. and Matsui, T.},
title = {A kernel for time series based on global alignment},
booktitle = {Proceedings of the IEEE International Conference on Acoustics, Speech
and Signal Processing (ICASSP 2007)},
year = {2007},
volume = {2},
pages = {II-413--II-416},
doi = {10.1109/ICASSP.2007.366260},
pdf = {../local/Cuturi2007kernel.pdf},
file = {Cuturi2007kernel.pdf:Cuturi2007kernel.pdf:PDF},
owner = {jp},
timestamp = {2008.11.01},
url = {http://dx.doi.org/10.1109/ICASSP.2007.366260}
}
@inproceedings{Cuturi2005Semigroup,
author = {Cuturi, M. and Vert, J.-P. },
title = {Semigroup kernels on finite sets},
booktitle = {Adv. {N}eural {I}nform. {P}rocess. {S}yst.},
year = {2005},
editor = {Lawrence K. Saul and Yair Weiss and L{\'e}on Bottou},
volume = {17},
pages = {329-336},
publisher = {MIT Press, Cambridge, MA},
pdf = {../local/cuturi_version_finale.pdf:http\://cg.ensmp.fr/~vert/publi/04nips_marco/cuturi_version_finale.pdf:PDF;cuturi_version_finale.pdf:http\},
file = {cuturi_version_finale.pdf:http\://cg.ensmp.fr/~vert/publi/04nips_marco/cuturi_version_finale.pdf:PDF;cuturi_version_finale.pdf:http\://cg.ensmp.fr/~vert/publi/04nips_marco/cuturi_version_finale.pdf:PDF},
owner = {vert}
}
@article{Cuturi2005context-tree,
author = {Cuturi, M. and Vert, J.-P.},
title = {The context-tree kernel for strings},
journal = {Neural {N}etwork.},
year = {2005},
volume = {18},
pages = {1111-1123},
number = {4},
abstract = {We propose a new kernel for strings which borrows ideas and techniques
from information theory and data compression. {T}his kernel can be
used in combination with any kernel method, in particular {S}upport
{V}ector {M}achines for string classi- fication, with notable applications
in proteomics. {B}y using a {B}ayesian averaging framework with conjugate
priors on a class of {M}arkovian models known as prob- abilistic
suffix trees or context-trees, we compute the value of this kernel
in linear time and space while only using the information contained
in the spectrum of the considered strings. {T}his is ensured through
an adaptation of a compression method known as the context-tree weighting
algorithm. {E}ncouraging classification results are reported on a
standard protein homology detection experiment, showing that the
context-tree kernel performs well with respect to other state-of-the-art
methods while using no biological prior knowledge.},
doi = {10.1016/j.neunet.2005.07.010},
pdf = {../local/Cuturi2005context-tree.pdf},
file = {Cuturi2005context-tree.pdf:local/Cuturi2005context-tree.pdf:PDF},
keywords = {biosvm},
url = {http://dx.doi.org/10.1016/j.neunet.2005.07.010}
}
@inproceedings{Cuturi2004mutual,
author = {Cuturi, M. and Vert, J.-P.},
title = {A mutual information kernel for strings},
booktitle = {Proceedings of {IJCNN} 2004},
year = {2004},
pages = {1904-1910},
pdf = {../local/ijcnn04.pdf:http\://cg.ensmp.fr/~vert/publi/04ijcnn/ijcnn04.pdf:PDF;ijcnn04.pdf:http\},
file = {ijcnn04.pdf:http\://cg.ensmp.fr/~vert/publi/04ijcnn/ijcnn04.pdf:PDF;ijcnn04.pdf:http\://cg.ensmp.fr/~vert/publi/04ijcnn/ijcnn04.pdf:PDF},
keywords = {biosvm},
owner = {vert}
}
@inproceedings{Curturi2009White,
author = {M. Cuturi and J.-P. Vert and A. D'Aspremont},
title = {White Functionals for Anomaly Detection in Dynamical Systems},
booktitle = {Advances in Neural Information Processing Systems 22},
year = {2009},
editor = {Y. Bengio and D. Schuurmans and J. Lafferty and C. K. I. Williams
and A. Culotta},
pages = {432--440},
pdf = {../local/Curturi2009White.pdf},
file = {Curturi2009White.pdf:Curturi2009White.pdf:PDF},
owner = {jp},
timestamp = {2009.12.21},
url = {http://books.nips.cc/papers/files/nips22/NIPS2009_1195.pdf}
}
@article{Doennes2002Prediction,
author = {Pierre D\"onnes and Arne Elofsson},
title = {Prediction of {MHC} class {I} binding peptides, using {SVMHC}.},
journal = {BMC Bioinformatics},
year = {2002},
volume = {3},
pages = {25},
month = {Sep},
abstract = {BACKGROUND: T-cells are key players in regulating a specific immune
response. Activation of cytotoxic T-cells requires recognition of
specific peptides bound to Major Histocompatibility Complex (MHC)
class I molecules. MHC-peptide complexes are potential tools for
diagnosis and treatment of pathogens and cancer, as well as for the
development of peptide vaccines. Only one in 100 to 200 potential
binders actually binds to a certain MHC molecule, therefore a good
prediction method for MHC class I binding peptides can reduce the
number of candidate binders that need to be synthesized and tested.
RESULTS: Here, we present a novel approach, SVMHC, based on support
vector machines to predict the binding of peptides to MHC class I
molecules. This method seems to perform slightly better than two
profile based methods, SYFPEITHI and HLA_BIND. The implementation
of SVMHC is quite simple and does not involve any manual steps, therefore
as more data become available it is trivial to provide prediction
for more MHC types. SVMHC currently contains prediction for 26 MHC
class I types from the MHCPEP database or alternatively 6 MHC class
I types from the higher quality SYFPEITHI database. The prediction
models for these MHC types are implemented in a public web service
available at http://www.sbc.su.se/svmhc/. CONCLUSIONS: Prediction
of MHC class I binding peptides using Support Vector Machines, shows
high performance and is easy to apply to a large number of MHC class
I types. As more peptide data are put into MHC databases, SVMHC can
easily be updated to give prediction for additional MHC class I types.
We suggest that the number of binding peptides needed for SVM training
is at least 20 sequences.},
keywords = {Animals; Artificial Intelligence; Comparative Study; Computational
Biology; Databases, Protein; Epitopes, T-Lymphocyte; HLA Antigens;
Histocompatibility Antigens Class I; Humans; Peptides; Predictive
Value of Tests; Protein Binding; Research Support, Non-U.S. Gov't;
Sensitivity and Specificity},
owner = {jacob},
pmid = {12225620},
timestamp = {2006.08.30}
}
@article{Dahlquist2002GenMAPP,
author = {Dahlquist, K. D. and Salomonis, N. and Vranizan, K. and Lawlor, S.
C. and Conklin, B. R.},
title = {{GenMAPP}, a new tool for viewing and analyzing microarray data on
biological pathways.},
journal = {Nat. Genet.},
year = {2002},
volume = {31},
pages = {19--20},
number = {1},
month = {May},
doi = {10.1038/ng0502-19},
pdf = {../local/Dahlquist2002GenMAPP.pdf},
file = {Dahlquist2002GenMAPP.pdf:Dahlquist2002GenMAPP.pdf:PDF},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {ng0502-19},
pmid = {11984561},
timestamp = {2011.10.04},
url = {http://dx.doi.org/10.1038/ng0502-19}
}
@article{Dai2005Evolving,
author = {Dai, M. and Wang, P. and Boyd, A.D. and Kostov, G. and Athey, B.
and Jones, E.G. and Bunney, W.E. and Myers, R.M. and Speed, T.P.
and Akil, H. and others},
title = {Evolving gene/transcript definitions significantly alter the interpretation
of GeneChip data},
journal = {Nucleic acids research},
year = {2005},
volume = {33},
pages = {e175--e175},
number = {20},
publisher = {Oxford Univ Press}
}
@article{Daily2007Distinct,
author = {Daily, J. P. and Scanfeld, D. and Pochet, N. and Le Roch, K. and
Plouffe, D. and Kamal, M. and Sarr, O. and Mboup, S. and Ndir, O.
and Wypi, D.j and Levasseur, K. and Thomas, E. and Tamayo, P. and
Dong, C. and Zhou, Y. and Lander, E. S. and Ndiaye, D. and Wirth,
D. and Winzeler, E. A. and Mesirov, J. P. and Regev, A.},
title = {Distinct physiological states of Plasmodium falciparum in malaria-infected
patients},
journal = {Nature},
year = {2007},
volume = {450},
pages = {1091--1095},
number = {7172},
month = {Dec},
abstract = {Infection with the malaria parasite Plasmodium falciparum leads to
widely different clinical conditions in children, ranging from mild
flu-like symptoms to coma and death. Despite the immense medical
implications, the genetic and molecular basis of this diversity remains
largely unknown. Studies of in vitro gene expression have found few
transcriptional differences between different parasite strains. Here
we present a large study of in vivo expression profiles of parasites
derived directly from blood samples from infected patients. The in
vivo expression profiles define three distinct transcriptional states.
The biological basis of these states can be interpreted by comparison
with an extensive compendium of expression data in the yeast Saccharomyces
cerevisiae. The three states in vivo closely resemble, first, active
growth based on glycolytic metabolism, second, a starvation response
accompanied by metabolism of alternative carbon sources, and third,
an environmental stress response. The glycolytic state is highly
similar to the known profile of the ring stage in vitro, but the
other states have not been observed in vitro. The results reveal
a previously unknown physiological diversity in the in vivo biology
of the malaria parasite, in particular evidence for a functional
mitochondrion in the asexual-stage parasite, and indicate in vivo
and in vitro studies to determine how this variation may affect disease
manifestations and treatment.},
doi = {10.1038/nature06311},
pdf = {../local/Daily2007Distinct.pdf},
file = {Daily2007Distinct.pdf:Daily2007Distinct.pdf:PDF},
institution = {Department of Immunology and Infectious Disease, [Harvard School
of Public Health, 665 Huntington Avenue, Boston, Massachusetts 02115,
USA.},
keywords = {plasmodium},
owner = {jp},
pii = {nature06311},
pmid = {18046333},
timestamp = {2009.04.28},
url = {http://dx.doi.org/10.1038/nature06311}
}
@article{Dalca2010VARiD,
author = {Dalca, A. V. and Rumble, S. M. and Levy, S. and Brudno, M.},
title = {{VARiD}: a variation detection framework for color-space and letter-space
platforms.},
journal = {Bioinformatics},
year = {2010},
volume = {26},
pages = {i343--i349},
number = {12},
month = {Jun},
abstract = {High-throughput sequencing (HTS) technologies are transforming the
study of genomic variation. The various HTS technologies have different
sequencing biases and error rates, and while most HTS technologies
sequence the residues of the genome directly, generating base calls
for each position, the Applied Biosystem's SOLiD platform generates
dibase-coded (color space) sequences. While combining data from the
various platforms should increase the accuracy of variation detection,
to date there are only a few tools that can identify variants from
color space data, and none that can analyze color space and regular
(letter space) data together.We present VARiD--a probabilistic method
for variation detection from both letter- and color-space reads simultaneously.
VARiD is based on a hidden Markov model and uses the forward-backward
algorithm to accurately identify heterozygous, homozygous and tri-allelic
SNPs, as well as micro-indels. Our analysis shows that VARiD performs
better than the AB SOLiD toolset at detecting variants from color-space
data alone, and improves the calls dramatically when letter- and
color-space reads are combined.The toolset is freely available at
http://compbio.cs.utoronto.ca/varid.},
doi = {10.1093/bioinformatics/btq184},
pdf = {../local/Dalca2010VARiD.pdf},
file = {Dalca2010VARiD.pdf:Dalca2010VARiD.pdf:PDF},
institution = {Department of Electrical Engineering and Computer Science, Massachusetts
Institute of Technology, Cambridge, MA, USA. varid@cs.toronto.edu},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {btq184},
pmid = {20529926},
timestamp = {2011.10.27},
url = {http://dx.doi.org/10.1093/bioinformatics/btq184}
}
@article{Dalma-Weiszhausz2006Affymetrix,
author = {Dennise D Dalma-Weiszhausz and Janet Warrington and Eugene Y Tanimoto
and C. Garrett Miyada},
title = {The affymetrix GeneChip platform: an overview.},
journal = {Methods Enzymol.},
year = {2006},
volume = {410},
pages = {3--28},
abstract = {The intent of this chapter is to provide the reader with a review
of GeneChip technology and the complete system it represents, including
its versatility, components, and the exciting applications that are
enabled by this platform. The following aspects of the technology
are reviewed: array design and manufacturing, target preparation,
instrumentation, data analysis, and both current and future applications.
There are key differentiators between Affymetrix' GeneChip technology
and other microarray-based methods. The most distinguishing feature
of GeneChip microarrays is that their manufacture is directed by
photochemical synthesis. Because of this manufacturing technology,
more than a million different probes can be synthesized on an array
roughly the size of a thumbnail. These numbers allow the inclusion
of multiple probes to interrogate the same target sequence, providing
statistical rigor to data interpretation. Over the years the GeneChip
platform has proven to be a reliable and robust system, enabling
many new discoveries and breakthroughs to be made by the scientific
community.},
doi = {10.1016/S0076-6879(06)10001-4},
pdf = {../local/Dalma-Weiszhausz2006Affymetrix.pdf},
file = {Dalma-Weiszhausz2006Affymetrix.pdf:Dalma-Weiszhausz2006Affymetrix.pdf:PDF},
institution = {Expression Product Development, AFFY-METRIX, INC., Santa Clara, California,
USA.},
keywords = {Animals; Humans; Oligonucleotide Array Sequence Analysis, instrumentation/methods},
language = {eng},
medline-pst = {ppublish},
owner = {philippe},
pii = {S0076-6879(06)10001-4},
pmid = {16938544},
timestamp = {2010.08.01},
url = {http://dx.doi.org/10.1016/S0076-6879(06)10001-4}
}
@article{Dalton2007Evaluation,
author = {James A R Dalton and Richard M Jackson},
title = {An evaluation of automated homology modelling methods at low target
template sequence similarity.},
journal = {Bioinformatics},
year = {2007},
volume = {23},
pages = {1901--1908},
number = {15},
month = {Aug},
abstract = {MOTIVATION: There are two main areas of difficulty in homology modelling
that are particularly important when sequence identity between target
and template falls below 50\%: sequence alignment and loop building.
These problems become magnified with automatic modelling processes,
as there is no human input to correct mistakes. As such we have benchmarked
several stand-alone strategies that could be implemented in a workflow
for automated high-throughput homology modelling. These include three
new sequence-structure alignment programs: 3D-Coffee, Staccato and
SAlign, plus five homology modelling programs and their respective
loop building methods: Builder, Nest, Modeller, SegMod/ENCAD and
Swiss-Model. The SABmark database provided 123 targets with at least
five templates from the same SCOP family and sequence identities
http://dx.doi.org/10.1093/bioinformatics/btm262}
}
@article{Darbellay2004Solid,
author = {Georges A Darbellay and Rebecca Duff and Jean-Marc Vesin and Paul-André
Despland and Dirk W Droste and Carlos Molina and Joachim Serena and
Roman Sztajzel and Patrick Ruchat and Theodoros Karapanayiotides
and Afksendyios Kalangos and Julien Bogousslavsky and Erich B Ringelstein
and Gérald Devuyst},
title = {Solid or gaseous circulating brain emboli: are they separable by
transcranial ultrasound?},
journal = {J {C}ereb {B}lood {F}low {M}etab},
year = {2004},
volume = {24},
pages = {860-8},
number = {8},
month = {Aug},
abstract = {High-intensity transient signals ({HITS}) detected by transcranial
{D}oppler ({TCD}) ultrasound may correspond to artifacts or to microembolic
signals, the latter being either solid or gaseous emboli. {T}he goal
of this study was to assess what can be achieved with an automatic
signal processing system for artifact/microembolic signals and solid/gas
differentiation in different clinical situations. {T}he authors studied
3,428 {HITS} in vivo in a multicenter study, i.e., 1,608 artifacts
in healthy subjects, 649 solid emboli in stroke patients with a carotid
stenosis, and 1,171 gaseous emboli in stroke patients with patent
foramen ovale. {T}hey worked with the dual-gate {TCD} combined to
three types of statistical classifiers: binary decision trees ({BDT}),
artificial neural networks ({ANN}), and support vector machines ({SVM}).
{T}he sensitivity and specificity to separate artifacts from microembolic
signals by {BDT} reached was 94\% and 97\%, respectively. {F}or the
discrimination between solid and gaseous emboli, the classifier achieved
a sensitivity and specificity of 81\% and 81\% for {BDT}, 84\% and
84\% for {ANN}, and 86\% and 86\% for {SVM}, respectively. {T}he
current results for artifact elimination and solid/gas differentiation
are already useful to extract data for future prospective clinical
studies.},
keywords = {Air, Algorithms, Amino Acids, Animals, Artifacts, Atrial, Carotid
Stenosis, Cerebrovascular Accident, Cerebrovascular Circulation,
Comparative Study, Cysteine, Decision Trees, Disulfides, Doppler,
Embolism, Heart Septal Defects, Humans, Intracranial Embolism, Models,
Molecular, Neural Networks (Computer), Non-U.S. Gov't, Oxidation-Reduction,
Protein Binding, Protein Folding, Proteins, Research Support, Sensitivity
and Specificity, Transcranial, Ultrasonography, 15362716}
}
@article{Daubechies1988Orthonormal,
author = {Daubechies, I.},
title = {Orthonormal bases of compactly supported weavelets},
journal = {Comm. Pure Appl. Math.},
year = {1988},
volume = {41},
pages = {909--996},
pdf = {../local/Daubechies1988Orthonormal.pdf},
file = {Daubechies1988Orthonormal.pdf:Daubechies1988Orthonormal.pdf:PDF},
owner = {jp},
timestamp = {2012.12.13}
}
@article{Daubechies2004iterative,
author = {Daubechies, Ingrid and Defrise, Michel and De Mol, Christine},
title = {An iterative thresholding algorithm for linear inverse problems with
a sparsity constraint},
journal = {Communications on {P}ure and {A}pplied {M}athematics},
year = {2004},
volume = {57},
pages = {1413--1457},
number = {11}
}
@inproceedings{Daume2009Bayesian,
author = {Daume, H.},
title = {{Bayesian Multitask Learning with Latent Hierarchies}},
booktitle = {25th Conference on Uncertainty in Artificial Intelligence},
year = {2009}
}
@article{David2010Cancer:,
author = {A. Rosalie David and Michael R Zimmerman},
title = {Cancer: an old disease, a new disease or something in between?},
journal = {Nat Rev Cancer},
year = {2010},
volume = {10},
pages = {728--733},
number = {10},
month = {Oct},
abstract = {In industrialized societies, cancer is second only to cardiovascular
disease as a cause of death. The history of this disorder has the
potential to improve our understanding of disease prevention, aetiology,
pathogenesis and treatment. A striking rarity of malignancies in
ancient physical remains might indicate that cancer was rare in antiquity,
and so poses questions about the role of carcinogenic environmental
factors in modern societies. Although the rarity of cancer in antiquity
remains undisputed, the first published histological diagnosis of
cancer in an Egyptian mummy demonstrates that new evidence is still
forthcoming.},
doi = {10.1038/nrc2914},
institution = {KNH Centre of Biomedical Egyptology, The University of Manchester,
3.614 Stopford Building, Oxford Road, Manchester M13 9PT, UK. rosalie.david@manchester.ac.uk},
keywords = {Animals; Art; Fossils; Hominidae; Humans; Neoplasms; Paleopathology},
language = {eng},
medline-pst = {ppublish},
owner = {phupe},
pii = {nrc2914},
pmid = {20814420},
timestamp = {2011.05.31},
url = {http://dx.doi.org/10.1038/nrc2914}
}
@article{Davies2001Discrete,
author = {Davies, E. B. and Gladwell, G. M. L. and Leydold, J. and Stadler,
P. F.},
title = {Discrete {N}odal {D}omain {T}heorems},
journal = {Lin. {A}lg. {A}ppl.},
year = {2001},
volume = {336},
pages = {51--60},
pdf = {../local/davi01.pdf},
file = {davi01.pdf:local/davi01.pdf:PDF},
subject = {net},
url = {http://citeseer.nj.nec.com/davies01discrete.html}
}
@article{Davies2007Poisson,
author = {Davies, J.R. and Jackson, R.M. and Mardia, K.V. and Taylor, C.C.},
title = {The Poisson Index: a new probabilistic model for protein ligand binding
site similarity.},
journal = {Bioinformatics},
year = {2007},
volume = {23},
pages = {3001--3008},
number = {22},
month = {Nov},
abstract = {MOTIVATION: The large-scale comparison of protein-ligand binding sites
is problematic, in that measures of structural similarity are difficult
to quantify and are not easily understood in terms of statistical
similarity that can ultimately be related to structure and function.
We present a binding site matching score the Poisson Index (PI) based
upon a well-defined statistical model. PI requires only the number
of matching atoms between two sites and the size of the two sites-the
same information used by the Tanimoto Index (TI), a comparable and
widely used measure for molecular similarity. We apply PI and TI
to a previously automatically extracted set of binding sites to determine
the robustness and usefulness of both scores. RESULTS: We found that
PI outperforms TI; moreover, site similarity is poorly defined for
TI at values around the 99.5\% confidence level for which PI is well
defined. A difference map at this confidence level shows that PI
gives much more meaningful information than TI. We show individual
examples where TI fails to distinguish either a false or a true site
paring in contrast to PI, which performs much better. TI cannot handle
large or small sites very well, or the comparison of large and small
sites, in contrast to PI that is shown to be much more robust. Despite
the difficulty of determining a biological 'ground truth' for binding
site similarity we conclude that PI is a suitable measure of binding
site similarity and could form the basis for a binding site classification
scheme comparable to existing protein domain classification schema.},
owner = {vero},
pmid = {17893083 },
timestamp = {2009.02.04}
}
@article{Davies2005Array,
author = {Davies, J. J. and Wilson, I. M. and Lam, W. L.},
title = {Array {CGH} technologies and their applications to cancer genomes},
journal = {Chromosome Res.},
year = {2005},
volume = {13},
pages = {237--248},
number = {3},
abstract = {Cancer is a disease characterized by genomic instability. Comparative
genomic hybridization (CGH) is a technique designed for detecting
segmental genomic alterations. Recent advances in array-based CGH
technology have enabled examination of chromosomal regions in unprecedented
detail, revolutionizing our understanding of tumour genomes. A number
of array-based technologies have been developed, aiming to improve
the resolution of CGH, enabling researchers to refine and define
regions in the genome that may be causal to cancer, and facilitating
gene discovery at a rapid rate. This article reviews the various
array CGH platforms and their use in the study of cancer genomes.
In addition, the need for high-resolution analysis is discussed as
well as the importance of studying early-stage disease to discover
genetic alterations that may be causal to cancer progression and
aetiology.},
doi = {10.1007/s10577-005-2168-x},
pdf = {../local/Davies2005Array.pdf},
file = {Davies2005Array.pdf:Davies2005Array.pdf:PDF},
institution = {British Columbia Cancer Research Centre, 675 W 10th Ave., Vancouver
BC, V5Z 1L3, Canada. jdavies@bccrc.ca},
keywords = {csbcbook, csbcbook-ch2},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pmid = {15868418},
timestamp = {2009.10.18},
url = {http://dx.doi.org/10.1007/s10577-005-2168-x}
}
@article{Davies2007Harnessing,
author = {Matthew N Davies and Darren R Flower},
title = {Harnessing bioinformatics to discover new vaccines.},
journal = {Drug Discov Today},
year = {2007},
volume = {12},
pages = {389--395},
number = {9-10},
month = {May},
abstract = {Vaccine design is highly suited to the application of in silico techniques,
for both the discovery and development of new and existing vaccines.
Here, we discuss computational contributions to epitope mapping and
reverse vaccinology, two techniques central to the new discipline
of immunomics. Also discussed are methods to improve the efficiency
of vaccination, such as codon optimization and adjuvant discovery
in addition to the identification of allergenic proteins. We also
review current software developed to facilitate vaccine design.},
doi = {10.1016/j.drudis.2007.03.010},
keywords = {Animals; Computational Biology; Drug Design; Epitope Mapping; Humans;
Software Design; Vaccination; Vaccines},
owner = {laurent},
pii = {S1359-6446(07)00135-3},
pmid = {17467575},
timestamp = {2007.08.23},
url = {http://dx.doi.org/10.1016/j.drudis.2007.03.010}
}
@article{Davis2006Reliable,
author = {Davis, C.A. and Gerick, F. and Hintermair, V. and Friedel, C.C. and
Fundel, K. and K{\"u}ffner, R. and Zimmer, R.},
title = {Reliable gene signatures for microarray classification: assessment
of stability and performance},
journal = {Bioinformatics},
year = {2006},
volume = {22},
pages = {2356--2363},
number = {19},
publisher = {Oxford Univ Press}
}
@article{Davisson1973Universal,
author = { Davisson, L. },
title = {Universal noiseless coding},
journal = {I{EEE} {T}rans. {I}nform. {T}heory},
year = {1973},
volume = {19},
pages = {783- 795},
number = {6},
month = {Nov},
abstract = { {U}niversal coding is any asymptotically optimum method of block-to-block
memoryless source coding for sources with unknown parameters. {T}his
paper considers noiseless coding for such sources, primarily in terms
of variable-length coding, with performance measured as a function
of the coding redundancy relative to the per-letter conditional source
entropy given the unknown parameter. {I}t is found that universal
(i.e., zero redundancy) coding in a weighted sense is possible if
and only if the per-letter average mutual information between the
parameter space and the message space is zero. {U}niversal coding
is possible in a maximin sense if and only if the channel capacity
between the two spaces is zero. {U}niversal coding is possible in
a minimax sense if and only if a probability mass function exists,
independent of the unknown parameter, for which the relative entropy
of the known conditional-probability mass-function is zero. {S}everal
examples are given to illustrate the ideas. {P}articular attention
is given to sources that are stationary and ergodic for any fixed
parameter although the whole ensemble is not. {F}or such sources,
weighted universal codes always exist if the alphabet is finite,
or more generally if the entropy is finite. {M}inimax universal codes
result if an additional entropy stability constraint is applied.
{A} discussion of fixed-rate universal coding is also given briefly
with performance measured by a probability of error.},
pdf = {../local/Davisson1973Universal.pdf},
file = {Davisson1973Universal.pdf:local/Davisson1973Universal.pdf:PDF},
keywords = {information-theory universal-coding},
owner = {vert}
}
@article{Bie2007Kernel-based,
author = {De Bie, T. and Tranchevent, L.-C. and van Oeffelen, L. M. M. and
Moreau, Y.},
title = {Kernel-based data fusion for gene prioritization},
journal = {Bioinformatics},
year = {2007},
volume = {23},
pages = {i125--i132},
number = {13},
month = {Jul},
abstract = {MOTIVATION: Hunting disease genes is a problem of primary importance
in biomedical research. Biologists usually approach this problem
in two steps: first a set of candidate genes is identified using
traditional positional cloning or high-throughput genomics techniques;
second, these genes are further investigated and validated in the
wet lab, one by one. To speed up discovery and limit the number of
costly wet lab experiments, biologists must test the candidate genes
starting with the most probable candidates. So far, biologists have
relied on literature studies, extensive queries to multiple databases
and hunches about expected properties of the disease gene to determine
such an ordering. Recently, we have introduced the data mining tool
ENDEAVOUR (Aerts et al., 2006), which performs this task automatically
by relying on different genome-wide data sources, such as Gene Ontology,
literature, microarray, sequence and more. RESULTS: In this article,
we present a novel kernel method that operates in the same setting:
based on a number of different views on a set of training genes,
a prioritization of test genes is obtained. We furthermore provide
a thorough learning theoretical analysis of the method's guaranteed
performance. Finally, we apply the method to the disease data sets
on which ENDEAVOUR (Aerts et al., 2006) has been benchmarked, and
report a considerable improvement in empirical performance. AVAILABILITY:
The MATLAB code used in the empirical results will be made publicly
available.},
doi = {10.1093/bioinformatics/btm187},
pdf = {../local/Bie2007Kernel-based.pdf},
file = {Bie2007Kernel-based.pdf:Bie2007Kernel-based.pdf:PDF},
institution = {Department of Engineering Mathematics, University of Bristol, University
Walk, BS8 1TR, Bristol, UK. tijl.debie@gmail.com},
owner = {jp},
pii = {23/13/i125},
pmid = {17646288},
timestamp = {2009.03.18},
url = {http://dx.doi.org/10.1093/bioinformatics/btm187}
}
@book{Degroot1970Optimal,
title = { Optimal statistical decisions / Morris H. De Groot },
publisher = { McGraw-Hill, New York :, },
year = { 1970 },
author = { De Groot, Morris H. },
pages = { xvi, 489 p. : }
}
@article{Deb2003Reliable,
author = {Kalyanmoy Deb and A. Raji Reddy},
title = {Reliable classification of two-class cancer data using evolutionary
algorithms.},
journal = {Biosystems},
year = {2003},
volume = {72},
pages = {111-29},
number = {1-2},
month = {Nov},
abstract = {In the area of bioinformatics, the identification of gene subsets
responsible for classifying available disease samples to two or more
of its variants is an important task. {S}uch problems have been solved
in the past by means of unsupervised learning methods (hierarchical
clustering, self-organizing maps, k-mean clustering, etc.) and supervised
learning methods (weighted voting approach, k-nearest neighbor method,
support vector machine method, etc.). {S}uch problems can also be
posed as optimization problems of minimizing gene subset size to
achieve reliable and accurate classification. {T}he main difficulties
in solving the resulting optimization problem are the availability
of only a few samples compared to the number of genes in the samples
and the exorbitantly large search space of solutions. {A}lthough
there exist a few applications of evolutionary algorithms ({EA}s)
for this task, here we treat the problem as a multiobjective optimization
problem of minimizing the gene subset size and minimizing the number
of misclassified samples. {M}oreover, for a more reliable classification,
we consider multiple training sets in evaluating a classifier. {C}ontrary
to the past studies, the use of a multiobjective {EA} ({NSGA}-{II})
has enabled us to discover a smaller gene subset size (such as four
or five) to correctly classify 100\% or near 100\% samples for three
cancer samples ({L}eukemia, {L}ymphoma, and {C}olon). {W}e have also
extended the {NSGA}-{II} to obtain multiple non-dominated solutions
discovering as much as 352 different three-gene combinations providing
a 100\% correct classification to the {L}eukemia data. {I}n order
to have further confidence in the identification task, we have also
introduced a prediction strength threshold for determining a sample's
belonging to one class or the other. {A}ll simulation results show
consistent gene subset identifications on three disease samples and
exhibit the flexibilities and efficacies in using a multiobjective
{EA} for the gene subset identification task.},
doi = {10.1016/S0303-2647(03)00138-2},
pii = {S0303264703001382},
url = {http://dx.doi.org/10.1016/S0303-2647(03)00138-2}
}
@article{Debnath1991Structure-Activity,
author = {Debnath, A.K. and Lopez de Compadre, R.L. and Debnath, G. and Schusterman,
A.J. and Hansch, C.},
title = {Structure-Activity Relationship of Mutagenic Aromatic and Heteroaromatic
Nitro compounds. Correlation with molecular orbital energies and
hydrophobicity},
journal = {J. Med. Chem.},
year = {1991},
volume = {34},
pages = {786-797},
number = {2},
keywords = {chemoinformatics},
owner = {mahe},
timestamp = {2006.07.31}
}
@article{Debouck1999DNA,
author = {C. Debouck and P. N. Goodfellow},
title = {{DNA} microarrays in drug discovery and development.},
journal = {Nat. Genet.},
year = {1999},
volume = {21},
pages = {48--50},
number = {1 Suppl},
month = {Jan},
abstract = {DNA microarrays can be used to measure the expression patterns of
thousands of genes in parallel, generating clues to gene function
that can help to identify appropriate targets for therapeutic intervention.
They can also be used to monitor changes in gene expression in response
to drug treatments. Here, we discuss the different ways in which
microarray analysis is likely to affect drug discovery.},
doi = {10.1038/4475},
keywords = {Agricultural, Alleles, Alternaria, Amino Acid, Amino Acid Chloromethyl
Ketones, Amino Acid Sequence, Animal, Animals, Apoptosis, Asthma,
Bacteria, Base Sequence, Binding Sites, Biotechnology, Blotting,
Bone Density, Bone Matrix, Bone and Bones, CCR5, Camptothecin, Caspases,
Cathepsins, Cell Surface, Central America, Chloroplast, Chondrocytes,
Chromosome Mapping, Chromosomes, Cloning, Cluster Analysis, Collagen,
Comparative Study, Coumarins, Crops, Crystallography, DNA, DNA Primers,
Dipeptides, Disease, Disease Models, Drug Design, Drug Evaluation,
Drug Industry, Enzyme Activation, Enzyme Inhibitors, Escherichia
coli, Evolution, Exons, Expressed Sequence Tags, Female, Fetus, Fluorescent
Dyes, Food Microbiology, Founder Effect, GTP-Binding Proteins, Gene
Expression, Gene Frequency, Gene Library, Genes, Genetic, Genetic
Predisposition to Disease, Genome, Geography, Growth Plate, Haplotypes,
Hordeum, Human, Humans, Inclusion Bodies, Injections, Intraperitoneal,
Introns, Isatin, Knockout, Male, Membrane Proteins, Messenger, Mice,
Models, Molecular, Molecular Sequence Data, Molecular Structure,
Mutation, Mycotoxins, Neutrophils, Non-U.S. Gov't, Northern, Oligonucleotide
Array Sequence Analysis, Osteoarthritis, Osteochondrodysplasias,
Osteoclasts, Osteopetrosis, Pair 15, Phaseolus, Polymorphism, Preclinical,
Pregnancy, Promoter Regions (Genetics), Protein Precursors, Proteomics,
RNA, Receptors, Recombinant Fusion Proteins, Recombinant Proteins,
Research Support, Restriction Fragment Length, Ribosomal Proteins,
Sequence Alignment, Sequence Analysis, Sequence Homology, South America,
Species Specificity, Splenomegaly, Sulfonamides, Synteny, Tissue
Distribution, Transcription, Trichothecenes, X-Ray, 9915501},
owner = {piedro},
pmid = {9915501},
timestamp = {2006.08.11},
url = {http://dx.doi.org/10.1038/4475}
}
@inproceedings{Decatur1997PAC,
author = {Decatur, S.E.},
title = {PAC Learning with Constant-Partition Classification Noise and Applications
to Decision Tree Induction},
booktitle = {Proceedings of the Fourteenth International Conference on Machine
Learning},
year = {1997},
series = {ICML '97},
pages = {83--91},
address = {San Francisco, CA, USA},
publisher = {Morgan Kaufmann Publishers Inc.},
acmid = {657273},
isbn = {1-55860-486-3},
numpages = {9},
owner = {mordelet},
timestamp = {2010.12.08},
url = {http://portal.acm.org/citation.cfm?id=645526.657273}
}
@article{Declercq2009RIP,
author = {Declercq, W. and Vanden Berghe, T. and Vandenabeele, P.},
title = {{RIP} Kinases at the Crossroads of Cell Death and Survival},
journal = {Cell},
year = {2009},
volume = {138},
pages = {229-232},
number = {2},
doi = {10.1016/j.cell.2009.07.006},
pdf = {../local/Declercq2009RIP.pdf},
file = {Declercq2009RIP.pdf:Declercq2009RIP.pdf:PDF},
keywords = {csbcbook},
url = {http://dx.doi.org/10.1016/j.cell.2009.07.006}
}
@article{Decoste2002Training,
author = {Decoste, D. and Sch\"{o}lkopf, B.},
title = {Training Invariant Support Vector Machines},
journal = {Mach. Learn.},
year = {2002},
volume = {46},
pages = {161--190},
number = {1-3},
pdf = {../local/Decoste2002Training.pdf},
file = {Decoste2002Training.pdf:Decoste2002Training.pdf:PDF},
owner = {jp},
timestamp = {2008.12.22}
}
@article{Degroeve2002Feature,
author = {Degroeve, S. and De Baets, B. and Van de Peer, Y. and Rouze, P.},
title = {Feature subset selection for splice site prediction},
journal = {Bioinformatics},
year = {2002},
volume = {18},
pages = {S75-S83},
number = {Suppl. 1},
abstract = {Motivation: {T}he large amount of available annotated {A}rabidopsis
thaliana sequences allows the induction of splice site prediction
models with supervised learning algorithms (see {H}aussler (1998)
for a review and references). {T}hese algorithms need information
sources or features from which the models can be computed. {F}or
splice site prediction, the features we consider in this study are
the presence or absence of certain nucleotides in close proximity
to the splice site. {S}ince it is not known how many and which nucleotides
are relevant for splice site prediction, the set of features is chosen
large enough such that the probability that all relevant information
sources are in the set is very high. {U}sing only those features
that are relevant for constructing a splice site prediction system
might improve the system and might also provide us with useful biological
knowledge. {U}sing fewer features will of course also improve the
prediction speed of the system. {R}esults: {A} wrapper-based feature
subset selection algorithm using a support vector machine or a naive
{B}ayes prediction method was evaluated against the traditional method
for selecting features relevant for splice site prediction. {O}ur
results show that this wrapper approach selects features that improve
the performance against the use of all features and against the use
of the features selected by the traditional method. {A}vailability:
{T}he data and additional interactive graphs on the selected feature
subsets are available at http://www.psb.rug.ac.be/gps {C}ontact:
svgro@gengenp.rug.ac.be yvdp@gengenp.rug.ac.be},
pdf = {../local/Degroeve2002Feature.pdf},
file = {Degroeve2002Feature.pdf:local/Degroeve2002Feature.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://bioinformatics.oupjournals.org/cgi/content/abstract/18/suppl_2/S75}
}
@article{Degroeve2005SpliceMachine,
author = {Degroeve, S. and Saeys, Y. and De Baets, B. and Rouze, P. and Van
de Peer, Y.},
title = {{{S}plice{M}achine}: predicting splice sites from high-dimensional
local context representations},
journal = {Bioinformatics},
year = {2005},
volume = {21},
pages = {1332-1338},
abstract = {Motivation: {I}n this age of complete genome sequencing, finding the
location and structure of genes is crucial for further molecular
research. {T}he accurate prediction of intron boundaries largely
facilitates the correct prediction of gene structure in nuclear genomes.
{M}any tools for localizing these boundaries on {DNA} sequences have
been developed and are available to researchers through the internet.
{N}evertheless, these tools still make many false positive predictions.{R}esults:
{T}his manuscript presents a novel publicly available splice site
prediction tool named {S}plice{M}achine that (i) shows state-of-the-art
prediction performance on {A}rabidopsis thaliana and human sequences,
(ii) performs a computationally fast annotation, and (iii) can be
trained by the user on its own data.{A}vailability: {R}esults, figures
and software are available at http://bioinformatics.psb.ugent.be/supplementary_data/.},
doi = {10.1093/bioinformatics/bti166},
pdf = {../local/Degroeve2005SpliceMachine.pdf},
file = {Degroeve2005SpliceMachine.pdf:local/Degroeve2005SpliceMachine.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://bioinformatics.oupjournals.org/cgi/content/abstract/bti166v1}
}
@article{Dekker2002Capturing,
author = {Job Dekker and Karsten Rippe and Martijn Dekker and Nancy Kleckner},
title = {Capturing chromosome conformation.},
journal = {Science},
year = {2002},
volume = {295},
pages = {1306--1311},
number = {5558},
month = {Feb},
abstract = {We describe an approach to detect the frequency of interaction between
any two genomic loci. Generation of a matrix of interaction frequencies
between sites on the same or different chromosomes reveals their
relative spatial disposition and provides information about the physical
properties of the chromatin fiber. This methodology can be applied
to the spatial organization of entire genomes in organisms from bacteria
to human. Using the yeast Saccharomyces cerevisiae, we could confirm
known qualitative features of chromosome organization within the
nucleus and dynamic changes in that organization during meiosis.
We also analyzed yeast chromosome III at the G1 stage of the cell
cycle. We found that chromatin is highly flexible throughout. Furthermore,
functionally distinct AT- and GC-rich domains were found to exhibit
different conformations, and a population-average 3D model of chromosome
III could be determined. Chromosome III emerges as a contorted ring.},
doi = {10.1126/science.1067799},
institution = {Department of Molecular and Cellular Biology, Harvard University,
Cambridge, MA 02138, USA. jdekker@fas.harvard.edu},
keywords = {AT Rich Sequence; Cell Fractionation; Cell Nucleus; Centromere; Chromatin;
Chromosomes, Fungal; Cross-Linking Reagents; Deoxyribonuclease EcoRI;
Formaldehyde; G1 Phase; GC Rich Sequence; Genome, Fungal; Mathematics;
Meiosis; Mitosis; Polymerase Chain Reaction; Protein Conformation;
Saccharomyces cerevisiae; Telomere},
owner = {phupe},
pii = {295/5558/1306},
pmid = {11847345},
timestamp = {2010.08.11},
url = {http://dx.doi.org/10.1126/science.1067799}
}
@article{Demvsar2006Statistical,
author = {Dem\v{s}ar, J.},
title = {Statistical {C}omparisons of {C}lassifiers over {M}ultiple {D}ata
{S}ets},
journal = {J. {M}ach. {L}earn. {R}es.},
year = {2006},
volume = {7},
pages = {1-30},
owner = {mahe},
timestamp = {2006.08.09},
url = {http://jmlr.csail.mit.edu/papers/v7/demsar06a.html}
}
@article{Dempster1972Covariance,
author = {Dempster, A. P.},
title = {Covariance selection},
journal = {Biometrics},
year = {1972},
volume = {28},
pages = {157--175},
owner = {jp},
timestamp = {2012.03.20},
url = {http://www.jstor.org/stable/2528966}
}
@article{Deng2001Unsupervised,
author = {Deng, Y. and Manjunath, B. S.},
title = {Unsupervised segmentation of color-texture regions in images and
video},
journal = {IEEE Trans. Pattern Anal. Mach. Intell.},
year = {2001},
volume = {23},
pages = {800--810},
number = {8},
month = {Aug},
abstract = {A method for unsupervised segmentation of color-texture regions in
images and video is presented. This method, which we refer to as
JSEG, consists of two independent steps: color quantization and spatial
segmentation. In the first step, colors in the image are quantized
to several representative classes that can be used to differentiate
regions in the image. The image pixels are then replaced by their
corresponding color class labels, thus forming a class-map of the
image. The focus of this work is on spatial segmentation, where a
criterion for "good" segmentation using the class-map is proposed.
Applying the criterion to local windows in the class-map results
in the "J-image," in which high and low values correspond to possible
boundaries and interiors of color-texture regions. A region growing
method is then used to segment the image based on the multiscale
J-images. A similar approach is applied to video sequences. An additional
region tracking scheme is embedded into the region growing process
to achieve consistent segmentation and tracking results, even for
scenes with nonrigid object motion. Experiments show the robustness
of the JSEG algorithm on real images and video.},
doi = {10.1109/34.946985},
pdf = {../local/Deng2001Unsupervised.pdf},
file = {Deng2001Unsupervised.pdf:local/Deng2001Unsupervised.pdf:PDF},
timestamp = {2008.07.29},
url = {http://dx.doi.org/10.1109/34.946985}
}
@inproceedings{Denis1998PAC,
author = {Denis, F.},
title = {PAC Learning from Positive Statistical Queries},
booktitle = {Proceedings of the 9th International Conference on Algorithmic Learning
Theory},
year = {1998},
series = {ALT '98},
pages = {112--126},
address = {London, UK},
publisher = {Springer-Verlag},
acmid = {757188},
isbn = {3-540-65013-X},
numpages = {15},
owner = {mordelet},
timestamp = {2010.11.03},
url = {http://portal.acm.org/citation.cfm?id=647716.757188}
}
@inproceedings{Denis2003Text,
author = {Denis, F. and Gilleron, R. and Laurent, A. and Tommasi, M.},
title = {Text Classification and Co-Training from Positive and Unlabeled Examples},
booktitle = {Proceedings of the ICML 2003 Workshop: The Continuum from Labeled
to Unlabeled Data},
year = {2003},
owner = {fantine},
timestamp = {2009.06.09},
url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.58.7101}
}
@article{Denis2005Learning,
author = {Denis, F. and Gilleron, R. and Letouzey, F.},
title = {Learning from positive and unlabeled examples},
journal = {Theor. Comput. Sci.},
year = {2005},
volume = {348},
pages = {70--83},
number = {1},
abstract = {In many machine learning settings, labeled examples are difficult
to collect while unlabeled data are abundant. Also, for some binary
classification problems, positive examples which are elements of
the target concept are available. Can these additional data be used
to improve accuracy of supervised learning algorithms? We investigate
in this paper the design of learning algorithms from positive and
unlabeled data only. Many machine learning and data mining algorithms,
such as decision tree induction algorithms and naive Bayes algorithms,
use examples only to evaluate statistical queries (SQ-like algorithms).
Kearns designed the statistical query learning model in order to
describe these algorithms. Here, we design an algorithm scheme which
transforms any SQ-like algorithm into an algorithm based on positive
statistical queries (estimate for probabilities over the set of positive
instances) and instance statistical queries (estimate for probabilities
over the instance space). We prove that any class learnable in the
statistical query learning model is learnable from positive statistical
queries and instance statistical queries only if a lower bound on
the weight of any target concept f can be estimated in polynomial
time. Then, we design a decision tree induction algorithm POSC4.5,
based on C4.5, that uses only positive and unlabeled examples and
we give experimental results for this algorithm. In the case of imbalanced
classes in the sense that one of the two classes (say the positive
class) is heavily underrepresented compared to the other class, the
learning problem remains open. This problem is challenging because
it is encountered in many real-world applications.},
doi = {http://dx.doi.org/10.1016/j.tcs.2005.09.007},
keywords = {PUlearning},
owner = {jp},
timestamp = {2010.01.29}
}
@inproceedings{Denis2002Text,
author = {Denis, F. and Gilleron, R. and Tommasi, M.},
title = {Text Classification from Positive and Unlabeled Examples},
booktitle = {Proc. of the 9th International Conference on Information Processing
and Management of Uncertainty in Knowledge-Based Systems},
year = {2002},
owner = {fantine},
timestamp = {2009.07.02},
url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.8.5291}
}
@article{Dennis2005Hunting,
author = {Jayne L Dennis and Karin A Oien},
title = {Hunting the primary: novel strategies for defining the origin of
tumours.},
journal = {J {P}athol},
year = {2005},
volume = {205},
pages = {236-47},
number = {2},
month = {Jan},
abstract = {In 1995, two methods of genome-wide expression profiling were first
described: expression microarrays and serial analysis of gene expression
({SAGE}). {I}n the subsequent 10 years, many hundreds of papers have
been published describing the application of these technologies to
a wide spectrum of biological and clinical questions. {C}ommon to
all of this research is a basic process of data gathering and analysis.
{T}he techniques and statistical and bio-informatic tools involved
in this process are reviewed. {T}he processes of class discovery
(using clustering and self-organizing maps), class prediction (weighted
voting, k nearest neighbour, support vector machines, and artificial
neural networks), target identification (fold change, discriminant
analysis, and principal component analysis), and target validation
({RT}-{PCR} and tissue microarrays) are described. {F}inally, the
diagnostic problem of adenocarcinomas that present as metastases
of unknown origin is reviewed, and it is demonstrated how integration
of expression profiling techniques promises to throw new light on
this important clinical challenge.},
doi = {10.1002/path.1702},
pdf = {../local/Dennis2005Hunting.pdf},
file = {Dennis2005Hunting.pdf:local/Dennis2005Hunting.pdf:PDF},
url = {http://dx.doi.org/10.1002/path.1702}
}
@inproceedings{Deodhar2007framework,
author = {Meghana Deodhar and Joydeep Ghosh},
title = {A framework for simultaneous co-clustering and learning from complex
data},
booktitle = {KDD '07: Proceedings of the 13th ACM SIGKDD international conference
on Knowledge discovery and data mining},
year = {2007},
pages = {250--259},
address = {New York, NY, USA},
publisher = {ACM},
doi = {http://doi.acm.org/10.1145/1281192.1281222},
isbn = {978-1-59593-609-7},
location = {San Jose, California, USA}
}
@article{DeRisi1997Exploring,
author = {DeRisi, J. L. and Iyer, V. R. and Brown, P. O.},
title = {Exploring the metabolic and genetic control of gene expression on
a genomic scale},
journal = {Science},
year = {1997},
volume = {278},
pages = {680--686},
number = {5338},
pdf = {../local/deri97.pdf},
file = {deri97.pdf:local/deri97.pdf:PDF},
subject = {microarray},
url = {http://www.sciencemag.org/cgi/reprint/278/5338/680.pdf}
}
@article{Deshpande2006Targeting,
author = {Deshpande, D. A. and Penn, R. B.},
title = {Targeting {G} protein-coupled receptor signaling in asthma.},
journal = {Cell. Signal.},
year = {2006},
volume = {18},
pages = {2105--2120},
number = {12},
month = {Dec},
abstract = {The complex disease asthma, an obstructive lung disease in which excessive
airway smooth muscle (ASM) contraction as well as increased ASM mass
reduces airway lumen size and limits airflow, can be viewed as a
consequence of aberrant airway G protein-coupled receptor (GPCR)
function. The central role of GPCRs in determining airway resistance
is underscored by the fact that almost every drug used in the treatment
of asthma directly or indirectly targets either GPCR-ligand interaction,
GPCR signaling, or processes that produce GPCR agonists. Although
many airway cells contribute to the regulation of airway resistance
and architecture, ASM properties and functions have the greatest
impact on airway homeostasis. The theme of this review is that GPCR-mediated
regulation of ASM tone and ASM growth is a major determinant of the
acute and chronic features of asthma, and multiple strategies targeting
GPCR signaling may be employed to prevent or manage these features.},
doi = {10.1016/j.cellsig.2006.04.008},
owner = {laurent},
pii = {S0898-6568(06)00130-6},
pmid = {16828259},
timestamp = {2008.01.16},
url = {http://dx.doi.org/10.1016/j.cellsig.2006.04.008}
}
@inproceedings{Deshpande2002Automated,
author = {M. Deshpande and G. Karypis},
title = {Automated {A}pproaches for {C}lassifying {S}tructures},
booktitle = {Proceedings of the 2nd Workshop on Data Mining in Bioinformatics
(BIOKDD '02), 2002},
year = {2002},
owner = {mahe},
timestamp = {2006.09.26}
}
@inproceedings{Deshpande2002Evaluation,
author = {Deshpande, M. and Karypis, G.},
title = {Evaluation of {T}echniques for {C}lassifying {B}iological {S}equences},
booktitle = {P{AKDD} '02: {P}roceedings of the 6th {P}acific-{A}sia {C}onference
on {A}dvances in {K}nowledge {D}iscovery and {D}ata {M}ining},
year = {2002},
pages = {417--431},
publisher = {Springer Verlag},
abstract = {In recent years we have witnessed an exponential increase in the amount
of biological information, either {DNA} or protein sequences, that
has become available in public databases. {T}his has been followed
by an increased interest in developing computational techniques to
automatically classify these large volumes of sequence data into
various categories corresponding to either their role in the chromosomes,
their structure, and/or their function. {I}n this paper we evaluate
some of the widely-used sequence classification algorithms and develop
a framework for modeling sequences in a fashion so that traditional
machine learning algorithms, such as support vector machines, can
be applied easily. {O}ur detailed experimental evaluation shows that
the {SVM}-based approaches are able to achieve higher classification
accuracy compared to the more traditional sequence classification
algorithms such as {M}arkov model based techniques and {K}-nearest
neighbor based approaches.},
pdf = {../local/Deshpande2002Evaluation.pdf},
file = {Deshpande2002Evaluation.pdf:local/Deshpande2002Evaluation.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert}
}
@article{Deshpande2005Frequent,
author = {M. Deshpande and M. Kuramochi and N. Wale and G. Karypis},
title = {Frequent {S}ubstructure-{B}ased {A}pproaches for {C}lassifying {C}hemical
{C}ompounds},
journal = {IEEE T. Knowl. Data. En.},
year = {2005},
volume = {17},
pages = {1036-1050},
number = {8},
month = {August},
doi = {http://doi.ieeecomputersociety.org/10.1109/TKDE.2005.127},
keywords = {chemoinformatics},
owner = {mahe},
timestamp = {2006.08.04}
}
@article{Desmedt2008Biological,
author = {Desmedt, C. and Haibe-Kains, B. and Wirapati, P. and Buyse, M. and
Larsimont, D. and Bontempi, G. and Delorenzi, M. and Piccart, M.
and Sotiriou, C.},
title = {Biological processes associated with breast cancer clinical outcome
depend on the molecular subtypes},
journal = {Clin. Cancer Res.},
year = {2008},
volume = {14},
pages = {5158--5165},
number = {16},
month = {Aug},
abstract = {Recently, several prognostic gene expression signatures have been
identified; however, their performance has never been evaluated according
to the previously described molecular subtypes based on the estrogen
receptor (ER) and human epidermal growth factor receptor 2 (HER2),
and their biological meaning has remained unclear. Here we aimed
to perform a comprehensive meta-analysis integrating both clinicopathologic
and gene expression data, focusing on the main molecular subtypes.We
developed gene expression modules related to key biological processes
in breast cancer such as tumor invasion, immune response, angiogenesis,
apoptosis, proliferation, and ER and HER2 signaling, and then analyzed
these modules together with clinical variables and several prognostic
signatures on publicly available microarray studies (>2,100 patients).Multivariate
analysis showed that in the ER+/HER2- subgroup, only the proliferation
module and the histologic grade were significantly associated with
clinical outcome. In the ER-/HER2- subgroup, only the immune response
module was associated with prognosis, whereas in the HER2+ tumors,
the tumor invasion and immune response modules displayed significant
association with survival. Proliferation was identified as the most
important component of several prognostic signatures, and their performance
was limited to the ER+/HER2- subgroup.Although proliferation is the
strongest parameter predicting clinical outcome in the ER+/HER2-
subtype and the common denominator of most prognostic gene signatures,
immune response and tumor invasion seem to be the main molecular
processes associated with prognosis in the ER-/HER2- and HER2+ subgroups,
respectively. These findings may help to define new clinicogenomic
models and to identify new therapeutic strategies in the specific
molecular subgroups.},
doi = {10.1158/1078-0432.CCR-07-4756},
pdf = {../local/Desmedt2008Biological.pdf},
file = {Desmedt2008Biological.pdf:Desmedt2008Biological.pdf:PDF},
institution = {Medical Oncology Department, Jules Bordet Institute.},
language = {eng},
medline-pst = {ppublish},
owner = {phupe},
pii = {14/16/5158},
pmid = {18698033},
timestamp = {2011.06.01},
url = {http://dx.doi.org/10.1158/1078-0432.CCR-07-4756}
}
@article{Desobry2005online,
author = {Desobry, F. and Davy, M. and Doncarli, C.},
title = {An online kernel change detection algorithm},
journal = {IEEE T. Signal. Proces.},
year = {2005},
volume = {53},
pages = {2961--2974},
number = {8},
doi = {10.1109/TSP.2005.851098},
pdf = {../local/Desobry2005online.pdf},
file = {Desobry2005online.pdf:Desobry2005online.pdf:PDF},
owner = {jp},
timestamp = {2011.04.13},
url = {http://dx.doi.org/10.1109/TSP.2005.851098}
}
@article{Deupi2007Structural,
author = {Deupi, X. and D\"olker, N. and L\`opez-Rodr\`iguez, M. L. and Campillo,
M. and Ballesteros, J. A. and Pardo, L.},
title = {Structural models of class a {G} protein-coupled receptors as a tool
for drug design: insights on transmembrane bundle plasticity.},
journal = {Curr. Top. Med. Chem.},
year = {2007},
volume = {7},
pages = {991--998},
number = {10},
abstract = {G protein-coupled receptors (GPCRs) interact with an extraordinary
diversity of ligands by means of their extracellular domains and/or
the extracellular part of the transmembrane (TM) segments. Each receptor
subfamily has developed specific sequence motifs to adjust the structural
characteristics of its cognate ligands to a common set of conformational
rearrangements of the TM segments near the G protein binding domains
during the activation process. Thus, GPCRs have fulfilled this adaptation
during their evolution by customizing a preserved 7TM scaffold through
conformational plasticity. We use this term to describe the structural
differences near the binding site crevices among different receptor
subfamilies, responsible for the selective recognition of diverse
ligands among different receptor subfamilies. By comparing the sequence
of rhodopsin at specific key regions of the TM bundle with the sequences
of other GPCRs we have found that the extracellular region of TMs
2 and 3 provides a remarkable example of conformational plasticity
within Class A GPCRs. Thus, rhodopsin-based molecular models need
to include the plasticity of the binding sites among GPCR families,
since the "quality" of these homology models is intimately linked
with the success in the processes of rational drug-design or virtual
screening of chemical databases.},
keywords = {chemogenomics},
owner = {laurent},
pmid = {17508932},
timestamp = {2008.07.21}
}
@book{Devillers1996Neural,
title = {Neural Networks in QSAR and Drug Design},
publisher = {Academic Press, London},
year = {1996},
author = {J. Devillers},
owner = {mahe},
timestamp = {2006.09.06}
}
@book{DeVore1993Constructive,
title = {Constructive {A}pproximation},
publisher = {Springer Verlag},
year = {1993},
author = {DeVore, R. A. and Lorentz, G. G.},
series = {Springer Grundlehren der Mathematischen Wissenschaften}
}
@article{Devos2004Classification,
author = {A. Devos and L. Lukas and J. A K Suykens and L. Vanhamme and A. R.
Tate and F. A. Howe and C. Majós and A. Moreno-Torres and M. van
der Graaf and C. Arús and S. Van Huffel},
title = {Classification of brain tumours using short echo time 1{H} {MR} spectra.},
journal = {J {M}agn {R}eson},
year = {2004},
volume = {170},
pages = {164-75},
number = {1},
month = {Sep},
abstract = {The purpose was to objectively compare the application of several
techniques and the use of several input features for brain tumour
classification using {M}agnetic {R}esonance {S}pectroscopy ({MRS}).
{S}hort echo time 1{H} {MRS} signals from patients with glioblastomas
(n = 87), meningiomas (n = 57), metastases (n = 39), and astrocytomas
grade {II} (n = 22) were provided by six centres in the {E}uropean
{U}nion funded {INTERPRET} project. {L}inear discriminant analysis,
least squares support vector machines ({LS}-{SVM}) with a linear
kernel and {LS}-{SVM} with radial basis function kernel were applied
and evaluated over 100 stratified random splittings of the dataset
into training and test sets. {T}he area under the receiver operating
characteristic curve ({AUC}) was used to measure the performance
of binary classifiers, while the percentage of correct classifications
was used to evaluate the multiclass classifiers. {T}he influence
of several factors on the classification performance has been tested:
{L}2- vs. water normalization, magnitude vs. real spectra and baseline
correction. {T}he effect of input feature reduction was also investigated
by using only the selected frequency regions containing the most
discriminatory information, and peak integrated values. {U}sing {L}2-normalized
complete spectra the automated binary classifiers reached a mean
test {AUC} of more than 0.95, except for glioblastomas vs. metastases.
{S}imilar results were obtained for all classification techniques
and input features except for water normalized spectra, where classification
performance was lower. {T}his indicates that data acquisition and
processing can be simplified for classification purposes, excluding
the need for separate water signal acquisition, baseline correction
or phasing.},
doi = {10.1016/j.jmr.2004.06.010},
pdf = {../local/Devos2004Classification.pdf},
file = {Devos2004Classification.pdf:local/Devos2004Classification.pdf:PDF},
pii = {S1090-7807(04)00181-8},
url = {http://dx.doi.org/10.1016/j.jmr.2004.06.010}
}
@article{Devos2005use,
author = {A. Devos and A. W. Simonetti and M. van der Graaf and L. Lukas and
J. A K Suykens and L. Vanhamme and L. M C Buydens and A. Heerschap
and S. Van Huffel},
title = {The use of multivariate {MR} imaging intensities versus metabolic
data from {MR} spectroscopic imaging for brain tumour classification.},
journal = {J {M}agn {R}eson},
year = {2005},
volume = {173},
pages = {218-28},
number = {2},
month = {Apr},
abstract = {This study investigated the value of information from both magnetic
resonance imaging and magnetic resonance spectroscopic imaging ({MRSI})
to automated discrimination of brain tumours. {T}he influence of
imaging intensities and metabolic data was tested by comparing the
use of {MR} spectra from {MRSI}, {MR} imaging intensities, peak integration
values obtained from the {MR} spectra and a combination of the latter
two. {T}hree classification techniques were objectively compared:
linear discriminant analysis, least squares support vector machines
({LS}-{SVM}) with a linear kernel as linear techniques and {LS}-{SVM}
with radial basis function kernel as a nonlinear technique. {C}lassifiers
were evaluated over 100 stratified random splittings of the dataset
into training and test sets. {T}he area under the receiver operating
characteristic ({ROC}) curve ({AUC}) was used as a global performance
measure on test data. {I}n general, all techniques obtained a high
performance when using peak integration values with or without {MR}
imaging intensities. {F}or example for low- versus high-grade tumours,
low- versus high-grade gliomas and gliomas versus meningiomas, the
mean test {AUC} was higher than 0.91, 0.94, and 0.99, respectively,
when both {MR} imaging intensities and peak integration values were
used. {T}he use of metabolic data from {MRSI} significantly improved
automated classification of brain tumour types compared to the use
of {MR} imaging intensities solely.},
doi = {10.1016/j.jmr.2004.12.007},
pdf = {../local/Devos2005use.pdf},
file = {Devos2005use.pdf:local/Devos2005use.pdf:PDF},
pii = {S1090-7807(04)00415-X},
url = {http://dx.doi.org/10.1016/j.jmr.2004.12.007}
}
@article{Devroye1988Automatic,
author = {Devroye, L.},
title = {Automatic pattern recognition: a study of the probability of error},
journal = {I{EEE} {T}rans. {P}attern {A}nal. {M}ach. {I}ntell.},
year = {1988},
volume = {10},
pages = {530-543},
number = {4},
month = {Jul},
abstract = {A test sequence is used to select the best rule from a class of discrimination
rules defined in terms of the training sequence. {T}he {V}apnik-{C}hervonenkis
and related inequalities are used to obtain distribution-free bounds
on the difference between the probability of error of the selected
rule and the probability of error of the best rule in the given class.
{T}he bounds are used to prove the consistency and asymptotic optimality
for several popular classes, including linear discriminators, nearest-neighbor
rules, kernel-based rules, histogram rules, binary tree classifiers,
and {F}ourier series classifiers. {I}n particular, the method can
be used to choose the smoothing parameter in kernel-based rules,
to choose k in the k-nearest neighbor rule, and to choose between
parametric and nonparametric rules },
pdf = {../local/Devroye1988Automatic.pdf},
file = {Devroye1988Automatic.pdf:local/Devroye1988Automatic.pdf:PDF},
keywords = {information-theory},
owner = {vert}
}
@book{Devroye1996Probabilistic,
title = {A {P}robabilistic {T}heory of {P}attern {R}ecognition},
publisher = {Springer},
year = {1996},
author = {Devroye, L. and Gy{\"o}rfi, L. and Lugosi, G.},
volume = {31},
series = {Applications of Mathematics}
}
@book{Devroye2000Combinatorial,
title = {Combinatorial {M}ethods in {D}ensity {E}stimation},
publisher = {Springer},
year = {2000},
author = {L. Devroye and G. Lugosi},
series = {Springer Series in Statistics}
}
@article{Dhingra2005Substantial,
author = {Vikas Dhingra and Mukta Gupta and Tracy Andacht and Zhen F Fu},
title = {New frontiers in proteomics research: a perspective.},
journal = {Int. J. Pharm.},
year = {2005},
volume = {299},
pages = {1--18},
number = {1-2},
month = {Aug},
abstract = {Substantial advances have been made in the fundamental understanding
of human biology, ranging from DNA structure to identification of
diseases associated with genetic abnormalities. Genome sequence information
is becoming available in unprecedented amounts. The absence of a
direct functional correlation between gene transcripts and their
corresponding proteins, however, represents a significant roadblock
for improving the efficiency of biological discoveries. The success
of proteomics depends on the ability to identify and analyze protein
products in a cell or tissue and, this is reliant on the application
of several key technologies. Proteomics is in its exponential growth
phase. Two-dimensional electrophoresis complemented with mass spectrometry
provides a global view of the state of the proteins from the sample.
Proteins identification is a requirement to understand their functional
diversity. Subtle difference in protein structure and function can
contribute to complexity and diversity of life. This review focuses
on the progress and the applications of proteomics science with special
reference to integration of the evolving technologies involved to
address biological questions.},
doi = {10.1016/j.ijpharm.2005.04.010},
institution = {Department of Pathology, University of Georgia, Athens, GA 30602,
USA. vdhingra@vet.uga.edu},
keywords = {Computational Biology; Electrophoresis, Gel, Two-Dimensional; Humans;
Peptide Mapping; Protein Interaction Mapping; Proteomics; Spectrometry,
Mass, Matrix-Assisted Laser Desorption-Ionization},
owner = {ljacob},
pii = {S0378-5173(05)00226-7},
pmid = {15979831},
timestamp = {2009.09.14},
url = {http://dx.doi.org/10.1016/j.ijpharm.2005.04.010}
}
@article{Dias2008Molecular,
author = {Raquel Dias and Walter Filgueira de Azevedo},
title = {Molecular docking algorithms.},
journal = {Curr. Drug Targets},
year = {2008},
volume = {9},
pages = {1040--1047},
number = {12},
month = {Dec},
abstract = {By means of virtual screening of small molecules databases it is possible
to identify new potential inhibitors against a target of interest.
Molecular docking is a computer simulation procedure to predict the
conformation of a receptor-ligand complex. Each docking program makes
use of one or more specific search algorithms, which are the methods
used to predict the possible conformations of a binary complex. In
the present review we describe several molecular-docking search algorithms,
and the programs which apply such methodologies. We also discuss
how virtual screening can be optimized, describing methods that may
increase accuracy of the simulation process, with relatively fast
docking algorithms.},
institution = {Faculdade de Bioci�ncias, Laborat�rio de Bioqu�mica Estrutural,
Pontif�cia Universidade Cat�lica do Rio Grande do Sul, Porto
Alegre, RS, Brazil.},
keywords = {Algorithms; Database Management Systems; Information Storage and Retrieval;
Models, Molecular; Molecular Conformation; Monte Carlo Method},
language = {eng},
medline-pst = {ppublish},
owner = {bricehoffmann},
pmid = {19128213},
timestamp = {2009.08.25}
}
@article{Didiano2008Molecular,
author = {Dominic Didiano and Oliver Hobert},
title = {Molecular architecture of a miRNA-regulated 3' UTR.},
journal = {RNA},
year = {2008},
volume = {14},
pages = {1297--1317},
number = {7},
month = {Jul},
abstract = {Animal genomes contain hundreds of microRNAs (miRNAs), small regulatory
RNAs that control gene expression by binding to complementary sites
in target mRNAs. Some rules that govern miRNA/target interaction
have been elucidated but their general applicability awaits further
experimentation on a case-by-case basis. We use here an assay system
in transgenic nematodes to analyze the interaction of the Caenorhabditis
elegans lsy-6 miRNA with 3' UTR sequences. In contrast to many previously
described assay systems used to analyze miRNA/target interactions,
our assay system operates within the cellular context in which lsy-6
normally functions, a single neuron in the nervous system of C. elegans.
Through extensive mutational analysis, we define features in the
known and experimentally validated target of lsy-6, the 3' UTR of
the cog-1 homeobox gene, that are required for a functional miRNA/target
interaction. We describe that both in the context of the cog-1 3'
UTR and in the context of heterologous 3' UTRs, one or more seed
matches are not a reliable predictor for a functional miRNA/target
interaction. We rather find that two nonsequence specific contextual
features beyond miRNA target sites are critical determinants of miRNA-mediated
3' UTR regulation. The contextual features reside 3' of lsy-6 binding
sites in the 3' UTR and act in a combinatorial manner; mutation of
each results in limited defects in 3' UTR regulation, but a combinatorial
deletion results in complete loss of 3' UTR regulation. Together
with two lsy-6 sites, these two contextual features are capable of
imparting regulation on a heterologous 3' UTR. Moreover, the contextual
features need to be present in a specific configuration relative
to miRNA binding sites and could either represent protein binding
sites or provide an appropriate structural context. We conclude that
a given target site resides in a 3' UTR context that evolved beyond
target site complementarity to support regulation by a specific miRNA.
The large number of 3' UTRs that we analyzed in this study will also
be useful to computational biologists in designing the next generation
of miRNA/target prediction algorithms.},
doi = {10.1261/rna.1082708},
pdf = {../local/Didiano2008Molecular.pdf},
file = {Didiano2008Molecular.pdf:Didiano2008Molecular.pdf:PDF},
institution = {Department of Biochemistry and Molecular Biophysics, Howard Hughes
Medical Institute, Columbia University Medical Center, New York,
New York 10032, USA.},
keywords = {sirna},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {rna.1082708},
pmid = {18463285},
timestamp = {2009.10.28},
url = {http://dx.doi.org/10.1261/rna.1082708}
}
@article{Didiano2006Perfect,
author = {Dominic Didiano and Oliver Hobert},
title = {Perfect seed pairing is not a generally reliable predictor for miRNA-target
interactions.},
journal = {Nat Struct Mol Biol},
year = {2006},
volume = {13},
pages = {849--851},
number = {9},
month = {Sep},
abstract = {We use Caenorhabditis elegans to test proposed general rules for microRNA
(miRNA)-target interactions. We show that G.U base pairing is tolerated
in the 'seed' region of the lsy-6 miRNA interaction with its in vivo
target cog-1, and that 6- to 8-base-pair perfect seed pairing is
not a generally reliable predictor for an interaction of lsy-6 with
a 3' untranslated region (UTR). Rather, lsy-6 can functionally interact
with its target site only in specific 3' UTR contexts. Our findings
illustrate the difficulty of establishing generalizable rules of
miRNA-target interactions.},
doi = {10.1038/nsmb1138},
pdf = {../local/Didiano2006Perfect.pdf},
file = {Didiano2006Perfect.pdf:Didiano2006Perfect.pdf:PDF},
institution = {Department of Biochemistry and Molecular Biophysics, Columbia University
Medical Center, Howard Hughes Medical Institute, 701 W. 168th Street,
New York, New York 10032, USA.},
keywords = {sirna},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {nsmb1138},
pmid = {16921378},
timestamp = {2009.10.28},
url = {http://dx.doi.org/10.1038/nsmb1138}
}
@article{Diekman2003Hybrid,
author = {Casey Diekman and Wei He and Nagabhushana Prabhu and Harvey Cramer},
title = {Hybrid methods for automated diagnosis of breast tumors.},
journal = {Anal {Q}uant {C}ytol {H}istol},
year = {2003},
volume = {25},
pages = {183-90},
number = {4},
month = {Aug},
abstract = {O{BJECTIVE}: {T}o design and analyze a new family of hybrid methods
for the diagnosis of breast tumors using fine needle aspirates. {STUDY}
{DESIGN}: {W}e present a radically new approach to the design of
diagnosis systems. {I}n the new approach, a nonlinear classifier
with high sensitivity but low specificity is hybridized with a linear
classifier having low sensitivity but high specificity. {D}ata from
the {W}isconsin {B}reast {C}ancer {D}atabase are used to evaluate,
computationally, the performance of the hybrid classifiers. {RESULTS}:
{T}he diagnosis scheme obtained by hybridizing the nonlinear classifier
ellipsoidal multisurface method ({EMSM}) with the linear classifier
proximal support vector machine ({PSVM}) was found to have a mean
sensitivity of 97.36\% and a mean specificity of 95.14\% and was
found to yield a 2.44\% improvement in the reliability of positive
diagnosis over that of {EMSM} at the expense of 0.4\% degradation
in the reliability of negative diagnosis, again compared to {EMSM}.
{A}t the 95\% confidence level we can trust the hybrid method to
be 96.19-98.53\% correct in its malignant diagnosis of new tumors
and 93.57-96.71\% correct in its benign diagnosis. {CONCLUSION}:
{H}ybrid diagnosis schemes represent a significant paradigm shift
and provide a promising new technique to improve the specificity
of nonlinear classifiers without seriously affecting the high sensitivity
of nonlinear classifiers.},
keywords = {Algorithms, Amino Acid Sequence, Amino Acids, Anion Exchange Resins,
Antigen-Antibody Complex, Artificial Intelligence, Automated, Automatic
Data Processing, Benchmarking, Biological, Biological Markers, Biopsy,
Blood Cells, Blood Proteins, Breast Neoplasms, Cell Line, Cellular
Structures, Chemical, Chromatography, Chromosome Aberrations, Cluster
Analysis, Colonic Neoplasms, Comparative Study, Computational Biology,
Computer Simulation, Computer-Assisted, Computing Methodologies,
DNA, Data Interpretation, Databases, Decision Making, Decision Trees,
Diagnosis, Diffusion Magnetic Resonance Imaging, Disease, English
Abstract, Epitopes, Expert Systems, Factual, Female, Fine-Needle,
Fusion, Fuzzy Logic, Gene Expression Profiling, Gene Expression Regulation,
Gene Targeting, Genetic, Genome, Histocompatibility Antigens Class
I, Humans, Hydrogen Bonding, Hydrophobicity, Image Interpretation,
Image Processing, In Vitro, Indicators and Reagents, Information
Storage and Retrieval, Ion Exchange, Least-Squares Analysis, Leiomyosarcoma,
Liver Cirrhosis, Lung Neoplasms, Magnetic Resonance Imaging, Male,
Mass, Mathematical Computing, Matrix-Assisted Laser Desorption-Ionization,
Models, Molecular, Molecular Sequence Data, Neoplasm Proteins, Neoplasms,
Neoplastic, Nephroblastoma, Neural Networks (Computer), Non-P.H.S.,
Non-U.S. Gov't, Nonl, Nucleic Acid Conformation, Nucleic Acid Hybridization,
Oligonucleotide Array Sequence Analysis, Oncogene Proteins, Ovarian
Neoplasms, P.H.S., Pattern Recognition, Predictive Value of Tests,
Pro, Prostatic Neoplasms, Protein, Protein Binding, Protein Interaction
Mapping, Protein Structure, Proteins, Quantitative Structure-Activity
Relationship, RNA, ROC Curve, Reproducibility of Results, Research
Support, Rhabdomyosarcoma, Secondary, Sensitivity and Specificity,
Sequence Alignment, Sequence Analysis, Severity of Illness Index,
Software, Solubility, Spectrometry, Statistical, Structure-Activity
Relationship, Subcellular Fractions, Subtraction Technique, T-Lymphocyte,
Tissue Distribution, Transcription Factors, Transfer, Treatment Outcome,
Tumor, Tumor Markers, U.S. Gov't, User-Computer Interface, inear
Dynamics, teome, 12961824}
}
@book{Diestel2000Graph,
title = {Graph theory},
publisher = {Springer-Verlag},
year = {2000},
author = {R. Diestel}
}
@article{Dieterle2003Urinary,
author = {Frank Dieterle and Silvia Müller-Hagedorn and Hartmut M Liebich
and Günter Gauglitz},
title = {Urinary nucleosides as potential tumor markers evaluated by learning
vector quantization.},
journal = {Artif. {I}ntell. {M}ed.},
year = {2003},
volume = {28},
pages = {265-79},
number = {3},
month = {Jul},
abstract = {Modified nucleosides were recently presented as potential tumor markers
for breast cancer. {T}he patterns of the levels of urinary nucleosides
are different for tumor bearing individuals and for healthy individuals.
{T}hus, a powerful pattern recognition method is needed. {A}lthough
backpropagation ({BP}) neural networks are becoming increasingly
common in medical literature for pattern recognition, it has been
shown that often-superior methods exist like learning vector quantization
({LVQ}) and support vector machines ({SVM}). {T}he aim of this feasibility
study is to get an indication of the performance of urinary nucleoside
levels evaluated by {LVQ} in contrast to the evaluation the popular
{BP} and {SVM} networks. {U}rine samples were collected from female
breast cancer patients and from healthy females. {T}welve different
ribonucleosides were isolated and quantified by a high performance
liquid chromatography ({HPLC}) procedure. {LVQ}, {SVM} and {BP} networks
were trained and the performance was evaluated by the classification
of the test sets into the categories "cancer" and "healthy". {A}ll
methods showed a good classification with a sensitivity ranging from
58.8 to 70.6\% at a specificity of 88.4-94.2\% for the test patterns.
{A}lthough the classification performance of all methods is comparable,
the {LVQ} implementations are superior in terms of more qualitative
features: the results of {LVQ} networks are more reproducible, as
the initialization is deterministic. {T}he {LVQ} networks can be
trained by unbalanced sizes of the different classes. {LVQ} networks
are fast during training, need only few parameters adjusted for training
and can be retrained by patterns of "local individuals". {A}s at
least some of these features play an important role in an implementation
into a medical decision support system, it is recommended to use
{LVQ} for an extended study.},
doi = {10.1016/S0933-3657(03)00058-7},
pdf = {../local/Dieterle2003Urinary.pdf},
file = {Dieterle2003Urinary.pdf:local/Dieterle2003Urinary.pdf:PDF},
keywords = {80 and over, Adnexal Diseases, Adult, Aged, Algorithms, Artificial
Intelligence, Automated, Bayes Theorem, Biological, Breast Neoplasms,
Case-Control Studies, Chromatography, Comparative Study, Computational
Biology, Computer-Assisted, Diagnosis, Differential, Feasibility
Studies, Female, High Pressure Liquid, Humans, Logistic Models, Middle
Aged, Neural Networks (Computer), Non-U.S. Gov't, Nucleosides, Ovarian
Neoplasms, Pattern Recognition, Predictive Value of Tests, ROC Curve,
Reproducibility of Results, Research Support, Sensitivity and Specificity,
Tumor Markers, 12927336},
pii = {S0933365703000587},
url = {http://dx.doi.org/10.1016/S0933-3657(03)00058-7}
}
@article{Dietterich1998Experimental,
author = {Dietterich, T.},
title = {An Experimental Comparison of Three Methods for Constructing Ensembles
of Decision Trees: Bagging, Boosting, and Randomization},
journal = {Mach. Learn.},
year = {1998},
volume = {40},
pages = {139--157},
number = {40},
doi = {10.1023/A:1007607513941},
pdf = {../local/Dietterich1998Experimental.pdf},
file = {Dietterich1998Experimental.pdf:Dietterich1998Experimental.pdf:PDF},
owner = {jp},
timestamp = {2012.03.21},
url = {http://dx.doi.org/10.1023/A:1007607513941}
}
@article{Dietterich1997Solving,
author = {Dietterich, T.G. and Lathrop, R.H. and Lozano-Perez, T.},
title = {Solving the {M}ultiple {I}nstance {P}roblem with {A}xis-{P}arallel
{R}ectangles},
journal = {Artificial Intelligence},
year = {1997},
volume = {89},
pages = {31-71},
number = {1-2},
citeseerurl = {http://citeseer.ist.psu.edu/dietterich97solving.html},
owner = {mahe},
timestamp = {2006.08.09}
}
@incollection{Dietterich2002Machine,
author = {Dietterich, T. G.},
title = {Machine {L}earning for {S}equential {D}ata: {A} {R}eview},
booktitle = {Structural, {S}yntactic, and {S}tatistical {P}attern {R}ecognition;
{L}ecture {N}otes in {C}omputer {S}cience, {V}ol. 2396},
publisher = {Springer-Verlag},
year = {2002},
editor = {Caelli, T.},
pages = {15--30},
pdf = {../local/Dietterich2002Machine.pdf},
file = {Dietterich2002Machine.pdf:local/Dietterich2002Machine.pdf:PDF},
keywords = {conditional-random-field},
owner = {vert}
}
@article{DiMasi2003price,
author = {J. A. DiMasi and R. W. Hansen and H. G. Grabowski},
title = {{T}he price of innovation: new estimates of drug development costs.},
journal = {J Health Econ},
year = {2003},
volume = {22},
pages = {151--185},
number = {2},
month = {Mar},
abstract = {The research and development costs of 68 randomly selected new drugs
were obtained from a survey of 10 pharmaceutical firms. These data
were used to estimate the average pre-tax cost of new drug development.
The costs of compounds abandoned during testing were linked to the
costs of compounds that obtained marketing approval. The estimated
average out-of-pocket cost per new drug is 403 million US dollars
(2000 dollars). Capitalizing out-of-pocket costs to the point of
marketing approval at a real discount rate of 11\% yields a total
pre-approval cost estimate of 802 million US dollars (2000 dollars).
When compared to the results of an earlier study with a similar methodology,
total capitalized costs were shown to have increased at an annual
rate of 7.4\% above general price inflation.},
keywords = {Capital Expenditures, Costs and Cost Analysis, Data Collection, Drug
Approval, Drug Evaluation, Drug Industry, Drugs, Economic, Humans,
Inflation, Investigational, Organizational Innovation, Preclinical,
Research Support, United States, 16087260},
owner = {mahe},
pii = {S0167629602001261},
pmid = {16087260},
timestamp = {2006.08.12}
}
@manual{Dimitriadou2011e1071,
title = {e1071: Misc Functions of the Department of Statistics (e1071), TU
Wien},
author = {Dimitriadou, E. and Hornik, K. and Leisch, F. and Meyer, D. and Weingessel,
A.},
year = {2011},
note = {R package version 1.6},
owner = {jp},
timestamp = {2012.07.31},
url = {http://CRAN.R-project.org/package=e1071}
}
@article{Ding2001Multi-class,
author = {Ding, C.H.Q. and Dubchak, I.},
title = {Multi-class protein fold recognition using support vector machines
and neural networks},
journal = {Bioinformatics},
year = {2001},
volume = {17},
pages = {349--358},
abstract = {Motivation: {P}rotein fold recognition is an important approach to
structure discovery without relying on sequence similarity. {W}e
study this approach with new multi-class classification methods and
examined many issues important for a practical recognition system.
{R}esults: {M}ost current discriminative methods for protein fold
prediction use the one-against-others method, which has the well-known
?{F}alse {P}ositives? problem. {W}e investigated two new methods:
the unique one-against-others and the all-against-all methods. {B}oth
improve prediction accuracy by 14?110% on a dataset containing 27
{SCOP} folds. {W}e used the {S}upport {V}ector {M}achine ({SVM})
and the {N}eural {N}etwork ({NN}) learning methods as base classifiers.
{SVM}s converges fast and leads to high accuracy. {W}hen scores of
multiple parameter datasets are combined, majority voting reduces
noise and increases recognition accuracy. {W}e examined many issues
involved with large number of classes, including dependencies of
prediction accuracy on the number of folds and on the number of representatives
in a fold. {O}verall, recognition systems achieve 56% fold prediction
accuracy on a protein test dataset, where most of the proteins have
below 25% sequence identity with the proteins used in training. {S}upplementary
information: {T}he protein parameter datasets used in this paper
are available online (http://www.nersc.gov/~cding/protein).},
pdf = {../local/Ding2001Multi-class.pdf},
file = {Ding2001Multi-class.pdf:local/Ding2001Multi-class.pdf:PDF},
keywords = {biosvm},
subject = {biokernel},
url = {http://bioinformatics.oupjournals.org/cgi/reprint/17/4/349.pdf}
}
@article{Ding2005Minimum,
author = {Chris Ding and Hanchuan Peng},
title = {Minimum redundancy feature selection from microarray gene expression
data.},
journal = {J {B}ioinform {C}omput {B}iol},
year = {2005},
volume = {3},
pages = {185-205},
number = {2},
month = {Apr},
abstract = {How to selecting a small subset out of the thousands of genes in microarray
data is important for accurate classification of phenotypes. {W}idely
used methods typically rank genes according to their differential
expressions among phenotypes and pick the top-ranked genes. {W}e
observe that feature sets so obtained have certain redundancy and
study methods to minimize it. {W}e propose a minimum redundancy -
maximum relevance ({MRMR}) feature selection framework. {G}enes selected
via {MRMR} provide a more balanced coverage of the space and capture
broader characteristics of phenotypes. {T}hey lead to significantly
improved class predictions in extensive experiments on 6 gene expression
data sets: {NCI}, {L}ymphoma, {L}ung, {C}hild {L}eukemia, {L}eukemia,
and {C}olon. {I}mprovements are observed consistently among 4 classification
methods: {N}aive {B}ayes, {L}inear discriminant analysis, {L}ogistic
regression, and {S}upport vector machines. {SUPPLIMENTARY}: {T}he
top 60 {MRMR} genes for each of the datasets are listed in http://crd.lbl.gov/~cding/{MRMR}/.
{M}ore information related to {MRMR} methods can be found at http://www.hpeng.net/.},
keywords = {Adult, Aged, Aging, Algorithms, Animals, Apoptosis, Artificial Intelligence,
Automated, Biological, Bone Marrow, Breast Neoplasms, Classification,
Cluster Analysis, Comparative Study, Computer Simulation, Computer-Assisted,
Diagnosis, Dose-Response Relationship, Drug, Female, Foot, Gait,
Gene Expression Profiling, Gene Expression Regulation, Gene Silencing,
Genetic Vectors, Humans, Image Interpretation, Information Storage
and Retrieval, Kidney, Liver, Logistic Models, Male, Messenger, Models,
Myocardium, Neoplasms, Non-U.S. Gov't, Oligonucleotide Array Sequence
Analysis, Pattern Recognition, Pharmaceutical Preparations, Polymerase
Chain Reaction, Principal Component Analysis, Proteins, RNA, Rats,
Reproducibility of Results, Research Support, Sensitivity and Specificity,
Small Interfering, Sprague-Dawley, Statistical, Subcellular Fractions,
Unknown Primary, 15852500},
pii = {S0219720005001004}
}
@article{Ding2003statistical,
author = {Ding, Y. and Lawrence, C. E.},
title = {A statistical sampling algorithm for {RNA} secondary structure prediction.},
journal = {Nucleic {A}cids {R}es.},
year = {2003},
volume = {31},
pages = {7280-301},
number = {24},
month = {Dec},
abstract = {An {RNA} molecule, particularly a long-chain m{RNA}, may exist as
a population of structures. {F}urther more, multiple structures have
been demonstrated to play important functional roles. {T}hus, a representation
of the ensemble of probable structures is of interest. {W}e present
a statistical algorithm to sample rigorously and exactly from the
{B}oltzmann ensemble of secondary structures. {T}he forward step
of the algorithm computes the equilibrium partition functions of
{RNA} secondary structures with recent thermodynamic parameters.
{U}sing conditional probabilities computed with the partition functions
in a recursive sampling process, the backward step of the algorithm
quickly generates a statistically representative sample of structures.
{W}ith cubic run time for the forward step, quadratic run time in
the worst case for the sampling step, and quadratic storage, the
algorithm is efficient for broad applicability. {W}e demonstrate
that, by classifying sampled structures, the algorithm enables a
statistical delineation and representation of the {B}oltzmann ensemble.
{A}pplications of the algorithm show that alternative biological
structures are revealed through sampling. {S}tatistical sampling
provides a means to estimate the probability of any structural motif,
with or without constraints. {F}or example, the algorithm enables
probability profiling of single-stranded regions in {RNA} secondary
structure. {P}robability profiling for specific loop types is also
illustrated. {B}y overlaying probability profiles, a mutual accessibility
plot can be displayed for predicting {RNA}:{RNA} interactions. {B}oltzmann
probability-weighted density of states and free energy distributions
of sampled structures can be readily computed. {W}e show that a sample
of moderate size from the ensemble of an enormous number of possible
structures is sufficient to guarantee statistical reproducibility
in the estimates of typical sampling statistics. {O}ur applications
suggest that the sampling algorithm may be well suited to prediction
of m{RNA} structure and target accessibility. {T}he algorithm is
applicable to the rational design of small interfering {RNA}s (si{RNA}s),
antisense oligonucleotides, and trans-cleaving ribozymes in gene
knock-down studies.}
}
@article{Ding2004Sfold,
author = {Ding, Y. and Yu, C. Chan and Lawrence, C. E.},
title = {{S}fold web server for statistical folding and rational design of
nucleic acids.},
journal = {Nucleic Acids Res.},
year = {2004},
volume = {32},
pages = {W135--W141},
number = {Web Server issue},
month = {Jul},
abstract = {The Sfold web server provides user-friendly access to Sfold, a recently
developed nucleic acid folding software package, via the World Wide
Web (WWW). The software is based on a new statistical sampling paradigm
for the prediction of RNA secondary structure. One of the main objectives
of this software is to offer computational tools for the rational
design of RNA-targeting nucleic acids, which include small interfering
RNAs (siRNAs), antisense oligonucleotides and trans-cleaving ribozymes
for gene knock-down studies. The methodology for siRNA design is
based on a combination of RNA target accessibility prediction, siRNA
duplex thermodynamic properties and empirical design rules. Our approach
to target accessibility evaluation is an original extension of the
underlying RNA folding algorithm to account for the likely existence
of a population of structures for the target mRNA. In addition to
the application modules Sirna, Soligo and Sribo for siRNAs, antisense
oligos and ribozymes, respectively, the module Srna offers comprehensive
features for statistical representation of sampled structures. Detailed
output in both graphical and text formats is available for all modules.
The Sfold server is available at http://sfold.wadsworth.org and http://www.bioinfo.rpi.edu/applications/sfold.},
doi = {10.1093/nar/gkh449},
keywords = {sirna},
owner = {vert},
pii = {32/suppl_2/W135},
pmid = {15215366},
timestamp = {2006.03.28},
url = {http://dx.doi.org/10.1093/nar/gkh449}
}
@article{Dixon2012Topological,
author = {Dixon, J. R. and Selvaraj, S. and Yue, F. and Kim, A. and Li, Y.
and Shen, Y. and Hu, M. and Liu, J. S. and Ren, B.},
title = {Topological domains in mammalian genomes identified by analysis of
chromatin interactions.},
journal = {Nature},
year = {2012},
volume = {485},
pages = {376-80},
number = {5},
doi = {10.1038/nature11082},
pdf = {../local/Dixon2012Topological.pdf},
file = {Dixon2012Topological.pdf:Dixon2012Topological.pdf:PDF},
keywords = {ngs, hic},
owner = {nelle},
timestamp = {2013.03.30},
url = {http://dx.doi.org/10.1038/nature11082}
}
@article{Djordjevic2003biophysical,
author = {Marko Djordjevic and Anirvan M Sengupta and Boris I Shraiman},
title = {A biophysical approach to transcription factor binding site discovery.},
journal = {Genome {R}es.},
year = {2003},
volume = {13},
pages = {2381-90},
number = {11},
month = {Nov},
abstract = {Identification of transcription factor binding sites within regulatory
segments of genomic {DNA} is an important step toward understanding
of the regulatory circuits that control expression of genes. {H}ere,
we describe a novel bioinformatics method that bases classification
of potential binding sites explicitly on the estimate of sequence-specific
binding energy of a given transcription factor. {T}he method also
estimates the chemical potential of the factor that defines the threshold
of binding. {I}n contrast with the widely used information-theoretic
weight matrix method, the new approach correctly describes saturation
in the transcription factor/{DNA} binding probability. {T}his results
in a significant improvement in the number of expected false positives,
particularly in the ubiquitous case of low-specificity factors. {I}n
the strong binding limit, the algorithm is related to the "support
vector machine" approach to pattern recognition. {T}he new method
is used to identify likely genomic binding sites for the {E}. coli
transcription factors collected in the {DPI}nteract database. {I}n
addition, for {CRP} (a global regulatory factor), the likely regulatory
modality (i.e., repressor or activator) of predicted binding sites
is determined.},
doi = {10.1101/gr.1271603},
pdf = {../local/Djordjevic2003biophysical.pdf},
file = {Djordjevic2003biophysical.pdf:local/Djordjevic2003biophysical.pdf:PDF},
pii = {13/11/2381},
url = {http://dx.doi.org/10.1101/gr.1271603}
}
@article{Do2006Normalization,
author = {Do, J. H. and Choi, D. K.},
title = {Normalization of microarray data: single-labeled and dual-labeled
arrays},
journal = {Molecules and Cells},
year = {2006},
volume = {22},
pages = {254-261},
owner = {philippe},
timestamp = {2010.08.04}
}
@article{Dobson2005Predicting,
author = {Dobson, P.D. and Doig, A.J.},
title = {Predicting enzyme class from protein structure without alignments},
journal = {J. {M}ol. {B}iol.},
year = {2005},
volume = {345},
pages = {187-199},
number = {1},
month = {Jan},
abstract = {Methods for predicting protein function from structure are becoming
more important as the rate at which structures are solved increases
more rapidly than experimental knowledge. {A}s a result, protein
structures now frequently lack functional annotations. {T}he majority
of methods for predicting protein function are reliant upon identifying
a similar protein and transferring its annotations to the query protein.
{T}his method fails when a similar protein cannot be identified,
or when any similar proteins identified also lack reliable annotations.
{H}ere, we describe a method that can assign function from structure
without the use of algorithms reliant upon alignments. {U}sing simple
attributes that can be calculated from any crystal structure, such
as secondary structure content, amino acid propensities, surface
properties and ligands, we describe each enzyme in a non-redundant
set. {T}he set is split according to {E}nzyme {C}lassification ({EC})
number. {W}e combine the predictions of one-class versus one-class
support vector machine models to make overall assignments of {EC}
number to an accuracy of 35% with the top-ranked prediction, rising
to 60% accuracy with the top two ranks. {I}n doing so we demonstrate
the utility of simple structural attributes in protein function prediction
and shed light on the link between structure and function. {W}e apply
our methods to predict the function of every currently unclassified
protein in the {P}rotein {D}ata {B}ank.},
doi = {10.1016/j.jmb.2004.10.024},
pdf = {../local/Dobson2005Predicting.pdf},
file = {Dobson2005Predicting.pdf:local/Dobson2005Predicting.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://dx.doi.org/10.1016/j.jmb.2004.10.024}
}
@article{Dobson2003Distinguishing,
author = {Dobson, P.D. and Doig, A.J.},
title = {Distinguishing enzyme structures from non-enzymes without alignments},
journal = {J. {M}ol. {B}iol.},
year = {2003},
volume = {330},
pages = {771-783},
number = {4},
abstract = {The ability to predict protein function from structure is becoming
increasingly important as the number of structures resolved is growing
more rapidly than our capacity to study function. {C}urrent methods
for predicting protein function are mostly reliant on identifying
a similar protein of known function. {F}or proteins that are highly
dissimilar or are only similar to proteins also lacking functional
annotations, these methods fail. {H}ere, we show that protein function
can be predicted as enzymatic or not without resorting to alignments.
{W}e describe 1178 high-resolution proteins in a structurally non-redundant
subset of the {P}rotein {D}ata {B}ank using simple features such
as secondary-structure content, amino acid propensities, surface
properties and ligands. {T}he subset is split into two functional
groupings, enzymes and non-enzymes. {W}e use the support vector machine-learning
algorithm to develop models that are capable of assigning the protein
class. {V}alidation of the method shows that the function can be
predicted to an accuracy of 77% using 52 features to describe each
protein. {A}n adaptive search of possible subsets of features produces
a simplified model based on 36 features that predicts at an accuracy
of 80%. {W}e compare the method to sequence-based methods that also
avoid calculating alignments and predict a recently released set
of unrelated proteins. {T}he most useful features for distinguishing
enzymes from non-enzymes are secondary-structure content, amino acid
frequencies, number of disulphide bonds and size of the largest cleft.
{T}his method is applicable to any structure as it does not require
the identification of sequence or structural similarity to a protein
of known function.},
doi = {10.1016/S0022-2836(03)00628-4},
pdf = {../local/Dobson2003Distinguishing.pdf},
file = {Dobson2003Distinguishing.pdf:local/Dobson2003Distinguishing.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://dx.doi.org/10.1016/S0022-2836(03)00628-4}
}
@inproceedings{Doi2000Hybrid,
author = {Doi, A. and Matsuno, H. and Nagasaki, M. and Miyano, S.},
title = {Hybrid {P}etri net representation of gene regulatory network},
booktitle = {Proceedings of the {P}acific {S}ymposium on {B}iocomputing},
year = {2000},
volume = {5},
pages = {341--352},
pdf = {../local/Doi2000Hybrid.pdf},
file = {Doi2000Hybrid.pdf:local/Doi2000Hybrid.pdf:PDF},
owner = {vert},
url = {http://helix-web.stanford.edu/psb00/matsuno.pdf}
}
@article{Domon2010Options,
author = {Bruno Domon and Ruedi Aebersold},
title = {Options and considerations when selecting a quantitative proteomics
strategy.},
journal = {Nat Biotechnol},
year = {2010},
volume = {28},
pages = {710--721},
number = {7},
month = {Jul},
abstract = {The vast majority of proteomic studies to date have relied on mass
spectrometric techniques to identify, and in some cases quantify,
peptides that have been generated by proteolysis. Current approaches
differ in the types of instrument used, their performance profiles,
the manner in which they interface with biological research strategies,
and their reliance on and use of prior information. Here, we consider
the three main mass spectrometry (MS)-based proteomic approaches
used today: shotgun (or discovery), directed and targeted strategies.
We discuss the principles of each technique, their strengths and
weaknesses and the dependence of their performance profiles on the
composition of the biological sample. Our goal is to provide a rational
framework for selecting strategies optimally suited to address the
specific research issue under consideration.},
doi = {10.1038/nbt.1661},
institution = {Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland.
bruno.domon@crp-santa.lu},
owner = {phupe},
pii = {nbt.1661},
pmid = {20622845},
timestamp = {2010.08.19},
url = {http://dx.doi.org/10.1038/nbt.1661}
}
@article{Donaldson2003PreBIND,
author = {Donaldson, I. and Martin, J. and de Bruijn, B. and Wolting, C. and
Lay, V. and Tuekam, B. and Zhang, S. and Baskin, B. and Bader, G.D.
and Michalickova, K. and Pawson, T. and Hogue, C.W.V. },
title = {{{P}re{BIND}} and {T}extomy - mining the biomedical literature for
protein-protein interactions using a support vector machine},
journal = {B{MC} {B}ioinformatics},
year = {2003},
volume = {4},
pages = {11},
number = {1},
month = {Mar},
abstract = {Background {T}he majority of experimentally verified molecular interaction
and biological pathway data are present in the unstructured text
of biomedical journal articles where they are inaccessible to computational
methods. {T}he {B}iomolecular interaction network database ({BIND})
seeks to capture these data in a machine-readable format. {W}e hypothesized
that the formidable task-size of backfilling the database could be
reduced by using {S}upport {V}ector {M}achine technology to first
locate interaction information in the literature. {W}e present an
information extraction system that was designed to locate protein-protein
interaction data in the literature and present these data to curators
and the public for review and entry into {BIND}. {R}esults {C}ross-validation
estimated the support vector machine's test-set precision, accuracy
and recall for classifying abstracts describing interaction information
was 92%, 90% and 92% respectively. {W}e estimated that the system
would be able to recall up to 60% of all non-high throughput interactions
present in another yeast-protein interaction database. {F}inally,
this system was applied to a real-world curation problem and its
use was found to reduce the task duration by 70% thus saving 176
days. {C}onclusions {M}achine learning methods are useful as tools
to direct interaction and pathway database back-filling; however,
this potential can only be realized if these techniques are coupled
with human review and entry into a factual database such as {BIND}.
{T}he {P}re{BIND} system described here is available to the public
at http://bind.ca. {C}urrent capabilities allow searching for human,
mouse and yeast protein-interaction information.},
doi = {10.1186/1471-2105-4-11},
pdf = {../local/Donaldson2003PreBIND.pdf},
file = {Donaldson2003PreBIND.pdf:local/Donaldson2003PreBIND.pdf:PDF},
keywords = {biosvm},
owner = {vert},
url = {http://www.biomedcentral.com/1471-2105/4/11/abstract}
}
@article{Dong2005Prediction,
author = {Hai-Long Dong and Yan-Fang Sui},
title = {Prediction of {HLA}-{A2}-restricted {CTL} epitope specific to {HCC}
by {SYFPEITHI} combined with polynomial method.},
journal = {World J Gastroenterol},
year = {2005},
volume = {11},
pages = {208--211},
number = {2},
month = {Jan},
abstract = {AIM: To predict the HLA-A2-restricted CTL epitopes of tumor antigens
associated with hepatocellular carcinoma (HCC). METHODS: MAGE-1,
MAGE-3, MAGE-8, P53 and AFP were selected as objective antigens in
this study for the close association with HCC. The HLA-A*0201 restricted
CTL epitopes of objective tumor antigens were predicted by SYFPEITHI
prediction method combined with the polynomial quantitative motifs
method. The threshold of polynomial scores was set to -24. RESULTS:
The SYFPEITHI prediction values of all possible nonamers of a given
protein sequence were added together and the ten high-scoring peptides
of each protein were chosen for further analysis in primary prediction.
Thirty-five candidates of CTL epitopes (nonamers) derived from the
primary prediction results were selected by analyzing with the polynomial
method and compared with reported CTL epitopes. CONCLUSION: The combination
of SYFPEITHI prediction method and polynomial method can improve
the prediction efficiency and accuracy. These nonamers may be useful
in the design of therapeutic peptide vaccine for HCC and as immunotherapeutic
strategies against HCC after identified by immunology experiment.},
keywords = {Amino Acid Sequence; Carcinoma, Hepatocellular; Databases, Protein;
Epitopes; HLA-A2 Antigen; Humans; Liver Neoplasms; Major Histocompatibility
Complex; Research Support, Non-U.S. Gov't; T-Lymphocytes, Cytotoxic},
owner = {jacob},
pmid = {15633217},
timestamp = {2006.08.30}
}
@article{Dong2005Fast,
author = {Jian-xiong Dong and Adam Krzyzak and Ching Y Suen},
title = {Fast {SVM} training algorithm with decomposition on very large data
sets.},
journal = {I{EEE} {T}rans {P}attern {A}nal {M}ach {I}ntell},
year = {2005},
volume = {27},
pages = {603-18},
number = {4},
month = {Apr},
abstract = {Training a support vector machine on a data set of huge size with
thousands of classes is a challenging problem. {T}his paper proposes
an efficient algorithm to solve this problem. {T}he key idea is to
introduce a parallel optimization step to quickly remove most of
the nonsupport vectors, where block diagonal matrices are used to
approximate the original kernel matrix so that the original problem
can be split into hundreds of subproblems which can be solved more
efficiently. {I}n addition, some effective strategies such as kernel
caching and efficient computation of kernel matrix are integrated
to speed up the training process. {O}ur analysis of the proposed
algorithm shows that its time complexity grows linearly with the
number of classes and size of the data set. {I}n the experiments,
many appealing properties of the proposed algorithm have been investigated
and the results show that the proposed algorithm has a much better
scaling capability than {L}ibsvm, {SVM}light, and {SVMT}orch. {M}oreover,
the good generalization performances on several large databases have
also been achieved.},
keywords = {Algorithms, Animals, Antibiotics, Antineoplastic, Artificial Intelligence,
Automated, Automatic Data Processing, Butadienes, Chloroplasts, Comparative
Study, Computer Simulation, Computer-Assisted, Database Management
Systems, Databases, Diagnosis, Disinfectants, Dose-Response Relationship,
Drug, Drug Toxicity, Electrodes, Electroencephalography, Ethylamines,
Expert Systems, Factual, Feedback, Fungicides, Gene Expression Profiling,
Genes, Genetic Markers, Humans, Image Enhancement, Image Interpretation,
Implanted, Industrial, Information Storage and Retrieval, Kidney,
Kidney Tubules, MEDLINE, Male, Mercuric Chloride, Microarray Analysis,
Molecular Biology, Motor Cortex, Movement, Natural Language Processing,
Neural Networks (Computer), Non-P.H.S., Non-U.S. Gov't, Numerical
Analysis, Pattern Recognition, Plant Proteins, Predictive Value of
Tests, Proteins, Proteome, Proximal, Puromycin Aminonucleoside, Rats,
Reproducibility of Results, Research Support, Sensitivity and Specificity,
Signal Processing, Sprague-Dawley, Subcellular Fractions, Terminology,
Therapy, Time Factors, Toxicogenetics, U.S. Gov't, User-Computer
Interface, 15794164}
}
@article{Doniger2002Predicting,
author = {Doniger, S. and Hofmann, T. and Yeh, J.},
title = {Predicting {CNS} permeability of drug molecules: comparison of neural
network and support vector machine algorithms},
journal = {J. {C}omput. {B}iol.},
year = {2002},
volume = {9},
pages = {849-864},
number = {6},
abstract = {Two different machine-learning algorithms have been used to predict
the blood-brain barrier permeability of different classes of molecules,
to develop a method to predict the ability of drug compounds to penetrate
the {CNS}. {T}he first algorithm is based on a multilayer perceptron
neural network and the second algorithm uses a support vector machine.
{B}oth algorithms are trained on an identical data set consisting
of 179 {CNS} active molecules and 145 {CNS} inactive molecules. {T}he
training parameters include molecular weight, lipophilicity, hydrogen
bonding, and other variables that govern the ability of a molecule
to diffuse through a membrane. {T}he results show that the support
vector machine outperforms the neural network. {B}ased on over 30
different validation sets, the {SVM} can predict up to 96% of the
molecules correctly, averaging 81.5% over 30 test sets, which comprised
of equal numbers of {CNS} positive and negative molecules. {T}his
is quite favorable when compared with the neural network's average
performance of 75.7% with the same 30 test sets. {T}he results of
the {SVM} algorithm are very encouraging and suggest that a classification
tool like this one will prove to be a valuable prediction approach.},
doi = {10.1089/10665270260518317},
pdf = {../local/Doniger2002Predicting.pdf},
file = {Doniger2002Predicting.pdf:local/Doniger2002Predicting.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert}
}
@article{Donoho1994Denoising,
author = {David L. Donoho},
title = {De-Noising By Soft-Thresholding},
journal = {{IEEE} {T}rans. {IT}},
year = {1994},
volume = {41},
pages = {613--627},
number = {3}
}
@article{Donoho2003Hessian,
author = {Donoho, D. L. and Grimes, C.},
title = {Hessian eigenmaps: {L}ocally linear embedding techniques for high-dimensional
data},
journal = {Proc. {N}atl. {A}cad. {S}ci. {USA}},
year = {2003},
volume = {100},
pages = {5591-5596},
number = {10},
doi = {10.1073/pnas.1031596100},
pdf = {../local/5591.pdf:http\},
file = {5591.pdf:http\://www.pnas.org/cgi/reprint/100/10/5591.pdf:PDF},
keywords = {dimred},
url = {http://www.pnas.org/cgi/content/abstract/100/10/5591}
}
@article{Donoho2000High,
author = {Donoho, D. L. and Johnstone, I. and Stine, B. and Piatetsky-Shapiro,
G.},
title = {High-Dimensional Data Analysis : The Curses and Blessings of Dimensionality},
journal = {Statistics},
year = {2000},
pages = {1--33},
owner = {jp},
timestamp = {2012.03.08}
}
@article{Doolan2003Identification,
author = {Denise L Doolan and Scott Southwood and Daniel A Freilich and John
Sidney and Norma L Graber and Lori Shatney and Lolita Bebris and
Laurence Florens and Carlota Dobano and Adam A Witney and Ettore
Appella and Stephen L Hoffman and John R Yates and Daniel J Carucci
and Alessandro Sette},
title = {{I}dentification of {P}lasmodium falciparum antigens by antigenic
analysis of genomic and proteomic data.},
journal = {Proc. Natl. Acad. Sci. U. S. A.},
year = {2003},
volume = {100},
pages = {9952--9957},
number = {17},
month = {Aug},
abstract = {The recent explosion in genomic sequencing has made available a wealth
of data that can now be analyzed to identify protein antigens, potential
targets for vaccine development. Here we present, in the context
of Plasmodium falciparum, a strategy that rapidly identifies target
antigens from large and complex genomes. Sixteen antigenic proteins
recognized by volunteers immunized with radiation-attenuated P. falciparum
sporozoites, but not by mock immunized controls, were identified.
Several of these were more antigenic than previously identified and
well characterized P. falciparum-derived protein antigens. The data
suggest that immune responses to Plasmodium are dispersed on a relatively
large number of parasite antigens. These studies have implications
for our understanding of immunodominance and breadth of responses
to complex pathogens.},
doi = {10.1073/pnas.1633254100},
pdf = {../local/Doolan2003Identification.pdf},
file = {Doolan2003Identification.pdf:local/Doolan2003Identification.pdf:PDF},
keywords = {plasmodium},
pii = {1633254100},
pmid = {12886016},
timestamp = {2006.04.13},
url = {http://dx.doi.org/10.1073/pnas.1633254100}
}
@article{Dostie2006Chromosome,
author = {Josée Dostie and Todd A Richmond and Ramy A Arnaout and Rebecca R
Selzer and William L Lee and Tracey A Honan and Eric D Rubio and
Anton Krumm and Justin Lamb and Chad Nusbaum and Roland D Green and
Job Dekker},
title = {Chromosome Conformation Capture Carbon Copy (5C): a massively parallel
solution for mapping interactions between genomic elements.},
journal = {Genome Res},
year = {2006},
volume = {16},
pages = {1299--1309},
number = {10},
month = {Oct},
abstract = {Physical interactions between genetic elements located throughout
the genome play important roles in gene regulation and can be identified
with the Chromosome Conformation Capture (3C) methodology. 3C converts
physical chromatin interactions into specific ligation products,
which are quantified individually by PCR. Here we present a high-throughput
3C approach, 3C-Carbon Copy (5C), that employs microarrays or quantitative
DNA sequencing using 454-technology as detection methods. We applied
5C to analyze a 400-kb region containing the human beta-globin locus
and a 100-kb conserved gene desert region. We validated 5C by detection
of several previously identified looping interactions in the beta-globin
locus. We also identified a new looping interaction in K562 cells
between the beta-globin Locus Control Region and the gamma-beta-globin
intergenic region. Interestingly, this region has been implicated
in the control of developmental globin gene switching. 5C should
be widely applicable for large-scale mapping of cis- and trans- interaction
networks of genomic elements and for the study of higher-order chromosome
structure.},
doi = {10.1101/gr.5571506},
pdf = {../local/Dostie2006Chromosome.pdf},
file = {Dostie2006Chromosome.pdf:Dostie2006Chromosome.pdf:PDF},
institution = {Program in Gene Function and Expression and Department of Biochemistry
and Molecular Pharmacology, University of Massachusetts Medical School,
Worcester, Massachusetts 01605-0103, USA.},
language = {eng},
medline-pst = {ppublish},
owner = {philippe},
pii = {gr.5571506},
pmid = {16954542},
timestamp = {2010.08.11},
url = {http://dx.doi.org/10.1101/gr.5571506}
}
@article{Dostie2007Chromosome,
author = {Josée Dostie and Ye Zhan and Job Dekker},
title = {Chromosome conformation capture carbon copy technology.},
journal = {Curr Protoc Mol Biol},
year = {2007},
volume = {Chapter 21},
pages = {Unit 21.14},
month = {Oct},
abstract = {Chromosome conformation capture (3C) is used to quantify physical
DNA contacts in vivo at high resolution. 3C was first used in yeast
to map the spatial chromatin organization of chromosome III, and
in higher eukaryotes to demonstrate that genomic DNA elements regulate
target genes by physically interacting with them. 3C has been widely
adopted for small-scale analysis of functional chromatin interactions
along (cis) or between (trans) chromosomes. For larger-scale applications,
chromosome conformation capture carbon copy (5C) combines 3C with
ligation-mediated amplification (LMA) to simultaneously quantify
hundreds of thousands of physical DNA contacts by microarray or ultra-high-throughput
DNA sequencing. 5C allows the mapping of extensive networks of physical
interactions among large sets of genomic elements throughout the
genome. Such networks can provide important biological insights,
e.g., by identifying relationships between regulatory elements and
their target genes. This unit describes 5C for large-scale analysis
of cis- and trans-chromatin interactions in mammalian cells.},
doi = {10.1002/0471142727.mb2114s80},
institution = {University of Massachusetts Medical School, Worcester, Massachusetts,
USA.},
keywords = {Chromosomes, Artificial, Bacterial; Chromosomes, chemistry; DNA Primers,
metabolism; Molecular Biology, methods; Nucleic Acid Conformation;
Oligonucleotide Array Sequence Analysis; Polymerase Chain Reaction;
Sequence Analysis, DNA; Templates, Genetic},
language = {eng},
medline-pst = {ppublish},
owner = {philippe},
pmid = {18265398},
timestamp = {2010.08.11},
url = {http://dx.doi.org/10.1002/0471142727.mb2114s80}
}
@article{Doubet1992CarbBank,
author = {S. Doubet and P. Albersheim},
title = {Carb{B}ank.},
journal = {Glycobiology},
year = {1992},
volume = {2},
pages = {505},
number = {6},
month = {Dec},
keywords = {glycans}
}
@article{Doubet1989Complex,
author = {S. Doubet and K. Bock and D. Smith and A. Darvill and P. Albersheim},
title = {The {C}omplex {C}arbohydrate {S}tructure {D}atabase.},
journal = {Trends {B}iochem {S}ci},
year = {1989},
volume = {14},
pages = {475-7},
number = {12},
month = {Dec},
abstract = {The {C}omplex {C}arbohydrate {S}tructure {D}atabase ({CCSD}) and {C}arb{B}ank,
an {IBM} {PC}/{AT} (or compatible) database management system, were
created to provide an information system to meet the needs of people
interested in carbohydrate science. {T}he {CCSD}, which presently
contains more than 2000 citations, is expected to double in size
in the next two years and to include, soon thereafter, all of the
published structures of carbohydrates larger than disaccharides.},
keywords = {Carbohydrate Sequence, Carbohydrates, Databases, Factual, Information
Systems, Molecular Structure, 2623761}
}
@article{Dover2002Methylation,
author = {Jim Dover and Jessica Schneider and Mary Anne Tawiah-Boateng and
Adam Wood and Kimberly Dean and Mark Johnston and Ali Shilatifard},
title = {Methylation of histone H3 by COMPASS requires ubiquitination of histone
H2B by Rad6.},
journal = {J Biol Chem},
year = {2002},
volume = {277},
pages = {28368--28371},
number = {32},
month = {Aug},
abstract = {The DNA of eukaryotes is wrapped around nucleosomes and packaged into
chromatin. Covalent modifications of the histone proteins that comprise
the nucleosome alter chromatin structure and have major effects on
gene expression. Methylation of lysine 4 of histone H3 by COMPASS
is required for silencing of genes located near chromosome telomeres
and within the rDNA (Krogan, N. J, Dover, J., Khorrami, S., Greenblatt,
J. F., Schneider, J., Johnston, M., and Shilatifard, A. (2002) J.
Biol. Chem. 277, 10753-10755; Briggs, S. D., Bryk, M., Strahl, B.
D., Cheung, W. L., Davie, J. K., Dent, S. Y., Winston, F., and Allis,
C. D. (2001) Genes. Dev. 15, 3286-3295). To learn about the mechanism
of histone methylation, we surveyed the genome of the yeast Saccharomyces
cerevisiae for genes necessary for this process. By analyzing approximately
4800 mutant strains, each deleted for a different non-essential gene,
we discovered that the ubiquitin-conjugating enzyme Rad6 is required
for methylation of lysine 4 of histone H3. Ubiquitination of histone
H2B on lysine 123 is the signal for the methylation of histone H3,
which leads to silencing of genes located near telomeres.},
doi = {10.1074/jbc.C200348200},
institution = {Department of Biochemistry, Saint Louis University School of Medicine,
St. Louis, Missouri 63104, USA.},
keywords = {DNA, Ribosomal, metabolism; Electrophoresis, Polyacrylamide Gel; Gene
Silencing; Histones, metabolism; Ligases, metabolism; Lysine, metabolism;
Methylation; Models, Biological; Mutation; Saccharomyces cerevisiae
Proteins; Saccharomyces cerevisiae, genetics; Ubiquitin, metabolism;
Ubiquitin-Conjugating Enzymes},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {C200348200},
pmid = {12070136},
timestamp = {2010.11.23},
url = {http://dx.doi.org/10.1074/jbc.C200348200}
}
@article{Doyle2005PlosBiol,
author = {John Doyle and Marie Csete},
title = {Motifs, control, and stability.},
journal = {PLoS Biol},
year = {2005},
volume = {3},
pages = {e392},
number = {11},
month = {Nov},
doi = {10.1371/journal.pbio.0030392},
institution = {Department of Control and Dynamical Systems, California Institute
of Technology, Pasadena, California, United States of America. doyle@caltech.edu},
keywords = {Amino Acid Motifs; Bacterial Physiological Phenomena; Bacterial Proteins,
chemistry; Escherichia coli, metabolism; Genes, Bacterial; Genes,
Plant; Glycolysis; Heat-Shock Proteins, chemistry; Models, Biological;
Models, Theoretical; Molecular Chaperones, chemistry; Plant Proteins,
chemistry; Protein Interaction Mapping; Protein Structure, Tertiary;
Transcription Factors, chemistry; Transcription, Genetic},
language = {eng},
medline-pst = {ppublish},
owner = {Andrei Zinovyev},
pii = {05-PLBI-P-0948},
pmid = {16277557},
timestamp = {2011.04.08},
url = {http://dx.doi.org/10.1371/journal.pbio.0030392}
}
@article{Doytchinova2004Identifying,
author = {Irini A Doytchinova and Pingping Guan and Darren R Flower},
title = {Identifying human {MHC} supertypes using bioinformatic methods.},
journal = {J. Immunol.},
year = {2004},
volume = {172},
pages = {4314--4323},
number = {7},
month = {Apr},
abstract = {Classification of MHC molecules into supertypes in terms of peptide-binding
specificities is an important issue, with direct implications for
the development of epitope-based vaccines with wide population coverage.
In view of extremely high MHC polymorphism (948 class I and 633 class
II HLA alleles) the experimental solution of this task is presently
impossible. In this study, we describe a bioinformatics strategy
for classifying MHC molecules into supertypes using information drawn
solely from three-dimensional protein structure. Two chemometric
techniques-hierarchical clustering and principal component analysis-were
used independently on a set of 783 HLA class I molecules to identify
supertypes based on structural similarities and molecular interaction
fields calculated for the peptide binding site. Eight supertypes
were defined: A2, A3, A24, B7, B27, B44, C1, and C4. The two techniques
gave 77\% consensus, i.e., 605 HLA class I alleles were classified
in the same supertype by both methods. The proposed strategy allowed
"supertype fingerprints" to be identified. Thus, the A2 supertype
fingerprint is Tyr(9)/Phe(9), Arg(97), and His(114) or Tyr(116);
the A3-Tyr(9)/Phe(9)/Ser(9), Ile(97)/Met(97) and Glu(114) or Asp(116);
the A24-Ser(9) and Met(97); the B7-Asn(63) and Leu(81); the B27-Glu(63)
and Leu(81); for B44-Ala(81); the C1-Ser(77); and the C4-Asn(77).},
keywords = {Alleles; Amino Acid Motifs; Binding Sites; Computational Biology;
DNA Fingerprinting; HLA Antigens; HLA-A Antigens; HLA-B Antigens;
HLA-C Antigens; Histocompatibility Antigens Class I; Histocompatibility
Testing; Humans; Multigene Family; Protein Interaction Mapping},
owner = {laurent},
pmid = {15034046},
timestamp = {2007.01.03}
}
@article{Doytchinova2005Towards,
author = {Irini A Doytchinova and Valerie Walshe and Persephone Borrow and
Darren R Flower},
title = {Towards the chemometric dissection of peptide--HLA-A*0201 binding
affinity: comparison of local and global QSAR models.},
journal = {J Comput Aided Mol Des},
year = {2005},
volume = {19},
pages = {203--212},
number = {3},
month = {Mar},
abstract = {The affinities of 177 nonameric peptides binding to the HLA-A*0201
molecule were measured using a FACS-based MHC stabilisation assay
and analysed using chemometrics. Their structures were described
by global and local descriptors, QSAR models were derived by genetic
algorithm, stepwise regression and PLS. The global molecular descriptors
included molecular connectivity chi indices, kappa shape indices,
E-state indices, molecular properties like molecular weight and log
P, and three-dimensional descriptors like polarizability, surface
area and volume. The local descriptors were of two types. The first
used a binary string to indicate the presence of each amino acid
type at each position of the peptide. The second was also position-dependent
but used five z-scales to describe the main physicochemical properties
of the amino acids forming the peptides. The models were developed
using a representative training set of 131 peptides and validated
using an independent test set of 46 peptides. It was found that the
global descriptors could not explain the variance in the training
set nor predict the affinities of the test set accurately. Both types
of local descriptors gave QSAR models with better explained variance
and predictive ability. The results suggest that, in their interactions
with the MHC molecule, the peptide acts as a complicated ensemble
of multiple amino acids mutually potentiating each other.},
doi = {10.1007/s10822-005-3993-x},
keywords = {Algorithms; Amino Acid Sequence; Binding Sites; HLA-A Antigens; Models,
Theoretical; Oligopeptides; Quantitative Structure-Activity Relationship;
Regression Analysis},
owner = {laurent},
pmid = {16059672},
timestamp = {2007.08.27},
url = {http://dx.doi.org/10.1007/s10822-005-3993-x}
}
@article{Dreiseitl2001comparison,
author = {S. Dreiseitl and L. Ohno-Machado and H. Kittler and S. Vinterbo and
H. Billhardt and M. Binder},
title = {A comparison of machine learning methods for the diagnosis of pigmented
skin lesions.},
journal = {J {B}iomed {I}nform},
year = {2001},
volume = {34},
pages = {28-36},
number = {1},
month = {Feb},
abstract = {We analyze the discriminatory power of k-nearest neighbors, logistic
regression, artificial neural networks ({ANN}s), decision tress,
and support vector machines ({SVM}s) on the task of classifying pigmented
skin lesions as common nevi, dysplastic nevi, or melanoma. {T}hree
different classification tasks were used as benchmarks: the dichotomous
problem of distinguishing common nevi from dysplastic nevi and melanoma,
the dichotomous problem of distinguishing melanoma from common and
dysplastic nevi, and the trichotomous problem of correctly distinguishing
all three classes. {U}sing {ROC} analysis to measure the discriminatory
power of the methods shows that excellent results for specific classification
problems in the domain of pigmented skin lesions can be achieved
with machine-learning methods. {O}n both dichotomous and trichotomous
tasks, logistic regression, {ANN}s, and {SVM}s performed on about
the same level, with k-nearest neighbors and decision trees performing
worse.},
doi = {10.1006/jbin.2001.1004},
pdf = {../local/Dreiseitl2001comparison.pdf},
file = {Dreiseitl2001comparison.pdf:local/Dreiseitl2001comparison.pdf:PDF},
keywords = {Algorithms, Amino Acid Sequence, Artificial Intelligence, Biological,
Cell Compartmentation, Comparative Study, Computer Simulation, Computer-Assisted,
Decision Trees, Diagnosis, Discriminant Analysis, Humans, Logistic
Models, Melanoma, Models, Neural Networks (Computer), Nevus, Non-U.S.
Gov't, Organelles, P.H.S., Pigmented, Predictive Value of Tests,
Proteins, Reproducibility of Results, Research Support, Skin Diseases,
Skin Neoplasms, Skin Pigmentation, U.S. Gov't, 11376540},
url = {http://dx.doi.org/10.1006/jbin.2001.1004}
}
@article{Drews2000Drug,
author = {J. Drews},
title = {Drug {D}iscovery: {A} {H}istorical {P}erspective},
journal = {Science},
year = {2000},
volume = {287},
pages = {1960-1964},
month = {March},
doi = {10.1126/science.287.5460.1960},
pdf = {../local/Drews2000Drug.pdf},
file = {Drews2000Drug.pdf:Drews2000Drug.pdf:PDF},
keywords = {chemoinformatics},
owner = {mahe},
timestamp = {2006.08.15},
url = {http://dx.doi.org/10.1126/science.287.5460.1960}
}
@article{Driel2006text-mining,
author = {van Driel, M.A. and Bruggeman, J. and Vriend, G. and Brunner, H.G.
and Leunissen, J.A.M.},
title = {A text-mining analysis of the human phenome.},
journal = {Eur. J. Hum. Genet.},
year = {2006},
volume = {14},
pages = {535--542},
number = {5},
month = {May},
abstract = {A number of large-scale efforts are underway to define the relationships
between genes and proteins in various species. But, few attempts
have been made to systematically classify all such relationships
at the phenotype level. Also, it is unknown whether such a phenotype
map would carry biologically meaningful information. We have used
text mining to classify over 5000 human phenotypes contained in the
Online Mendelian Inheritance in Man database. We find that similarity
between phenotypes reflects biological modules of interacting functionally
related genes. These similarities are positively correlated with
a number of measures of gene function, including relatedness at the
level of protein sequence, protein motifs, functional annotation,
and direct protein-protein interaction. Phenotype grouping reflects
the modular nature of human disease genetics. Thus, phenotype mapping
may be used to predict candidate genes for diseases as well as functional
relations between genes and proteins. Such predictions will further
improve if a unified system of phenotype descriptors is developed.
The phenotype similarity data are accessible through a web interface
at http://www.cmbi.ru.nl/MimMiner/.},
doi = {10.1038/sj.ejhg.5201585},
institution = {Centre for Molecular and Biomolecular Informatics, Radboud University
Nijmegen, Toernooiveld 1, 6525ED Nijmegen, the Netherlands.},
keywords = {Chromosome Mapping; Databases, Genetic; Genetic Predisposition to
Disease; Genetic Vectors; Genome, Human; Genotype; Humans; Models,
Genetic; Models, Statistical; Multigene Family; Phenotype},
owner = {mordelet},
pii = {5201585},
pmid = {16493445},
timestamp = {2010.09.27},
url = {http://dx.doi.org/10.1038/sj.ejhg.5201585}
}
@article{Driel2003new,
author = {van Driel, M. and Cuelenaere, K. and Kemmeren, P.P.C.W. and Leunissen,
J.A.M. and Brunner, H.G.},
title = {A new web-based data mining tool for the identification of candidate
genes for human genetic disorders.},
journal = {Eur. J. Hum. Genet.},
year = {2003},
volume = {11},
pages = {57--63},
number = {1},
month = {Jan},
abstract = {To identify the gene underlying a human genetic disorder can be difficult
and time-consuming. Typically, positional data delimit a chromosomal
region that contains between 20 and 200 genes. The choice then lies
between sequencing large numbers of genes, or setting priorities
by combining positional data with available expression and phenotype
data, contained in different internet databases. This process of
examining positional candidates for possible functional clues may
be performed in many different ways, depending on the investigator's
knowledge and experience. Here, we report on a new tool called the
GeneSeeker, which gathers and combines positional data and expression/phenotypic
data in an automated way from nine different web-based databases.
This results in a quick overview of interesting candidate genes in
the region of interest. The GeneSeeker system is built in a modular
fashion allowing for easy addition or removal of databases if required.
Databases are searched directly through the web, which obviates the
need for data warehousing. In order to evaluate the GeneSeeker tool,
we analysed syndromes with known genesis. For each of 10 syndromes
the GeneSeeker programme generated a shortlist that contained a significantly
reduced number of candidate genes from the critical region, yet still
contained the causative gene. On average, a list of 163 genes based
on position alone was reduced to a more manageable list of 22 genes
based on position and expression or phenotype information. We are
currently expanding the tool by adding other databases. The GeneSeeker
is available via the web-interface (http://www.cmbi.kun.nl/GeneSeeker/).},
doi = {10.1038/sj.ejhg.5200918},
institution = {Centre for Molecular and Biomolecular Informatics, University of
Nijmegen, The Netherlands. M.vanDriel@cmbi.kun.nl},
keywords = {Computational Biology; Databases, Genetic; Databases, Nucleic Acid;
Gene Expression; Genetic Diseases, Inborn; Humans; Internet; Noonan
Syndrome; Software},
owner = {mordelet},
pii = {5200918},
pmid = {12529706},
timestamp = {2010.09.27},
url = {http://dx.doi.org/10.1038/sj.ejhg.5200918}
}
@article{Drier2011two,
author = {Drier, Y. and Domany, E.},
title = {Do two machine-learning based prognostic signatures for breast cancer
capture the same biological processes?},
journal = {PloS one},
year = {2011},
volume = {6},
pages = {e17795},
number = {3},
publisher = {Public Library of Science}
}
@article{Dror2005Accurate,
author = {Dror, G. and Sorek, R. and Shamir, R.},
title = {Accurate identification of alternatively spliced exons using support
vector machine},
journal = {Bioinformatics},
year = {2005},
volume = {21},
pages = {897-901},
number = {7},
month = {Apr},
abstract = {Motivation: {A}lternative splicing is a major component of the regulation
acting on mammalian transcriptomes. {I}t is estimated that over half
of all human genes have more than one splice variant. {P}revious
studies have shown that alternatively spliced exons possess several
features that distinguish them from constitutively spliced ones.
{R}ecently, we have demonstrated that such features can be used to
distinguish alternative from constitutive exons. {I}n the current
study we use advanced machine learning methods to generate robust
alternative exons classifier.{R}esults: {W}e extracted several hundred
local sequence features of constitutive as well as alternative exons.
{U}sing feature selection methods we find seven attributes that are
dominant for the task of classification. {S}everal less informative
features help to slightly increase the performance of the classifier.
{T}he classifier achieves a true positive rate of 50% for a false
positive rate of 0.5%. {T}his result enables one to reliably identify
alternatively spliced exons in exon databases that are believed to
be dominated by constitutive exons.{A}vailability: {U}pon request
from the authors.},
doi = {10.1093/bioinformatics/bti132},
pdf = {../local/Dror2005Accurate.pdf},
file = {Dror2005Accurate.pdf:local/Dror2005Accurate.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://bioinformatics.oupjournals.org/cgi/content/abstract/bti132v1}
}
@article{Dubey2005Support,
author = {Anshul Dubey and Matthew J Realff and Jay H Lee and Andreas S Bommarius},
title = {Support vector machines for learning to identify the critical positions
of a protein.},
journal = {J {T}heor {B}iol},
year = {2005},
volume = {234},
pages = {351-61},
number = {3},
month = {Jun},
abstract = {A method for identifying the positions in the amino acid sequence,
which are critical for the catalytic activity of a protein using
support vector machines ({SVM}s) is introduced and analysed. {SVM}s
are supported by an efficient learning algorithm and can utilize
some prior knowledge about the structure of the problem. {T}he amino
acid sequences of the variants of a protein, created by inducing
mutations, along with their fitness are required as input data by
the method to predict its critical positions. {T}o investigate the
performance of this algorithm, variants of the beta-lactamase enzyme
were created in silico using simulations of both mutagenesis and
recombination protocols. {R}esults from literature on beta-lactamase
were used to test the accuracy of this method. {I}t was also compared
with the results from a simple search algorithm. {T}he algorithm
was also shown to be able to predict critical positions that can
tolerate two different amino acids and retain function.},
doi = {10.1016/j.jtbi.2004.11.037},
pdf = {../local/Dubey2005Support.pdf},
file = {Dubey2005Support.pdf:local/Dubey2005Support.pdf:PDF},
keywords = {biosvm},
pii = {S0022-5193(04)00585-5},
url = {http://dx.doi.org/10.1016/j.jtbi.2004.11.037}
}
@article{Dubus2006In,
author = {Dubus, E. and Ijjaali, I. and Petitet, F. and Michel, A.},
title = {{I}n {S}ilico {C}lassification of h{ERG} {C}hannel {B}lockers: a
{K}nowledge-{B}ased {S}trategy.},
journal = {Chem. Med. Chem.},
year = {2006},
volume = {1},
pages = {622--630},
number = {6},
month = {Jun},
abstract = {The blockage of the hERG potassium channel by a wide number of diverse
compounds has become a major pharmacological safety concern as it
can lead to sudden cardiac death. In silico models can be potent
tools to screen out potential hERG blockers as early as possible
during the drug-discovery process. In this study, predictive models
developed using the recursive partitioning method and created using
diverse datasets from 203 molecules tested on the hERG channel are
described. The first model was built with hERG compounds grouped
into two classes, with a separation limit set at an IC(50) value
of 1 mum, and reaches an overall accuracy of 81 \%. The misclassification
of molecules having a range of activity between 1 and 10 muM led
to the generation of a tri-class model able to correctly classify
high, moderate, and weak hERG blockers with an overall accuracy of
90 \%. Another model, constructed with the high and weak hERG-blocker
categories, successfully increases the accuracy to 96 \%. The results
reported herein indicate that a combination of precise, knowledge
management resources and powerful modeling tools are invaluable to
assessing potential cardiotoxic side effects related to hERG blockage.},
doi = {10.1002/cmdc.200500099},
keywords = {herg chemoinformatics},
pmid = {16892402},
timestamp = {2006.10.06},
url = {http://dx.doi.org/10.1002/cmdc.200500099}
}
@inproceedings{Duchenne2009Tensor,
author = {Duchenne, O. and Bach, F. and Kweon, I. and Ponce, J.},
title = {A tensor-based algorithm for high-order graph matching},
booktitle = {Proc. IEEE Conference on Computer Vision and Pattern Recognition
CVPR 2009},
year = {2009},
pages = {1980--1987},
month = {20--25 June },
doi = {10.1109/CVPRW.2009.5206619},
owner = {michael},
timestamp = {2009.11.17}
}
@inproceedings{Duchi2008Efficient,
author = {J. Duchi and S. Shalev-Shwartz and Y. Singer and T. Chandra},
title = {Efficient projections onto the L1-ball for learning in high dimensions},
booktitle = {Proceedings of the 25th {A}nnual {I}nternational {C}onference on
{M}achine {L}earning ({ICML} 2008)},
year = {2008},
editor = {Andrew McCallum and Sam Roweis},
pages = {272--279},
publisher = {Omnipress},
location = {Helsinki, Finland}
}
@book{Duda2001Pattern,
title = {Pattern Classification},
publisher = {Wiley-Interscience},
year = {2001},
author = {R. O. Duda and P. E. Hart and D. G. Stork},
owner = {mahe},
timestamp = {2006.09.07}
}
@article{Dudoit2002Comparison,
author = {Dudoit, S. and Fridlyand, J. and Speed, T.},
title = {Comparison of discrimination methods for classification of tumors
using gene expression data},
journal = {J. Am. Stat. Assoc.},
year = {2002},
volume = {97},
pages = {77--87},
owner = {jp},
timestamp = {2012.03.04}
}
@article{Dudoit2002Statistical,
author = {Dudoit, S. and Yang, Y. H. and Callow, M. J. and Speed, T. P.},
title = {Statistical methods for identifying differentially expressed genes
in replicated {cDNA} microarray experiments},
journal = {Statistica Sinica},
year = {2002},
volume = {12},
pages = {111--139},
pdf = {../local/Dudoit2002Statistical.pdf},
file = {Dudoit2002Statistical.pdf:Dudoit2002Statistical.pdf:PDF},
owner = {jp},
timestamp = {2011.10.03}
}
@article{Dunson2008The,
author = {Dunson, D. and Xue, Y. and Carin, L.},
title = {The Matrix Stick-Breaking Process: Flexible Bayes Meta-Analysis},
journal = {Journal of the {A}merican {S}tatistical {A}ssociation},
year = {2008},
volume = {103},
pages = {317--327},
number = {481},
month = {March},
abstract = {In analyzing data from multiple related studies, it often is of interest
to borrow information across studies and to cluster similar studies.
Although parametric hierarchical models are commonly used, of concern
is sensitivity to the form chosen for the random-effects distribution.
A Dirichlet process (DP) prior can allow the distribution to be unknown,
while clustering studies; however, the DP does not allow local clustering
of studies with respect to a subset of the coefficients without making
independence assumptions. Motivated by this problem, we propose a
matrix stick-breaking process (MSBP) as a prior for a matrix of random
probability measures. Properties of the MSBP are considered, and
methods are developed for posterior computation using Markov chain
Monte Carlo. Using the MSBP as a prior for a matrix of study-specific
regression coefficients, we demonstrate advantages over parametric
modeling in simulated examples. The methods are further illustrated
using a multinational uterotrophic bioassay study.},
url = {http://www.ingentaconnect.com/content/asa/jasa/2008/00000103/00000481/art00036}
}
@book{Durbin1998Biological,
title = {Biological {S}equence {A}nalysis: {P}robabilistic {M}odels of {P}roteins
and {N}ucleic {A}cids},
publisher = {Cambridge University Press},
year = {1998},
author = {Durbin, R. and Eddy, S. and Krogh, A. and Mitchison, G.}
}
@article{Donnes2002Prediction,
author = {D{\"o}nnes, P. and Elofsson, A.},
title = {Prediction of {MHC} class {I} binding peptides, using {SVMHC}},
journal = {B{MC} {B}ioinformatics},
year = {2002},
volume = {3},
pages = {25},
number = {1},
month = {Sep},
abstract = {Background {T}-cells are key players in regulating a specific immune
response. {A}ctivation of cytotoxic {T}-cells requires recognition
of specific peptides bound to {M}ajor {H}istocompatibility {C}omplex
({MHC}) class {I} molecules. {MHC}-peptide complexes are potential
tools for diagnosis and treatment of pathogens and cancer, as well
as for the development of peptide vaccines. {O}nly one in 100 to
200 potential binders actually binds to a certain {MHC} molecule,
therefore a good prediction method for {MHC} class {I} binding peptides
can reduce the number of candidate binders that need to be synthesized
and tested. {R}esults {H}ere, we present a novel approach, {SVMHC},
based on support vector machines to predict the binding of peptides
to {MHC} class {I} molecules. {T}his method seems to perform slightly
better than two profile based methods, {SYFPEITHI} and {HLA}_{BIND}.
{T}he implementation of {SVMHC} is quite simple and does not involve
any manual steps, therefore as more data become available it is trivial
to provide prediction for more {MHC} types. {SVMHC} currently contains
prediction for 26 {MHC} class {I} types from the {MHCPEP} database
or alternatively 6 {MHC} class {I} types from the higher quality
{SYFPEITHI} database. {T}he prediction models for these {MHC} types
are implemented in a public web service available at http://www.sbc.su.se/svmhc/.
{C}onclusions {P}rediction of {MHC} class {I} binding peptides using
{S}upport {V}ector {M}achines, shows high performance and is easy
to apply to a large number of {MHC} class {I} types. {A}s more peptide
data are put into {MHC} databases, {SVMHC} can easily be updated
to give prediction for additional {MHC} class {I} types. {W}e suggest
that the number of binding peptides needed for {SVM} training is
at least 20 sequences.},
doi = {10.1186/1471-2105-3-25},
pdf = {../local/Donnes2002Prediction.pdf},
file = {Donnes2002Prediction.pdf:local/Donnes2002Prediction.pdf:PDF},
keywords = {biosvm immunoinformatics},
owner = {vert},
url = {http://www.biomedcentral.com/1471-2105/3/25/abstract}
}
@article{Donnes2005Integrated,
author = {D{\"o}nnes, P. and Kohlbacher, O.},
title = {Integrated modeling of the major events in the {MHC} class {I} antigen
processing pathway.},
journal = {Protein {S}ci.},
year = {2005},
volume = {14},
pages = {2132-2140},
month = {Jun},
abstract = {Rational design of epitope-driven vaccines is a key goal of immunoinformatics.
{T}ypically, candidate selection relies on the prediction of {MHC}-peptide
binding only, as this is known to be the most selective step in the
{MHC} class {I} antigen processing pathway. {H}owever, proteasomal
cleavage and transport by the transporter associated with antigen
processing ({TAP}) are essential steps in antigen processing as well.
{W}hile prediction methods exist for the individual steps, no method
has yet offered an integrated prediction of all three major processing
events. {H}ere we present {WAPP}, a method combining prediction of
proteasomal cleavage, {TAP} transport, and {MHC} binding into a single
prediction system. {T}he proteasomal cleavage site prediction employs
a new matrix-based method that is based on experimentally verified
proteasomal cleavage sites. {S}upport vector regression is used for
predicting peptides transported by {TAP}. {MHC} binding is the last
step in the antigen processing pathway and was predicted using a
support vector machine method, {SVMHC}. {T}he individual methods
are combined in a filtering approach mimicking the natural processing
pathway. {WAPP} thus predicts peptides that are cleaved by the proteasome
at the {C} terminus, transported by {TAP}, and show significant affinity
to {MHC} class {I} molecules. {T}his results in a decrease in false
positive rates compared to {MHC} binding prediction alone. {C}ompared
to prediction of {MHC} binding only, we report an increased overall
accuracy and a lower rate of false positive predictions for the {HLA}-{A}*0201,
{HLA}-{B}*2705, {HLA}-{A}*01, and {HLA}-{A}*03 alleles using {WAPP}.
{T}he method is available online through our prediction server at
http://www-bs.informatik.uni-tuebingen.de/{WAPP}.},
doi = {10.1110/ps.051352405},
pdf = {../local/Donnes2005Integrated.pdf},
file = {Donnes2005Integrated.pdf:local/Donnes2005Integrated.pdf:PDF},
keywords = {biosvm immunoinformatics},
pii = {ps.051352405},
url = {http://dx.doi.org/10.1110/ps.051352405}
}
@article{Diaz-Uriarte2006Gene,
author = {D{\'\i}az-Uriarte, R. and De Andres, S.A.},
title = {Gene selection and classification of microarray data using random
forest},
journal = {BMC bioinformatics},
year = {2006},
volume = {7},
pages = {3},
number = {1},
publisher = {BioMed Central Ltd}
}
@article{Early1998Polychemotherapy,
author = {{Early Breast Cancer Trialists’ Collaborative Group}},
title = {Polychemotherapy for early breast cancer: an overview of the randomised
trials. Early Breast Cancer Trialists' Collaborative Group.},
journal = {Lancet},
year = {1998},
volume = {352},
pages = {930--942},
number = {9132},
month = {Sep},
abstract = {There have been many randomised trials of adjuvant prolonged polychemotherapy
among women with early breast cancer, and an updated overview of
their results is presented.In 1995, information was sought on each
woman in any randomised trial that began before 1990 and involved
treatment groups that differed only with respect to the chemotherapy
regimens that were being compared. Analyses involved about 18,000
women in 47 trials of prolonged polychemotherapy versus no chemotherapy,
about 6000 in 11 trials of longer versus shorter polychemotherapy,
and about 6000 in 11 trials of anthracycline-containing regimens
versus CMF (cyclophosphamide, methotrexate, and fluorouracil).For
recurrence, polychemotherapy produced substantial and highly significant
proportional reductions both among women aged under 50 at randomisation
(35\% [SD 4] reduction; 2p<0.00001) and among those aged 50-69 (20\%
[SD 3] reduction; 2p<0.00001); few women aged 70 or over had been
studied. For mortality, the reductions were also significant both
among women aged under 50 (27\% [SD 5] reduction; 2p<0.00001) and
among those aged 50-69 (11\% [SD 3] reduction; 2p=0.0001). The recurrence
reductions emerged chiefly during the first 5 years of follow-up,
whereas the difference in survival grew throughout the first 10 years.
After standardisation for age and time since randomisation, the proportional
reductions in risk were similar for women with node-negative and
node-positive disease. Applying the proportional mortality reduction
observed in all women aged under 50 at randomisation would typically
change a 10-year survival of 71\% for those with node-negative disease
to 78\% (an absolute benefit of 7\%), and of 42\% for those with
node-positive disease to 53\% (an absolute benefit of 11\%). The
smaller proportional mortality reduction observed in all women aged
50-69 at randomisation would translate into smaller absolute benefits,
changing a 10-year survival of 67\% for those with node-negative
disease to 69\% (an absolute gain of 2\%) and of 46\% for those with
node-positive disease to 49\% (an absolute gain of 3\%). The age-specific
benefits of polychemotherapy appeared to be largely irrespective
of menopausal status at presentation, oestrogen receptor status of
the primary tumour, and of whether adjuvant tamoxifen had been given.
In terms of other outcomes, there was a reduction of about one-fifth
(2p=0.05) in contralateral breast cancer, which has already been
included in the analyses of recurrence, and no apparent adverse effect
on deaths from causes other than breast cancer (death rate ratio
0.89 [SD 0.09]). The directly randomised comparisons of longer versus
shorter durations of polychemotherapy did not indicate any survival
advantage with the use of more than about 3-6 months of polychemotherapy.
By contrast, directly randomised comparisons did suggest that, compared
with CMF alone, the anthracycline-containing regimens studied produced
somewhat greater effects on recurrence (2p=0.006) and mortality (69\%
vs 72\% 5-year survival; log-rank 2p=0.02). But this comparison is
one of many that could have been selected for emphasis, the 99\%
CI reaches zero, and the results of several of the relevant trials
are not yet available.Some months of adjuvant polychemotherapy (eg,
with CMF or an anthracycline-containing regimen) typically produces
an absolute improvement of about 7-11\% in 10-year survival for women
aged under 50 at presentation with early breast cancer, and of about
2-3\% for those aged 50-69 (unless their prognosis is likely to be
extremely good even without such treatment). Treatment decisions
involve consideration not only of improvements in cancer recurrence
and survival but also of adverse side-effects of treatment, and this
report makes no recommendations as to who should or should not be
treated.},
keywords = {Adult; Aged; Antineoplastic Combined Chemotherapy Protocols, therapeutic
use; Breast Neoplasms, chemistry/drug therapy/mortality; Chemotherapy,
Adjuvant; Drug Administration Schedule; Female; Humans; Lymphatic
Metastasis; Menopause; Middle Aged; Neoplasm Recurrence, Local; Randomized
Controlled Trials as Topic; Receptors, Estrogen, analysis; Tamoxifen,
administration /&/ dosage},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {S0140673698033017},
pmid = {9752815},
timestamp = {2012.03.01}
}
@article{Edwards2000Escherichia,
author = {Edwards, J. S. and Palsson, B. O.},
title = {The \emph{{E}scherichia coli} MG1655 in silico metabolic genotype:
its definition, characteristics, and capabilities.},
journal = {Proc Natl Acad Sci U S A},
year = {2000},
volume = {97},
pages = {5528--5533},
number = {10},
month = {May},
abstract = {The Escherichia coli MG1655 genome has been completely sequenced.
The annotated sequence, biochemical information, and other information
were used to reconstruct the E. coli metabolic map. The stoichiometric
coefficients for each metabolic enzyme in the E. coli metabolic map
were assembled to construct a genome-specific stoichiometric matrix.
The E. coli stoichiometric matrix was used to define the system's
characteristics and the capabilities of E. coli metabolism. The effects
of gene deletions in the central metabolic pathways on the ability
of the in silico metabolic network to support growth were assessed,
and the in silico predictions were compared with experimental observations.
It was shown that based on stoichiometric and capacity constraints
the in silico analysis was able to qualitatively predict the growth
potential of mutant strains in 86\% of the cases examined. Herein,
it is demonstrated that the synthesis of in silico metabolic genotypes
based on genomic, biochemical, and strain-specific information is
possible, and that systems analysis methods are available to analyze
and interpret the metabolic phenotype.},
doi = {10.1073/pnas.97.10.5528},
pdf = {../local/Edwards2000Escherichia.pdf},
file = {Edwards2000Escherichia.pdf:Edwards2000Escherichia.pdf:PDF},
institution = {Department of Bioengineering, University of California, San Diego,
La Jolla, CA 92093-0412, USA.},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {97/10/5528},
pmid = {10805808},
timestamp = {2011.11.30},
url = {http://dx.doi.org/10.1073/pnas.97.10.5528}
}
@article{Efcavitch2010Single-molecule,
author = {J. William Efcavitch and John F Thompson},
title = {Single-molecule DNA analysis.},
journal = {Annu Rev Anal Chem (Palo Alto Calif)},
year = {2010},
volume = {3},
pages = {109--128},
abstract = {The ability to detect single molecules of DNA or RNA has led to an
extremely rich area of exploration of the single most important biomolecule
in nature. In cases in which the nucleic acid molecules are tethered
to a solid support, confined to a channel, or simply allowed to diffuse
into a detection volume, novel techniques have been developed to
manipulate the DNA and to examine properties such as structural dynamics
and protein-DNA interactions. Beyond the analysis of the properties
of nucleic acids themselves, single-molecule detection has enabled
dramatic improvements in the throughput of DNA sequencing and holds
promise for continuing progress. Both optical and nonoptical detection
methods that use surfaces, nanopores, and zero-mode waveguides have
been attempted, and one optically based instrument is already commercially
available. The breadth of literature related to single-molecule DNA
analysis is vast; this review focuses on a survey of efforts in molecular
dynamics and nucleic acid sequencing.},
doi = {10.1146/annurev.anchem.111808.073558},
institution = {Helicos BioSciences Corporation, Cambridge, Massachusetts 02139,
USA. jwefcavitch@helicosbio.com},
owner = {phupe},
pmid = {20636036},
timestamp = {2010.08.20},
url = {http://dx.doi.org/10.1146/annurev.anchem.111808.073558}
}
@article{Efron1979Bootstrap,
author = {Efron, B.},
title = {Bootstrap methods: another look at the jackknife},
journal = {Ann. Stat.},
year = {1979},
volume = {7},
pages = {1--26},
number = {1},
pdf = {../local/Efron1979Bootstrap.pdf},
file = {Efron1979Bootstrap.pdf:Efron1979Bootstrap.pdf:PDF},
publisher = {Institute of Mathematical Statistics}
}
@article{Efron2004Least,
author = {Efron, B. and Hastie, T. and Johnstone, I. and Tibshirani, R.},
title = {Least angle regression},
journal = {Ann. Stat.},
year = {2004},
volume = {32},
pages = {407--499},
number = {2},
pdf = {../local/Efron2004Least.pdf},
file = {Efron2004Least.pdf:Efron2004Least.pdf:PDF},
timestamp = {2006.07.08}
}
@article{Efroni2007Identification,
author = {Efroni, S. and Schaefer, C. F. and Buetow, K. H.},
title = {Identification of key processes underlying cancer phenotypes using
biologic pathway analysis.},
journal = {PLoS One},
year = {2007},
volume = {2},
pages = {e425},
number = {5},
abstract = {Cancer is recognized to be a family of gene-based diseases whose causes
are to be found in disruptions of basic biologic processes. An increasingly
deep catalogue of canonical networks details the specific molecular
interaction of genes and their products. However, mapping of disease
phenotypes to alterations of these networks of interactions is accomplished
indirectly and non-systematically. Here we objectively identify pathways
associated with malignancy, staging, and outcome in cancer through
application of an analytic approach that systematically evaluates
differences in the activity and consistency of interactions within
canonical biologic processes. Using large collections of publicly
accessible genome-wide gene expression, we identify small, common
sets of pathways - Trka Receptor, Apoptosis response to DNA Damage,
Ceramide, Telomerase, CD40L and Calcineurin - whose differences robustly
distinguish diverse tumor types from corresponding normal samples,
predict tumor grade, and distinguish phenotypes such as estrogen
receptor status and p53 mutation state. Pathways identified through
this analysis perform as well or better than phenotypes used in the
original studies in predicting cancer outcome. This approach provides
a means to use genome-wide characterizations to map key biological
processes to important clinical features in disease.},
doi = {10.1371/journal.pone.0000425},
pdf = {../local/Efroni2007Identification.pdf},
file = {Efroni2007Identification.pdf:Efroni2007Identification.pdf:PDF},
institution = {National Cancer Institute Center for Bioinformatics, Rockville, Maryland,
United States of America.},
language = {eng},
medline-pst = {epublish},
owner = {jp},
pmid = {17487280},
timestamp = {2011.10.05},
url = {http://dx.doi.org/10.1371/journal.pone.0000425}
}
@article{Egan2000Prediction,
author = {Egan, W. J. and Merz, K. M. and Baldwin, J. J.},
title = {Prediction of drug absorption using multivariate statistics},
journal = {J. Med. Chem.},
year = {2000},
volume = {43},
pages = {3867--3877},
number = {21},
month = {Oct},
abstract = {Literature data on compounds both well- and poorly-absorbed in humans
were used to build a statistical pattern recognition model of passive
intestinal absorption. Robust outlier detection was utilized to analyze
the well-absorbed compounds, some of which were intermingled with
the poorly-absorbed compounds in the model space. Outliers were identified
as being actively transported. The descriptors chosen for inclusion
in the model were PSA and AlogP98, based on consideration of the
physical processes involved in membrane permeability and the interrelationships
and redundancies between available descriptors. These descriptors
are quite straightforward for a medicinal chemist to interpret, enhancing
the utility of the model. Molecular weight, while often used in passive
absorption models, was shown to be superfluous, as it is already
a component of both PSA and AlogP98. Extensive validation of the
model on hundreds of known orally delivered drugs, "drug-like" molecules,
and Pharmacopeia, Inc. compounds, which had been assayed for Caco-2
cell permeability, demonstrated a good rate of successful predictions
(74-92\%, depending on the dataset and exact criterion used).},
keywords = {chemogenomics},
owner = {laurent},
pii = {jm000292e},
pmid = {11052792},
timestamp = {2008.07.16}
}
@article{Egolf1993Prediction,
author = {L. M. Egolf and P. C. Jurs},
title = {Prediction of {B}oiling {P}oints of {O}rganic {H}terocyclic {C}ompounds
{U}sing {R}egression and {N}eural {N}etworks {T}echniques},
journal = {J Chem Inf Comput Sci},
year = {1993},
volume = {33},
pages = {616-635},
owner = {mahe},
timestamp = {2006.09.07}
}
@article{Ehlers2005NBS1,
author = {Justis P Ehlers and J. William Harbour},
title = {N{BS}1 expression as a prognostic marker in uveal melanoma.},
journal = {Clin. {C}ancer {R}es.},
year = {2005},
volume = {11},
pages = {1849-53},
number = {5},
month = {Mar},
abstract = {P{URPOSE}: {U}p to half of uveal melanoma patients die of metastatic
disease. {T}reatment of the primary eye tumor does not improve survival
in high-risk patients due to occult micrometastatic disease, which
is present at the time of eye tumor diagnosis but is not detected
and treated until months to years later. {H}ere, we use microarray
gene expression data to identify a new prognostic marker. {EXPERIMENTAL}
{DESIGN}: {M}icroarray gene expression profiles were analyzed in
25 primary uveal melanomas. {T}umors were ranked by support vector
machine ({SVM}) and by cytologic severity. {N}bs1 protein expression
was assessed by quantitative immunohistochemistry in 49 primary uveal
melanomas. {S}urvival was assessed using {K}aplan-{M}eier life-table
analysis. {RESULTS}: {E}xpression of the {N}ijmegen breakage syndrome
({NBS}1) gene correlated strongly with {SVM} and cytologic tumor
rankings ({P} < 0.0001). {F}urther, immunohistochemistry expression
of the {N}bs1 protein correlated strongly with both {SVM} and cytologic
rankings ({P} < 0.0001). {T}he 6-year actuarial survival was 100\%
in patients with low immunohistochemistry expression of {N}bs1 and
22\% in those with high {N}bs1 expression ({P} = 0.01). {CONCLUSIONS}:
{NBS}1 is a strong predictor of uveal melanoma survival and potentially
could be used as a clinical marker for guiding clinical management.},
doi = {10.1158/1078-0432.CCR-04-2054},
pdf = {../local/Ehlers2005NBS1.pdf},
file = {Ehlers2005NBS1.pdf:local/Ehlers2005NBS1.pdf:PDF},
keywords = {80 and over, Adult, Aged, Algorithms, Amino Acid Sequence, Amino Acids,
Analysis of Variance, Animals, Area Under Curve, Artifacts, Automated,
Bacteriophage T4, Base Sequence, Biological, Birefringence, Brain
Chemistry, Brain Neoplasms, Cell Cycle Proteins, Comparative Study,
Computational Biology, Computer-Assisted, Cornea, Cross-Sectional
Studies, Databases, Decision Trees, Diagnosis, Diagnostic Imaging,
Diagnostic Techniques, Discriminant Analysis, Evolution, Extramural,
Face, Female, Gene Expression Profiling, Genetic, Glaucoma, Humans,
Immunohistochemistry, Intraocular Pressure, Lasers, Least-Squares
Analysis, Likelihood Functions, Magnetic Resonance Imaging, Magnetic
Resonance Spectroscopy, Male, Markov Chains, Melanoma, Middle Aged,
Models, Molecular, Mutation, N.I.H., Nerve Fibers, Non-P.H.S., Non-U.S.
Gov't, Nuclear Proteins, Nucleic Acid, Nucleic Acid Conformation,
Numerical Analysis, Oligonucleotide Array Sequence Analysis, Ophthalmological,
Optic Nerve Diseases, Optical Coherence, P.H.S., Pattern Recognition,
Photic Stimulation, Polymorphism, Prognosis, Prospective Studies,
Protein, Protein Structure, Proteins, RNA, ROC Curve, Regression
Analysis, Reproducibility of Results, Research Support, Retinal Ganglion
Cells, Secondary, Sensitivity and Specificity, Sequence Analysis,
Single Nucleotide, Single-Stranded Conformational, Software, Statistics,
Survival Analysis, Tertiary, Tomography, Tumor Markers, U.S. Gov't,
Untranslated, Uveal Neoplasms, Visual Fields, beta-Lactamases, 15756009},
pii = {11/5/1849},
url = {http://clincancerres.aacrjournals.org/cgi/content/abstract/11/5/1849}
}
@article{Eid2009Real,
author = {John Eid and Adrian Fehr and Jeremy Gray and Khai Luong and John
Lyle and Geoff Otto and Paul Peluso and David Rank and Primo Baybayan
and Brad Bettman and Arkadiusz Bibillo and Keith Bjornson and Bidhan
Chaudhuri and Frederick Christians and Ronald Cicero and Sonya Clark
and Ravindra Dalal and Alex Dewinter and John Dixon and Mathieu Foquet
and Alfred Gaertner and Paul Hardenbol and Cheryl Heiner and Kevin
Hester and David Holden and Gregory Kearns and Xiangxu Kong and Ronald
Kuse and Yves Lacroix and Steven Lin and Paul Lundquist and Congcong
Ma and Patrick Marks and Mark Maxham and Devon Murphy and Insil Park
and Thang Pham and Michael Phillips and Joy Roy and Robert Sebra
and Gene Shen and Jon Sorenson and Austin Tomaney and Kevin Travers
and Mark Trulson and John Vieceli and Jeffrey Wegener and Dawn Wu
and Alicia Yang and Denis Zaccarin and Peter Zhao and Frank Zhong
and Jonas Korlach and Stephen Turner},
title = {Real-time DNA sequencing from single polymerase molecules.},
journal = {Science},
year = {2009},
volume = {323},
pages = {133--138},
number = {5910},
month = {Jan},
abstract = {We present single-molecule, real-time sequencing data obtained from
a DNA polymerase performing uninterrupted template-directed synthesis
using four distinguishable fluorescently labeled deoxyribonucleoside
triphosphates (dNTPs). We detected the temporal order of their enzymatic
incorporation into a growing DNA strand with zero-mode waveguide
nanostructure arrays, which provide optical observation volume confinement
and enable parallel, simultaneous detection of thousands of single-molecule
sequencing reactions. Conjugation of fluorophores to the terminal
phosphate moiety of the dNTPs allows continuous observation of DNA
synthesis over thousands of bases without steric hindrance. The data
report directly on polymerase dynamics, revealing distinct polymerization
states and pause sites corresponding to DNA secondary structure.
Sequence data were aligned with the known reference sequence to assay
biophysical parameters of polymerization for each template position.
Consensus sequences were generated from the single-molecule reads
at 15-fold coverage, showing a median accuracy of 99.3\%, with no
systematic error beyond fluorophore-dependent error rates.},
doi = {10.1126/science.1162986},
institution = {Pacific Biosciences, 1505 Adams Drive, Menlo Park, CA 94025, USA.},
keywords = {Base Sequence; Consensus Sequence; DNA, Circular, chemistry; DNA,
Single-Stranded, chemistry; DNA, biosynthesis; DNA-Directed DNA Polymerase,
metabolism; Deoxyribonucleotides, metabolism; Enzymes, Immobilized;
Fluorescent Dyes; Kinetics; Nanostructures; Sequence Analysis, DNA,
methods; Spectrometry, Fluorescence},
language = {eng},
medline-pst = {ppublish},
owner = {philippe},
pii = {1162986},
pmid = {19023044},
timestamp = {2010.07.28},
url = {http://dx.doi.org/10.1126/science.1162986}
}
@article{Ein-Dor2005Outcome,
author = {Ein-Dor, L. and Kela, I. and Getz, G. and Givol, D. and Domany, E.},
title = {Outcome signature genes in breast cancer: is there a unique set?},
journal = {Bioinformatics},
year = {2005},
volume = {21},
pages = {171--178},
number = {2},
month = {Jan},
abstract = {MOTIVATION: Predicting the metastatic potential of primary malignant
tissues has direct bearing on the choice of therapy. Several microarray
studies yielded gene sets whose expression profiles successfully
predicted survival. Nevertheless, the overlap between these gene
sets is almost zero. Such small overlaps were observed also in other
complex diseases, and the variables that could account for the differences
had evoked a wide interest. One of the main open questions in this
context is whether the disparity can be attributed only to trivial
reasons such as different technologies, different patients and different
types of analyses. RESULTS: To answer this question, we concentrated
on a single breast cancer dataset, and analyzed it by a single method,
the one which was used by van't Veer et al. to produce a set of outcome-predictive
genes. We showed that, in fact, the resulting set of genes is not
unique; it is strongly influenced by the subset of patients used
for gene selection. Many equally predictive lists could have been
produced from the same analysis. Three main properties of the data
explain this sensitivity: (1) many genes are correlated with survival;
(2) the differences between these correlations are small; (3) the
correlations fluctuate strongly when measured over different subsets
of patients. A possible biological explanation for these properties
is discussed. CONTACT: eytan.domany@weizmann.ac.il SUPPLEMENTARY
INFORMATION: http://www.weizmann.ac.il/physics/complex/compphys/downloads/liate/},
doi = {10.1093/bioinformatics/bth469},
pdf = {../local/Ein-Dor2005Outcome.pdf},
file = {Ein-Dor2005Outcome.pdf:Ein-Dor2005Outcome.pdf:PDF},
institution = {Department of Physics of Complex Systems, Weizmann Institute of Science
Rehovot 76100, Israel.},
keywords = {breastcancer, microarray, featureselection},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {bth469},
pmid = {15308542},
timestamp = {2010.10.12},
url = {http://dx.doi.org/10.1093/bioinformatics/bth469}
}
@article{Ein-Dor2006Thousands,
author = {Ein-Dor, L. and Zuk, O. and Domany, E.},
title = {Thousands of samples are needed to generate a robust gene list for
predicting outcome in cancer},
journal = {Proc. Natl. Acad. Sci. USA},
year = {2006},
volume = {103},
pages = {5923--5928},
number = {15},
doi = {10.1073/pnas.0601231103},
pdf = {../local/Ein-Dor2006Thousands.pdf},
file = {Ein-Dor2006Thousands.pdf:Ein-Dor2006Thousands.pdf:PDF},
owner = {jp},
timestamp = {2011.01.12},
url = {http://dx.doi.org/10.1073/pnas.0601231103}
}
@article{Einmahl1992Generalized,
author = {Einmahl, J. H. J. and Mason, D. M.},
title = {Generalized {Q}uantile {P}rocess},
journal = {Ann. {S}tat.},
year = {1992},
volume = {20},
pages = {1062-1078},
month = {June},
pdf = {../local/Einmahl1992Generalized.pdf},
file = {Einmahl1992Generalized.pdf:local/Einmahl1992Generalized.pdf:PDF},
url = {http://links.jstor.org/sici?sici=0090-5364%28199206%2920%3A2%3C1062%3AGQP%3E2.0.CO%3B2-6}
}
@article{Eisen1998Cluster,
author = {Eisen, M. B. and Spellman, P. T. and Brown, P. O. and Botstein, D.},
title = {Cluster analysis and display of genome-wide expression patterns},
journal = {Proc. Natl. Acad. Sci. USA},
year = {1998},
volume = {95},
pages = {14863--14868},
month = {Dec},
pdf = {../local/Eisen1998Cluster.pdf},
file = {Eisen1998Cluster.pdf:Eisen1998Cluster.pdf:PDF},
subject = {microarray},
url = {http://www.pnas.org/cgi/reprint/95/25/14863.pdf}
}
@article{Eissing2004Bistability,
author = {Eissing, T. and Conzelmann, H. and Gilles, E. D. and Allgower, F.
and Bullinger, E. and Scheurich, P.},
title = {Bistability Analyses of a Caspase Activation Model for Receptor-induced
Apoptosis},
journal = {J. Biol. Chem.},
year = {2004},
volume = {279},
pages = {36892-36897},
number = {35},
abstract = {Apoptosis is an important physiological process crucially involved
in development and homeostasis of multicellular organisms. Although
the major signaling pathways have been unraveled, a detailed mechanistic
understanding of the complex underlying network remains elusive.
We have translated here the current knowledge of the molecular mechanisms
of the death-receptor-activated caspase cascade into a mathematical
model. A reduction down to the apoptotic core machinery enables the
application of analytical mathematical methods to evaluate the system
behavior within a wide range of parameters. Using parameter values
from the literature, the model reveals an unstable status of survival
indicating the need for further control. Based on recent publications
we tested one additional regulatory mechanism at the level of initiator
caspase activation and demonstrated that the resulting system displays
desired characteristics such as bistability. In addition, the results
from our model studies allowed us to reconcile the fast kinetics
of caspase 3 activation observed at the single cell level with the
much slower kinetics found at the level of a cell population.},
doi = {10.1074/jbc.M404893200},
eprint = {http://www.jbc.org/content/279/35/36892.full.pdf+html},
pdf = {../local/Eissing2004Bistability.pdf},
file = {Eissing2004Bistability.pdf:Eissing2004Bistability.pdf:PDF},
keywords = {csbcbook},
url = {http://www.jbc.org/content/279/35/36892.abstract}
}
@article{Eissing2007Response,
author = {Eissing, T. and Waldherr, S. and Allgower, F. and Scheurich, P. and
Bullinger, E.},
title = {Response to Bistability in Apoptosis: Roles of Bax, Bcl-2, and Mitochondrial
Permeability Transition Pores},
journal = {Biophysical Journal},
year = {2007},
volume = {92},
pages = {3332 - 3334},
number = {9},
doi = {10.1529/biophysj.106.100362},
pdf = {../local/Eissing2007Response.pdf},
file = {Eissing2007Response.pdf:Eissing2007Response.pdf:PDF},
issn = {0006-3495},
keywords = {csbcbook},
url = {http://www.sciencedirect.com/science/article/B94RW-4TR4KB1-1B/2/8c4f12571fa01055b7dea12984318e9f}
}
@article{Eissing2007Responsea,
author = {Eissing, T. and Waldherr, S. and Allgower, F. and Scheurich, P. and
Bullinger, E.},
title = {Response to Bistability in Apoptosis: Roles of Bax, Bcl-2, and Mitochondrial
Permeability Transition Pores},
journal = {Biophys. J.},
year = {2007},
volume = {92},
pages = {3332--3334},
number = {9},
doi = {10.1529/biophysj.106.100362},
issn = {0006-3495},
keywords = {csbcbook},
owner = {jp},
timestamp = {2012.05.11},
url = {http://www.sciencedirect.com/science/article/B94RW-4TR4KB1-1B/2/8c4f12571fa01055b7dea12984318e9f}
}
@article{Ekins2003In,
author = {Ekins, S.},
title = {{I}n silico approaches to predicting drug metabolism, toxicology
and beyond.},
journal = {Biochem. Soc. Trans.},
year = {2003},
volume = {31},
pages = {611--614},
number = {Pt 3},
month = {Jun},
abstract = {The discovery and optimization of new drug candidates is becoming
increasingly reliant upon the combination of experimental and computational
approaches related to drug metabolism, toxicology and general biopharmaceutical
properties. With the considerable output of high-throughput assays
for cytochrome-P450-mediated drug-drug interactions, metabolic stability
and assays for toxicology, we have orders of magnitude more data
that will facilitate model building. A recursive partitioning model
for human liver microsomal metabolic stability based on over 800
structurally diverse molecules was used to predict molecules with
known log in vitro clearance data (Spearman's rho -0.64, P <0.0001).
In addition, with solely published data, a quantitative structure-activity
relationship for 66 inhibitors of the potassium channel human ether-a-gogo
(hERG) that has been implicated in the failure of a number of recent
drugs has been generated. This model has been validated with further
published data for 25 molecules (Spearman's rho 0.83, P <0.0001).
If continued value is to be realized from these types of computational
models, there needs to be some applied research on their validation
and optimization with new data. Some relatively simple approaches
may have value when it comes to combining data from multiple models
in order to improve and focus drug discovery on the molecules most
likely to succeed.},
doi = {10.1042/},
keywords = {herg},
pmid = {12773166},
timestamp = {2007.03.27},
url = {http://dx.doi.org/10.1042/}
}
@article{Ekins2002Towards,
author = {S. Ekins and B. Boulanger and P. W. Swaan and M. A. Z. Hupcey},
title = {{T}owards a new age of virtual {ADME}/{TOX} and multidimensional
drug discovery.},
journal = {J Comput Aided Mol Des},
year = {2002},
volume = {16},
pages = {381--401},
number = {5-6},
abstract = {With the continual pressure to ensure follow-up molecules to billion
dollar blockbuster drugs, there is a hurdle in profitability and
growth for pharmaceutical companies in the next decades. With each
success and failure we increasingly appreciate that a key to the
success of synthesized molecules through the research and development
process is the possession of drug-like properties. These properties
include an adequate bioactivity as well as adequate solubility, an
ability to cross critical membranes (intestinal and sometimes blood-brain
barrier), reasonable metabolic stability and of course safety in
humans. Dependent on the therapeutic area being investigated it might
also be desirable to avoid certain enzymes or transporters to circumvent
potential drug-drug interactions. It may also be important to limit
the induction of these same proteins that can result in further toxicities.
We have clearly moved the assessment of in vitro absorption, distribution,
metabolism, excretion and toxicity (ADME/TOX) parameters much earlier
in the discovery organization than a decade ago with the inclusion
of higher throughput systems. We are also now faced with huge amounts
of ADME/TOX data for each molecule that need interpretation and also
provide a valuable resource for generating predictive computational
models for future drug discovery. The present review aims to show
what tools exist today for visualizing and modeling ADME/TOX data,
what tools need to be developed, and how both the present and future
tools are valuable for virtual filtering using ADME/TOX and bioactivity
properties in parallel as a viable addition to present practices.},
keywords = {ATP-Binding Cassette Transporters, Algorithms, Animals, Biological,
Biological Availability, Computer Simulation, Drug Design, Drug Evaluation,
Drug Industry, Gene Expression Profiling, Humans, Models, Organic
Anion Transporters, P.H.S., Pharmaceutical, Pharmaceutical Preparations,
Pharmacogenetics, Pharmacokinetics, Preclinical, Proteomics, Research
Support, Software, Systems Biology, Technology, Toxicity Tests, U.S.
Gov't, 12489686},
owner = {mahe},
pmid = {12489686},
timestamp = {2006.08.16}
}
@article{Ekins2002Three-dimensional,
author = {Ekins, S. and Crumb, W. J. and Sarazan, R. D. and Wikel, J. H. and
Wrighton, S. A.},
title = {{T}hree-dimensional quantitative structure-activity relationship
for inhibition of human ether-a-go-go-related gene potassium channel.},
journal = {J. Pharmacol. Exp. Ther.},
year = {2002},
volume = {301},
pages = {427--434},
number = {2},
month = {May},
abstract = {The protein product of the human ether-a-go-go gene (hERG) is a potassium
channel that when inhibited by some drugs may lead to cardiac arrhythmia.
Previously, a three-dimensional quantitative structure-activity relationship
(3D-QSAR) pharmacophore model was constructed using Catalyst with
in vitro inhibition data for antipsychotic agents. The rationale
of the current study was to use a combination of in vitro and in
silico technologies to further test the pharmacophore model and qualitatively
predict whether molecules are likely to inhibit this potassium channel.
These predictions were assessed with the experimental data using
the Spearman's rho rank correlation. The antipsychotic-based hERG
inhibitor model produced a statistically significant Spearman's rho
of 0.71 for 11 molecules. In addition, 15 molecules from the literature
were used as a further test set and were also well ranked by the
same model with a statistically significant Spearman's rho value
of 0.76. A Catalyst General hERG pharmacophore model was generated
with these literature molecules, which contained four hydrophobic
features and one positive ionizable feature. Linear regression of
log-transformed observed versus predicted IC(50) values for this
training set resulted in an r(2) value of 0.90. The model based on
literature data was evaluated with the in vitro data generated for
the original 22 molecules (including the antipsychotics) and illustrated
a significant Spearman's rho of 0.77. Thus, the Catalyst 3D-QSAR
approach provides useful qualitative predictions for test set molecules.
The model based on literature data therefore provides a potentially
valuable tool for discovery chemistry as future molecules may be
synthesized that are less likely to inhibit hERG based on information
provided by a pharmacophore for the inhibition of this potassium
channel.},
keywords = {herg},
pmid = {11961040},
timestamp = {2007.03.27}
}
@article{El-Naqa2004similarity,
author = {El-Naqa, I. and Yang, Y. and Galatsanos, N. P. and Nishikawa, R.
M. and Wernick, M. N.},
title = {A similarity learning approach to content-based image retrieval:
application to digital mammography.},
journal = {I{EEE} {T}rans {M}ed {I}maging},
year = {2004},
volume = {23},
pages = {1233-44},
number = {10},
month = {Oct},
abstract = {In this paper, we describe an approach to content-based retrieval
of medical images from a database, and provide a preliminary demonstration
of our approach as applied to retrieval of digital mammograms. {C}ontent-based
image retrieval ({CBIR}) refers to the retrieval of images from a
database using information derived from the images themselves, rather
than solely from accompanying text indices. {I}n the medical-imaging
context, the ultimate aim of {CBIR} is to provide radiologists with
a diagnostic aid in the form of a display of relevant past cases,
along with proven pathology and other suitable information. {CBIR}
may also be useful as a training tool for medical students and residents.
{T}he goal of information retrieval is to recall from a database
information that is relevant to the user's query. {T}he most challenging
aspect of {CBIR} is the definition of relevance (similarity), which
is used to guide the retrieval machine. {I}n this paper, we pursue
a new approach, in which similarity is learned from training examples
provided by human observers. {S}pecifically, we explore the use of
neural networks and support vector machines to predict the user's
notion of similarity. {W}ithin this framework we propose using a
hierarchal learning approach, which consists of a cascade of a binary
classifier and a regression module to optimize retrieval effectiveness
and efficiency. {W}e also explore how to incorporate online human
interaction to achieve relevance feedback in this learning framework.
{O}ur experiments are based on a database consisting of 76 mammograms,
all of which contain clustered microcalcifications ({MC}s). {O}ur
goal is to retrieve mammogram images containing similar {MC} clusters
to that in a query. {T}he performance of the retrieval system is
evaluated using precision-recall curves computed using a cross-validation
procedure. {O}ur experimental results demonstrate that: 1) the learning
framework can accurately predict the perceptual similarity reported
by human observers, thereby serving as a basis for {CBIR}; 2) the
learning-based framework can significantly outperform a simple distance-based
similarity metric; 3) the use of the hierarchical two-stage network
can improve retrieval performance; and 4) relevance feedback can
be effectively incorporated into this learning framework to achieve
improvement in retrieval precision based on online interaction with
users; and 5) the retrieved images by the network can have predicting
value for the disease condition of the query.}
}
@article{El-Naqa2002support,
author = {El-Naqa, I. and Yang, Y. and Wernick, M. N. and Galatsanos, N. P.
and Nishikawa, R. M.},
title = {A support vector machine approach for detection of microcalcifications.},
journal = {I{EEE} {T}rans {M}ed {I}maging},
year = {2002},
volume = {21},
pages = {1552-63},
number = {12},
month = {Dec},
abstract = {In this paper, we investigate an approach based on support vector
machines ({SVM}s) for detection of microcalcification ({MC}) clusters
in digital mammograms, and propose a successive enhancement learning
scheme for improved performance. {SVM} is a machine-learning method,
based on the principle of structural risk minimization, which performs
well when applied to data outside the training set. {W}e formulate
{MC} detection as a supervised-learning problem and apply {SVM} to
develop the detection algorithm. {W}e use the {SVM} to detect at
each location in the image whether an {MC} is present or not. {W}e
tested the proposed method using a database of 76 clinical mammograms
containing 1120 {MC}s. {W}e use free-response receiver operating
characteristic curves to evaluate detection performance, and compare
the proposed algorithm with several existing methods. {I}n our experiments,
the proposed {SVM} framework outperformed all the other methods tested.
{I}n particular, a sensitivity as high as 94\% was achieved by the
{SVM} method at an error rate of one false-positive cluster per image.
{T}he ability of {SVM} to out perform several well-known methods
developed for the widely studied problem of {MC} detection suggests
that {SVM} is a promising technique for object detection in a medical
imaging application.}
}
@article{Elbashir2001Duplexes,
author = {Elbashir, S. M. and Harborth, J. and Lendeckel, W. and Yalcin, A.
and Weber, K. and Tuschl, T.},
title = {{D}uplexes of 21-nucleotide {RNA}s mediate {RNA} interference in
cultured mammalian cells.},
journal = {Nature},
year = {2001},
volume = {411},
pages = {494--498},
number = {6836},
month = {May},
abstract = {RNA interference (RNAi) is the process of sequence-specific, post-transcriptional
gene silencing in animals and plants, initiated by double-stranded
RNA (dsRNA) that is homologous in sequence to the silenced gene.
The mediators of sequence-specific messenger RNA degradation are
21- and 22-nucleotide small interfering RNAs (siRNAs) generated by
ribonuclease III cleavage from longer dsRNAs. Here we show that 21-nucleotide
siRNA duplexes specifically suppress expression of endogenous and
heterologous genes in different mammalian cell lines, including human
embryonic kidney (293) and HeLa cells. Therefore, 21-nucleotide siRNA
duplexes provide a new tool for studying gene function in mammalian
cells and may eventually be used as gene-specific therapeutics.},
doi = {10.1038/35078107},
pdf = {../local/Elbashir2001Duplexes.pdf},
file = {Elbashir2001Duplexes.pdf:Elbashir2001Duplexes.pdf:PDF},
keywords = {sirna},
owner = {vert},
pii = {35078107},
pmid = {11373658},
timestamp = {2006.03.28},
url = {http://dx.doi.org/10.1038/35078107}
}
@article{Elbashir2001RNA,
author = {Elbashir, S. M. and Lendeckel, W. and Tuschl, T.},
title = {R{NA} interference is mediated by 21- and 22-nucleotide {RNA}s.},
journal = {Genes {D}ev.},
year = {2001},
volume = {15},
pages = {188-200},
number = {2},
month = {Jan},
abstract = {Double-stranded {RNA} (ds{RNA}) induces sequence-specific posttranscriptional
gene silencing in many organisms by a process known as {RNA} interference
({RNA}i). {U}sing a {D}rosophila in vitro system, we demonstrate
that 21- and 22-nt {RNA} fragments are the sequence-specific mediators
of {RNA}i. {T}he short interfering {RNA}s (si{RNA}s) are generated
by an {RN}ase {III}-like processing reaction from long ds{RNA}. {C}hemically
synthesized si{RNA} duplexes with overhanging 3' ends mediate efficient
target {RNA} cleavage in the lysate, and the cleavage site is located
near the center of the region spanned by the guiding si{RNA}. {F}urthermore,
we provide evidence that the direction of ds{RNA} processing determines
whether sense or antisense target {RNA} can be cleaved by the si{RNA}-protein
complex.},
keywords = {sirna}
}
@article{Elfilali2006ITTACA,
author = {Elfilali, A. and Lair, S. and Verbeke, C. and La Rosa, P. and Radvanyi,
F. and Barillot, E.},
title = {{ITTACA}: a new database for integrated tumor transcriptome array
and clinical data analysis.},
journal = {Nucleic Acids Res.},
year = {2006},
volume = {34},
pages = {D613--D616},
number = {Database issue},
month = {Jan},
abstract = {Transcriptome microarrays have become one of the tools of choice for
investigating the genes involved in tumorigenesis and tumor progression,
as well as finding new biomarkers and gene expression signatures
for the diagnosis and prognosis of cancer. Here, we describe a new
database for Integrated Tumor Transcriptome Array and Clinical data
Analysis (ITTACA). ITTACA centralizes public datasets containing
both gene expression and clinical data. ITTACA currently focuses
on the types of cancer that are of particular interest to research
teams at Institut Curie: breast carcinoma, bladder carcinoma and
uveal melanoma. A web interface allows users to carry out different
class comparison analyses, including the comparison of expression
distribution profiles, tests for differential expression and patient
survival analyses. ITTACA is complementary to other databases, such
as GEO and SMD, because it offers a better integration of clinical
data and different functionalities. It also offers more options for
class comparison analyses when compared with similar projects such
as Oncomine. For example, users can define their own patient groups
according to clinical data or gene expression levels. This added
flexibility and the user-friendly web interface makes ITTACA especially
useful for comparing personal results with the results in the existing
literature. ITTACA is accessible online at http://bioinfo.curie.fr/ittaca.},
doi = {10.1093/nar/gkj022},
institution = {Institut Curie, Service Bioinformatique, 26 rue d'Ulm, Paris, 75248
cedex 05, France.},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {34/suppl_1/D613},
pmid = {16381943},
timestamp = {2010.10.13},
url = {http://dx.doi.org/10.1093/nar/gkj022}
}
@inproceedings{Elkan2008Learning,
author = {Elkan, C. and Noto, K.},
title = {Learning classifiers from only positive and unlabeled data},
booktitle = {KDD '08: Proceeding of the 14th ACM SIGKDD international conference
on Knowledge discovery and data mining},
year = {2008},
pages = {213--220},
address = {New York, NY, USA},
publisher = {ACM},
bibsource = {DBLP, http://dblp.uni-trier.de},
doi = {10.1145/1401890.1401920},
ee = {http://doi.acm.org/10.1145/1401890.1401920},
pdf = {../local/Elkan2008Learning.pdf},
file = {Elkan2008Learning.pdf:Elkan2008Learning.pdf:PDF},
keywords = {PUlearning},
owner = {fantine},
timestamp = {2009.06.09},
url = {http://dx.doi.org/10.1145/1401890.1401920}
}
@article{Elkon2003Genome-wide,
author = {Elkon, R. and Linhart, C. and Sharan, R. and Shamir, R. and Shiloh,
Y.},
title = {Genome-wide in silico identification of transcriptional regulators
controlling the cell cycle in human cells.},
journal = {Genome Res.},
year = {2003},
volume = {13},
pages = {773--780},
number = {5},
month = {May},
abstract = {Dissection of regulatory networks that control gene transcription
is one of the greatest challenges of functional genomics. Using human
genomic sequences, models for binding sites of known transcription
factors, and gene expression data, we demonstrate that the reverse
engineering approach, which infers regulatory mechanisms from gene
expression patterns, can reveal transcriptional networks in human
cells. To date, such methodologies were successfully demonstrated
only in prokaryotes and low eukaryotes. We developed computational
methods for identifying putative binding sites of transcription factors
and for evaluating the statistical significance of their prevalence
in a given set of promoters. Focusing on transcriptional mechanisms
that control cell cycle progression, our computational analyses revealed
eight transcription factors whose binding sites are significantly
overrepresented in promoters of genes whose expression is cell-cycle-dependent.
The enrichment of some of these factors is specific to certain phases
of the cell cycle. In addition, several pairs of these transcription
factors show a significant co-occurrence rate in cell-cycle-regulated
promoters. Each such pair indicates functional cooperation between
its members in regulating the transcriptional program associated
with cell cycle progression. The methods presented here are general
and can be applied to the analysis of transcriptional networks controlling
any biological process.},
doi = {10.1101/gr.947203},
institution = {The David and Inez Myers Laboratory for Genetic Research, Department
of Human Genetics, Sackler School of Medicine, and School of Computer
Science, Tel Aviv University, Tel Aviv 69978, Israel.},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {13/5/773},
pmid = {12727897},
timestamp = {2011.09.21},
url = {http://dx.doi.org/10.1101/gr.947203}
}
@article{Elliott2009Current,
author = {Elliott, M and Parker, C and Smith, D and Borchers, CH},
title = {Current Trends in Quantitative Proteomics},
journal = {The Journal of Mass Spectrometry},
year = {2009},
volume = {44},
pages = {1637-60},
owner = {phupe},
timestamp = {2010.08.19}
}
@article{Ellis1992Pathological,
author = {Ellis, I. O. and Galea, M. and Broughton, N. and Locker, A. and Blamey,
R. W. and Elston, C. W.},
title = {Pathological prognostic factors in breast cancer. II. Histological
type. Relationship with survival in a large study with long-term
follow-up.},
journal = {Histopathology},
year = {1992},
volume = {20},
pages = {479--489},
number = {6},
month = {Jun},
abstract = {The histological tumour type determined by current criteria has been
investigated in a consecutive series of 1621 women with primary operable
breast carcinoma, presenting between 1973 and 1987. All women underwent
definitive surgery with node biopsy and none received adjuvant systemic
therapy. Special types, tubular, invasive cribriform and mucinous,
with a very favourable prognosis can be identified. A common type
of tumour recognized by our group and designated tubular mixed carcinoma
is shown to be prognostically distinct from carcinomas of no special
type; it has a characteristic histological appearance and is the
third most common type in this series. Analysis of subtypes of lobular
carcinoma confirms differing prognoses. The classical, tubulo-lobular
and lobular mixed types are associated with a better prognosis than
carcinomas of no special type; this is not so for the solid variant.
Tubulo-lobular carcinoma in particular has an extremely good prognosis
similar to tumours included in the 'special type' category above.
Neither medullary carcinoma nor atypical medullary carcinoma are
found to carry a survival advantage over carcinomas of no special
type. The results confirm that histological typing of human breast
carcinoma can provide useful prognostic information.},
doi = {10.1111/j.1365-2559.1992.tb01032.x},
pdf = {../local/Ellis1992Pathological.pdf},
file = {Ellis1992Pathological.pdf:Ellis1992Pathological.pdf:PDF},
institution = {Department of Histopathology, City Hospital, Nottingham, UK.},
keywords = {csbcbook, csbcbook-ch3},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pmid = {1607149},
timestamp = {2009.10.18},
url = {http://dx.doi.org/10.1111/j.1365-2559.1992.tb01032}
}
@article{Elston1991Pathological,
author = {Elston, C. W. and Ellis, I. O.},
title = {Pathological prognostic factors in breast cancer. I. The value of
histological grade in breast cancer: experience from a large study
with long-term follow-up.},
journal = {Histopathology},
year = {1991},
volume = {19},
pages = {403--410},
number = {5},
month = {Nov},
abstract = {Morphological assessment of the degree of differentiation has been
shown in numerous studies to provide useful prognostic information
in breast cancer, but until recently histological grading has not
been accepted as a routine procedure, mainly because of perceived
problems with reproducibility and consistency. In the Nottingham/Tenovus
Primary Breast Cancer Study the most commonly used method, described
by Bloom & Richardson, has been modified in order to make the criteria
more objective. The revised technique involves semiquantitative evaluation
of three morphological features--the percentage of tubule formation,
the degree of nuclear pleomorphism and an accurate mitotic count
using a defined field area. A numerical scoring system is used and
the overall grade is derived from a summation of individual scores
for the three variables: three grades of differentiation are used.
Since 1973, over 2200 patients with primary operable breast cancer
have been entered into a study of multiple prognostic factors. Histological
grade, assessed in 1831 patients, shows a very strong correlation
with prognosis; patients with grade I tumours have a significantly
better survival than those with grade II and III tumours (P less
than 0.0001). These results demonstrate that this method for histological
grading provides important prognostic information and, if the grading
protocol is followed consistently, reproducible results can be obtained.
Histological grade forms part of the multifactorial Nottingham prognostic
index, together with tumour size and lymph node stage, which is used
to stratify individual patients for appropriate therapy.},
doi = {10.1111/j.1365-2559.1991.tb00229.x},
pdf = {../local/Elston1991Pathological.pdf},
file = {Elston1991Pathological.pdf:Elston1991Pathological.pdf:PDF},
institution = {Department of Histopathology, City Hospital, Nottingham, UK.},
keywords = {csbcbook, csbcbook-ch3},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pmid = {1757079},
timestamp = {2009.10.18},
url = {http://dx.doi.org/10.1111/j.1365-2559.1991.tb00229.x}
}
@book{Emsley1998Elements,
title = {The {E}lements (third edition)},
publisher = {Oxford University Press},
year = {1998},
author = {John Emsley},
owner = {mahe},
timestamp = {2006.02.03}
}
@article{Engelhardt2005Protein,
author = {Engelhardt, B. E. and Jordan, M. I. and Muratore, K. E. and Brenner,
S. E.},
title = {Protein {M}olecular {F}unction {P}rediction by {B}ayesian {P}hylogenomics.},
journal = {P{L}o{S} {C}omput. {B}iol.},
year = {2005},
volume = {1},
pages = {e45},
number = {5},
month = {Oct},
abstract = {We present a statistical graphical model to infer specific molecular
function for unannotated protein sequences using homology. {B}ased
on phylogenomic principles, {SIFTER} ({S}tatistical {I}nference of
{F}unction {T}hrough {E}volutionary {R}elationships) accurately predicts
molecular function for members of a protein family given a reconciled
phylogeny and available function annotations, even when the data
are sparse or noisy. {O}ur method produced specific and consistent
molecular function predictions across 100 {P}fam families in comparison
to the {G}ene {O}ntology annotation database, {BLAST}, {GO}tcha,
and {O}rthostrapper. {W}e performed a more detailed exploration of
functional predictions on the adenosine-5'-monophosphate/adenosine
deaminase family and the lactate/malate dehydrogenase family, in
the former case comparing the predictions against a gold standard
set of published functional characterizations. {G}iven function annotations
for 3\% of the proteins in the deaminase family, {SIFTER} achieves
96\% accuracy in predicting molecular function for experimentally
characterized proteins as reported in the literature. {T}he accuracy
of {SIFTER} on this dataset is a significant improvement over other
currently available methods such as {BLAST} (75\%), {G}ene{Q}uiz
(64\%), {GO}tcha (89\%), and {O}rthostrapper (11\%). {W}e also experimentally
characterized the adenosine deaminase from {P}lasmodium falciparum,
confirming {SIFTER}'s prediction. {T}he results illustrate the predictive
power of exploiting a statistical model of function evolution in
phylogenomic problems. {A} software implementation of {SIFTER} is
available from the authors.},
doi = {10.1371/journal.pcbi.0010045},
pdf = {../local/Engelhardt2005Protein.pdf},
file = {Engelhardt2005Protein.pdf:local/Engelhardt2005Protein.pdf:PDF},
keywords = {biogm},
owner = {vert},
pmid = {16217548},
timestamp = {2006.01.18},
url = {http://dx.doi.org/10.1371/journal.pcbi.0010045}
}
@article{Eroes2004Comparison,
author = {D. Er\"os and G. K\'eri and I. K\"ovesdi and C. Sz\'antai-Kis and
G. M\'esz\'aros and L. Orfi},
title = {{C}omparison of predictive ability of water solubility {QSPR} models
generated by {MLR}, {PLS} and {ANN} methods.},
journal = {Mini Rev Med Chem},
year = {2004},
volume = {4},
pages = {167--177},
number = {2},
month = {Feb},
abstract = {ADME/Tox computational screening is one of the most hot topics of
modern drug research. About one half of the potential drug candidates
fail because of poor ADME/Tox properties. Since the experimental
determination of water solubility is time-consuming also, reliable
computational predictions are needed for the pre-selection of acceptable
"drug-like" compounds from diverse combinatorial libraries. Recently
many successful attempts were made for predicting water solubility
of compounds. A comprehensive review of previously developed water
solubility calculation methods is presented here, followed by the
description of the solubility prediction method designed and used
in our laboratory. We have selected carefully 1381 compounds from
scientific publications in a unified database and used this dataset
in the calculations. The externally validated models were based on
calculated descriptors only. The aim of model optimization was to
improve repeated evaluations statistics of the predictions and effective
descriptor scoring functions were used to facilitate quick generation
of multiple linear regression analysis (MLR), partial least squares
method (PLS) and artificial neural network (ANN) models with optimal
predicting ability. Standard error of prediction of the best model
generated with ANN (with 39-7-1 network structure) was 0.72 in logS
units while the cross validated squared correlation coefficient (Q(2))
was better than 0.85. These values give a good chance for successful
pre-selection of screening compounds from virtual libraries, based
on the predicted water solubility.},
keywords = {Chemical, Chemistry, Comparative Study, Cytochrome P-450 Enzyme System,
Estradiol, Least-Squares Analysis, Ligands, Linear Models, Models,
Molecular, Naphthalenes, Neural Networks (Computer), Non-U.S. Gov't,
Physical, Quantitative Structure-Activity Relationship, Reproducibility
of Results, Research Support, Solubility, Spectrum Analysis, Statistical,
Water, 14965289},
owner = {mahe},
pmid = {14965289},
timestamp = {2006.09.07}
}
@article{Erhan2006Collaborative,
author = {Erhan, D. and L'heureux, P.-J. and Yue, S. Y. and Bengio, Y.},
title = {Collaborative filtering on a family of biological targets.},
journal = {J. Chem. Inf. Model.},
year = {2006},
volume = {46},
pages = {626--635},
number = {2},
abstract = {Building a QSAR model of a new biological target for which few screening
data are available is a statistical challenge. However, the new target
may be part of a bigger family, for which we have more screening
data. Collaborative filtering or, more generally, multi-task learning,
is a machine learning approach that improves the generalization performance
of an algorithm by using information from related tasks as an inductive
bias. We use collaborative filtering techniques for building predictive
models that link multiple targets to multiple examples. The more
commonalities between the targets, the better the multi-target model
that can be built. We show an example of a multi-target neural network
that can use family information to produce a predictive model of
an undersampled target. We evaluate JRank, a kernel-based method
designed for collaborative filtering. We show their performance on
compound prioritization for an HTS campaign and the underlying shared
representation between targets. JRank outperformed the neural network
both in the single- and multi-target models.},
doi = {10.1021/ci050367t},
pdf = {../local/Erhan2006Collaborative.pdf},
file = {Erhan2006Collaborative.pdf:Erhan2006Collaborative.pdf:PDF},
keywords = {chemogenomics},
owner = {laurent},
pmid = {16562992},
timestamp = {2007.10.11},
url = {http://dx.doi.org/10.1021/ci050367t}
}
@article{Ernst2010Discovery,
author = {Ernst, J. and Kellis, M.},
title = {Discovery and characterization of chromatin states for systematic
annotation of the human genome.},
journal = {Nat. Biotechnol.},
year = {2010},
volume = {28},
pages = {817--825},
number = {8},
month = {Aug},
abstract = {A plethora of epigenetic modifications have been described in the
human genome and shown to play diverse roles in gene regulation,
cellular differentiation and the onset of disease. Although individual
modifications have been linked to the activity levels of various
genetic functional elements, their combinatorial patterns are still
unresolved and their potential for systematic de novo genome annotation
remains untapped. Here, we use a multivariate Hidden Markov Model
to reveal 'chromatin states' in human T cells, based on recurrent
and spatially coherent combinations of chromatin marks. We define
51 distinct chromatin states, including promoter-associated, transcription-associated,
active intergenic, large-scale repressed and repeat-associated states.
Each chromatin state shows specific enrichments in functional annotations,
sequence motifs and specific experimentally observed characteristics,
suggesting distinct biological roles. This approach provides a complementary
functional annotation of the human genome that reveals the genome-wide
locations of diverse classes of epigenetic function.},
doi = {10.1038/nbt.1662},
institution = {MIT Computer Science and Artificial Intelligence Laboratory, Cambridge,
Massachusetts, USA.},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {nbt.1662},
pmid = {20657582},
timestamp = {2010.11.23},
url = {http://dx.doi.org/10.1038/nbt.1662}
}
@inproceedings{Eskin2000Protein,
author = {Eskin, E. and Grundy, W.N. and Singer, Y.},
title = {Protein family classification using sparse {M}arkov transducers},
booktitle = {Proceedings of the {E}ighth {I}nternational {C}onference on {I}ntelligent
{S}ystems for {M}olecular {B}iology ({ISMB} 2000)},
year = {2000},
pages = {134-145},
owner = {vert}
}
@article{Esquela-Kerscher2006Oncomirs,
author = {Esquela-Kerscher, A. and Slack, F. J.},
title = {Oncomirs - micro{RNA}s with a role in cancer.},
journal = {Nat. Rev. Cancer},
year = {2006},
volume = {6},
pages = {259--269},
number = {4},
month = {Apr},
abstract = {MicroRNAs (miRNAs) are an abundant class of small non-protein-coding
RNAs that function as negative gene regulators. They regulate diverse
biological processes, and bioinformatic data indicates that each
miRNA can control hundreds of gene targets, underscoring the potential
influence of miRNAs on almost every genetic pathway. Recent evidence
has shown that miRNA mutations or mis-expression correlate with various
human cancers and indicates that miRNAs can function as tumour suppressors
and oncogenes. miRNAs have been shown to repress the expression of
important cancer-related genes and might prove useful in the diagnosis
and treatment of cancer.},
doi = {10.1038/nrc1840},
pdf = {../local/Esquela-Kerscher2006Oncomirs.pdf},
file = {Esquela-Kerscher2006Oncomirs.pdf:Esquela-Kerscher2006Oncomirs.pdf:PDF},
institution = { Developmental Biology, 266 Whitney Avenue, New Haven, Connecticut
06520, USA.},
keywords = {csbcbook},
owner = {jp},
pii = {nrc1840},
pmid = {16557279},
timestamp = {2009.10.09},
url = {http://dx.doi.org/10.1038/nrc1840}
}
@article{Esteller2008Epigenetics,
author = {Esteller, M.},
title = {Epigenetics in cancer},
journal = {N. Engl. J. Med.},
year = {2008},
volume = {358},
pages = {1148--1159},
number = {11},
month = {Mar},
doi = {10.1056/NEJMra072067},
pdf = {../local/Esteller2008Epigenetics.pdf},
file = {Esteller2008Epigenetics.pdf:Esteller2008Epigenetics.pdf:PDF},
institution = {Cancer Epigenetics Laboratory, Spanish National Cancer Research Center,
Madrid, Spain. mesteller@cnio.es},
keywords = {csbcbook},
owner = {jp},
pii = {358/11/1148},
pmid = {18337604},
timestamp = {2009.10.09},
url = {http://dx.doi.org/10.1056/NEJMra072067}
}
@article{Esteller2007Cancer,
author = {Esteller, M.},
title = {Cancer epigenomics: {DNA} methylomes and histone-modification maps},
journal = {Nat. Rev. Genet.},
year = {2007},
volume = {8},
pages = {286--298},
number = {4},
month = {Apr},
abstract = {An altered pattern of epigenetic modifications is central to many
common human diseases, including cancer. Many studies have explored
the mosaic patterns of DNA methylation and histone modification in
cancer cells on a gene-by-gene basis; among their results has been
the seminal finding of transcriptional silencing of tumour-suppressor
genes by CpG-island-promoter hypermethylation. However, recent technological
advances are now allowing cancer epigenetics to be studied genome-wide
- an approach that has already begun to provide both biological insight
and new avenues for translational research. It is time to 'upgrade'
cancer epigenetics research and put together an ambitious plan to
tackle the many unanswered questions in this field using epigenomics
approaches.},
doi = {10.1038/nrg2005},
pdf = {../local/Esteller2007Cancer.pdf},
file = {Esteller2007Cancer.pdf:Esteller2007Cancer.pdf:PDF},
institution = {Cancer Epigenetics Laboratory, Spanish National Cancer Centre (CNIO),
Melchor Fernandez Almagro 3, 28029 Madrid, Spain. mesteller@cnio.es},
keywords = {csbcbook},
owner = {jp},
pii = {nrg2005},
pmid = {17339880},
timestamp = {2009.10.09},
url = {http://dx.doi.org/10.1038/nrg2005}
}
@phdthesis{Estrach2012Scattering,
author = {Estrach, J. B.},
title = {Scattering representations for recognition},
school = {Ecole Polytechnique},
year = {2012},
pdf = {../local/Estrach2012Scattering.pdf},
file = {Estrach2012Scattering.pdf:Estrach2012Scattering.pdf:PDF},
owner = {jp},
timestamp = {2013.03.29}
}
@article{Eulalio2008Getting,
author = {Ana Eulalio and Eric Huntzinger and Elisa Izaurralde},
title = {Getting to the root of miRNA-mediated gene silencing.},
journal = {Cell},
year = {2008},
volume = {132},
pages = {9--14},
number = {1},
month = {Jan},
abstract = {MicroRNAs are approximately 22 nucleotide-long RNAs that silence gene
expression posttranscriptionally by binding to the 3' untranslated
regions of target mRNAs. Although much is known about their biogenesis
and biological functions, the mechanisms allowing miRNAs to silence
gene expression in animal cells are still under debate. Here, we
discuss current models for miRNA-mediated gene silencing and formulate
a hypothesis to reconcile differences.},
doi = {10.1016/j.cell.2007.12.024},
institution = {Max-Planck-Institute for Developmental Biology, Spemannstrasse 35,
D-72076 Tübingen, Germany.},
keywords = {sirna},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {S0092-8674(07)01697-2},
pmid = {18191211},
timestamp = {2009.10.28},
url = {http://dx.doi.org/10.1016/j.cell.2007.12.024}
}
@article{Evers2005Structure-based,
author = {Evers, A. and Klabunde, T.},
title = {Structure-based drug discovery using {GPCR} homology modeling: successful
virtual screening for antagonists of the {alpha1A} adrenergic receptor.},
journal = {J. Med. Chem.},
year = {2005},
volume = {48},
pages = {1088--1097},
number = {4},
month = {Feb},
abstract = {In this paper, we describe homology modeling of the alpha1A receptor
based on the X-ray structure of bovine rhodopsin. The protein model
has been generated by applying ligand-supported homology modeling,
using mutational and ligand SAR data to guide the protein modeling
procedure. We performed a virtual screening of the company's compound
collection to test how well this model is suited to identify alpha1A
antagonists. We applied a hierarchical virtual screening procedure
guided by 2D filters and three-dimensional pharmacophore models.
The ca. 23,000 filtered compounds were docked into the alpha1A homology
model with GOLD and scored with PMF. From the top-ranked compounds,
80 diverse compounds were tested in a radioligand displacement assay.
37 compounds revealed K(i) values better than 10 microM; the most
active compound binds with 1.4 nM to the alpha1A receptor. Our findings
suggest that rhodopsin-based homology models may be used as the structural
basis for GPCR lead finding and compound optimization.},
doi = {10.1021/jm0491804},
keywords = {chemogenomics},
owner = {laurent},
pmid = {15715476},
timestamp = {2008.01.16},
url = {http://dx.doi.org/10.1021/jm0491804}
}
@article{Evgeniou2005Learning,
author = {Evgeniou, T. and Micchelli, C. and Pontil, M.},
title = {Learning multiple tasks with kernel methods},
journal = {J. Mach. Learn. Res.},
year = {2005},
volume = {6},
pages = {615-637},
abstract = {We study the problem of learning many related tasks simultaneously
using kernel methods and
regularization. The standard single-task kernel methods, such as support
vector machines and
regularization networks, are extended to the case of multi-task learning.
Our analysis shows that
the problem of estimating many task functions with regularization
can be cast as a single task
learning problem if a family of multi-task kernel functions we define
is used. These kernels model
relations among the tasks and are derived from a novel form of regularizers.
Specific kernels that
can be used for multi-task learning are provided and experimentally
tested on two real data sets.
In agreement with past empirical work on multi-task learning, the
experiments show that learning
multiple related tasks simultaneously using the proposed approach
can significantly outperform
standard single-task learning particularly when there are many related
tasks but few data per task.},
timestamp = {2006.05.18},
url = {http://jmlr.csail.mit.edu/papers/volume6/evgeniou05a}
}
@inproceedings{Evgeniou2004Regularized,
author = {Evgeniou, Theodoros and Pontil, Massimiliano},
title = {Regularized multi--task learning},
booktitle = {KDD '04: Proceedings of the tenth ACM SIGKDD international conference
on Knowledge discovery and data mining},
year = {2004},
pages = {109--117},
address = {New York, NY, USA},
publisher = {ACM},
doi = {http://doi.acm.org/10.1145/1014052.1014067},
isbn = {1-58113-888-1},
location = {Seattle, WA, USA}
}
@article{Evgeniou2000Regularization,
author = {Evgeniou, T. and Pontil, M. and Poggio, T.},
title = {Regularization {N}etworks and {S}upport {V}ector {M}achines},
journal = {Adv. {C}omput. {M}ath.},
year = {2000},
volume = {13},
pages = {1--50},
number = {1},
doi = {10.1023/A:1018946025316},
pdf = {../local/Evgeniou2000Regularization.pdf},
file = {Evgeniou2000Regularization.pdf:local/Evgeniou2000Regularization.pdf:PDF},
url = {http://dx.doi.org/10.1023/A:1018946025316}
}
@article{Fabbri2008MicroRNAs,
author = {Fabbri, M. and Croce, C. M. and Calin, G. A.},
title = {{MicroRNAs}},
journal = {Cancer J.},
year = {2008},
volume = {14},
pages = {1--6},
number = {1},
abstract = {MicroRNAs (miRNAs) are small, noncoding RNAs with regulatory functions,
which play an important role in many human diseases, including cancer.
An emerging number of studies show that miRNAs can act either as
oncogenes or as tumor suppressor genes or sometimes as both. Germline,
somatic mutations and polymorphisms can contribute to cancer predisposition.
miRNA expression levels have diagnostic and prognostic implications,
and their roles as anticancer therapeutic agents is promising and
currently under investigation.},
doi = {10.1097/PPO.0b013e318164145e},
institution = {Human Cancer Genetics, Molecular Virology, Immunology and Medical
Genetics, Ohio State University, Columbus, OH, USA.},
keywords = {csbcbook},
owner = {jp},
pii = {00130404-200801000-00001},
pmid = {18303474},
timestamp = {2009.10.09},
url = {http://dx.doi.org/10.1097/PPO.0b013e318164145e}
}
@article{Theres2006Structural,
author = {Theres Fagerberg and Jean-Charles Cerottini and Olivier Michielin},
title = {{S}tructural prediction of peptides bound to {MHC} class {I}.},
journal = {J. Mol. Biol.},
year = {2006},
volume = {356},
pages = {521--546},
number = {2},
month = {Feb},
abstract = {An ab initio structure prediction approach adapted to the peptide-major
histocompatibility complex (MHC) class I system is presented. Based
on structure comparisons of a large set of peptide-MHC class I complexes,
a molecular dynamics protocol is proposed using simulated annealing
(SA) cycles to sample the conformational space of the peptide in
its fixed MHC environment. A set of 14 peptide-human leukocyte antigen
(HLA) A0201 and 27 peptide-non-HLA A0201 complexes for which X-ray
structures are available is used to test the accuracy of the prediction
method. For each complex, 1000 peptide conformers are obtained from
the SA sampling. A graph theory clustering algorithm based on heavy
atom root-mean-square deviation (RMSD) values is applied to the sampled
conformers. The clusters are ranked using cluster size, mean effective
or conformational free energies, with solvation free energies computed
using Generalized Born MV 2 (GB-MV2) and Poisson-Boltzmann (PB) continuum
models. The final conformation is chosen as the center of the best-ranked
cluster. With conformational free energies, the overall prediction
success is 83\% using a 1.00 Angstroms crystal RMSD criterion for
main-chain atoms, and 76\% using a 1.50 Angstroms RMSD criterion
for heavy atoms. The prediction success is even higher for the set
of 14 peptide-HLA A0201 complexes: 100\% of the peptides have main-chain
RMSD values < or =1.00 Angstroms and 93\% of the peptides have heavy
atom RMSD values < or =1.50 Angstroms. This structure prediction
method can be applied to complexes of natural or modified antigenic
peptides in their MHC environment with the aim to perform rational
structure-based optimizations of tumor vaccines.},
doi = {10.1016/j.jmb.2005.11.059},
keywords = {, Algorithms, Amino Acid Sequence, Antibodies, Artificial Intelligence,
Automated, Binding Sites, Chemical, Computer Simulation, Databases,
Epitope Mapping, Genes, HLA-A Antigens, HLA-DQ Antigens, Histocompatibility
Antigens Class I, Humans, Immunoassay, Immunological, MHC Class I,
Models, Molecular, Molecular Sequence Data, Pattern Recognition,
Peptides, Protein, Protein Binding, Protein Conformation, Protein
Interaction Mapping, Protein Structure, Sequence Alignment, Sequence
Analysis, Software, Tertiary, Water, 16368108},
pii = {S0022-2836(05)01462-2},
pmid = {16368108},
timestamp = {2007.01.25},
url = {http://dx.doi.org/10.1016/j.jmb.2005.11.059}
}
@article{Faith2008Many,
author = {Faith, J.J. and Driscoll, M.E. and Fusaro, V.A. and Cosgrove, E.J.
and Hayete, B. and Juhn, F.S. and Schneider, S.J. and Gardner, T.S.},
title = {Many Microbe Microarrays Database: uniformly normalized Affymetrix
compendia with structured experimental metadata.},
journal = {Nucleic Acids Res.},
year = {2008},
volume = {36},
pages = {D866--D870},
number = {Database issue},
month = {Jan},
abstract = {Many Microbe Microarrays Database (M3D) is designed to facilitate
the analysis and visualization of expression data in compendia compiled
from multiple laboratories. M3D contains over a thousand Affymetrix
microarrays for Escherichia coli, Saccharomyces cerevisiae and Shewanella
oneidensis. The expression data is uniformly normalized to make the
data generated by different laboratories and researchers more comparable.
To facilitate computational analyses, M3D provides raw data (CEL
file) and normalized data downloads of each compendium. In addition,
web-based construction, visualization and download of custom datasets
are provided to facilitate efficient interrogation of the compendium
for more focused analyses. The experimental condition metadata in
M3D is human curated with each chemical and growth attribute stored
as a structured and computable set of experimental features with
consistent naming conventions and units. All versions of the normalized
compendia constructed for each species are maintained and accessible
in perpetuity to facilitate the future interpretation and comparison
of results published on M3D data. M3D is accessible at http://m3d.bu.edu/.},
doi = {10.1093/nar/gkm815},
pdf = {../local/Faith2008Many.pdf},
file = {Faith2008Many.pdf:Faith2008Many.pdf:PDF},
institution = {Program in Bioinformatics, Boston University, 24 Cummington St. and
Department of Biomedical Engineering, Boston University, 44 Cummington
St., Boston, Massachusetts, 02215, USA.},
owner = {mordelet},
pii = {gkm815},
pmid = {17932051},
timestamp = {2010.07.16},
url = {http://dx.doi.org/10.1093/nar/gkm815}
}
@article{Faith2007Large-scale,
author = {Faith, J. J. and Hayete, B. and Thaden, J. T. and Mogno, I. and Wierzbowski,
J. and Cottarel, G. and Kasif, S. and Collins, J. J. and Gardner,
T. S.},
title = {Large-scale mapping and validation of {E}scherichia coli transcriptional
regulation from a compendium of expression profiles},
journal = {PLoS Biol.},
year = {2007},
volume = {5},
pages = {e8},
number = {1},
month = {Jan},
abstract = {Machine learning approaches offer the potential to systematically
identify transcriptional regulatory interactions from a compendium
of microarray expression profiles. However, experimental validation
of the performance of these methods at the genome scale has remained
elusive. Here we assess the global performance of four existing classes
of inference algorithms using 445 Escherichia coli Affymetrix arrays
and 3,216 known E. coli regulatory interactions from RegulonDB. We
also developed and applied the context likelihood of relatedness
(CLR) algorithm, a novel extension of the relevance networks class
of algorithms. CLR demonstrates an average precision gain of 36\%
relative to the next-best performing algorithm. At a 60\% true positive
rate, CLR identifies 1,079 regulatory interactions, of which 338
were in the previously known network and 741 were novel predictions.
We tested the predicted interactions for three transcription factors
with chromatin immunoprecipitation, confirming 21 novel interactions
and verifying our RegulonDB-based performance estimates. CLR also
identified a regulatory link providing central metabolic control
of iron transport, which we confirmed with real-time quantitative
PCR. The compendium of expression data compiled in this study, coupled
with RegulonDB, provides a valuable model system for further improvement
of network inference algorithms using experimental data.},
doi = {10.1371/journal.pbio.0050008},
pdf = {../local/Faith2007Large-scale.pdf},
file = {Faith2007Large-scale.pdf:Faith2007Large-scale.pdf:PDF},
pii = {06-PLBI-RA-0740R3},
pmid = {17214507},
timestamp = {2008.02.03},
url = {http://dx.doi.org/10.1371/journal.pbio.0050008}
}
@article{Faloutsos1999On,
author = {Faloutsos, M. and Faloutsos, P. and Faloutsos, C.},
title = {On power-law relationships of the internet topology},
journal = {Comput. {C}omm. {R}ev.},
year = {1999},
volume = {29},
pages = {251--262},
pdf = {../local/falo99.pdf},
file = {falo99.pdf:local/falo99.pdf:PDF},
subject = {compnet},
url = {http://www.acm.org/sigcomm/sigcomm99/papers/session7-2.html}
}
@inproceedings{Fan2001Stock,
author = {Fan, A. and Palaniswami, M.},
title = {Stock selection using support vector machines},
booktitle = {Proc. Int. Joint Conf. Neural Networks IJCNN '01},
year = {2001},
volume = {3},
pages = {1793--1798},
abstract = {We used the support vector machines (SVM) in a classification approach
to `beat the market'. Given the fundamental accounting and price
information of stocks trading on the Australian Stock Exchange, we
attempt to use SVM to identify stocks that are likely to outperform
the market by having exceptional returns. The equally weighted portfolio
formed by the stocks selected by SVM has a total return of 208% over
a five years period, significantly outperformed the benchmark of
71%. We also give a new perspective with a class sensitivity tradeoff,
whereby the output of SVM is interpreted as a probability measure
and ranked, such that the stocks selected can be fixed to the top
25%},
doi = {10.1109/IJCNN.2001.938434},
pdf = {../local/Fan2001Stock.pdf},
file = {Fan2001Stock.pdf:Fan2001Stock.pdf:PDF},
owner = {jp},
timestamp = {2011.04.08}
}
@article{Fan2006Concordance,
author = {Fan, C. and Oh, D.S. and Wessels, L. and Weigelt, B. and Nuyten,
D.S.A. and Nobel, A.B. and van't Veer, L.J. and Perou, C.M.},
title = {Concordance among gene-expression-based predictors for breast cancer},
journal = {N. Engl. J. Med.},
year = {2006},
volume = {355},
pages = {560},
number = {6},
doi = {10.1056/NEJMoa052933},
pdf = {../local/Fan2006Concordance.pdf},
file = {Fan2006Concordance.pdf:Fan2006Concordance.pdf:PDF},
keywords = {breastcancer, microarray},
owner = {jp},
publisher = {Mass Med Soc},
timestamp = {2011.01.13},
url = {http://dx.doi.org/10.1056/NEJMoa052933}
}
@article{Fan2006Illumina,
author = {Jian-Bing Fan and Kevin L Gunderson and Marina Bibikova and Joanne
M Yeakley and Jing Chen and Eliza Wickham Garcia and Lori L Lebruska
and Marc Laurent and Richard Shen and David Barker},
title = {Illumina universal bead arrays.},
journal = {Methods Enzymol},
year = {2006},
volume = {410},
pages = {57--73},
abstract = {This chapter describes an accurate, scalable, and flexible microarray
technology. It includes a miniaturized array platform where each
individual feature is quality controlled and a versatile assay that
can be adapted for various genetic analyses, such as single nucleotide
polymorphism genotyping, DNA methylation detection, and gene expression
profiling. This chapter describes the concept of the BeadArray technology,
two different Array of Arrays formats, the assay scheme and protocol,
the performance of the system, and its use in large-scale genetic,
epigenetic, and expression studies.},
doi = {10.1016/S0076-6879(06)10003-8},
institution = {Illumina, Inc., San Diego, California, USA.},
keywords = {Animals; Humans; Microspheres; Oligonucleotide Array Sequence Analysis,
instrumentation/methods},
language = {eng},
medline-pst = {ppublish},
owner = {philippe},
pii = {S0076-6879(06)10003-8},
pmid = {16938546},
timestamp = {2010.08.04},
url = {http://dx.doi.org/10.1016/S0076-6879(06)10003-8}
}
@article{Fang2011Design,
author = {Zhide Fang and Xiangqin Cui},
title = {Design and validation issues in RNA-seq experiments.},
journal = {Brief Bioinform},
year = {2011},
volume = {12},
pages = {280--287},
number = {3},
month = {May},
abstract = {The next-generation sequencing technologies are being rapidly applied
in biological research. Tens of millions of short sequences generated
in a single experiment provide us enormous information on genome
composition, genetic variants, gene expression levels and protein
binding sites depending on the applications. Various methods are
being developed for analyzing the data generated by these technologies.
However, the relevant experimental design issues have rarely been
discussed. In this review, we use RNA-seq as an example to bring
this topic into focus and to discuss experimental design and validation
issues pertaining to next-generation sequencing in the quantification
of transcripts.},
doi = {10.1093/bib/bbr004},
institution = {Assistant Professor, Department of Biostatistics, Section on Statistical
Genetics, University of Alabama at Birmingham, 327 Ryals Public Health
Building, 1665 University BLVD, Birmingham, AL 35294, USA. xcui@uab.edu.},
language = {eng},
medline-pst = {ppublish},
owner = {phupe},
pii = {bbr004},
pmid = {21498551},
timestamp = {2011.06.01},
url = {http://dx.doi.org/10.1093/bib/bbr004}
}
@article{Farago1993Strong,
author = {Farago, A. and Lugosi, G.},
title = {Strong universal consistency of neural network classifiers},
journal = {I{EEE} {T}rans. {I}nform. {T}heory},
year = {1993},
volume = {39},
pages = {1146-1151},
number = {4},
month = {Jul},
abstract = {In statistical pattern recognition, a classifier is called universally
consistent if its error probability converges to the {B}ayes-risk
as the size of the training data grows for all possible distributions
of the random variable pair of the observation vector and its class.
{I}t is proven that if a one-layered neural network with properly
chosen number of nodes is trained to minimize the empirical risk
on the training data, then a universally consistent classifier results.
{I}t is shown that the exponent in the rate of convergence does not
depend on the dimension if certain smoothness conditions on the distribution
are satisfied. {T}hat is, this class of universally consistent classifiers
does not suffer from the curse of dimensionality. {A} training algorithm
is presented that finds the optimal set of parameters in polynomial
time if the number of nodes and the space dimension is fixed and
the amount of training data grows },
pdf = {../local/Farago1993Strong.pdf},
file = {Farago1993Strong.pdf:local/Farago1993Strong.pdf:PDF},
owner = {vert}
}
@article{Fare2003Effects,
author = {Thomas L Fare and Ernest M Coffey and Hongyue Dai and Yudong D He
and Deborah A Kessler and Kristopher A Kilian and John E Koch and
Eric LeProust and Matthew J Marton and Michael R Meyer and Roland
B Stoughton and George Y Tokiwa and Yanqun Wang},
title = {Effects of atmospheric ozone on microarray data quality.},
journal = {Anal Chem},
year = {2003},
volume = {75},
pages = {4672--4675},
number = {17},
month = {Sep},
abstract = {A data anomaly was observed that affected the uniformity and reproducibility
of fluorescent signal across DNA microarrays. Results from experimental
sets designed to identify potential causes (from microarray production
to array scanning) indicated that the anomaly was linked to a batch
process; further work allowed us to localize the effect to the posthybridization
array stringency washes. Ozone levels were monitored and highly correlated
with the batch effect. Controlled exposures of microarrays to ozone
confirmed this factor as the root cause, and we present data that
show susceptibility of a class of cyanine dyes (e.g., Cy5, Alexa
647) to ozone levels as low as 5-10 ppb for periods as short as 10-30
s. Other cyanine dyes (e.g., Cy3, Alexa 555) were not significantly
affected until higher ozone levels (> 100 ppb). To address this environmental
effect, laboratory ozone levels should be kept below 2 ppb (e.g.,
with filters in HVAC) to achieve high quality microarray data.},
institution = {Rosetta Inpharmatics LLC, 12040 115th Avenue NE, Kirkland, Washington
98034, USA.},
keywords = {Artifacts; Atmosphere, chemistry; Carbocyanines, chemistry; Desiccation;
Fluorescence; Oligonucleotide Array Sequence Analysis, instrumentation/standards;
Ozone, analysis/chemistry; Quality Control; Reproducibility of Results},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pmid = {14632079},
timestamp = {2012.02.29}
}
@article{Farid2006New,
author = {Farid, R. and Day, T. and Friesner, R. A. and Pearlstein, R. A.},
title = {{N}ew insights about {HERG} blockade obtained from protein modeling,
potential energy mapping, and docking studies.},
journal = {Bioorg. Med. Chem.},
year = {2006},
volume = {14},
pages = {3160--3173},
number = {9},
month = {May},
abstract = {We created a homology model of the homo-tetrameric pore domain of
HERG using the crystal structure of the bacterial potassium channel,
KvAP, as a template. We docked a set of known blockers with well-characterized
effects on channel function into the lumen of the pore between the
selectivity filter and extracellular entrance using a novel docking
and refinement procedure incorporating Glide and Prime. Key aromatic
groups of the blockers are predicted to form multiple simultaneous
ring stacking and hydrophobic interactions among the eight aromatic
residues lining the pore. Furthermore, each blocker can achieve these
interactions via multiple docking configurations. To further interpret
the docking results, we mapped hydrophobic and hydrophilic potentials
within the lumen of each refined docked complex. Hydrophilic iso-potential
contours define a 'propeller-shaped' volume at the selectivity filter
entrance. Hydrophobic contours define a hollow 'crown-shaped' volume
located above the 'propeller', whose hydrophobic 'rim' extends along
the pore axis between Tyr652 and Phe656. Blockers adopt conformations/binding
orientations that closely mimic the shapes and properties of these
contours. Blocker basic groups are localized in the hydrophilic 'propeller',
forming electrostatic interactions with Ser624 rather than a generally
accepted pi-cation interaction with Tyr652. Terfenadine, cisapride,
sertindole, ibutilide, and clofilium adopt similar docked poses,
in which their N-substituents bridge radially across the hollow interior
of the 'crown' (analogous to the hub and spokes of a wheel), and
project aromatic/hydrophobic portions into the hydrophobic 'rim'.
MK-499 docks with its longitudinal axis parallel to the axis of the
pore and 'crown', and its hydrophobic groups buried within the hydrophobic
'rim'.},
doi = {10.1016/j.bmc.2005.12.032},
pdf = {../local/Farid2006New.pdf},
file = {Farid2006New.pdf:local/Farid2006New.pdf:PDF},
keywords = {chemoinformatics herg},
pii = {S0968-0896(05)01214-9},
pmid = {16413785},
timestamp = {2007.02.03},
url = {http://dx.doi.org/10.1016/j.bmc.2005.12.032}
}
@article{Faugeras2004Variational,
author = {Olivier Faugeras and Geoffray Adde and Guillaume Charpiat and Christophe
Chefd'hotel and Maureen Clerc and Thomas Deneux and Rachid Deriche
and Gerardo Hermosillo and Renaud Keriven and Pierre Kornprobst and
Jan Kybic and Christophe Lenglet and Lucero Lopez-Perez and Théo
Papadopoulo and Jean-Philippe Pons and Florent Segonne and Bertrand
Thirion and David Tschumperlé and Thierry Viéville and Nicolas
Wotawa},
title = {Variational, geometric, and statistical methods for modeling brain
anatomy and function.},
journal = {Neuroimage},
year = {2004},
volume = {23 Suppl 1},
pages = {S46-55},
abstract = {We survey the recent activities of the {O}dyssée {L}aboratory in
the area of the application of mathematics to the design of models
for studying brain anatomy and function. {W}e start with the problem
of reconstructing sources in {MEG} and {EEG}, and discuss the variational
approach we have developed for solving these inverse problems. {T}his
motivates the need for geometric models of the head. {W}e present
a method for automatically and accurately extracting surface meshes
of several tissues of the head from anatomical magnetic resonance
({MR}) images. {A}natomical connectivity can be extracted from diffusion
tensor magnetic resonance images but, in the current state of the
technology, it must be preceded by a robust estimation and regularization
stage. {W}e discuss our work based on variational principles and
show how the results can be used to track fibers in the white matter
({WM}) as geodesics in some {R}iemannian space. {W}e then go to the
statistical modeling of functional magnetic resonance imaging (f{MRI})
signals from the viewpoint of their decomposition in a pseudo-deterministic
and stochastic part that we then use to perform clustering of voxels
in a way that is inspired by the theory of support vector machines
and in a way that is grounded in information theory. {M}ultimodal
image matching is discussed next in the framework of image statistics
and partial differential equations ({PDE}s) with an eye on registering
f{MRI} to the anatomy. {T}he paper ends with a discussion of a new
theory of random shapes that may prove useful in building anatomical
and functional atlases.},
doi = {10.1016/j.neuroimage.2004.07.015},
pdf = {../local/Faugeras2004Variational.pdf},
file = {Faugeras2004Variational.pdf:local/Faugeras2004Variational.pdf:PDF},
keywords = {Adolescent, Adult, Algorithms, Anatomic, Bacterial Proteins, Brain,
Brain Mapping, Comparative Study, Computer Simulation, Computer-Assisted,
Diffusion Magnetic Resonance Imaging, Facial Asymmetry, Facial Expression,
Facial Paralysis, Female, Gene Expression Profiling, Gram-Negative
Bacteria, Gram-Positive Bacteria, Humans, Image Interpretation, Magnetoencephalography,
Male, Middle Aged, Models, Motion, Neural Pathways, Non-U.S. Gov't,
Photography, Protein, Proteome, Research Support, Retina, Sequence
Alignment, Sequence Analysis, Severity of Illness Index, Software,
Statistical, Subcellular Fractions, 15501100},
pii = {S1053-8119(04)00380-5},
url = {http://dx.doi.org/10.1016/j.neuroimage.2004.07.015}
}
@techreport{Fawcett2003ROC,
author = {Fawcett, T.},
title = {R{OC} graphs: notes and practical considerations for data mining
researchers},
institution = {HP Laboratories},
year = {2003},
number = {2003-4},
address = {Palo Alto, CA, USA},
owner = {vert},
timestamp = {2006.01.19}
}
@inproceedings{Fazel2001rank,
author = {M. Fazel and H. Hindi and S. Boyd},
title = {A rank minimization heuristic with application to minimum order system
approximation},
booktitle = {Proceedings of the 2001 American Control Conference},
year = {2001},
volume = {6},
pages = {4734--4739},
doi = {http://dx.doi.org/10.1109/ACC.2001.945730}
}
@article{Feder1986Maximum,
author = {Feder, M. },
title = {Maximum entropy as a special case of the minimum description length
criterion},
journal = {I{EEE} {T}rans. {I}nform. {T}heory},
year = {1986},
volume = {32},
pages = {847 - 849},
number = {6},
month = {Nov},
abstract = {The {M}aximum {E}ntropy ({ME}) and {M}aximum {L}ikelihood ({ML}) criteria
are the bases for two approaches to statistical inference problems.
{A} new criterion, called the {M}inimum {D}escription {L}ength ({MDL}),
has been recently introduced. {T}his criterion generalizes the {ML}
method so it can be applied to more general situations, e.g., when
the number of parameters is unknown. {I}t is shown that {ME} is also
a special case of the {MDL} criterion; maximizing the entropy subject
to some constraints on the underlying probability function is identical
to minimizing the code length required to represent all possible
i.i.d, realizations of the random variable such that the sample frequencies
(or histogram) satisfy those given constraints.},
pdf = {../local/Feder1986Maximum.pdf},
file = {Feder1986Maximum.pdf:local/Feder1986Maximum.pdf:PDF},
keywords = {information-theory},
owner = {vert}
}
@article{Feder1996Hierarchical,
author = {Feder, M. and Merhav, N. },
title = {Hierarchical universal coding},
journal = {I{EEE} {T}rans. {I}nform. {T}heory},
year = {1996},
volume = {42},
pages = {1354-1364},
number = {5},
month = {Sep},
abstract = {In an earlier paper, we proved a strong version of the redundancy-capacity
converse theorem of universal coding, stating that for ?most? sources
in a given class, the universal coding redundancy is essentially
lower-bounded by the capacity of the channel induced by this class.
{S}ince this result holds for general classes of sources, it extends
{R}issanen's (1986) strong converse theorem for parametric families.
{W}hile our earlier result has established strong optimality only
for mixture codes weighted by the capacity-achieving prior, our first
result herein extends this finding to a general prior. {F}or some
cases our technique also leads to a simplified proof of the above
mentioned strong converse theorem. {T}he major interest in this paper,
however, is in extending the theory of universal coding to hierarchical
structures of classes, where each class may have a different capacity.
{I}n this setting, one wishes to incur redundancy essentially as
small as that corresponding to the active class, and not the union
of classes. {O}ur main result is that the redundancy of a code based
on a two-stage mixture (first, within each class, and then over the
classes), is no worse than that of any other code for ?most? sources
of ?most? classes. {I}f, in addition, the classes can be efficiently
distinguished by a certain decision rule, then the best attainable
redundancy is given explicitly by the capacity of the active class
plus the normalized negative logarithm of the prior probability assigned
to this class. {T}hese results suggest some interesting guidelines
as for the choice of the prior. {W}e also discuss some examples with
a natural hierarchical partition into classes },
pdf = {../local/Feder1996Hierarchical.pdf},
file = {Feder1996Hierarchical.pdf:local/Feder1996Hierarchical.pdf:PDF},
keywords = {information-theory source-coding},
owner = {vert}
}
@article{Feder1994Relations,
author = {Feder, M. and Merhav, N.},
title = {Relations between entropy and error probability},
journal = {I{EEE} {T}rans. {I}nform. {T}heory},
year = {1994},
volume = {40},
pages = {259 - 266},
number = {1},
month = {Jan},
abstract = {The relation between the entropy of a discrete random variable and
the minimum attainable probability of error made in guessing its
value is examined. {W}hile {F}ano's inequality provides a tight lower
bound on the error probability in terms of the entropy, the present
authors derive a converse result-a tight upper bound on the minimal
error probability in terms of the entropy. {B}oth bounds are sharp,
and can draw a relation, as well, between the error probability for
the maximum a posteriori ({MAP}) rule, and the conditional entropy
(equivocation), which is a useful uncertainty measure in several
applications. {C}ombining this relation and the classical channel
coding theorem, the authors present a channel coding theorem for
the equivocation which, unlike the channel coding theorem for error
probability, is meaningful at all rates. {T}his theorem is proved
directly for {DMC}s, and from this proof it is further concluded
that for {R}⩾{C} the equivocation achieves its minimal value
of {R}-{C} at the rate of n1/2 where n is the block length},
pdf = {../local/Feder1994Relations.pdf},
file = {Feder1994Relations.pdf:local/Feder1994Relations.pdf:PDF},
keywords = {information-theory},
owner = {vert}
}
@article{Feder1992Universal,
author = {Feder, M. and Merhav, N. and Gutman, M. },
title = {Universal prediction of individual sequences},
journal = {I{EEE} {T}rans. {I}nform. {T}heory},
year = {1992},
volume = {38},
pages = {1258-1270},
number = {4},
month = {Jul},
abstract = {The problem of predicting the next outcome of an individual binary
sequence using finite memory is considered. {T}he finite-state predictability
of an infinite sequence is defined as the minimum fraction of prediction
errors that can be made by any finite-state ({FS}) predictor. {I}t
is proven that this {FS} predictability can be achieved by universal
sequential prediction schemes. {A}n efficient prediction procedure
based on the incremental parsing procedure of the {L}empel-{Z}iv
data compression algorithm is shown to achieve asymptotically the
{FS} predictability. {S}ome relations between compressibility and
predictability are discussed, and the predictability is proposed
as an additional measure of the complexity of a sequence },
pdf = {../local/Feder1992Universal.pdf},
file = {Feder1992Universal.pdf:local/Feder1992Universal.pdf:PDF},
keywords = {information-theory universal-coding},
owner = {vert}
}
@article{Feder1998Universal,
author = {Feder, M. and Singer, A.C.},
title = {Universal data compression and linear prediction},
journal = {Data {C}ompression {C}onference},
year = {1998},
abstract = {The relationship between prediction and data compression can be extended
to universal prediction schemes and universal data compression. {P}revious
work shows that minimizing the sequential squared prediction error
for individual sequences can be achieved using the same strategies
which minimize the sequential code length for data compression of
individual sequences. {D}efining a ?probability? as an exponential
function of sequential loss, results from universal data compression
can be used to develop universal linear prediction algorithms. {S}pecifically,
we present an algorithm for linear prediction of individual sequences
which is twice-universal, over parameters and model orders},
pdf = {../local/Feder1998Universal.pdf},
file = {Feder1998Universal.pdf:local/Feder1998Universal.pdf:PDF},
keywords = {information-theory},
owner = {vert}
}
@article{Felsenstein1981Evolutionary,
author = {J. Felsenstein},
title = {Evolutionary trees from {DNA} sequences: a maximum likelihood approach},
journal = {Journal of {M}olecular {E}volution},
year = {1981},
volume = {17},
pages = {368--376},
subject = {bio}
}
@article{Feng2005Boosting,
author = {Kai-Yan Feng and Yu-Dong Cai and Kuo-Chen Chou},
title = {Boosting classifier for predicting protein domain structural class.},
journal = {Biochem {B}iophys {R}es {C}ommun},
year = {2005},
volume = {334},
pages = {213-7},
number = {1},
month = {Aug},
abstract = {A novel classifier, the so-called "{L}ogit{B}oost" classifier, was
introduced to predict the structural class of a protein domain according
to its amino acid sequence. {L}ogit{B}oost is featured by introducing
a log-likelihood loss function to reduce the sensitivity to noise
and outliers, as well as by performing classification via combining
many weak classifiers together to build up a very strong and robust
classifier. {I}t was demonstrated thru jackknife cross-validation
tests that {L}ogit{B}oost outperformed other classifiers including
"support vector machine," a very powerful classifier widely used
in biological literatures. {I}t is anticipated that {L}ogit{B}oost
can also become a useful vehicle in classifying other attributes
of proteins according to their sequences, such as subcellular localization
and enzyme family class, among many others.},
doi = {10.1016/j.bbrc.2005.06.075},
pdf = {../local/Feng2005Boosting.pdf},
file = {Feng2005Boosting.pdf:local/Feng2005Boosting.pdf:PDF},
keywords = {Archaeal, Artificial Intelligence, Bacterial, Cytomegalovirus, Gene
Transfer, Genome, Genomics, Horizontal, Non-U.S. Gov't, Research
Support, Viral, 15993842},
pii = {S0006-291X(05)01299-4},
url = {http://dx.doi.org/10.1016/j.bbrc.2005.06.075}
}
@article{Ferea1999Systematic,
author = {Ferea, T. L. and Botstein, D. and Brown, P. O. and Rosenzweig, R.
F.},
title = {Systematic changes in gene expression patterns following adaptive
evolution in yeast},
journal = {Proc. {N}atl. {A}cad. {S}ci. {USA}},
year = {1999},
volume = {96},
pages = {9721--9726},
number = {17},
pdf = {../local/fere99.pdf},
file = {fere99.pdf:local/fere99.pdf:PDF},
subject = {microarray},
url = {http://www.pnas.org/cgi/reprint/96/17/9721.pdf}
}
@article{Ferrer2005Offline,
author = {Miguel A Ferrer and Jesús B Alonso and Carlos M Travieso},
title = {Offline geometric parameters for automatic signature verification
using fixed-point arithmetic.},
journal = {I{EEE} {T}rans {P}attern {A}nal {M}ach {I}ntell},
year = {2005},
volume = {27},
pages = {993-7},
number = {6},
month = {Jun},
abstract = {This paper presents a set of geometric signature features for offline
automatic signature verification based on the description of the
signature envelope and the interior stroke distribution in polar
and {C}artesian coordinates. {T}he features have been calculated
using 16 bits fixed-point arithmetic and tested with different classifiers,
such as hidden {M}arkov models, support vector machines, and {E}uclidean
distance classifier. {T}he experiments have shown promising results
in the task of discriminating random and simple forgeries.}
}
@article{Fiebitz2008High-throughput,
author = {Andrea Fiebitz and Lajos Nyarsik and Bernard Haendler and Yu-Hui
Hu and Florian Wagner and Sabine Thamm and Hans Lehrach and Michal
Janitz and Dominique Vanhecke},
title = {High-throughput mammalian two-hybrid screening for protein-protein
interactions using transfected cell arrays.},
journal = {BMC Genomics},
year = {2008},
volume = {9},
pages = {68},
abstract = {BACKGROUND: Most of the biological processes rely on the formation
of protein complexes. Investigation of protein-protein interactions
(PPI) is therefore essential for understanding of cellular functions.
It is advantageous to perform mammalian PPI analysis in mammalian
cells because the expressed proteins can then be subjected to essential
post-translational modifications. Until now mammalian two-hybrid
assays have been performed on individual gene scale. We here describe
a new and cost-effective method for the high-throughput detection
of protein-protein interactions in mammalian cells that combines
the advantages of mammalian two-hybrid systems with those of DNA
microarrays. RESULTS: In this cell array protein-protein interaction
assay (CAPPIA), mixtures of bait and prey expression plasmids together
with an auto-fluorescent reporter are immobilized on glass slides
in defined array formats. Adherent cells that grow on top of the
micro-array will become fluorescent only if the expressed proteins
interact and subsequently trans-activate the reporter. Using known
interaction partners and by screening 160 different combinations
of prey and bait proteins associated with the human androgen receptor
we demonstrate that this assay allows the quantitative detection
of specific protein interactions in different types of mammalian
cells and under the influence of different compounds. Moreover, different
strategies in respect to bait-prey combinations are presented. CONCLUSION:
We demonstrate that the CAPPIA assay allows the quantitative detection
of specific protein interactions in different types of mammalian
cells and under the influence of different compounds. The high number
of preys that can be tested per slide together with the flexibility
to interrogate any bait of interest and the small amounts of reagents
that are required makes this assay currently one of the most economical
high-throughput detection assays for protein-protein interactions
in mammalian cells.},
doi = {10.1186/1471-2164-9-68},
pdf = {../local/Fiebitz2008High-throughput.pdf},
file = {Fiebitz2008High-throughput.pdf:Fiebitz2008High-throughput.pdf:PDF},
institution = {Max Planck Institute for Molecular Genetics, Department Vertebrate
Genomics, Fabeckstr, 60-62, 14195 Berlin, Germany. fiebitz@molgen.mpg.de},
owner = {phupe},
pii = {1471-2164-9-68},
pmid = {18254948},
timestamp = {2010.08.31},
url = {http://dx.doi.org/10.1186/1471-2164-9-68}
}
@article{Fields1999Functional,
author = {Fields, S. and Kohara, Y. and Lockhart, D. J.},
title = {Functional genomics},
journal = {Proc. {N}atl. {A}cad. {S}ci. {USA}},
year = {1999},
volume = {96},
pages = {8825--8826},
month = {August},
pdf = {../local/fiel99.pdf},
file = {fiel99.pdf:local/fiel99.pdf:PDF},
subject = {bio},
url = {http://www.pnas.org/cgi/reprint/96/16/8825.pdf}
}
@article{Fields1989novel,
author = {Fields, S. and Song, O.},
title = {A novel genetic system to detect protein-protein interactions},
journal = {Nature},
year = {1989},
volume = {340},
pages = {245--246},
number = {6230},
month = {Jul},
abstract = {Protein-protein interactions between two proteins have generally been
studied using biochemical techniques such as crosslinking, co-immunoprecipitation
and co-fractionation by chromatography. We have generated a novel
genetic system to study these interactions by taking advantage of
the properties of the GAL4 protein of the yeast Saccharomyces cerevisiae.
This protein is a transcriptional activator required for the expression
of genes encoding enzymes of galactose utilization. It consists of
two separable and functionally essential domains: an N-terminal domain
which binds to specific DNA sequences (UASG); and a C-terminal domain
containing acidic regions, which is necessary to activate transcription.
We have generated a system of two hybrid proteins containing parts
of GAL4: the GAL4 DNA-binding domain fused to a protein 'X' and a
GAL4 activating region fused to a protein 'Y'. If X and Y can form
a protein-protein complex and reconstitute proximity of the GAL4
domains, transcription of a gene regulated by UASG occurs. We have
tested this system using two yeast proteins that are known to interact--SNF1
and SNF4. High transcriptional activity is obtained only when both
hybrids are present in a cell. This system may be applicable as a
general method to identify proteins that interact with a known protein
by the use of a simple galactose selection.},
comment = {The paper that introduces the yeast two-hybrid system for detection
of protein-protein interactions},
doi = {10.1038/340245a0},
institution = {Department of Microbiology, State University of New York at Stony
Brook, Stony Brook 11794.},
owner = {jp},
pmid = {2547163},
timestamp = {2008.10.02},
url = {http://dx.doi.org/10.1038/340245a0}
}
@inproceedings{Filatov1995Graph,
author = {Filatov, A. and Gitis, A. and Kil, I.},
title = {Graph-based handwritten digit string recognition},
booktitle = {ICDAR '95: Proceedings of the Third International Conference on Document
Analysis and Recognition (Volume 2)},
year = {1995},
pages = {845},
address = {Washington, DC, USA},
publisher = {IEEE Computer Society},
isbn = {0-8186-7128-9}
}
@article{Filipowicz2008Mechanisms,
author = {Filipowicz, W. and Bhattacharyya, S. N. and Sonenberg, N.},
title = {Mechanisms of post-transcriptional regulation by micro{RNA}s: are
the answers in sight?},
journal = {Nat. Rev. Genet.},
year = {2008},
volume = {9},
pages = {102--114},
number = {2},
month = {Feb},
abstract = {MicroRNAs constitute a large family of small, approximately 21-nucleotide-long,
non-coding RNAs that have emerged as key post-transcriptional regulators
of gene expression in metazoans and plants. In mammals, microRNAs
are predicted to control the activity of approximately 30\% of all
protein-coding genes, and have been shown to participate in the regulation
of almost every cellular process investigated so far. By base pairing
to mRNAs, microRNAs mediate translational repression or mRNA degradation.
This Review summarizes the current understanding of the mechanistic
aspects of microRNA-induced repression of translation and discusses
some of the controversies regarding different modes of microRNA function.},
doi = {10.1038/nrg2290},
pdf = {../local/Filipowicz2008Mechanisms.pdf},
file = {Filipowicz2008Mechanisms.pdf:Filipowicz2008Mechanisms.pdf:PDF},
institution = {Friedrich Miescher Institute for Biomedical Research, 4002 Basel,
Switzerland. witold.filipowicz@fmi.ch},
keywords = {csbcbook},
owner = {jp},
pii = {nrg2290},
pmid = {18197166},
timestamp = {2009.10.09},
url = {http://dx.doi.org/10.1038/nrg2290}
}
@article{Fine2001Efficient,
author = {Fine, S. and Scheinberg, K.},
title = {Efficient {SVM} training using low-rank kernel representations},
journal = {J. Mach. Learn. Res.},
year = {2001},
volume = {2},
pages = {243--264}
}
@article{Finetti2008Sixteen-kinase,
author = {Finetti, P. and Cervera, N. and Charafe-Jauffret, E. and Chabannon,
C. and Charpin, C. and Chaffanet, M. and Jacquemier, J. and Viens,
P. and Birnbaum, D. and Bertucci, F.},
title = {Sixteen-kinase gene expression identifies luminal breast cancers
with poor prognosis},
journal = {Cancer Res.},
year = {2008},
volume = {68},
pages = {767--776},
number = {3},
month = {Feb},
abstract = {Breast cancer is a heterogeneous disease made of various molecular
subtypes with different prognosis. However, evolution remains difficult
to predict within some subtypes, such as luminal A, and treatment
is not as adapted as it should be. Refinement of prognostic classification
and identification of new therapeutic targets are needed. Using oligonucleotide
microarrays, we profiled 227 breast cancers. We focused our analysis
on two major breast cancer subtypes with opposite prognosis, luminal
A (n = 80) and basal (n = 58), and on genes encoding protein kinases.
Whole-kinome expression separated luminal A and basal tumors. The
expression (measured by a kinase score) of 16 genes encoding serine/threonine
kinases involved in mitosis distinguished two subgroups of luminal
A tumors: Aa, of good prognosis and Ab, of poor prognosis. This classification
and its prognostic effect were validated in 276 luminal A cases from
three independent series profiled across different microarray platforms.
The classification outperformed the current prognostic factors in
univariate and multivariate analyses in both training and validation
sets. The luminal Ab subgroup, characterized by high mitotic activity
compared with luminal Aa tumors, displayed clinical characteristics
and a kinase score intermediate between the luminal Aa subgroup and
the luminal B subtype, suggesting a continuum in luminal tumors.
Some of the mitotic kinases of the signature represent therapeutic
targets under investigation. The identification of luminal A cases
of poor prognosis should help select appropriate treatment, whereas
the identification of a relevant kinase set provides potential targets.},
doi = {10.1158/0008-5472.CAN-07-5516},
pdf = {../local/Finetti2008Sixteen-kinase.pdf},
file = {Finetti2008Sixteen-kinase.pdf:Finetti2008Sixteen-kinase.pdf:PDF},
institution = {UMR599 Inserm, Institut Paoli-Calmettes, Laboratoire d'Oncologie
Moléculaire, Centre de Recherche en Cancérologie de Marseille, Marseille,
France.},
keywords = {csbcbook, csbcbook-ch3},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {68/3/767},
pmid = {18245477},
timestamp = {2009.10.18},
url = {http://dx.doi.org/10.1158/0008-5472.CAN-07-5516}
}
@article{Finn1998Pharmacophore,
author = {P. Finn and S. Muggleton and D. Page and A. Srinivasan},
title = {Pharmacophore discovery using the inductive logic programming language
{P}rogol},
journal = {Machine {L}earning},
year = {1998},
volume = {30},
pages = {241-270},
citeseerurl = {http://citeseer.ist.psu.edu/finn98pharmacophore.html},
keywords = {chemoinformatics},
owner = {mahe},
timestamp = {2006.02.03}
}
@article{Fire1998Potent,
author = {Fire, A. and Xu, S. and Montgomery, M. K. and Kostas, S. A. and Driver,
S. E. and Mello, C. C.},
title = {{P}otent and specific genetic interference by double-stranded {RNA}
in {C}aenorhabditis elegans.},
journal = {Nature},
year = {1998},
volume = {391},
pages = {806--811},
number = {6669},
month = {Feb},
abstract = {Experimental introduction of RNA into cells can be used in certain
biological systems to interfere with the function of an endogenous
gene. Such effects have been proposed to result from a simple antisense
mechanism that depends on hybridization between the injected RNA
and endogenous messenger RNA transcripts. RNA interference has been
used in the nematode Caenorhabditis elegans to manipulate gene expression.
Here we investigate the requirements for structure and delivery of
the interfering RNA. To our surprise, we found that double-stranded
RNA was substantially more effective at producing interference than
was either strand individually. After injection into adult animals,
purified single strands had at most a modest effect, whereas double-stranded
mixtures caused potent and specific interference. The effects of
this interference were evident in both the injected animals and their
progeny. Only a few molecules of injected double-stranded RNA were
required per affected cell, arguing against stochiometric interference
with endogenous mRNA and suggesting that there could be a catalytic
or amplification component in the interference process.},
doi = {10.1038/35888},
pdf = {../local/Fire1998Potent.pdf},
file = {Fire1998Potent.pdf:Fire1998Potent.pdf:PDF},
keywords = {sirna},
owner = {vert},
pmid = {9486639},
timestamp = {2006.03.28},
url = {http://dx.doi.org/10.1038/35888}
}
@inproceedings{Fischetti02Generalized,
author = {M. Fischetti and J.J. Salazar-Gonzalez and P. Toth},
title = {The Generalized Travelling Salesman and Orienteering Problems},
booktitle = {The Travelling Salesman Problem and Its Variations},
year = {2002},
pages = {609--662}
}
@article{Fischler1981Random,
author = {Fischler, M. A. and Bolles, R. C.},
title = {Random Sample Consensus: A Paradigm for Model Fitting with Applications
to Image Analysis and Automated Cartography},
journal = {Commun. of the ACM},
year = {1981},
volume = {24},
pages = {381-395},
number = {6},
abstract = {A new paradigm, Random Sample Consensus (RANSAC), for fitting a model
to experimental data is introduced. RANSAC is capable of interpreting/smoothing
data containing a significant percentage of gross errors, and is
thus ideally suited for applications in automated image analysis
where interpretation is based on the data provided by error-prone
feature detectors. A major portion of this paper describes the application
of RANSAC to the Location Determination Problem (LDP): Given an image
depicting a set of landmarks with know locations, determine that
point in space from which the image was obtained. In response to
a RANSAC requirement, new results are derived on the minimum number
of landmarks needed to obtain a solution, and algorithms are presented
for computing these minimum-landmark solutions in closed form. These
results provide the basis for an automatic system that can solve
the LDP under difficult viewing.}
}
@article{Fisher1950Gene,
author = {Fisher, R.A.},
title = {Gene frequencies in a cline determined by selection and diffusion},
journal = {Biometrics},
year = {1950},
volume = {6},
pages = {353--361},
number = {4},
publisher = {JSTOR}
}
@article{Flannick2006Graemlin,
author = {Flannick, J. and Novak, A. and Srinivasan, B.S. and McAdams, H.H.
and Batzoglou, S.},
title = {Graemlin: general and robust alignment of multiple large interaction
networks},
journal = {Genome Res.},
year = {2006},
volume = {16},
pages = {1169--1181},
number = {9},
month = {Sep},
abstract = {The recent proliferation of protein interaction networks has motivated
research into network alignment: the cross-species comparison of
conserved functional modules. Previous studies have laid the foundations
for such comparisons and demonstrated their power on a select set
of sparse interaction networks. Recently, however, new computational
techniques have produced hundreds of predicted interaction networks
with interconnection densities that push existing alignment algorithms
to their limits. To find conserved functional modules in these new
networks, we have developed Graemlin, the first algorithm capable
of scalable multiple network alignment. Graemlin's explicit model
of functional evolution allows both the generalization of existing
alignment scoring schemes and the location of conserved network topologies
other than protein complexes and metabolic pathways. To assess Graemlin's
performance, we have developed the first quantitative benchmarks
for network alignment, which allow comparisons of algorithms in terms
of their ability to recapitulate the KEGG database of conserved functional
modules. We find that Graemlin achieves substantial scalability gains
over previous methods while improving sensitivity.},
doi = {10.1101/gr.5235706},
pdf = {../local/Flannick2006Graemlin.pdf},
file = {Flannick2006Graemlin.pdf:local/Flannick2006Graemlin.pdf:PDF},
institution = {Department of Computer Science, Stanford University, Stanford, California
94305, USA.},
owner = {jp},
pii = {gr.5235706},
pmid = {16899655},
timestamp = {2008.10.03},
url = {http://dx.doi.org/10.1101/gr.5235706}
}
@article{Fliri2005Analysis,
author = {Fliri, A. F. and Loging, W. T. and Thadeio, P. F. and Volkmann, R.
A.},
title = {Analysis of drug-induced effect patterns to link structure and side
effects of medicines.},
journal = {Nat. Chem. Biol.},
year = {2005},
volume = {1},
pages = {389--397},
number = {7},
month = {Dec},
abstract = {The high failure rate of experimental medicines in clinical trials
accentuates inefficiencies of current drug discovery processes caused
by a lack of tools for translating the information exchange between
protein and organ system networks. Recently, we reported that biological
activity spectra (biospectra), derived from in vitro protein binding
assays, provide a mechanism for assessing a molecule's capacity to
modulate the function of protein-network components. Herein we describe
the translation of adverse effect data derived from 1,045 prescription
drug labels into effect spectra and show their utility for diagnosing
drug-induced effects of medicines. In addition, notwithstanding the
limitation imposed by the quality of drug label information, we show
that biospectrum analysis, in concert with effect spectrum analysis,
provides an alignment between preclinical and clinical drug-induced
effects. The identification of this alignment provides a mechanism
for forecasting clinical effect profiles of medicines.},
doi = {10.1038/nchembio747},
pdf = {../local/Fliri2005Analysis.pdf},
file = {Fliri2005Analysis.pdf:Fliri2005Analysis.pdf:PDF},
keywords = {chemoinformatics},
owner = {vert},
pmid = {16370374},
timestamp = {2006.11.24},
url = {http://dx.doi.org/10.1038/nchembio747}
}
@article{Fliri2005Biological,
author = {Fliri, A. F. and Loging, W. T. and Thadeio, P. F. and Volkmann, R.
A.},
title = {Biological spectra analysis: Linking biological activity profiles
to molecular structure.},
journal = {Proc. Natl. Acad. Sci. USA},
year = {2005},
volume = {102},
pages = {261--266},
number = {2},
month = {Jan},
abstract = {Establishing quantitative relationships between molecular structure
and broad biological effects has been a longstanding challenge in
science. Currently, no method exists for forecasting broad biological
activity profiles of medicinal agents even within narrow boundaries
of structurally similar molecules. Starting from the premise that
biological activity results from the capacity of small organic molecules
to modulate the activity of the proteome, we set out to investigate
whether descriptor sets could be developed for measuring and quantifying
this molecular property. Using a 1,567-compound database, we show
that percent inhibition values, determined at single high drug concentration
in a battery of in vitro assays representing a cross section of the
proteome, provide precise molecular property descriptors that identify
the structure of molecules. When broad biological activity of molecules
is represented in spectra form, organic molecules can be sorted by
quantifying differences between biological spectra. Unlike traditional
structure-activity relationship methods, sorting of molecules by
using biospectra comparisons does not require knowledge of a molecule's
putative drug targets. To illustrate this finding, we selected as
starting point the biological activity spectra of clotrimazole and
tioconazole because their putative target, lanosterol demethylase
(CYP51), was not included in the bioassay array. Spectra similarity
obtained through profile similarity measurements and hierarchical
clustering provided an unbiased means for establishing quantitative
relationships between chemical structures and biological activity
spectra. This methodology, which we have termed biological spectra
analysis, provides the capability not only of sorting molecules on
the basis of biospectra similarity but also of predicting simultaneous
interactions of new molecules with multiple proteins.},
doi = {10.1073/pnas.0407790101},
pdf = {../local/Fliri2005Biological.pdf},
file = {Fliri2005Biological.pdf:Fliri2005Biological.pdf:PDF},
keywords = {chemoinformatics},
owner = {vert},
pii = {0407790101},
pmid = {15625110},
timestamp = {2006.11.24},
url = {http://dx.doi.org/10.1073/pnas.0407790101}
}
@article{Fliri2005Biospectra,
author = {Anton F Fliri and William T Loging and Peter F Thadeio and Robert
A Volkmann},
title = {Biospectra analysis: model proteome characterizations for linking
molecular structure and biological response.},
journal = {J. Med. Chem.},
year = {2005},
volume = {48},
pages = {6918--6925},
number = {22},
month = {Nov},
abstract = {Establishing quantitative relationships between molecular structure
and broad biological effects has been a long-standing goal in drug
discovery. Evaluation of the capacity of molecules to modulate protein
functions is a prerequisite for understanding the relationship between
molecular structure and in vivo biological response. A particular
challenge in these investigations is to derive quantitative measurements
of a molecule's functional activity pattern across different proteins.
Herein we describe an operationally simple probabilistic structure-activity
relationship (SAR) approach, termed biospectra analysis, for identifying
agonist and antagonist effect profiles of medicinal agents by using
pattern similarity between biological activity spectra (biospectra)
of molecules as the determinant. Accordingly, in vitro binding data
(percent inhibition values of molecules determined at single high
drug concentration in a battery of assays representing a cross section
of the proteome) are useful for identifying functional effect profile
similarity between medicinal agents. To illustrate this finding,
the relationship between biospectra similarity of 24 molecules, identified
by hierarchical clustering of a 1567 molecule dataset as being most
closely aligned with the neurotransmitter dopamine, and their agonist
or antagonist properties was probed. Distinguishing the results described
in this study from those obtained with affinity-based methods, the
observed association between biospectra and biological response profile
similarity remains intact even upon removal of putative drug targets
from the dataset (four dopaminergic [D1/D2/D3/D4] and two adrenergic
[alpha1 and alpha2] receptors). These findings indicate that biospectra
analysis provides an unbiased new tool for forecasting structure-response
relationships and for translating broad biological effect information
into chemical structure design.},
doi = {10.1021/jm050494g},
pdf = {../local/Fliri2005Biospectra.pdf},
file = {Fliri2005Biospectra.pdf:Fliri2005Biospectra.pdf:PDF},
keywords = {chemoinformatics},
owner = {vert},
pmid = {16250650},
timestamp = {2006.11.24},
url = {http://dx.doi.org/10.1021/jm050494g}
}
@article{Florens2002proteomic,
author = {Laurence Florens and Michael P Washburn and J. Dale Raine and Robert
M Anthony and Munira Grainger and J. David Haynes and J. Kathleen
Moch and Nemone Muster and John B Sacci and David L Tabb and Adam
A Witney and Dirk Wolters and Yimin Wu and Malcolm J Gardner and
Anthony A Holder and Robert E Sinden and John R Yates and Daniel
J Carucci},
title = {{A} proteomic view of the {P}lasmodium falciparum life cycle.},
journal = {Nature},
year = {2002},
volume = {419},
pages = {520--526},
number = {6906},
month = {Oct},
abstract = {The completion of the Plasmodium falciparum clone 3D7 genome provides
a basis on which to conduct comparative proteomics studies of this
human pathogen. Here, we applied a high-throughput proteomics approach
to identify new potential drug and vaccine targets and to better
understand the biology of this complex protozoan parasite. We characterized
four stages of the parasite life cycle (sporozoites, merozoites,
trophozoites and gametocytes) by multidimensional protein identification
technology. Functional profiling of over 2,400 proteins agreed with
the physiology of each stage. Unexpectedly, the antigenically variant
proteins of var and rif genes, defined as molecules on the surface
of infected erythrocytes, were also largely expressed in sporozoites.
The detection of chromosomal clusters encoding co-expressed proteins
suggested a potential mechanism for controlling gene expression.},
doi = {10.1038/nature01107},
pdf = {../local/Florens2002proteomic.pdf},
file = {Florens2002proteomic.pdf:local/Florens2002proteomic.pdf:PDF},
keywords = {plasmodium},
pii = {nature01107},
pmid = {12368862},
timestamp = {2006.04.13},
url = {http://dx.doi.org/10.1038/nature01107}
}
@article{Flower1998Properties,
author = {D. R. Flower},
title = {On the Properties of Bit String-Based Measures of Chemical Similarity},
journal = {J Chem Inf Comput Sci},
year = {1998},
volume = {38},
pages = {379-386},
owner = {mahe},
timestamp = {2006.09.03}
}
@article{Flusberg2010Direct,
author = {Benjamin A Flusberg and Dale R Webster and Jessica H Lee and Kevin
J Travers and Eric C Olivares and Tyson A Clark and Jonas Korlach
and Stephen W Turner},
title = {Direct detection of DNA methylation during single-molecule, real-time
sequencing.},
journal = {Nat Methods},
year = {2010},
volume = {7},
pages = {461--465},
number = {6},
month = {Jun},
abstract = {We describe the direct detection of DNA methylation, without bisulfite
conversion, through single-molecule, real-time (SMRT) sequencing.
In SMRT sequencing, DNA polymerases catalyze the incorporation of
fluorescently labeled nucleotides into complementary nucleic acid
strands. The arrival times and durations of the resulting fluorescence
pulses yield information about polymerase kinetics and allow direct
detection of modified nucleotides in the DNA template, including
N6-methyladenine, 5-methylcytosine and 5-hydroxymethylcytosine. Measurement
of polymerase kinetics is an intrinsic part of SMRT sequencing and
does not adversely affect determination of primary DNA sequence.
The various modifications affect polymerase kinetics differently,
allowing discrimination between them. We used these kinetic signatures
to identify adenine methylation in genomic samples and found that,
in combination with circular consensus sequencing, they can enable
single-molecule identification of epigenetic modifications with base-pair
resolution. This method is amenable to long read lengths and will
likely enable mapping of methylation patterns in even highly repetitive
genomic regions.},
doi = {10.1038/nmeth.1459},
institution = {Pacific Biosciences, Menlo Park, California, USA.},
keywords = {DNA Methylation; DNA-Directed DNA Polymerase, metabolism; Escherichia
coli, genetics; Kinetics; Principal Component Analysis; Sequence
Analysis, DNA, methods},
language = {eng},
medline-pst = {ppublish},
owner = {philippe},
pii = {nmeth.1459},
pmid = {20453866},
timestamp = {2010.07.27},
url = {http://dx.doi.org/10.1038/nmeth.1459}
}
@article{Fodor1991Light-directed,
author = {Fodor, S. P. and Read, J. L. and Pirrung, M. C. and Stryer, L. and
Lu, A. T. and Solas, D.},
title = {Light-directed, spatially addressable parallel chemical synthesis},
journal = {Science},
year = {1991},
volume = {251},
pages = {767--773},
pdf = {../local/Fodor1991Light-directed.pdf},
file = {Fodor1991Light-directed.pdf:local/Fodor1991Light-directed.pdf:PDF},
url = {http://links.jstor.org/sici?sici=0036-8075%2819910215%293%3A251%3A4995%3C767%3ALSAPCS%3E2.0.CO%3B2-J}
}
@article{Fong2004Predicting,
author = {Fong, J. H. and Keating, A. E. and Singh, M.},
title = {Predicting specificity in b{ZIP} coiled-coil protein interactions},
journal = {Genome {B}iol.},
year = {2004},
volume = {5},
number = {R11},
abstract = {We present a method for predicting protein-protein interactions mediated
by the coiled-coil motif. {W}hen tested on interactions between nearly
all human and yeast b{ZIP} proteins, our method identifies 70% of
strong interactions while maintaining that 92% of predictions are
correct. {F}urthermore, cross-validation testing shows that including
the b{ZIP} experimental data significantly improves performance.
{O}ur method can be used to predict b{ZIP} interactions in other
genomes and is a promising approach for predicting coiled-coil interactions
more generally.},
pdf = {../local/Fong2004Predicting.pdf},
file = {Fong2004Predicting.pdf:local/Fong2004Predicting.pdf:PDF},
keywords = {biosvm},
owner = {vert},
url = {http://genomebiology.com/2004/5/2/R11}
}
@article{Formosa2003Changing,
author = {T. Formosa},
title = {Changing the DNA landscape: putting a SPN on chromatin.},
journal = {Curr Top Microbiol Immunol},
year = {2003},
volume = {274},
pages = {171--201},
abstract = {In eukaryotic cells, transcription and replication each occur on DNA
templates that are incorporated into nucleosomes. Formation of chromatin
generally limits accessibility of specific DNA sequences and inhibits
progression of polymerases as they copy information from the DNA.
The processes that select sites for initiating either transcription
or replication are therefore strongly influenced by factors that
modulate the properties of chromatin proteins. Further, in order
to elongate their products, both DNA and RNA polymerases must be
able to overcome the inhibition presented by chromatin (Lipford and
Bell 2001; Workman and Kingston 1998). One way to adjust the properties
of chromatin proteins is to covalently modify them by adding or removing
chemical moieties. Both histone and non-histone chromatin proteins
are altered by acetylation, methylation, and other changes, and the
'nucleosome modifying' complexes that perform these reactions are
important components of pathways of transcriptional regulation (Cote
2002; Orphanides and Reinberg 2000; Roth et al. 2001; Strahl and
Allis 2000; Workman and Kingston 1998). Another way to alter the
effects of nucleosomes is to change the position of the histone octamers
relative to specific DNA sequences (Orphanides and Reinberg 2000;
Verrijzer 2002; Wang 2002; Workman and Kingston 1998). Since the
ability of a sequence to be bound by specific proteins can vary significantly
whether the sequence is in the linkers between nucleosomes or at
various positions within a nucleosome, 'nucleosome remodeling' complexes
that rearrange nucleosome positioning are also important regulators
of transcription. Since the DNA replication machinery has to encounter
many of the same challenges posed by chromatin, it seems likely that
modifying and remodeling complexes also act during duplication of
the genome, but most of the current information on these factors
relates to regulation of transcription. This chapter describes the
factor known variously as FACT in humans, where it promotes elongation
of RNA polymerase II on nucleosomal templates in vitro (Orphanides
et al. 1998, 1999), DUF in frogs, where it is needed for DNA replication
in oocyte extracts (Okuhara et al. 1999), and CP or SPN in yeast,
where it is linked in vivo to both transcription and replication
(Brewster et al. 2001; Formosa et al. 2001). Like the nucleosome
modifying and remodeling complexes, it is broadly conserved among
eukaryotes, affects a wide range of processes that utilize chromatin,
and directly alters the properties of nucleosomes. However, it does
not have nucleosome modifying or standard ATP-dependent remodeling
activity, and therefore represents a third class of chromatin modulating
factors. It is also presently unique in the extensive connections
it displays with both transcription and replication: FACT/DUF/CP/SPN
appears to modify nucleosomes in a way that is directly important
for the efficient functioning of both RNA polymerases and DNA polymerases.
While less is known about the mechanisms it uses to promote its functions
than for other factors that affect chromatin, it is clearly an essential
part of the complex mixture of activities that modulate access to
DNA within chromatin. Physical and genetic interactions suggest that
FACT/DUF/CP/SPN affects multiple pathways within replication and
transcription as a member of several distinct complexes. Some of
the interactions are easy to assimilate into models for replication
or transcription, such as direct binding to DNA polymerase alpha
(Wittmeyer and Formosa 1997; Wittmeyer et al. 1999), association
with nucleosome modifying complexes (John et al. 2000), and interaction
with factors that participate in elongation of RNA Polymerase II
(Gavin et al. 2002; Squazzo et al. 2002). Others are more surprising
such as an association with the 19S complex that regulates the function
of the 20S proteasome (Ferdous et al. 2001; Xu et al. 1995), and
the indication that FACT/DUF/CP/SPN can act as a specificity factor
for casein kinase II (Keller et al. 2001). This chapter reviews the
varied approaches that have each revealed different aspects of the
function of FACT/DUF/CP/SPN, and presents a picture of a factor that
can both alter nucleosomes and orchestrate the assembly or activity
of a broad range of complexes that act upon chromatin.},
institution = {University of Utah, Biochemistry, 20 N 1900 E RM 211, Salt Lake City,
UT 84132-3201, USA. Tim.Formosa@hsc.utah.edu},
keywords = {Animals; Cell Cycle Proteins, metabolism; Chromatin, metabolism; DNA,
metabolism; Eukaryotic Cells, metabolism; Gene Expression Regulation;
Humans; Saccharomyces cerevisiae Proteins; Transcription Factors,
metabolism; Transcription, Genetic; Transcriptional Elongation Factors},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pmid = {12596908},
timestamp = {2010.11.23}
}
@article{Forney1973Viterbi,
author = {G. D. Forney},
title = {The Viterbi algorithm},
journal = {Proc. IEEE},
year = {1973},
volume = {61},
pdf = {../local/Forney1973Viterbi.pdf},
file = {Forney1973Viterbi.pdf:Forney1973Viterbi.pdf:PDF},
owner = {michael},
timestamp = {2008.10.02}
}
@techreport{Fortin1996graph,
author = {S. Fortin},
title = {The Graph Isomorphism Problem},
institution = {MIT},
year = {1996}
}
@article{Foster1994A,
author = {Foster, Dean P. and George, Edward I.},
title = {The Risk Inflation Criterion for Multiple Regression},
journal = {The Annals of Statistics},
year = {1994},
volume = {22},
pages = {1947--1975},
number = {4},
abstract = {A new criterion is proposed for the evaluation of variable selection
procedures in multiple regression. This criterion, which we call
the risk inflation, is based on an adjustment to the risk. Essentially,
the risk inflation is the maximum increase in risk due to selecting
rather than knowing the "correct" predictors. A new variable selection
procedure is obtained which, in the case of orthogonal predictors,
substantially improves on AIC, Cp and BIC and is close to optimal.
In contrast to AIC, Cp and BIC which use dimensionality penalties
of 2, 2 and log n, respectively, this new procedure uses a penalty
2 log p, where p is the number of available predictors. For the case
of nonorthogonal predictors, bounds for the optimal penalty are obtained.},
doi = {10.2307/2242493},
issn = {00905364},
keywords = {mdl, regression},
publisher = {Institute of Mathematical Statistics},
url = {http://dx.doi.org/10.2307/2242493}
}
@article{Foucart2009Sparsest,
author = {Foucart, Simon and Lai, Ming-Jun},
title = {Sparsest solutions of underdetermined linear systems via $\ell_q$-minimization
for $0 < q \leq 1$},
journal = {Applied and {C}omputational {H}armonic {A}nalysis},
year = {2009},
volume = {26},
pages = {395--407},
number = {3},
month = {May},
url = {http://dx.doi.org/10.1016/j.acha.2008.09.001}
}
@article{Frank1956Algorithm,
author = {M. Frank and P. Wolfe},
title = {An algorithm for quadratic programming},
journal = {Naval Research Logistics Quarterly},
year = {1956},
volume = {3},
pages = {95-110},
publisher = {INFORMS}
}
@article{Franke2006Reconstruction,
author = {Franke, L. and van Bakel, H. and Fokkens, L. and D de Jong, E.D and
Egmont-Petersen, M. and Wijmenga, C.},
title = {Reconstruction of a functional human gene network, with an application
for prioritizing positional candidate genes.},
journal = {Am. J. Hum. Genet.},
year = {2006},
volume = {78},
pages = {1011--1025},
number = {6},
month = {Jun},
abstract = {Most common genetic disorders have a complex inheritance and may result
from variants in many genes, each contributing only weak effects
to the disease. Pinpointing these disease genes within the myriad
of susceptibility loci identified in linkage studies is difficult
because these loci may contain hundreds of genes. However, in any
disorder, most of the disease genes will be involved in only a few
different molecular pathways. If we know something about the relationships
between the genes, we can assess whether some genes (which may reside
in different loci) functionally interact with each other, indicating
a joint basis for the disease etiology. There are various repositories
of information on pathway relationships. To consolidate this information,
we developed a functional human gene network that integrates information
on genes and the functional relationships between genes, based on
data from the Kyoto Encyclopedia of Genes and Genomes, the Biomolecular
Interaction Network Database, Reactome, the Human Protein Reference
Database, the Gene Ontology database, predicted protein-protein interactions,
human yeast two-hybrid interactions, and microarray co-expressions.
We applied this network to interrelate positional candidate genes
from different disease loci and then tested 96 heritable disorders
for which the Online Mendelian Inheritance in Man database reported
at least three disease genes. Artificial susceptibility loci, each
containing 100 genes, were constructed around each disease gene,
and we used the network to rank these genes on the basis of their
functional interactions. By following up the top five genes per artificial
locus, we were able to detect at least one known disease gene in
54\% of the loci studied, representing a 2.8-fold increase over random
selection. This suggests that our method can significantly reduce
the cost and effort of pinpointing true disease genes in analyses
of disorders for which numerous loci have been reported but for which
most of the genes are unknown.},
doi = {10.1086/504300},
pdf = {../local/Franke2006Reconstruction.pdf},
file = {Franke2006Reconstruction.pdf:Franke2006Reconstruction.pdf:PDF},
institution = {Complex Genetics Section, Department of Biomedical Genetics-Department
of Medical Genetics, University Medical Centre Utrecht, Utrecht,
The Netherlands.},
owner = {mordelet},
pii = {S0002-9297(07)63922-6},
pmid = {16685651},
timestamp = {2010.09.28},
url = {http://dx.doi.org/10.1086/504300}
}
@article{Fraunholz2005Systems,
author = {M. J. Fraunholz},
title = {{S}ystems biology in malaria research.},
journal = {Trends Parasitol.},
year = {2005},
volume = {21},
pages = {393--395},
number = {9},
month = {Sep},
abstract = {A recent publication of genome and expression analyses of the murine
parasites Plasmodium chabaudi chabaudi and Plasmodium berghei presents
the state of the art in Plasmodium systems biology. By integrating
genomics, transcriptomics and proteomics, the authors can classify
and annotate genes by their expression profiles and can even detect
evidence of posttranscriptional gene silencing in the murine malaria
species.},
doi = {10.1016/j.pt.2005.07.007},
pdf = {../local/Fraunholz2005Systems.pdf},
file = {Fraunholz2005Systems.pdf:Fraunholz2005Systems.pdf:PDF},
keywords = {plasmodium},
pii = {S1471-4922(05)00194-7},
pmid = {16043412},
timestamp = {2006.04.13},
url = {http://dx.doi.org/10.1016/j.pt.2005.07.007}
}
@article{Fredholm2007G-protein-coupled,
author = {Fredholm, B. B. and H{\"o}kfelt, T. and Milligan, G.},
title = {G-protein-coupled receptors: an update.},
journal = {Acta Physiol.},
year = {2007},
volume = {190},
pages = {3--7},
number = {1},
month = {May},
abstract = {The receptors that couple to G proteins (GPCR) and which span the
cell membranes seven times (7-TM receptors) were the focus of a symposium
in Stockholm 2006. The ensemble of GPCR has now been mapped in several
animal species. They remain a major focus of interest in drug development,
and their diverse physiological and pathophysiological roles are
being clarified, i.a. by genetic targeting. Recent developments hint
at novel levels of complexity. First, many, if not all, GPCRs are
part of multimeric ensembles, and physiology and pharmacology of
a given GPCR may be at least partly guided by the partners it was
formed together with. Secondly, at least some GPCRs may be constitutively
active. Therefore, drugs that are inverse agonists may prove useful.
Furthermore, the level of activity may vary in such a profound way
between cells and tissues that this could offer new ways of achieving
specificity of drug action. Finally, it is becoming increasingly
clear that some of these receptors can signal via novel types of
pathways, and hence that 'GPCRs' may not always be G-protein-coupled.
Thus there are many challenges for the basic scientist and the drug
industry.},
doi = {10.1111/j.1365-201X.2007.01689.x},
keywords = {chemogenomics},
owner = {laurent},
pii = {APS1689},
pmid = {17428227},
timestamp = {2008.01.16},
url = {http://dx.doi.org/10.1111/j.1365-201X.2007.01689.x}
}
@article{Freedman2010Lies,
author = {Freedman, D.H.},
title = {Lies, damned lies, and medical science},
journal = {The Atlantic},
year = {2010},
volume = {306},
pages = {76--84},
number = {4}
}
@article{Freedman2006Statistical,
author = {Freedman, D.A.},
title = {Statistical Models for Causation What Inferential Leverage Do They
Provide?},
journal = {Evaluation Review},
year = {2006},
volume = {30},
pages = {691--713},
number = {6},
publisher = {Sage Publications}
}
@article{Freeman2006Copy,
author = {Freeman, J. L. and Perry, G. H. and Feuk, L. and Redon, R. and McCarroll,
S. A. and Altshuler, D. M. and Aburatani, H. and Jones, K. W. and
Tyler-Smith, C. and Hurles, M. E. and Carter, N. P. and Scherer,
S. W. and Lee, C.},
title = {Copy number variation: new insights in genome diversity},
journal = {Genome Res},
year = {2006},
volume = {16},
pages = {949--961},
number = {8},
month = {Aug},
abstract = {DNA copy number variation has long been associated with specific chromosomal
rearrangements and genomic disorders, but its ubiquity in mammalian
genomes was not fully realized until recently. Although our understanding
of the extent of this variation is still developing, it seems likely
that, at least in humans, copy number variants (CNVs) account for
a substantial amount of genetic variation. Since many CNVs include
genes that result in differential levels of gene expression, CNVs
may account for a significant proportion of normal phenotypic variation.
Current efforts are directed toward a more comprehensive cataloging
and characterization of CNVs that will provide the basis for determining
how genomic diversity impacts biological function, evolution, and
common human diseases.},
doi = {10.1101/gr.3677206},
pdf = {../local/Freeman2006Copy.pdf},
file = {Freeman2006Copy.pdf:Freeman2006Copy.pdf:PDF},
institution = {Department of Pathology, Brigham and Women's Hospital, Boston, Massachusetts
02115, USA.},
keywords = {cgh, csbcbook, csbcbook-ch2},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {gr.3677206},
pmid = {16809666},
timestamp = {2009.10.18},
url = {http://dx.doi.org/10.1101/gr.3677206}
}
@article{Freier1986Improved,
author = {Freier, S. M. and Kierzek, R. and Jaeger, J. A. and Sugimoto, N.
and Caruthers, M. H. and Neilson, T. and Turner, D. H.},
title = {Improved free-energy parameters for predictions of {RNA} duplex stability.},
journal = {Proc. {N}atl. {A}cad. {S}ci. {USA}},
year = {1986},
volume = {83},
pages = {9373-7},
number = {24},
month = {Dec},
abstract = {Thermodynamic parameters for prediction of {RNA} duplex stability
are reported. {O}ne parameter for duplex initiation and 10 parameters
for helix propagation are derived from enthalpy and free-energy changes
for helix formation by 45 {RNA} oligonucleotide duplexes. {T}he oligomer
sequences were chosen to maximize reliability of secondary structure
predictions. {E}ach of the 10 nearest-neighbor sequences is well-represented
among the 45 oligonucleotides, and the sequences were chosen to minimize
experimental errors in delta {GO} at 37 degrees {C}. {T}hese parameters
predict melting temperatures of most oligonucleotide duplexes within
5 degrees {C}. {T}his is about as good as can be expected from the
nearest-neighbor model. {F}ree-energy changes for helix propagation
at dangling ends, terminal mismatches, and internal {G} {X} {U} mismatches,
and free-energy changes for helix initiation at hairpin loops, internal
loops, or internal bulges are also tabulated.}
}
@article{Freudenberg2002similarity-based,
author = {Freudenberg, J. and Propping, P.},
title = {A similarity-based method for genome-wide prediction of disease-relevant
human genes},
journal = {Bioinformatics},
year = {2002},
volume = {18 Suppl 2},
pages = {S110--S115},
abstract = {MOTIVATION: A method for prediction of disease relevant human genes
from the phenotypic appearance of a query disease is presented. Diseases
of known genetic origin are clustered according to their phenotypic
similarity. Each cluster entry consists of a disease and its underlying
disease gene. Potential disease genes from the human genome are scored
by their functional similarity to known disease genes in these clusters,
which are phenotypically similar to the query disease. RESULTS: For
assessment of the approach, a leave-one-out cross-validation of 878
diseases from the OMIM database, using 10672 candidate genes from
the human genome, is performed. Depending on the applied parameters,
in roughly one-third of cases the true solution is contained within
the top scoring 3\% of predictions and in two-third of cases the
true solution is contained within the top scoring 15\% of predictions.
The prediction results can either be used to identify target genes,
when searching for a mutation in monogenic diseases or for selection
of loci in genotyping experiments in genetically complex diseases.},
pdf = {../local/Freudenberg2002similarity-based.pdf},
file = {Freudenberg2002similarity-based.pdf:Freudenberg2002similarity-based.pdf:PDF},
institution = {Institute of Human Genetics, Bonn University Hospital, Germany. jan.freudenberg@uni-bonn.de},
owner = {jp},
pmid = {12385992},
timestamp = {2009.03.18}
}
@article{Freund2003efficient,
author = {Freund, Y. and Iyer, R. and Schapire, R. E. and Singer, Y.},
title = {An efficient boosting algorithm for combining preferences},
journal = {J. Mach. Learn. Res.},
year = {2003},
volume = {4},
pages = {933--969},
pdf = {../local/Freund2003efficient.pdf},
file = {Freund2003efficient.pdf:Freund2003efficient.pdf:PDF},
owner = {jp},
timestamp = {2013.02.05},
url = {http://www.ai.mit.edu/projects/jmlr/papers/volume4/freund03a/freund03a.pdf}
}
@inproceedings{Freund2000Analysis,
author = {Freund, Y. and Mansour, Y. and Schapire, R. E.},
title = {Analysis of a {P}seudo-{B}ayesian {P}rediction {M}ethod},
booktitle = {Conference on {I}nformation {S}ciences and {S}ystems, {P}rinceton
{U}niversity, {M}arch 15-17},
year = {2000},
pdf = {../local/freu00.pdf},
file = {freu00.pdf:local/freu00.pdf:PDF},
subject = {ml}
}
@article{Freyhult2005Unbiased,
author = {Freyhult, E. and Prusis, P. and Lapinsh, M. and Wikberg, J. E. S.
and Moulton, V. and Gustafsson, M. G.},
title = {Unbiased descriptor and parameter selection confirms the potential
of proteochemometric modelling.},
journal = {BMC Bioinformatics},
year = {2005},
volume = {6},
pages = {50},
abstract = {BACKGROUND: Proteochemometrics is a new methodology that allows prediction
of protein function directly from real interaction measurement data
without the need of 3D structure information. Several reported proteochemometric
models of ligand-receptor interactions have already yielded significant
insights into various forms of bio-molecular interactions. The proteochemometric
models are multivariate regression models that predict binding affinity
for a particular combination of features of the ligand and protein.
Although proteochemometric models have already offered interesting
results in various studies, no detailed statistical evaluation of
their average predictive power has been performed. In particular,
variable subset selection performed to date has always relied on
using all available examples, a situation also encountered in microarray
gene expression data analysis. RESULTS: A methodology for an unbiased
evaluation of the predictive power of proteochemometric models was
implemented and results from applying it to two of the largest proteochemometric
data sets yet reported are presented. A double cross-validation loop
procedure is used to estimate the expected performance of a given
design method. The unbiased performance estimates (P2) obtained for
the data sets that we consider confirm that properly designed single
proteochemometric models have useful predictive power, but that a
standard design based on cross validation may yield models with quite
limited performance. The results also show that different commercial
software packages employed for the design of proteochemometric models
may yield very different and therefore misleading performance estimates.
In addition, the differences in the models obtained in the double
CV loop indicate that detailed chemical interpretation of a single
proteochemometric model is uncertain when data sets are small. CONCLUSION:
The double CV loop employed offer unbiased performance estimates
about a given proteochemometric modelling procedure, making it possible
to identify cases where the proteochemometric design does not result
in useful predictive models. Chemical interpretations of single proteochemometric
models are uncertain and should instead be based on all the models
selected in the double CV loop employed here.},
doi = {10.1186/1471-2105-6-50},
keywords = {chemogenomics},
owner = {laurent},
pii = {1471-2105-6-50},
pmid = {15760465},
timestamp = {2008.07.16},
url = {http://dx.doi.org/10.1186/1471-2105-6-50}
}
@article{Friedel2005Support,
author = {Friedel, C. C. and Jahn, K. H. V. and Sommer, S. and Rudd, S. and
Mewes, H. W. and Tetko, I. V.},
title = {Support vector machines for separation of mixed plant-pathogen {EST}
collections based on codon usage},
journal = {Bioinformatics},
year = {2005},
volume = {21},
pages = {1383-1388},
abstract = {Motivation: {D}iscovery of host and pathogen genes expressed at the
plant-pathogen interface often requires the construction of mixed
libraries that contain sequences from both genomes. {S}equence identification
requires high-throughput and reliable classification of genome origin.
{W}hen using single-pass c{DNA} sequences difficulties arise from
the short sequence length, the lack of sufficient taxonomically relevant
sequence data in public databases and ambiguous sequence homology
between plant and pathogen genes.{R}esults: {A} novel method is described,
which is independent of the availability of homologous genes and
relies on subtle differences in codon usage between plant and fungal
genes. {W}e used support vector machines ({SVM}s) to identify the
probable origin of sequences. {SVM}s were compared to several other
machine learning techniques and to a probabilistic algorithm ({PF}-{IND},
{M}aor et al., 2003) for {EST} classification also based on codon
bias differences. {O}ur software ({ECLAT}) has achieved a classification
accuracy of 93.1% on a test set of 3217 {EST} sequences from {H}.
vulgare and {B}. graminis, which is a significant improvement compared
to {PF}-{IND} (prediction accuracy of 81.2% on the same test set).
{EST} sequences with at least 50 nt of coding sequence can be classified
by {ECLAT} with high confidence. {ECLAT} allows training of classifiers
for any host-pathogen combination for which there are sufficient
classified training sequences.{A}vailability: {ECLAT} is freely available
on the internet (http://mips.gsf.de/proj/est) or on request as a
standalone version.},
doi = {10.1093/bioinformatics/bti200},
pdf = {../local/Friedel2005Support.pdf},
file = {Friedel2005Support.pdf:local/Friedel2005Support.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://bioinformatics.oupjournals.org/cgi/content/abstract/bti200v1}
}
@article{Friedman1993Some,
author = {Friedman, J.},
title = {Some geometric aspects of graphs and their eigenfunctions},
journal = {Duke {M}ath. {J}.},
year = {1993},
volume = {69},
pages = {487--525},
month = {March},
subject = {net}
}
@article{Friedman2007Pathwise,
author = {Friedman, J. and Hastie, T. and H{\"o}fling, H. and Tibshirani, R.},
title = {Pathwise coordinate optimization},
journal = {Ann. Appl. Statist.},
year = {2007},
volume = {1},
pages = {302--332},
number = {1},
abstract = {We consider "one-at-a-time" coordinate-wise descent algorithms for
a class of convex optimization problems. An algorithm of this kind
has been proposed for the L1-penalized regression (lasso) in the
literature, but it seems to have been largely ignored. Indeed, it
seems that coordinate-wise algorithms are not often used in convex
optimization. We show that this algorithm is very competitive with
the well-known LARS (or homotopy) procedure in large lasso problems,
and that it can be applied to related methods such as the garotte
and elastic net. It turns out that coordinate-wise descent does not
work in the "fused lasso", however, so we derive a generalized algorithm
that yields the solution in much less time that a standard convex
optimizer. Finally, we generalize the procedure to the two-dimensional
fused lasso, and demonstrate its performance on some image smoothing
problems.},
doi = {10.1214/07-AOAS131},
pdf = {../local/Friedman2007Pathwise.pdf},
file = {Friedman2007Pathwise.pdf:Friedman2007Pathwise.pdf:PDF},
owner = {jp},
timestamp = {2008.12.08},
url = {http://dx.doi.org/10.1214/07-AOAS131}
}
@article{Friedman2008Sparse,
author = {Friedman, J. and Hastie, T. and Tibshirani, R.},
title = {Sparse inverse covariance estimation with the graphical lasso},
journal = {Biostatistics},
year = {2008},
volume = {9},
pages = {432--441},
number = {3},
month = {Jul},
abstract = {We consider the problem of estimating sparse graphs by a lasso penalty
applied to the inverse covariance matrix. Using a coordinate descent
procedure for the lasso, we develop a simple algorithm--the graphical
lasso--that is remarkably fast: It solves a 1000-node problem ( approximately
500,000 parameters) in at most a minute and is 30-4000 times faster
than competing methods. It also provides a conceptual link between
the exact problem and the approximation suggested by Meinshausen
and Bühlmann (2006). We illustrate the method on some cell-signaling
data from proteomics.},
doi = {10.1093/biostatistics/kxm045},
pdf = {../local/Friedman2008Sparse.pdf},
file = {Friedman2008Sparse.pdf:Friedman2008Sparse.pdf:PDF},
institution = {Department of Statistics, Stanford University, CA 94305, USA.},
owner = {jp},
pii = {kxm045},
pmid = {18079126},
timestamp = {2008.11.27},
url = {http://dx.doi.org/10.1093/biostatistics/kxm045}
}
@article{Friedman2004Inferring,
author = {Friedman, N.},
title = {Inferring cellular networks using probabilistic graphical models},
journal = {Science},
year = {2004},
volume = {303},
pages = {799},
number = {5659},
publisher = {AAAS}
}
@article{Friedman2000Using,
author = {Friedman, N. and Linial, M. and Nachman, I. and Pe'er, D.},
title = {Using {B}ayesian Networks to Analyze Expression Data},
journal = {J. Comput. Biol.},
year = {2000},
volume = {7},
pages = {601--620},
number = {3-4},
abstract = {D{NA} hybridization arrays simultaneously measure the expression level
for thousands of genes. {T}hese measurements provide a "snapshot"
of transcription levels within the cell. {A} major challenge in computational
biology is to uncover, from such measurements, gene/protein interactions
and key biological features of cellular systems. {I}n this paper,
we propose a new framework for discovering interactions between genes
based on multiple expression measurements. {T}his framework builds
on the use of {B}ayesian networks for representing statistical dependencies.
{A} {B}ayesian network is a graph-based model of joint multivariate
probability distributions that captures properties of conditional
independence between variables. {S}uch models are attractive for
their ability to describe complex stochastic processes and because
they provide a clear methodology for learning from (noisy) observations.
{W}e start by showing how {B}ayesian networks can describe interactions
between genes. {W}e then describe a method for recovering gene interactions
from microarray data using tools for learning {B}ayesian networks.
{F}inally, we demonstrate this method on the {S}. cerevisiae cell-cycle
measurements of {S}pellman et al. (1998).},
doi = {10.1089/106652700750050961},
pdf = {../local/Friedman2000Using.pdf},
file = {Friedman2000Using.pdf:local/Friedman2000Using.pdf:PDF},
keywords = {biogm},
subject = {microarray},
url = {http://dx.doi.org/10.1089/106652700750050961}
}
@article{Frigola2006Epigenetic,
author = {Frigola, J. and Song, J. and Stirzaker, C. and Hinshelwood, R. A.
and Peinado, M. A. and Clark, S. J.},
title = {Epigenetic remodeling in colorectal cancer results in coordinate
gene suppression across an entire chromosome band},
journal = {Nat. Genet.},
year = {2006},
volume = {38},
pages = {540--549},
number = {5},
month = {May},
abstract = {We report a new mechanism in carcinogenesis involving coordinate long-range
epigenetic gene silencing. Epigenetic silencing in cancer has always
been envisaged as a local event silencing discrete genes. However,
in this study of silencing in colorectal cancer, we found common
repression of the entire 4-Mb band of chromosome 2q.14.2, associated
with global methylation of histone H3 Lys9. DNA hypermethylation
within the repressed genomic neighborhood was localized to three
separate enriched CpG island 'suburbs', with the largest hypermethylated
suburb spanning 1 Mb. These data change our understanding of epigenetic
gene silencing in cancer cells: namely, epigenetic silencing can
span large regions of the chromosome, and both DNA-methylated and
neighboring unmethylated genes can be coordinately suppressed by
global changes in histone modification. We propose that loss of gene
expression can occur through long-range epigenetic silencing, with
similar implications as loss of heterozygosity in cancer.},
doi = {10.1038/ng1781},
pdf = {../local/Frigola2006Epigenetic.pdf},
file = {Frigola2006Epigenetic.pdf:Frigola2006Epigenetic.pdf:PDF},
institution = {Cancer Program, Garvan Institute of Medical Research, 384 Victoria
Street, Darlinghurst, Sydney 2010, New South Wales, Australia.},
keywords = {csbcbook},
owner = {jp},
pii = {ng1781},
pmid = {16642018},
timestamp = {2009.10.09},
url = {http://dx.doi.org/10.1038/ng1781}
}
@article{Frimurer2005physicogenetic,
author = {Frimurer, T. M. and Ulven, T. and Elling, C. E. and Gerlach, L.-O.
and Kostenis, E. and H{\"o}gberg, T.},
title = {A physicogenetic method to assign ligand-binding relationships between
7TM receptors.},
journal = {Bioorg. Med. Chem. Lett.},
year = {2005},
volume = {15},
pages = {3707--3712},
number = {16},
month = {Aug},
abstract = {A computational protocol has been devised to relate 7TM receptor proteins
(GPCRs) with respect to physicochemical features of the core ligand-binding
site as defined from the crystal structure of bovine rhodopsin. The
identification of such receptors that already are associated with
ligand information (e.g., small molecule ligands with mutagenesis
or SAR data) is used to support structure-guided drug design of novel
ligands. A case targeting the newly identified prostaglandin D2 receptor
CRTH2 serves as a primary example to illustrate the procedure.},
doi = {10.1016/j.bmcl.2005.05.102},
pdf = {../local/Frimurer2005physicogenetic.pdf},
file = {Frimurer2005physicogenetic.pdf:Frimurer2005physicogenetic.pdf:PDF},
keywords = {chemogenomics},
owner = {vert},
pii = {S0960-894X(05)00704-3},
pmid = {15993056},
timestamp = {2007.12.12},
url = {http://dx.doi.org/10.1016/j.bmcl.2005.05.102}
}
@article{Frith2008Discovering,
author = {Frith, Martin C. and Saunders, Neil F. W. and Kobe, Bostjan and Bailey,
Timothy L.},
title = {Discovering Sequence Motifs with Arbitrary Insertions and Deletions},
journal = {PLoS Comput. Biol.},
year = {2008},
volume = {4},
pages = {e1000071+},
number = {5},
month = {May},
address = {Computational Biology Research Center, National Institute of Advanced
Industrial Science and Technology (AIST), Tokyo, Japan.},
citeulike-article-id = {2771903},
citeulike-linkout-0 = {http://dx.doi.org/10.1371/journal.pcbi.1000071},
citeulike-linkout-1 = {http://view.ncbi.nlm.nih.gov/pubmed/18437229},
citeulike-linkout-2 = {http://www.hubmed.org/display.cgi?uids=18437229},
doi = {10.1371/journal.pcbi.1000071},
issn = {1553-7358},
keywords = {glam},
posted-at = {2009-07-31 17:34:55},
priority = {1},
publisher = {Public Library of Science},
url = {http://dx.doi.org/10.1371/journal.pcbi.1000071}
}
@article{Fritz2002Microarray-based,
author = {Fritz, B. and Schubert, F. and Wrobel, G. and Schwaenen, C. and Wessendorf,
S. and Nessling, M. and Korz, C. and Rieker, R. J. and Montgomery,
K. and Kucherlapati, R. and Mechtersheimer, G. and Eils, R. and Joos,
S. and Lichter, P.},
title = {Microarray-based {C}opy {N}umber and {E}xpression {P}rofiling in
{D}edifferentiated and {P}leomorphic {L}iposarcoma},
journal = {Cancer {R}es.},
year = {2002},
volume = {62},
pages = {2993-2998},
number = {11},
abstract = {Sixteen dedifferentiated and pleomorphic liposarcomas were analyzed
by comparative genomic hybridization ({CGH}) to genomic microarrays
(matrix-{CGH}), c{DNA}-derived microarrays for expression profiling,
and by quantitative {PCR}. {M}atrix-{CGH} revealed copy number gains
of numerous oncogenes, i.e., {CCND}1, {MDM}2, {GLI}, {CDK}4, {MYB},
{ESR}1, and {AIB}1, several of which correlate with a high level
of transcripts from the respective gene. {I}n addition, a number
of genes were found differentially expressed in dedifferentiated
and pleomorphic liposarcomas. {A}pplication of dedicated clustering
algorithms revealed that both tumor subtypes are clearly separated
by the genomic profiles but only with a lesser power by the expression
profiles. {U}sing a support vector machine, a subset of five clones
was identified as "class discriminators." {T}hus, for the distinction
of these types of liposarcomas, genomic profiling appears to be more
advantageous than {RNA} expression analysis.},
pdf = {../local/Fritz2002Microarray-based.pdf},
file = {Fritz2002Microarray-based.pdf:local/Fritz2002Microarray-based.pdf:PDF},
keywords = {biosvm, cgh},
owner = {jeanphilippevert},
url = {http://cancerres.aacrjournals.org/cgi/content/abstract/62/11/2993}
}
@inproceedings{Frohlich2005Optimal,
author = {H. Fr{\"o}hlich and J. K. Wegner and F. Sieker and A. Zell},
title = {Optimal assignment kernels for attributed molecular graphs},
booktitle = {Proceedings of the 22nd international conference on Machine learning},
year = {2005},
pages = {225 - 232},
address = {New York, NY, USA},
publisher = {ACM Press},
doi = {http://doi.acm.org/10.1145/1102351.1102380},
keywords = {chemoinformatics},
owner = {mahe},
timestamp = {2006.08.28}
}
@article{Fu2005Image,
author = {J. C. Fu and S. K. Lee and S. T C Wong and J. Y. Yeh and A. H. Wang
and H. K. Wu},
title = {Image segmentation feature selection and pattern classification for
mammographic microcalcifications.},
journal = {Comput {M}ed {I}maging {G}raph},
year = {2005},
month = {Jul},
abstract = {Since microcalcifications in {X}-ray mammograms are the primary indicator
of breast cancer, detection of microcalcifications is central to
the development of an effective diagnostic system. {T}his paper proposes
a two-stage detection procedure. {I}n the first stage, a data driven,
closed form mathematical model is used to calculate the location
and shape of suspected microcalcifications. {W}hen tested on the
{N}ijmegen {U}niversity {H}ospital ({N}etherlands) database, data
analysis shows that the proposed model can effectively detect the
occurrence of microcalcifications. {T}he proposed mathematical model
not only eliminates the need for system training, but also provides
information on the borders of suspected microcalcifications for further
feature extraction. {I}n the second stage, 61 features are extracted
for each suspected microcalcification, representing texture, the
spatial domain and the spectral domain. {F}rom these features, a
sequential forward search ({SFS}) algorithm selects the classification
input vector, which consists of features sensitive only to microcalcifications.
{T}wo types of classifiers-a general regression neural network ({GRNN})
and a support vector machine ({SVM})-are applied, and their classification
performance is compared using the {A}z value of the {R}eceiver {O}perating
{C}haracteristic curve. {F}or all 61 features used as input vectors,
the test data set yielded {A}z values of 97.01\% for the {SVM} and
96.00\% for the {GRNN}. {W}ith input features selected by {SFS},
the corresponding {A}z values were 98.00\% for the {SVM} and 97.80\%
for the {GRNN}. {T}he {SVM} outperformed the {GRNN}, whether or not
the input vectors first underwent {SFS} feature selection. {I}n both
cases, feature selection dramatically reduced the dimension of the
input vectors (82\% for the {SVM} and 59\% for the {GRNN}). {M}oreover,
{SFS} feature selection improved the classification performance,
increasing the {A}z value from 97.01 to 98.00\% for the {SVM} and
from 96.00 to 97.80\% for the {GRNN}.},
doi = {10.1016/j.compmedimag.2005.03.002},
keywords = {Archaeal, Artificial Intelligence, Bacterial, Cytomegalovirus, Gene
Transfer, Genome, Genomics, Horizontal, Non-U.S. Gov't, Research
Support, Viral, 16002263},
pii = {S0895-6111(05)00038-8},
url = {http://dx.doi.org/10.1016/j.compmedimag.2005.03.002}
}
@article{Fu1998Penalized,
author = {W. Fu},
title = {Penalized regressions: the Bridge versus the Lasso},
journal = {Journal of {C}omputational and {G}raphical {S}tatistics},
year = {1998},
volume = {7},
pages = {397--416}
}
@article{Fu2009DISCOVER,
author = {Fu, Wenjie and Ray, Pradipta and Xing, Eric P.},
title = {DISCOVER: a feature-based discriminative method for motif search
in complex genomes.},
journal = {Bioinformatics (Oxford, England)},
year = {2009},
volume = {25},
pages = {i321--329},
number = {12},
month = {June},
abstract = {MOTIVATION: Identifying transcription factor binding sites (TFBSs)
encoding complex regulatory signals in metazoan genomes remains a
challenging problem in computational genomics. Due to degeneracy
of nucleotide content among binding site instances or motifs, and
intricate 'grammatical organization' of motifs within cis-regulatory
modules (CRMs), extant pattern matching-based in silico motif search
methods often suffer from impractically high false positive rates,
especially in the context of analyzing large genomic datasets, and
noisy position weight matrices which characterize binding sites.
Here, we try to address this problem by using a framework to maximally
utilize the information content of the genomic DNA in the region
of query, taking cues from values of various biologically meaningful
genetic and epigenetic factors in the query region such as clade-specific
evolutionary parameters, presence/absence of nearby coding regions,
etc. We present a new method for TFBS prediction in metazoan genomes
that utilizes both the CRM architecture of sequences and a variety
of features of individual motifs. Our proposed approach is based
on a discriminative probabilistic model known as conditional random
fields that explicitly optimizes the predictive probability of motif
presence in large sequences, based on the joint effect of all such
features. RESULTS: This model overcomes weaknesses in earlier methods
based on less effective statistical formalisms that are sensitive
to spurious signals in the data. We evaluate our method on both simulated
CRMs and real Drosophila sequences in comparison with a wide spectrum
of existing models, and outperform the state of the art by 22\% in
F1 score. Availability and Implementation: The code is publicly available
at http://www.sailing.cs.cmu.edu/discover.html. SUPPLEMENTARY INFORMATION:
Supplementary data are available at Bioinformatics online.},
doi = {10.1093/bioinformatics/btp230},
issn = {1460-2059},
keywords = {complex, discovery, genomes, motif, tfbs},
posted-at = {2009-06-17 15:21:34},
priority = {2},
url = {http://dx.doi.org/10.1093/bioinformatics/btp230}
}
@techreport{fujibuchi1998,
author = {W. Fujibuchi and K. Sato and H. Ogata and S. Goto and M. Kanehisa},
title = {K{EGG} and {DBGET}/{L}ink{DB}: {I}ntegration of biological relationships
in divergenet molecular biology data},
institution = {AAAI Press},
year = {1998},
type = {Knowledge Sharing Across Biological and Medical Knowledge-Based Systems},
number = {WS-98-04},
owner = {franck},
timestamp = {2006.02.22}
}
@article{Fukumizu2008Statistical,
author = {Fukumizu, K. and Bach, F. R. and Gretton, A.},
title = {Statistical consistency of kernel canonical correlation analysis},
journal = {J. Mach. Learn. Res.},
year = {2008},
volume = {8},
pages = {361--383},
pdf = {../local/Fukumizu2008Statistical.pdf},
file = {Fukumizu2008Statistical.pdf:Fukumizu2008Statistical.pdf:PDF},
owner = {jp},
timestamp = {2009.01.05},
url = {http://jmlr.csail.mit.edu/papers/volume8/fukumizu07a/fukumizu07a.pdf}
}
@article{Fullwood2010Chromatin,
author = {Melissa J Fullwood and Yuyuan Han and Chia-Lin Wei and Xiaoan Ruan
and Yijun Ruan},
title = {Chromatin interaction analysis using paired-end tag sequencing.},
journal = {Curr Protoc Mol Biol},
year = {2010},
volume = {Chapter 21},
pages = {Unit 21.15.1--Unit 21.1525},
month = {Jan},
abstract = {Chromatin Interaction Analysis using Paired-End Tag sequencing (ChIA-PET)
is a technique developed for large-scale, de novo analysis of higher-order
chromatin structures. Cells are treated with formaldehyde to cross-link
chromatin interactions, DNA segments bound by protein factors are
enriched by chromatin immunoprecipitation, and interacting DNA fragments
are then captured by proximity ligation. The Paired-End Tag (PET)
strategy is applied to the construction of ChIA-PET libraries, which
are sequenced by high-throughput next-generation sequencing technologies.
Finally, raw PET sequences are subjected to bioinformatics analysis,
resulting in a genome-wide map of binding sites and chromatin interactions
mediated by the protein factor under study. This unit describes ChIA-PET
for genome-wide analysis of chromatin interactions in mammalian cells,
with the application of Roche/454 and Illumina sequencing technologies.},
doi = {10.1002/0471142727.mb2115s89},
institution = {Genome Institute of Singapore, Agency for Science, Technology and
Research, Singapore.},
keywords = {Animals; Chromatin; Computational Biology; Databases, Nucleic Acid;
Genome-Wide Association Study; Humans; Sequence Analysis, DNA},
owner = {phupe},
pmid = {20069536},
timestamp = {2010.08.26},
url = {http://dx.doi.org/10.1002/0471142727.mb2115s89}
}
@article{Fullwood2009oestrogen-receptor-alpha-bound,
author = {Melissa J Fullwood and Mei Hui Liu and You Fu Pan and Jun Liu and
Han Xu and Yusoff Bin Mohamed and Yuriy L Orlov and Stoyan Velkov
and Andrea Ho and Poh Huay Mei and Elaine G Y Chew and Phillips Yao
Hui Huang and Willem-Jan Welboren and Yuyuan Han and Hong Sain Ooi
and Pramila N Ariyaratne and Vinsensius B Vega and Yanquan Luo and
Peck Yean Tan and Pei Ye Choy and K. D Senali Abayratna Wansa and
Bing Zhao and Kar Sian Lim and Shi Chi Leow and Jit Sin Yow and Roy
Joseph and Haixia Li and Kartiki V Desai and Jane S Thomsen and Yew
Kok Lee and R. Krishna Murthy Karuturi and Thoreau Herve and Guillaume
Bourque and Hendrik G Stunnenberg and Xiaoan Ruan and Valere Cacheux-Rataboul
and Wing-Kin Sung and Edison T Liu and Chia-Lin Wei and Edwin Cheung
and Yijun Ruan},
title = {An oestrogen-receptor-alpha-bound human chromatin interactome.},
journal = {Nature},
year = {2009},
volume = {462},
pages = {58--64},
number = {7269},
month = {Nov},
abstract = {Genomes are organized into high-level three-dimensional structures,
and DNA elements separated by long genomic distances can in principle
interact functionally. Many transcription factors bind to regulatory
DNA elements distant from gene promoters. Although distal binding
sites have been shown to regulate transcription by long-range chromatin
interactions at a few loci, chromatin interactions and their impact
on transcription regulation have not been investigated in a genome-wide
manner. Here we describe the development of a new strategy, chromatin
interaction analysis by paired-end tag sequencing (ChIA-PET) for
the de novo detection of global chromatin interactions, with which
we have comprehensively mapped the chromatin interaction network
bound by oestrogen receptor alpha (ER-alpha) in the human genome.
We found that most high-confidence remote ER-alpha-binding sites
are anchored at gene promoters through long-range chromatin interactions,
suggesting that ER-alpha functions by extensive chromatin looping
to bring genes together for coordinated transcriptional regulation.
We propose that chromatin interactions constitute a primary mechanism
for regulating transcription in mammalian genomes.},
doi = {10.1038/nature08497},
institution = {Genome Institute of Singapore, Agency for Science, Technology and
Research, Singapore 138672.},
keywords = {Binding Sites; Cell Line; Chromatin; Chromatin Immunoprecipitation;
Cross-Linking Reagents; Estrogen Receptor alpha; Formaldehyde; Genome,
Human; Humans; Promoter Regions, Genetic; Protein Binding; Reproducibility
of Results; Sequence Analysis, DNA; Transcription, Genetic; Transcriptional
Activation},
owner = {phupe},
pii = {nature08497},
pmid = {19890323},
timestamp = {2010.08.26},
url = {http://dx.doi.org/10.1038/nature08497}
}
@article{Fullwood2009Next-generation,
author = {Melissa J Fullwood and Chia-Lin Wei and Edison T Liu and Yijun Ruan},
title = {Next-generation DNA sequencing of paired-end tags (PET) for transcriptome
and genome analyses.},
journal = {Genome Res},
year = {2009},
volume = {19},
pages = {521--532},
number = {4},
month = {Apr},
abstract = {Comprehensive understanding of functional elements in the human genome
will require thorough interrogation and comparison of individual
human genomes and genomic structures. Such an endeavor will require
improvements in the throughputs and costs of DNA sequencing. Next-generation
sequencing platforms have impressively low costs and high throughputs
but are limited by short read lengths. An immediate and widely recognized
solution to this critical limitation is the paired-end tag (PET)
sequencing for various applications, collectively called the PET
sequencing strategy, in which short and paired tags are extracted
from the ends of long DNA fragments for ultra-high-throughput sequencing.
The PET sequences can be accurately mapped to the reference genome,
thus demarcating the genomic boundaries of PET-represented DNA fragments
and revealing the identities of the target DNA elements. PET protocols
have been developed for the analyses of transcriptomes, transcription
factor binding sites, epigenetic sites such as histone modification
sites, and genome structures. The exclusive advantage of the PET
technology is its ability to uncover linkages between the two ends
of DNA fragments. Using this unique feature, unconventional fusion
transcripts, genome structural variations, and even molecular interactions
between distant genomic elements can be unraveled by PET analysis.
Extensive use of PET data could lead to efficient assembly of individual
human genomes, transcriptomes, and interactomes, enabling new biological
and clinical insights. With its versatile and powerful nature for
DNA analysis, the PET sequencing strategy has a bright future ahead.},
doi = {10.1101/gr.074906.107},
pdf = {../local/Fullwood2009Next-generation.pdf},
file = {Fullwood2009Next-generation.pdf:Fullwood2009Next-generation.pdf:PDF},
institution = {Genome Institute of Singapore, Agency for Science, Technology and
Research, Singapore 138672, Singapore.},
owner = {phupe},
pii = {19/4/521},
pmid = {19339662},
timestamp = {2010.08.20},
url = {http://dx.doi.org/10.1101/gr.074906.107}
}
@article{Fundel2005simple,
author = {Katrin Fundel and Daniel Güttler and Ralf Zimmer and Joannis Apostolakis},
title = {A simple approach for protein name identification: prospects and
limits.},
journal = {B{MC} {B}ioinformatics},
year = {2005},
volume = {6 Suppl 1},
pages = {S15},
abstract = {B{ACKGROUND}: {S}ignificant parts of biological knowledge are available
only as unstructured text in articles of biomedical journals. {B}y
automatically identifying gene and gene product (protein) names and
mapping these to unique database identifiers, it becomes possible
to extract and integrate information from articles and various data
sources. {W}e present a simple and efficient approach that identifies
gene and protein names in texts and returns database identifiers
for matches. {I}t has been evaluated in the recent {B}io{C}re{A}t{I}v{E}
entity extraction and mention normalization task by an independent
jury. {METHODS}: {O}ur approach is based on the use of synonym lists
that map the unique database identifiers for each gene/protein to
the different synonym names. {F}or yeast and mouse, synonym lists
were used as provided by the organizers who generated them from public
model organism databases. {T}he synonym list for fly was generated
directly from the corresponding organism database. {T}he lists were
then extensively curated in largely automated procedure and matched
against {MEDLINE} abstracts by exact text matching. {R}ule-based
and support vector machine-based post filters were designed and applied
to improve precision. {RESULTS}: {O}ur procedure showed high recall
and precision with {F}-measures of 0.897 for yeast and 0.764/0.773
for mouse in the {B}io{C}re{A}t{I}v{E} assessment ({T}ask 1{B}) and
0.768 for fly in a post-evaluation. {CONCLUSION}: {T}he results were
close to the best over all submissions. {D}epending on the synonym
properties it can be crucial to consider context and to filter out
erroneous matches. {T}his is especially important for fly, which
has a very challenging nomenclature for the protein name identification
task. {H}ere, the support vector machine-based post filter proved
to be very effective.},
doi = {10.1186/1471-2105-6-S1-S15},
pdf = {../local/Fundel2005simple.pdf},
file = {Fundel2005simple.pdf:local/Fundel2005simple.pdf:PDF},
keywords = {, , 15960827},
pii = {1471-2105-6-S1-S15},
url = {http://dx.doi.org/10.1186/1471-2105-6-S1-S15}
}
@article{Furey2000Support,
author = {Furey, T. S. and Cristianini, N. and Duffy, N. and Bednarski, D.
W. and Schummer, M. and Haussler, D.},
title = {Support vector machine classification and validation of cancer tissue
samples using microarray expression data},
journal = {Bioinformatics},
year = {2000},
volume = {16},
pages = {906-914},
number = {10},
month = {Oct},
abstract = {Motivation: {DNA} microarray experiments generating thousands of gene
expression measurements, are being used to gather information from
tissue and cell samples regarding gene expression differences that
will be useful in diagnosing disease. {W}e have developed a new method
to analyse this kind of data using support vector machines ({SVM}s).
{T}his analysis consists of both classification of the tissue samples,
and an exploration of the data for mis-labeled or questionable tissue
results. {R}esults: {W}e demonstrate the method in detail on samples
consisting of ovarian cancer tissues, normal ovarian tissues, and
other normal tissues. {T}he dataset consists of expression experiment
results for 97802 c{DNA}s for each tissue. {A}s a result of computational
analysis, a tissue sample is discovered and confirmed to be wrongly
labeled. {U}pon correction of this mistake and the removal of an
outlier, perfect classification of tissues is achieved, but not with
high confidence. {W}e identify and analyse a subset of genes from
the ovarian dataset whose expression is highly differentiated between
the types of tissues. {T}o show robustness of the {SVM} method, two
previously published datasets from other types of tissues or cells
are analysed. {T}he results are comparable to those previously obtained.
{W}e show that other machine learning methods also perform comparably
to the {SVM} on many of those datasets. {A}vailability: {T}he {SVM}
software is available at http://www.cs.columbia.edu/~bgrundy/svm.
{C}ontact: booch@cse.ucsc.edu},
pdf = {../local/Furey2000Support.pdf},
file = {Furey2000Support.pdf:local/Furey2000Support.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://bioinformatics.oupjournals.org/cgi/content/abstract/16/10/906}
}
@article{Furlanello2003Entropy-based,
author = {Furlanello, C. and Serafini, M. and Merler, S. and Jurman, G.},
title = {Entropy-based gene ranking without selection bias for the predictive
classification of microarray data},
journal = {B{MC} {B}ioinformatics},
year = {2003},
volume = {4},
number = {54},
abstract = {Background {W}e describe the {E}-{RFE} method for gene ranking, which
is useful for the identification of markers in the predictive classification
of array data. {T}he method supports a practical modeling scheme
designed to avoid the construction of classification rules based
on the selection of too small gene subsets (an effect known as the
selection bias, in which the estimated predictive errors are too
optimistic due to testing on samples already considered in the feature
selection process). {R}esults {W}ith {E}-{RFE}, we speed up the recursive
feature elimination ({RFE}) with {SVM} classifiers by eliminating
chunks of uninteresting genes using an entropy measure of the {SVM}
weights distribution. {A}n optimal subset of genes is selected according
to a two-strata model evaluation procedure: modeling is replicated
by an external stratified-partition resampling scheme, and, within
each run, an internal {K}-fold cross-validation is used for {E}-{RFE}
ranking. {A}lso, the optimal number of genes can be estimated according
to the saturation of {Z}ipf's law profiles. {C}onclusions {W}ithout
a decrease of classification accuracy, {E}-{RFE} allows a speed-up
factor of 100 with respect to standard {RFE}, while improving on
alternative parametric {RFE} reduction strategies. {T}hus, a process
for gene selection and error estimation is made practical, ensuring
control of the selection bias, and providing additional diagnostic
indicators of gene importance.},
doi = {10.1186/1471-2105-4-54},
pdf = {../local/Furlanello2003Entropy-based.pdf},
file = {Furlanello2003Entropy-based.pdf:local/Furlanello2003Entropy-based.pdf:PDF},
keywords = {biosvm},
owner = {vert},
url = {http://www.biomedcentral.com/1471-2105/4/54}
}
@article{Furnival1974Regressions,
author = {Furnival, G.M. and Wilson, R.W.},
title = {Regressions by leaps and bounds},
journal = {Technometrics},
year = {1974},
volume = {16},
pages = {499--511},
number = {4},
publisher = {Taylor \& Francis}
}
@article{Fussenegger2000NatBio,
author = {Fussenegger, M. and Bailey, J. and Varner, J.},
title = {A mathematical model of caspase function in apoptosis},
journal = {Nat. Biotechnol.},
year = {2000},
volume = {18},
pages = {768-774},
abstract = {Caspases (cysteine-containing aspartate-specific proteases) are at
the core of the cell's suicide machinery. These enzymes, once activated,
dismantle the cell by selectively cleaving key proteins after aspartate
residues. The events culminating in caspase activation are the subject
of intense study because of their role in cancer, and neurodegenerative
and autoimmune disorders. Here we present a mechanistic mathematical
model, formulated on the basis of newly emerging information, describing
key elements of receptor-mediated and stress-induced caspase activation.
We have used mass-conservation principles in conjunction with kinetic
rate laws to formulate ordinary differential equations that describe
the temporal evolution of caspase activation. Qualitative strategies
for the prevention of caspase activation are simulated and compared
with experimental data. We show that model predictions are consistent
with available information. Thus, the model could aid in better understanding
caspase activation and identifying therapeutic approaches promoting
or retarding apoptotic cell death.},
doi = {doi:10.1038/77589},
pdf = {../local/Fuster2005sweet.pdf},
file = {Fuster2005sweet.pdf:Fuster2005sweet.pdf:PDF},
keywords = {csbcbook}
}
@article{Fuster2005sweet,
author = {Fuster, M. N. and Esko, J. D.},
title = {The sweet and sour of cancer: glycans as novel therapeutic targets.},
journal = {Nat. {R}ev. {C}ancer},
year = {2005},
volume = {5},
pages = {526-42},
number = {7},
month = {Jul},
abstract = {A growing body of evidence supports crucial roles for glycans at various
pathophysiological steps of tumour progression. {G}lycans regulate
tumour proliferation, invasion, haematogenous metastasis and angiogenesis,
and increased understanding of these roles sets the stage for developing
pharmaceutical agents that target these molecules. {S}uch novel agents
might be used alone or in combination with operative and/or chemoradiation
strategies for treating cancer.},
doi = {10.1038/nrc1649},
pdf = {../local/Fuster2005sweet.pdf},
file = {Fuster2005sweet.pdf:Fuster2005sweet.pdf:PDF},
keywords = {glycans},
pii = {nrc1649},
url = {http://dx.doi.org/10.1038/nrc1649}
}
@article{Galluzzi2008Cell,
author = {Galluzzi, L. and Kroemer, G.},
title = {Necroptosis: A Specialized Pathway of Programmed Necrosis},
journal = {Cell},
year = {2008},
volume = {135},
pages = {1161-1163},
number = {7},
note = {doi: DOI: 10.1016/j.cell.2008.12.004},
keywords = {csbcbook}
}
@book{Galton1869Hereditary,
title = {Hereditary genius},
publisher = {Macmillan and Company},
year = {1869},
author = {Galton, S.F.}
}
@article{Galan2004Odor-driven,
author = {Roberto Fdez Galán and Silke Sachse and C. Giovanni Galizia and
Andreas V M Herz},
title = {Odor-driven attractor dynamics in the antennal lobe allow for simple
and rapid olfactory pattern classification.},
journal = {Neural {C}omput},
year = {2004},
volume = {16},
pages = {999-1012},
number = {5},
month = {May},
abstract = {The antennal lobe plays a central role for odor processing in insects,
as demonstrated by electrophysiological and imaging experiments.
{H}ere we analyze the detailed temporal evolution of glomerular activity
patterns in the antennal lobe of honeybees. {W}e represent these
spatiotemporal patterns as trajectories in a multidimensional space,
where each dimension accounts for the activity of one glomerulus.
{O}ur data show that the trajectories reach odor-specific steady
states (attractors) that correspond to stable activity patterns at
about 1 second after stimulus onset. {A}s revealed by a detailed
mathematical investigation, the trajectories are characterized by
different phases: response onset, steady-state plateau, response
offset, and periods of spontaneous activity. {A}n analysis based
on support-vector machines quantifies the odor specificity of the
attractors and the optimal time needed for odor discrimination. {T}he
results support the hypothesis of a spatial olfactory code in the
antennal lobe and suggest a perceptron-like readout mechanism that
is biologically implemented in a downstream network, such as the
mushroom body.},
doi = {10.1162/089976604773135078},
url = {http://dx.doi.org/10.1162/089976604773135078}
}
@article{Gama-Castro2011RegulonDB,
author = {Gama-Castro, S. and Salgado, H. and Peralta-Gil, M. and Santos-Zavaleta,
A. and Mu{\~n}iz-Rascado, L. and Solano-Lira, H. and Jimenez-Jacinto,
V. and Weiss, Verena and Garc{\'i}a-Sotelo, J. S. and L{\'o}pez-Fuentes,
A. and Porr{\'o}n-Sotelo, L. and Alquicira-Hern{\'a}ndez, S. and
Medina-Rivera, A. and Mart{\'i}nez-Flores, I. and Alquicira-Hern{\'a}ndez,
K. and Mart{\'i}nez-Adame, R. and Bonavides-Mart{\'i}nez, C. and
Miranda-R{\'i}os, J. and Huerta, A. M. and Mendoza-Vargas, A. and
Collado-Torres, L. and Taboada, B. and Vega-Alvarado, L. and Olvera,
M. and Olvera, L. and Grande, R. and Morett, E. and Collado-Vides,
J.},
title = {{RegulonDB} version 7.0: transcriptional regulation of {E}scherichia
coli {K-12} integrated within genetic sensory response units (Gensor
Units)},
journal = {Nucleic Acids Res.},
year = {2011},
volume = {39},
pages = {D98-D105},
number = {suppl 1},
abstract = {RegulonDB (http://regulondb.ccg.unam.mx/) is the primary reference
database of the best-known regulatory network of any free-living
organism, that of Escherichia coli K-12. The major conceptual change
since 3 years ago is an expanded biological context so that transcriptional
regulation is now part of a unit that initiates with the signal and
continues with the signal transduction to the core of regulation,
modifying expression of the affected target genes responsible for
the response. We call these genetic sensory response units, or Gensor
Units. We have initiated their high-level curation, with graphic
maps and superreactions with links to other databases. Additional
connectivity uses expandable submaps. RegulonDB has summaries for
every transcription factor (TF) and TF-binding sites with internal
symmetry. Several DNA-binding motifs and their sizes have been redefined
and relocated. In addition to data from the literature, we have incorporated
our own information on transcription start sites (TSSs) and transcriptional
units (TUs), obtained by using high-throughput whole-genome sequencing
technologies. A new portable drawing tool for genomic features is
also now available, as well as new ways to download the data, including
web services, files for several relational database manager systems
and text files including BioPAX format.},
doi = {10.1093/nar/gkq1110},
eprint = {http://nar.oxfordjournals.org/content/39/suppl_1/D98.full.pdf+html},
url = {http://nar.oxfordjournals.org/content/39/suppl_1/D98.abstract}
}
@article{Gangal2005Human,
author = {Rajeev Gangal and Pankaj Sharma},
title = {Human pol {II} promoter prediction: time series descriptors and machine
learning.},
journal = {Nucleic {A}cids {R}es},
year = {2005},
volume = {33},
pages = {1332-6},
number = {4},
abstract = {Although several in silico promoter prediction methods have been developed
to date, they are still limited in predictive performance. {T}he
limitations are due to the challenge of selecting appropriate features
of promoters that distinguish them from non-promoters and the generalization
or predictive ability of the machine-learning algorithms. {I}n this
paper we attempt to define a novel approach by using unique descriptors
and machine-learning methods for the recognition of eukaryotic polymerase
{II} promoters. {I}n this study, non-linear time series descriptors
along with non-linear machine-learning algorithms, such as support
vector machine ({SVM}), are used to discriminate between promoter
and non-promoter regions. {T}he basic idea here is to use descriptors
that do not depend on the primary {DNA} sequence and provide a clear
distinction between promoter and non-promoter regions. {T}he classification
model built on a set of 1000 promoter and 1500 non-promoter sequences,
showed a 10-fold cross-validation accuracy of 87\% and an independent
test set had an accuracy >85\% in both promoter and non-promoter
identification. {T}his approach correctly identified all 20 experimentally
verified promoters of human chromosome 22. {T}he high sensitivity
and selectivity indicates that n-mer frequencies along with non-linear
time series descriptors, such as {L}yapunov component stability and
{T}sallis entropy, and supervised machine-learning methods, such
as {SVM}s, can be useful in the identification of pol {II} promoters.},
doi = {10.1093/nar/gki271},
pdf = {../local/Gangal2005Human.pdf},
file = {Gangal2005Human.pdf:local/Gangal2005Human.pdf:PDF},
keywords = {biosvm},
pii = {33/4/1332},
url = {http://dx.doi.org/10.1093/nar/gki271}
}
@article{Gao2005Improving,
author = {Yuan Gao and George Church},
title = {Improving molecular cancer class discovery through sparse non-negative
matrix factorization.},
journal = {Bioinformatics},
year = {2005},
volume = {21},
pages = {3970--3975},
number = {21},
month = {Nov},
abstract = {MOTIVATION: Identifying different cancer classes or subclasses with
similar morphological appearances presents a challenging problem
and has important implication in cancer diagnosis and treatment.
Clustering based on gene-expression data has been shown to be a powerful
method in cancer class discovery. Non-negative matrix factorization
is one such method and was shown to be advantageous over other clustering
techniques, such as hierarchical clustering or self-organizing maps.
In this paper, we investigate the benefit of explicitly enforcing
sparseness in the factorization process. RESULTS: We report an improved
unsupervised method for cancer classification by the use of gene-expression
profile via sparse non-negative matrix factorization. We demonstrate
the improvement by direct comparison with classic non-negative matrix
factorization on the three well-studied datasets. In addition, we
illustrate how to identify a small subset of co-expressed genes that
may be directly involved in cancer.},
doi = {10.1093/bioinformatics/bti653},
pdf = {../local/Gao2005Improving.pdf},
file = {Gao2005Improving.pdf:Gao2005Improving.pdf:PDF},
institution = {Department of Genetics, Harvard Medical School Boston, MA 02115,
USA. g1m1c1@receptor.med.harvard.edu},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {21/21/3970},
pmid = {16244221},
timestamp = {2012.02.28},
url = {http://dx.doi.org/10.1093/bioinformatics/bti653}
}
@article{Garber2001Diversity,
author = {Garber, M. E. and Troyanskaya, O. G. and Schluens, K. and Petersen,
S. and Thaesler, Z. and Pacyna-Gengelbach, M. and {van de Rijn},
M. and Rosen, G. D. and Perou, C. M. and Whyte, R. I. and Altman,
R. B. and Brown, P. O. and Botstein, D. and Petersen, I.},
title = {Diversity of gene expression in adenocarcinoma of the lung.},
journal = {Proc Natl Acad Sci U S A},
year = {2001},
volume = {98},
pages = {13784--13789},
number = {24},
month = {Nov},
abstract = {The global gene expression profiles for 67 human lung tumors representing
56 patients were examined by using 24,000-element cDNA microarrays.
Subdivision of the tumors based on gene expression patterns faithfully
recapitulated morphological classification of the tumors into squamous,
large cell, small cell, and adenocarcinoma. The gene expression patterns
made possible the subclassification of adenocarcinoma into subgroups
that correlated with the degree of tumor differentiation as well
as patient survival. Gene expression analysis thus promises to extend
and refine standard pathologic analysis.},
doi = {10.1073/pnas.241500798},
pdf = {../local/Garber2001Diversity.pdf},
file = {Garber2001Diversity.pdf:Garber2001Diversity.pdf:PDF},
institution = {Department of Genetics, Stanford University School of Medicine, Stanford,
CA 94305, USA.},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {241500798},
pmid = {11707590},
timestamp = {2012.02.27},
url = {http://dx.doi.org/10.1073/pnas.241500798}
}
@article{Garcia2007Organismal,
author = {Benjamin A Garcia and Sandra B Hake and Robert L Diaz and Monika
Kauer and Stephanie A Morris and Judith Recht and Jeffrey Shabanowitz
and Nilamadhab Mishra and Brian D Strahl and C. David Allis and Donald
F Hunt},
title = {Organismal differences in post-translational modifications in histones
H3 and H4.},
journal = {J Biol Chem},
year = {2007},
volume = {282},
pages = {7641--7655},
number = {10},
month = {Mar},
abstract = {Post-translational modifications (PTMs) of histones play an important
role in many cellular processes, notably gene regulation. Using a
combination of mass spectrometric and immunobiochemical approaches,
we show that the PTM profile of histone H3 differs significantly
among the various model organisms examined. Unicellular eukaryotes,
such as Saccharomyces cerevisiae (yeast) and Tetrahymena thermophila
(Tet), for example, contain more activation than silencing marks
as compared with mammalian cells (mouse and human), which are generally
enriched in PTMs more often associated with gene silencing. Close
examination reveals that many of the better-known modified lysines
(Lys) can be either methylated or acetylated and that the overall
modification patterns become more complex from unicellular eukaryotes
to mammals. Additionally, novel species-specific H3 PTMs from wild-type
asynchronously grown cells are also detected by mass spectrometry.
Our results suggest that some PTMs are more conserved than previously
thought, including H3K9me1 and H4K20me2 in yeast and H3K27me1, -me2,
and -me3 in Tet. On histone H4, methylation at Lys-20 showed a similar
pattern as H3 methylation at Lys-9, with mammals containing more
methylation than the unicellular organisms. Additionally, modification
profiles of H4 acetylation were very similar among the organisms
examined.},
doi = {10.1074/jbc.M607900200},
institution = {Department of Chemistry, University of Virginia, Charlottesville,
Virginia 22901, USA.},
keywords = {Acetylation; Animals; Hela Cells; Histones, chemistry/metabolism;
Humans; Methylation; Mice; NIH 3T3 Cells; Protein Processing, Post-Translational;
Saccharomyces cerevisiae, metabolism; Species Specificity; Tandem
Mass Spectrometry; Tetrahymena, metabolism},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {M607900200},
pmid = {17194708},
timestamp = {2010.11.23},
url = {http://dx.doi.org/10.1074/jbc.M607900200}
}
@article{Garcia-Gomez2004Benign,
author = {Juan M GarcÃa-Gómez and César Vidal and Luis MartÃ-Bonmatà and
JoaquÃn Galant and Nicolas Sans and Montserrat Robles and Francisco
Casacuberta},
title = {Benign/malignant classifier of soft tissue tumors using {MR} imaging.},
journal = {M{AGMA}},
year = {2004},
volume = {16},
pages = {194-201},
number = {4},
month = {Mar},
abstract = {This article presents a pattern-recognition approach to the soft tissue
tumors ({STT}) benign/malignant character diagnosis using magnetic
resonance ({MR}) imaging applied to a large multicenter database.
{OBJECTIVE}: {T}o develop and test an automatic classifier of {STT}
into benign or malignant by using classical {MR} imaging findings
and epidemiological information. {MATERIALS} {AND} {METHODS}: {A}
database of 430 patients (62\% benign and 38\% malignant) from several
{E}uropean multicenter registers. {T}here were 61 different histologies
(36 with benign and 25 with malignant nature). {T}hree pattern-recognition
methods (artificial neural networks, support vector machine, k-nearest
neighbor) were applied to learn the discrimination between benignity
and malignancy based on a defined {MR} imaging findings protocol.
{A}fter the systems had learned by using training samples (with 302
cases), the clinical decision support system was tested in the diagnosis
of 128 new {STT} cases. {RESULTS}: {A}n 88-92\% efficacy was obtained
in a not-viewed set of tumors using the pattern-recognition techniques.
{T}he best results were obtained with a back-propagation artificial
neural network. {CONCLUSION}: {B}enign vs. malignant {STT} discrimination
is accurate by using pattern-recognition methods based on classical
{MR} image findings. {T}his objective tool will assist radiologists
in {STT} grading.},
doi = {10.1007/s10334-003-0023-7},
pdf = {../local/Garcia-Gomez2004Benign.pdf},
file = {Garcia-Gomez2004Benign.pdf:local/Garcia-Gomez2004Benign.pdf:PDF},
url = {http://dx.doi.org/10.1007/s10334-003-0023-7}
}
@article{Gardner2002Genome,
author = {Malcolm J Gardner and Neil Hall and Eula Fung and Owen White and
Matthew Berriman and Richard W Hyman and Jane M Carlton and Arnab
Pain and Karen E Nelson and Sharen Bowman and Ian T Paulsen and Keith
James and Jonathan A Eisen and Kim Rutherford and Steven L Salzberg
and Alister Craig and Sue Kyes and Man-Suen Chan and Vishvanath Nene
and Shamira J Shallom and Bernard Suh and Jeremy Peterson and Sam
Angiuoli and Mihaela Pertea and Jonathan Allen and Jeremy Selengut
and Daniel Haft and Michael W Mather and Akhil B Vaidya and David
M A Martin and Alan H Fairlamb and Martin J Fraunholz and David S
Roos and Stuart A Ralph and Geoffrey I McFadden and Leda M Cummings
and G. Mani Subramanian and Chris Mungall and J. Craig Venter and
Daniel J Carucci and Stephen L Hoffman and Chris Newbold and Ronald
W Davis and Claire M Fraser and Bart Barrell},
title = {{G}enome sequence of the human malaria parasite {P}lasmodium falciparum.},
journal = {Nature},
year = {2002},
volume = {419},
pages = {498--511},
number = {6906},
month = {Oct},
abstract = {The parasite Plasmodium falciparum is responsible for hundreds of
millions of cases of malaria, and kills more than one million African
children annually. Here we report an analysis of the genome sequence
of P. falciparum clone 3D7. The 23-megabase nuclear genome consists
of 14 chromosomes, encodes about 5,300 genes, and is the most (A
+ T)-rich genome sequenced to date. Genes involved in antigenic variation
are concentrated in the subtelomeric regions of the chromosomes.
Compared to the genomes of free-living eukaryotic microbes, the genome
of this intracellular parasite encodes fewer enzymes and transporters,
but a large proportion of genes are devoted to immune evasion and
host-parasite interactions. Many nuclear-encoded proteins are targeted
to the apicoplast, an organelle involved in fatty-acid and isoprenoid
metabolism. The genome sequence provides the foundation for future
studies of this organism, and is being exploited in the search for
new drugs and vaccines to fight malaria.},
doi = {10.1038/nature01097},
pdf = {../local/Gardner2002Genome.pdf},
file = {Gardner2002Genome.pdf:local/Gardner2002Genome.pdf:PDF},
keywords = {plasmodium},
pii = {nature01097},
pmid = {12511928},
timestamp = {2006.04.13},
url = {http://dx.doi.org/10.1038/nature01097}
}
@article{Gardner2003Inferring,
author = {Gardner, T. S. and Bernardo, D. and Lorenz, D. and Collins, J. J.},
title = {Inferring genetic networks and identifying compound mode of action
via expression profiling},
journal = {Science},
year = {2003},
volume = {301},
pages = {102--105},
number = {5629},
month = {Jul},
abstract = {The complexity of cellular gene, protein, and metabolite networks
can hinder attempts to elucidate their structure and function. To
address this problem, we used systematic transcriptional perturbations
to construct a first-order model of regulatory interactions in a
nine-gene subnetwork of the SOS pathway in Escherichia coli. The
model correctly identified the major regulatory genes and the transcriptional
targets of mitomycin C activity in the subnetwork. This approach,
which is experimentally and computationally scalable, provides a
framework for elucidating the functional properties of genetic networks
and identifying molecular targets of pharmacological compounds.},
doi = {10.1126/science.1081900},
pdf = {../local/Gardner2003Inferring.pdf},
file = {Gardner2003Inferring.pdf:Gardner2003Inferring.pdf:PDF},
institution = {Center for BioDynamics and Department of Biomedical Engineering,
Boston University, 44 Cummington Street, Boston, MA 02215, USA.},
owner = {fantine},
pii = {301/5629/102},
pmid = {12843395},
timestamp = {2008.01.22},
url = {http://dx.doi.org/10.1126/science.1081900}
}
@article{Gardner2010Reverse,
author = {Gardner, T. S. and Faith, J. J.},
title = {Reverse-engineering transcription control networks.},
journal = {Phys. Life Rev.},
year = {2010},
volume = {2},
pages = {65--88},
number = {1},
month = {Apr},
abstract = {Microarray technologies, which enable the simultaneous measurement
of all RNA transcripts in a cell, have spawned the development of
algorithms for reverse-engineering transcription control networks.
In this article, we classify the algorithms into two general strategies:
physical modeling and influence modeling. We discuss the biological
and computational principles underlying each strategy, and provide
leading examples of each. We also discuss the practical considerations
for developing and applying the various methods.},
doi = {10.1016/j.plrev.2005.01.001},
pdf = {../local/Gardner2010Reverse.pdf},
file = {Gardner2010Reverse.pdf:Gardner2010Reverse.pdf:PDF},
institution = {Department of Biomedical Engineering, Boston University, 44 Cummington
St., Boston, MA 02215, USA.},
language = {eng},
medline-pst = {aheadofprint},
owner = {jp},
pii = {S1571-0645(05)00003-5},
pmid = {20416858},
timestamp = {2011.11.30},
url = {http://dx.doi.org/10.1016/j.plrev.2005.01.001}
}
@article{Gardy2005PSORTb,
author = {Gardy, J. L. and Laird, M. R. and Chen, F. and Rey, S. and Walsh,
C. J. and Ester, M. and Brinkman, F. S. L.},
title = {{{PSORT}b v.2.0}: expanded prediction of bacterial protein subcellular
localization and insights gained from comparative proteome analysis},
journal = {Bioinformatics},
year = {2005},
volume = {21},
pages = {617-623},
number = {5},
month = {Mar},
abstract = {Motivation: {PSORT}b v.1.1 is the most precise bacterial localization
prediction tool available. {H}owever the program's predictive coverage
and recall are low and the method is only applicable to {G}ram-negative
bacteria. {T}he goals of the present work were: increase {PSORT}b's
coverage while maintaining the existing precision level, expand it
to include {G}ram-positive bacteria, and then carry out a comparative
analysis of localization.{R}esults: {A}n expanded database of proteins
of known localization and new modules using frequent subsequence-based
support vector machines were introduced into {PSORT}b v.2.0. {T}he
program attains a precision of 96% for {G}ram-positive and {G}ram-negative
bacteria and predictive coverage comparable to other tools for whole
proteome analysis. {W}e show that the proportion of proteins at each
localization is remarkably consistent across species, even in species
with varying proteome size.{A}vailability: {W}eb-based version: http://www.psort.org/psortb.
{S}tandalone version: {A}vailable through the website under {GNU}
{G}eneral {P}ublic {L}icense.{S}upplementary {I}nformation: http://www.psort.org/psortb/supplementaryinfo.html.},
doi = {10.1093/bioinformatics/bti057},
pdf = {../local/Gardy2005PSORTb.pdf},
file = {Gardy2005PSORTb.pdf:local/Gardy2005PSORTb.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://dx.doi.org/10.1093/bioinformatics/bti057}
}
@book{Garey1979Computer,
title = {Computer and intractability: A guide to the theory of NP-completeness},
publisher = {San Francisco, CA: W. H. Freeman},
year = {1979},
author = {M. R. Garey and D. S. Johnson}
}
@article{Garg2005SVM-based,
author = {Garg, A. and Bhasin, M. and Raghava, G.P.},
title = {S{VM}-based method for subcellular localization of human proteins
using amino acid compositions, their order and similarity search},
journal = {J. {B}iol. {C}hem.},
year = {2005},
volume = {280},
pages = {14427-32},
number = {15},
month = {Apr},
abstract = {Here we report a systematic approach for predicting subcellular localization
(cytoplasm, mitochondrial, nuclear and plasma membrane) of human
proteins. {F}irstly, {SVM} based modules for predicting subcellular
localization using traditional amino acid and dipeptide (i+1) composition
achieved overall accuracy of 76.6% and 77.8%, respectively. {PSI}-{BLAST}
when carried out using similarity-based search against non-redundant
database of experimentally annotated proteins yielded 73.3% accuracy.
{T}o gain further insight, hybrid module (hybrid1) was developed
based on amino acid composition, dipeptide composition, and similarity
information and attained better accuracy of 84.9%. {I}n addition,
{SVM} module based on different higher order dipeptide i.e. i+2,
i+3, and i+4 were also constructed for the prediction of subcellular
localization of human proteins and overall accuracy of 79.7%, 77.5%
and 77.1% was accomplished respectively. {F}urthermore, another {SVM}
module hybrid2 was developed using traditional dipeptide (i+1) and
higher order dipeptide (i+2, i+3, and i+4) compositions, which gave
an overall accuracy of 81.3%. {W}e also developed {SVM} module hybrid3
based on amino acid composition, traditional and higher order dipeptide
compositions and {PSI}-{BLAST} output and achieved an overall accuracy
of 84.4%. {A} web server {HSLP}red (http://www.imtech.res.in/raghava/hslpred/
or http://bioinformatics.uams.edu/raghava/hslpred/) has been designed
to predict subcellular localization of human proteins using the above
approaches.},
doi = {10.1074/jbc.M411789200},
pdf = {../local/Garg2005SVM-based.pdf},
file = {Garg2005SVM-based.pdf:local/Garg2005SVM-based.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://dx.doi.org/10.1074/jbc.M411789200}
}
@article{Garnis2006High,
author = {C. Garnis and W. W. Lockwood and E. Vucic and Y. Ge and L. Girard
and J. D. Minna and A. F. Gazdar and S. Lam and C. MacAulay and W.
L. Lam},
title = {High resolution analysis of non-small cell lung cancer cell lines
by whole genome tiling path array {CGH}.},
journal = {Int. J. Cancer},
year = {2006},
volume = {118},
pages = {1556--1564},
number = {6},
abstract = {Chromosomal regions harboring tumor suppressors and oncogenes are
often deleted or amplified. Array comparative genomic hybridization
detects segmental DNA copy number alterations in tumor DNA relative
to a normal control. The recent development of a bacterial artificial
chromosome array, which spans the human genome in a tiling path manner
with >32,000 clones, has facilitated whole genome profiling at an
unprecedented resolution. Using this technology, we comprehensively
describe and compare the genomes of 28 commonly used non-small cell
lung carcinoma (NSCLC) cell models, derived from 18 adenocarcinomas
(AC), 9 squamous cell carcinomas and 1 large cell carcinoma. Analysis
at such resolution not only provided a detailed genomic alteration
template for each of these model cell lines, but revealed novel regions
of frequent duplication and deletion. Significantly, a detailed analysis
of chromosome 7 identified 6 distinct regions of alterations across
this chromosome, implicating the presence of multiple novel oncogene
loci on this chromosome. As well, a comparison between the squamous
and AC cells revealed alterations common to both subtypes, such as
the loss of 3p and gain of 5p, in addition to multiple hotspots more
frequently associated with only 1 subtype. Interestingly, chromosome
3q, which is known to be amplified in both subtypes, showed 2 distinct
regions of alteration, 1 frequently altered in squamous and 1 more
frequently altered in AC. In summary, our data demonstrate the unique
information generated by high resolution analysis of NSCLC genomes
and uncover the presence of genetic alterations prevalent in the
different NSCLC subtypes.},
doi = {10.1002/ijc.21491},
institution = {British Columbia Cancer Research Centre, Vancouver, BC, Canada. cgarnis@bccrc.ca},
keywords = {Carcinoma, Non-Small-Cell Lung, genetics/pathology; Cell Line, Tumor;
Chromosomes, Artificial, Bacterial, genetics; Gene Amplification;
Gene Dosage; Gene Expression Profiling; Genome, Human, genetics;
Humans; Loss of Heterozygosity; Lung Neoplasms, genetics/pathology;
Microarray Analysis, methods; Nucleic Acid Hybridization, methods},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pmid = {16187286},
timestamp = {2010.01.08},
url = {http://dx.doi.org/10.1002/ijc.21491}
}
@article{Garrett2003Comparison,
author = {D. Garrett and D. A Peterson and C. Anderson and M. Thaut},
title = {Comparison of linear, nonlinear, and feature selection methods for
{EEG} signal classification.},
journal = {I{EEE} {T}rans {N}eural {S}yst {R}ehabil {E}ng},
year = {2003},
volume = {11},
pages = {141-4},
number = {2},
month = {Jun},
abstract = {The reliable operation of brain-computer interfaces ({BCI}s) based
on spontaneous electroencephalogram ({EEG}) signals requires accurate
classification of multichannel {EEG}. {T}he design of {EEG} representations
and classifiers for {BCI} are open research questions whose difficulty
stems from the need to extract complex spatial and temporal patterns
from noisy multidimensional time series obtained from {EEG} measurements.
{T}he high-dimensional and noisy nature of {EEG} may limit the advantage
of nonlinear classification methods over linear ones. {T}his paper
reports the results of a linear (linear discriminant analysis) and
two nonlinear classifiers (neural networks and support vector machines)
applied to the classification of spontaneous {EEG} during five mental
tasks, showing that nonlinear classifiers produce only slightly better
classification results. {A}n approach to feature selection based
on genetic algorithms is also presented with preliminary results
of application to {EEG} during finger movement.},
keywords = {80 and over, Adnexal Diseases, Adult, Aged, Algorithms, Artificial
Intelligence, Automated, Bayes Theorem, Biological, Brain, Brain
Mapping, Breast Neoplasms, Case-Control Studies, Chromatography,
Comparative Study, Computational Biology, Computer Simulation, Computer-Assisted,
DNA, Diagnosis, Differential, Discriminant Analysis, Electroencephalography,
Evoked Potentials, Feasibility Studies, Female, Fingers, Gene Expression
Profiling, Gene Expression Regulation, Genetic, Genetic Markers,
Genetic Predisposition to Disease, Genetic Screening, Habituation
(Psychophysiology), High Pressure Liquid, Humans, Linear Models,
Logistic Models, Male, Middle Aged, Migraine, Models, Movement, Neural
Networks (Computer), Neurological, Non-P.H.S., Non-U.S. Gov't, Nonlinear
Dynamics, Nucleosides, Ovarian Neoplasms, Pattern Recognition, Photic
Stimulation, Predictive Value of Tests, ROC Curve, Reproducibility
of Results, Research Support, Sensitivity and Specificity, Signal
Processing, Software, Statistical, Thinking, Tumor Markers, U.S.
Gov't, User-Computer Interface, Visual, 12899257}
}
@article{Gasch2001Genomic,
author = {A.P. Gasch and M. Huang and S. Metzner and D. Botstein and S.J. Elledge
and P.O. Brown},
title = {Genomic expression responses to {DNA}-damaging agents and the regulatory
role of the yeast {ATR} homolog {M}ec1p},
journal = {Mol. {B}iol. {C}ell},
year = {2001},
volume = {12},
pages = {2987--3003},
number = {10},
pdf = {../local/gasc01.pdf},
file = {gasc01.pdf:local/gasc01.pdf:PDF},
subject = {microarray},
url = {http://www.molbiolcell.org/cgi/content/full/12/10/2987}
}
@article{Gasch2000Genomic,
author = {Gasch, A. P. and Spellman, P. T. and Kao, C. M. and Carmel-Harel,
O. and Eisen, M. B. and Storz, G. and Botstein, D. and Brown, P.
O.},
title = {Genomic {E}xpression {P}rograms in the {R}esponse of {Y}east {C}ells
to {E}nvironmental {C}hanges},
journal = {Mol. {B}iol. {C}ell},
year = {2000},
volume = {11},
pages = {4241--4257},
month = {Dec},
pdf = {../local/gasc00.pdf},
file = {gasc00.pdf:local/gasc00.pdf:PDF},
subject = {microarray},
url = {http://www.molbiolcell.org/cgi/reprint/11/12/4241.pdf}
}
@article{Gasteigner1996Chemical,
author = {Gasteiger, J. and Sadowski, J. and Schuur, J. and Selzer, P. and
Steinhauer, L. and Steinhauer, V.},
title = {Chemical Information in 3D Space},
journal = {J. Chem. Inform. Comput. Sci.},
year = {1996},
volume = {36},
pages = {1030-1037},
number = {5},
doi = {10.1021/ci960343+},
eprint = {http://pubs.acs.org/doi/pdf/10.1021/ci960343%2B},
url = {http://pubs.acs.org/doi/abs/10.1021/ci960343%2B}
}
@article{GatViks2004,
author = {Gat-Viks, I. and Tanay, A. and Shamir, R.},
title = {Modeling and analysis of heterogeneous regulation in biological networks},
journal = {J {C}omput {B}iol},
year = {2004},
volume = {11},
pages = {1034-49},
number = {6},
abstract = {In this study, we propose a novel model for the representation of
biological networks and provide algorithms for learning model parameters
from experimental data. {O}ur approach is to build an initial model
based on extant biological knowledge and refine it to increase the
consistency between model predictions and experimental data. {O}ur
model encompasses networks which contain heterogeneous biological
entities (m{RNA}, proteins, metabolites) and aims to capture diverse
regulatory circuitry on several levels (metabolism, transcription,
translation, post-translation and feedback loops, among them). {A}lgorithmically,
the study raises two basic questions: how to use the model for predictions
and inference of hidden variables states, and how to extend and rectify
model components. {W}e show that these problems are hard in the biologically
relevant case where the network contains cycles. {W}e provide a prediction
methodology in the presence of cycles and a polynomial time, constant
factor approximation for learning the regulation of a single entity.
{A} key feature of our approach is the ability to utilize both high-throughput
experimental data, which measure many model entities in a single
experiment, as well as specific experimental measurements of few
entities or even a single one. {I}n particular, we use together gene
expression, growth phenotypes, and proteomics data. {W}e tested our
strategy on the lysine biosynthesis pathway in yeast. {W}e constructed
a model of more than 150 variables based on an extensive literature
survey and evaluated it with diverse experimental data. {W}e used
our learning algorithms to propose novel regulatory hypotheses in
several cases where the literature-based model was inconsistent with
the experiments. {W}e showed that our approach has better accuracy
than extant methods of learning regulation.}
}
@article{Gati1979Further,
author = {G. Gati},
title = {Further annotated bibliography on the isomorphism disease},
journal = {J. Graph Theor.},
year = {1979},
volume = {3},
pages = {95--109},
acknowledgement = {#ack-fg#},
coden = {JGTHDO},
issn = {0364-9024}
}
@article{Gaudan2005Resolving,
author = {Gaudan, S. and Kirsch, H. and Rebholz-Schuhmann, D.},
title = {Resolving abbreviations to their senses in {M}edline.},
journal = {Bioinformatics},
year = {2005},
month = {Jul},
abstract = {M{OTIVATION}: {B}iological literature contains many abbreviations
with one particular sense in each document. {H}owever, most abbreviations
do not have a unique sense across the literature. {F}urthermore,
many documents do not contain the long-forms of the abbreviations.
{R}esolving an abbreviation in a document consists of retrieving
its sense in use. {A}bbreviation resolution improves accuracy of
document retrieval engines and of information extraction systems.
{RESULTS}: {W}e combine an automatic analysis of {M}edline abstracts
and linguistic methods to build a dictionary of abbreviation/sense
pairs. {T}he dictionary is used for the resolution of abbreviations
occurring with their long-forms. {A}mbiguous global abbreviations
are resolved using {S}upport {V}ector {M}achines that have been trained
on the context of each instance of the abbreviation/sense pairs,
previously extracted for the dictionary setup. {T}he system disambiguates
abbreviations with a precision of 98.9\% for a recall of 98.2\% (98.5\%
accuracy). {T}his performance is superior in comparison to previously
reported research work. {AVAILABILITY}: {T}he abbreviation resolution
module is available at http://www.ebi.ac.uk/{R}ebholz/software.html.},
doi = {10.1093/bioinformatics/bti586},
pdf = {../local/Gaudan2005Resolving.pdf},
file = {Gaudan2005Resolving.pdf:local/Gaudan2005Resolving.pdf:PDF},
keywords = {biosvm nlp},
pii = {bti586},
url = {http://dx.doi.org/10.1093/bioinformatics/bti586}
}
@article{Gaudet2005MCPorteomics,
author = {Gaudet, S. and Janes, K. A. and Albeck, J. G. and Pace, E. A. and
Lauffenburger, D. A. and Sorger, P. K.},
title = {A Compendium of Signals and Responses Triggered by Prodeath and Prosurvival
Cytokines},
journal = {Mol Cell Proteomics},
year = {2005},
volume = {4},
pages = {1569-1590},
number = {10},
abstract = {Cell-signaling networks consist of proteins with a variety of functions
(receptors, adaptor proteins, GTPases, kinases, proteases, and transcription
factors) working together to control cell fate. Although much is
known about the identities and biochemical activities of these signaling
proteins, the ways in which they are combined into networks to process
and transduce signals are poorly understood. Network-level understanding
of signaling requires data on a wide variety of biochemical processes
such as posttranslational modification, assembly of macromolecular
complexes, enzymatic activity, and localization. No single method
can gather such heterogeneous data in high throughput, and most studies
of signal transduction therefore rely on series of small, discrete
experiments. Inspired by the power of systematic datasets in genomics,
we set out to build a systematic signaling dataset that would enable
the construction of predictive models of cell-signaling networks.
Here we describe the compilation and fusion of [~]10,000 signal and
response measurements acquired from HT-29 cells treated with tumor
necrosis factor-{alpha}, a proapoptotic cytokine, in combination
with epidermal growth factor or insulin, two prosurvival growth factors.
Nineteen protein signals were measured over a 24-h period using kinase
activity assays, quantitative immunoblotting, and antibody microarrays.
Four different measurements of apoptotic response were also collected
by flow cytometry for each time course. Partial least squares regression
models that relate signaling data to apoptotic response data reveal
which aspects of compendium construction and analysis were important
for the reproducibility, internal consistency, and accuracy of the
fused set of signaling measurements. We conclude that it is possible
to build self-consistent compendia of cell-signaling data that can
be mined computationally to yield important insights into the control
of mammalian cell responses.},
keywords = {csbcbook}
}
@article{Gavin2002Functionala,
author = {Anne-Claude Gavin and Markus Bösche and Roland Krause and Paola
Grandi and Martina Marzioch and Andreas Bauer and Jörg Schultz and
Jens M Rick and Anne-Marie Michon and Cristina-Maria Cruciat and
Marita Remor and Christian Höfert and Malgorzata Schelder and Miro
Brajenovic and Heinz Ruffner and Alejandro Merino and Karin Klein
and Manuela Hudak and David Dickson and Tatjana Rudi and Volker Gnau
and Angela Bauch and Sonja Bastuck and Bettina Huhse and Christina
Leutwein and Marie-Anne Heurtier and Richard R Copley and Angela
Edelmann and Erich Querfurth and Vladimir Rybin and Gerard Drewes
and Manfred Raida and Tewis Bouwmeester and Peer Bork and Bertrand
Seraphin and Bernhard Kuster and Gitte Neubauer and Giulio Superti-Furga},
title = {Functional organization of the yeast proteome by systematic analysis
of protein complexes.},
journal = {Nature},
year = {2002},
volume = {415},
pages = {141-7},
number = {6868},
month = {Jan},
abstract = {Most cellular processes are carried out by multiprotein complexes.
{T}he identification and analysis of their components provides insight
into how the ensemble of expressed proteins (proteome) is organized
into functional units. {W}e used tandem-affinity purification ({TAP})
and mass spectrometry in a large-scale approach to characterize multiprotein
complexes in {S}accharomyces cerevisiae. {W}e processed 1,739 genes,
including 1,143 human orthologues of relevance to human biology,
and purified 589 protein assemblies. {B}ioinformatic analysis of
these assemblies defined 232 distinct multiprotein complexes and
proposed new cellular roles for 344 proteins, including 231 proteins
with no previous functional annotation. {C}omparison of yeast and
human complexes showed that conservation across species extends from
single proteins to their molecular environment. {O}ur analysis provides
an outline of the eukaryotic proteome as a network of protein complexes
at a level of organization beyond binary interactions. {T}his higher-order
map contains fundamental biological information and offers the context
for a more reasoned and informed approach to drug discovery.},
doi = {10.1038/415141a},
pdf = {../local/gavi02.pdf},
file = {gavi02.pdf:local/gavi02.pdf:PDF},
keywords = {Affinity, Affinity Labels, Amino Acid Sequence, Animals, Cell Cycle
Proteins, Cells, Chromatography, Cloning, Comparative Study, Cultured,
DNA, DNA Damage, DNA Repair, Electrospray Ionization, Fungal, Gene
Targeting, Genetic, Humans, Macromolecular Substances, Mass, Matrix-Assisted
Laser Desorption-Ionization, Mitosis, Molecular, Molecular Sequence
Data, Non-P.H.S., Non-U.S. Gov't, P.H.S., Phosphoric Monoester Hydrolases,
Protein Binding, Protein Interaction Mapping, Protein Kinases, Proteome,
Proteomics, Recombinant Fusion Proteins, Research Support, Ribonucleoproteins,
Ribosomes, Saccharomyces cerevisiae, Saccharomyces cerevisiae Proteins,
Sensitivity and Specificity, Sequence Alignment, Signal Transduction,
Species Specificity, Spectrometry, Spectrum Analysis, Transcription,
U.S. Gov't, 11805813},
owner = {vert},
pii = {415141a},
url = {http://dx.doi.org/10.1038/415141a}
}
@article{Ge2003Reducing,
author = {Xijin Ge and Shuichi Tsutsumi and Hiroyuki Aburatani and Shuichi
Iwata},
title = {Reducing false positives in molecular pattern recognition.},
journal = {Genome {I}nform {S}er {W}orkshop {G}enome {I}nform},
year = {2003},
volume = {14},
pages = {34-43},
abstract = {In the search for new cancer subtypes by gene expression profiling,
it is essential to avoid misclassifying samples of unknown subtypes
as known ones. {I}n this paper, we evaluated the false positive error
rates of several classification algorithms through a 'null test'
by presenting classifiers a large collection of independent samples
that do not belong to any of the tumor types in the training dataset.
{T}he benchmark dataset is available at www2.genome.rcast.u-tokyo.ac.jp/pm/.
{W}e found that k-nearest neighbor ({KNN}) and support vector machine
({SVM}) have very high false positive error rates when fewer genes
(<100) are used in prediction. {T}he error rate can be partially
reduced by including more genes. {O}n the other hand, prototype matching
({PM}) method has a much lower false positive error rate. {S}uch
robustness can be achieved without loss of sensitivity by introducing
suitable measures of prediction confidence. {W}e also proposed a
cluster-and-select technique to select genes for classification.
{T}he nonparametric {K}ruskal-{W}allis {H} test is employed to select
genes differentially expressed in multiple tumor types. {T}o reduce
the redundancy, we then divided these genes into clusters with similar
expression patterns and selected a given number of genes from each
cluster. {T}he reliability of the new algorithm is tested on three
public datasets.},
keywords = {Amino Acid Sequence, Amino Acids, Animals, Automated, Base Sequence,
Bayes Theorem, Biological, Carbohydrate Conformation, Carbohydrate
Sequence, Cattle, Computational Biology, Computer Simulation, Crystallography,
DNA, Databases, Factual, False Positive Reactions, Gene Expression
Profiling, Genes, Genetic, Genetic Techniques, Genome, Histocompatibility
Antigens Class I, Human, Humans, Introns, Least-Squares Analysis,
MHC Class I, Major Histocompatibility Complex, Markov Chains, Messenger,
Mice, Models, Monosaccharides, Neoplasms, Non-U.S. Gov't, Nonparametric,
Pattern Recognition, Peptides, Phylogeny, Plants, Poly A, Polysaccharides,
Predictive Value of Tests, Protein, Protein Structure, Proteins,
RNA, Rats, Reproducibility of Results, Research Support, Saccharomyces
cerevisiae, Secondary, Sequence Alignment, Software, Species Specificity,
Statistics, Theoretical, X-Ray, 15706518}
}
@article{Gedela2011Integration,
author = {Srinubabu Gedela},
title = {Integration, warehousing, and analysis strategies of Omics data.},
journal = {Methods Mol Biol},
year = {2011},
volume = {719},
pages = {399--414},
abstract = {"-Omics" is a current suffix for numerous types of large-scale biological
data generation procedures, which naturally demand the development
of novel algorithms for data storage and analysis. With next generation
genome sequencing burgeoning, it is pivotal to decipher a coding
site on the genome, a gene's function, and information on transcripts
next to the pure availability of sequence information. To explore
a genome and downstream molecular processes, we need umpteen results
at the various levels of cellular organization by utilizing different
experimental designs, data analysis strategies and methodologies.
Here comes the need for controlled vocabularies and data integration
to annotate, store, and update the flow of experimental data. This
chapter explores key methodologies to merge Omics data by semantic
data carriers, discusses controlled vocabularies as eXtensible Markup
Languages (XML), and provides practical guidance, databases, and
software links supporting the integration of Omics data.},
doi = {10.1007/978-1-61779-027-0\_18},
institution = {Stanford University School of Medicine, Stanford, CA, USA. gedela@stanford.edu},
language = {eng},
medline-pst = {ppublish},
owner = {phupe},
pmid = {21370094},
timestamp = {2011.05.31},
url = {http://dx.doi.org/10.1007/978-1-61779-027-0\_18}
}
@article{Gehlenborg2010Visualization,
author = {Nils Gehlenborg and Seán I O'Donoghue and Nitin S Baliga and Alexander
Goesmann and Matthew A Hibbs and Hiroaki Kitano and Oliver Kohlbacher
and Heiko Neuweger and Reinhard Schneider and Dan Tenenbaum and Anne-Claude
Gavin},
title = {Visualization of omics data for systems biology.},
journal = {Nat Methods},
year = {2010},
volume = {7},
pages = {S56--S68},
number = {3 Suppl},
month = {Mar},
abstract = {High-throughput studies of biological systems are rapidly accumulating
a wealth of 'omics'-scale data. Visualization is a key aspect of
both the analysis and understanding of these data, and users now
have many visualization methods and tools to choose from. The challenge
is to create clear, meaningful and integrated visualizations that
give biological insight, without being overwhelmed by the intrinsic
complexity of the data. In this review, we discuss how visualization
tools are being used to help interpret protein interaction, gene
expression and metabolic profile data, and we highlight emerging
new directions.},
doi = {10.1038/nmeth.1436},
institution = {European Bioinformatics Institute, Cambridge, UK.},
keywords = {Genomics; Image Processing, Computer-Assisted; Mass Spectrometry;
Metabolomics; Nuclear Magnetic Resonance, Biomolecular; Protein Binding;
Proteomics; Systems Biology},
language = {eng},
medline-pst = {ppublish},
owner = {philippe},
pii = {nmeth.1436},
pmid = {20195258},
timestamp = {2010.07.27},
url = {http://dx.doi.org/10.1038/nmeth.1436}
}
@article{Genuer2010Variable,
author = {Genuer, R. and Poggi, J.M. and Tuleau-Malot, C.},
title = {Variable selection using random forests},
journal = {Pattern Recognition Letters},
year = {2010},
volume = {31},
pages = {2225--2236},
number = {14},
publisher = {Elsevier}
}
@article{Geppert2008Support-vector-machine-based,
author = {Geppert, H. and Horv{\'a}th, T. and G{\"a}rtner, T. and Wrobel, S.
and Bajorath, J.},
title = {Support-vector-machine-based ranking significantly improves the effectiveness
of similarity searching using 2D fingerprints and multiple reference
compounds.},
journal = {J Chem Inf Model},
year = {2008},
volume = {48},
pages = {742--746},
number = {4},
month = {Apr},
abstract = {Similarity searching using molecular fingerprints is computationally
efficient and a surprisingly effective virtual screening tool. In
this study, we have compared ranking methods for similarity searching
using multiple active reference molecules. Different 2D fingerprints
were used as search tools and also as descriptors for a support vector
machine (SVM) algorithm. In systematic database search calculations,
a SVM-based ranking scheme consistently outperformed nearest neighbor
and centroid approaches, regardless of the fingerprints that were
tested, even if only very small training sets were used for SVM learning.
The superiority of SVM-based ranking over conventional fingerprint
methods is ascribed to the fact that SVM makes use of information
about database molecules, in addition to known active compounds,
during the learning phase.},
doi = {10.1021/ci700461s},
pdf = {../local/Geppert2008Support-vector-machine-based.pdf},
file = {Geppert2008Support-vector-machine-based.pdf:Geppert2008Support-vector-machine-based.pdf:PDF},
institution = {Fraunhofer IAIS, Schloss Birlinghoven, D-53754 Sankt Augustin, Germany.},
keywords = {chemoinformatics, PUlearning},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pmid = {18318473},
timestamp = {2010.04.02},
url = {http://dx.doi.org/10.1021/ci700461s}
}
@inproceedings{Germann01Fast,
author = {U. Germann and M. Jahr and K. Knight and D. Marcu},
title = {Fast decoding and optimal decoding for machine translation},
booktitle = {In Proceedings of ACL 39},
year = {2001},
pages = {228--235}
}
@article{Gerstein2000current,
author = {Gerstein, M. and Jansen, R.},
title = {The current excitement in bioinformatics-analysis of whole-genome
expression data: how does it relate to protein structure and function?},
journal = {Curr. Opin. Struct. Biol.},
year = {2000},
volume = {10},
pages = {574--584},
number = {5},
month = {Oct},
abstract = {Whole-genome expression profiles provide a rich new data-trove for
bioinformatics. Initial analyses of the profiles have included clustering
and cross-referencing to 'external' information on protein structure
and function. Expression profile clusters do relate to protein function,
but the correlation is not perfect, with the discrepancies partially
resulting from the difficulty in consistently defining function.
Other attributes of proteins can also be related to expression-in
particular, structure and localization-and sometimes show a clearer
relationship than function.},
pdf = {../local/Gerstein2000current.pdf},
file = {Gerstein2000current.pdf:Gerstein2000current.pdf:PDF},
institution = {Department of Molecular Biophysics and Biochemistry, 266 Whitney
Avenue, Yale University, PO Box 208114, New Haven, CT 06520, USA.
Mark.Gerstein@yale.edu},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {S0959-440X(00)00134-2},
pmid = {11042457},
timestamp = {2011.10.04}
}
@article{Gestel2002Bayesian,
author = {T. Van Gestel and J. A K Suykens and G. Lanckriet and A. Lambrechts
and B. De Moor and J. Vandewalle},
title = {Bayesian framework for least-squares support vector machine classifiers,
gaussian processes, and kernel {F}isher discriminant analysis.},
journal = {Neural {C}omput},
year = {2002},
volume = {14},
pages = {1115-47},
number = {5},
month = {May},
abstract = {The {B}ayesian evidence framework has been successfully applied to
the design of multilayer perceptrons ({MLP}s) in the work of {M}ac{K}ay.
{N}evertheless, the training of {MLP}s suffers from drawbacks like
the nonconvex optimization problem and the choice of the number of
hidden units. {I}n support vector machines ({SVM}s) for classification,
as introduced by {V}apnik, a nonlinear decision boundary is obtained
by mapping the input vector first in a nonlinear way to a high-dimensional
kernel-induced feature space in which a linear large margin classifier
is constructed. {P}ractical expressions are formulated in the dual
space in terms of the related kernel function, and the solution follows
from a (convex) quadratic programming ({QP}) problem. {I}n least-squares
{SVM}s ({LS}-{SVM}s), the {SVM} problem formulation is modified by
introducing a least-squares cost function and equality instead of
inequality constraints, and the solution follows from a linear system
in the dual space. {I}mplicitly, the least-squares formulation corresponds
to a regression formulation and is also related to kernel {F}isher
discriminant analysis. {T}he least-squares regression formulation
has advantages for deriving analytic expressions in a {B}ayesian
evidence framework, in contrast to the classification formulations
used, for example, in gaussian processes ({GP}s). {T}he {LS}-{SVM}
formulation has clear primal-dual interpretations, and without the
bias term, one explicitly constructs a model that yields the same
expressions as have been obtained with {GP}s for regression. {I}n
this article, the {B}ayesian evidence framework is combined with
the {LS}-{SVM} classifier formulation. {S}tarting from the feature
space formulation, analytic expressions are obtained in the dual
space on the different levels of {B}ayesian inference, while posterior
class probabilities are obtained by marginalizing over the model
parameters. {E}mpirical results obtained on 10 public domain data
sets show that the {LS}-{SVM} classifier designed within the {B}ayesian
evidence framework consistently yields good generalization performances.},
doi = {10.1162/089976602753633411},
pdf = {../local/Gestel2002Bayesian.pdf},
file = {Gestel2002Bayesian.pdf:Gestel2002Bayesian.pdf:PDF},
url = {http://dx.doi.org/10.1162/089976602753633411}
}
@article{Gether2000Uncovering,
author = {U. Gether},
title = {Uncovering molecular mechanisms involved in activation of G protein-coupled
receptors.},
journal = {Endocr Rev},
year = {2000},
volume = {21},
pages = {90--113},
number = {1},
month = {Feb},
abstract = {G protein-coupled, seven-transmembrane segment receptors (GPCRs or
7TM receptors), with more than 1000 different members, comprise the
largest superfamily of proteins in the body. Since the cloning of
the first receptors more than a decade ago, extensive experimental
work has uncovered multiple aspects of their function and challenged
many traditional paradigms. However, it is only recently that we
are beginning to gain insight into some of the most fundamental questions
in the molecular function of this class of receptors. How can, for
example, so many chemically diverse hormones, neurotransmitters,
and other signaling molecules activate receptors believed to share
a similar overall tertiary structure? What is the nature of the physical
changes linking agonist binding to receptor activation and subsequent
transduction of the signal to the associated G protein on the cytoplasmic
side of the membrane and to other putative signaling pathways? The
goal of the present review is to specifically address these questions
as well as to depict the current awareness about GPCR structure-function
relationships in general.},
keywords = {Animals; GTP-Binding Proteins; Humans; Ligands; Models, Biological;
Molecular Conformation; Receptors, Cell Surface},
owner = {laurent},
pmid = {10696571},
timestamp = {2007.09.22}
}
@article{Geurts2011Learning,
author = {Geurts, Pierre},
title = {Learning from positive and unlabeled examples by enforcing statistical
significance.},
journal = {Journal of Machine Learning Research - Proceedings Track},
year = {2011},
volume = {15},
pages = {305-314},
biburl = {http://www.bibsonomy.org/bibtex/2d8d9fb95ceea0cae30df5f6c1b46a04a/dblp},
ee = {http://www.jmlr.org/proceedings/papers/v15/geurts11a/geurts11a.pdf},
interhash = {9290ccc55163a155f7b7387fe01f7845},
intrahash = {d8d9fb95ceea0cae30df5f6c1b46a04a},
keywords = {dblp},
owner = {fantinemordelet},
timestamp = {2013.01.09},
url = {http://dblp.uni-trier.de/db/journals/jmlr/jmlrp15.html#Geurts11}
}
@article{GevaZatorsky2006MSB,
author = {Geva-Zatorsky, N. and Rosenfeld, N. and Itzkovitz, S. and Milo, R.
and Sigal, A. and Dekel, E. and Yarnitzky, T. and Liron, Y. and Polak,
P. and Lahav, G. and Alon, U.},
title = {Oscillations and variability in the p53 system},
journal = {Mol Syst Biol},
year = {2006},
volume = {2},
pages = {2006 0033},
abstract = {Understanding the dynamics and variability of protein circuitry requires
accurate measurements in living cells as well as theoretical models.
To address this, we employed one of the best-studied protein circuits
in human cells, the negative feedback loop between the tumor suppressor
p53 and the oncogene Mdm2. We measured the dynamics of fluorescently
tagged p53 and Mdm2 over several days in individual living cells.
We found that isogenic cells in the same environment behaved in highly
variable ways following DNA-damaging gamma irradiation: some cells
showed undamped oscillations for at least 3 days (more than 10 peaks).
The amplitude of the oscillations was much more variable than the
period. Sister cells continued to oscillate in a correlated way after
cell division, but lost correlation after about 11 h on average.
Other cells showed low-frequency fluctuations that did not resemble
oscillations. We also analyzed different families of mathematical
models of the system, including a novel checkpoint mechanism. The
models point to the possible source of the variability in the oscillations:
low-frequency noise in protein production rates, rather than noise
in other parameters such as degradation rates. This study provides
a view of the extensive variability of the behavior of a protein
circuit in living human cells, both from cell to cell and in the
same cell over time.},
keywords = {csbcbook}
}
@article{Gevaert2006Predicting,
author = {Gevaert, O. and Smet, F.D. and Timmerman, D. and Moreau, Y. and Moor,
B.D.},
title = {Predicting the prognosis of breast cancer by integrating clinical
and microarray data with Bayesian networks},
journal = {Bioinformatics},
year = {2006},
volume = {22},
pages = {e184--e190},
number = {14},
publisher = {Oxford Univ Press}
}
@article{Ghosh2005Classification,
author = {Debashis Ghosh and Arul M Chinnaiyan},
title = {Classification and {S}election of {B}iomarkers in {G}enomic {D}ata
{U}sing {LASSO}.},
journal = {J {B}iomed {B}iotechnol},
year = {2005},
volume = {2005},
pages = {147-54},
number = {2},
abstract = {High-throughput gene expression technologies such as microarrays have
been utilized in a variety of scientific applications. {M}ost of
the work has been done on assessing univariate associations between
gene expression profiles with clinical outcome (variable selection)
or on developing classification procedures with gene expression data
(supervised learning). {W}e consider a hybrid variable selection/classification
approach that is based on linear combinations of the gene expression
profiles that maximize an accuracy measure summarized using the receiver
operating characteristic curve. {U}nder a specific probability model,
this leads to the consideration of linear discriminant functions.
{W}e incorporate an automated variable selection approach using {LASSO}.
{A}n equivalence between {LASSO} estimation with support vector machines
allows for model fitting using standard software. {W}e apply the
proposed method to simulated data as well as data from a recently
published prostate cancer study.},
doi = {10.1155/JBB.2005.147},
pdf = {../local/Ghosh2005Classification.pdf},
file = {Ghosh2005Classification.pdf:local/Ghosh2005Classification.pdf:PDF},
keywords = {, , 16046820},
pii = {S1110724304406020_THIS_PII_IS_INCORRECT_},
url = {http://dx.doi.org/10.1155/JBB.2005.147}
}
@article{Giallourakis2005Disease,
author = {Giallourakis, C. and Henson, C. and Reich, M. and Xie, X. and Mootha,
V. K.},
title = {Disease gene discovery through integrative genomics.},
journal = {Annu. Rev. Genomics Hum. Genet.},
year = {2005},
volume = {6},
pages = {381--406},
abstract = {The availability of complete genome sequences and the wealth of large-scale
biological data sets now provide an unprecedented opportunity to
elucidate the genetic basis of rare and common human diseases. Here
we review some of the emerging genomics technologies and data resources
that can be used to infer gene function to prioritize candidate genes.
We then describe some computational strategies for integrating these
large-scale data sets to provide more faithful descriptions of gene
function, and how such approaches have recently been applied to discover
genes underlying Mendelian disorders. Finally, we discuss future
prospects and challenges for using integrative genomics to systematically
discover not only single genes but also entire gene networks that
underlie and modify human disease.},
doi = {10.1146/annurev.genom.6.080604.162234},
pdf = {../local/Giallourakis2005Disease.pdf},
file = {Giallourakis2005Disease.pdf:Giallourakis2005Disease.pdf:PDF},
institution = {Broad Institute of Harvard and MIT, Cambridge, Massachusetts 02139,
USA.},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pmid = {16124867},
timestamp = {2010.11.01},
url = {http://dx.doi.org/10.1146/annurev.genom.6.080604.162234}
}
@article{Gibney2010Epigenetics,
author = {E. R. Gibney and C. M. Nolan},
title = {Epigenetics and gene expression.},
journal = {Heredity},
year = {2010},
volume = {105},
pages = {4--13},
number = {1},
month = {Jul},
abstract = {Transcription, translation and subsequent protein modification represent
the transfer of genetic information from the archival copy of DNA
to the short-lived messenger RNA, usually with subsequent production
of protein. Although all cells in an organism contain essentially
the same DNA, cell types and functions differ because of qualitative
and quantitative differences in their gene expression. Thus, control
of gene expression is at the heart of differentiation and development.
Epigenetic processes, including DNA methylation, histone modification
and various RNA-mediated processes, are thought to influence gene
expression chiefly at the level of transcription; however, other
steps in the process (for example, translation) may also be regulated
epigenetically. The following paper will outline the role epigenetics
is believed to have in influencing gene expression.},
doi = {10.1038/hdy.2010.54},
institution = {UCD Institute of Food and Health, Dublin, Ireland.},
keywords = {Animals; Cell Differentiation; Cell Lineage; DNA Modification Methylases,
metabolism; Epigenesis, Genetic; Gene Expression; Heredity; Humans},
language = {eng},
medline-pst = {ppublish},
owner = {philippe},
pii = {hdy201054},
pmid = {20461105},
timestamp = {2011.06.04},
url = {http://dx.doi.org/10.1038/hdy.2010.54}
}
@article{Gillet2003Similarity,
author = {V. Gillet and P. Willett and J. Bradshaw},
title = {Similarity searching using reduced graphs},
journal = {J. Chem. Inf. Comput. Sci.},
year = {2003},
volume = {43},
pages = {338-345},
owner = {mahe},
timestamp = {2006.09.13}
}
@article{Girirajan2009Sequencing,
author = {Santhosh Girirajan and Lin Chen and Tina Graves and Tomas Marques-Bonet
and Mario Ventura and Catrina Fronick and Lucinda Fulton and Mariano
Rocchi and Robert S Fulton and Richard K Wilson and Elaine R Mardis
and Evan E Eichler},
title = {Sequencing human-gibbon breakpoints of synteny reveals mosaic new
insertions at rearrangement sites},
journal = {Genome Res.},
year = {2009},
volume = {19},
pages = {178--190},
number = {2},
month = {Feb},
abstract = {The gibbon genome exhibits extensive karyotypic diversity with an
increased rate of chromosomal rearrangements during evolution. In
an effort to understand the mechanistic origin and implications of
these rearrangement events, we sequenced 24 synteny breakpoint regions
in the white-cheeked gibbon (Nomascus leucogenys, NLE) in the form
of high-quality BAC insert sequences (4.2 Mbp). While there is a
significant deficit of breakpoints in genes, we identified seven
human gene structures involved in signaling pathways (DEPDC4, GNG10),
phospholipid metabolism (ENPP5, PLSCR2), beta-oxidation (ECH1), cellular
structure and transport (HEATR4), and transcription (ZNF461), that
have been disrupted in the NLE gibbon lineage. Notably, only three
of these genes show the expected evolutionary signatures of pseudogenization.
Sequence analysis of the breakpoints suggested both nonclassical
nonhomologous end-joining (NHEJ) and replication-based mechanisms
of rearrangement. A substantial number (11/24) of human-NLE gibbon
breakpoints showed new insertions of gibbon-specific repeats and
mosaic structures formed from disparate sequences including segmental
duplications, LINE, SINE, and LTR elements. Analysis of these sites
provides a model for a replication-dependent repair mechanism for
double-strand breaks (DSBs) at rearrangement sites and insights into
the structure and formation of primate segmental duplications at
sites of genomic rearrangements during evolution.},
doi = {10.1101/gr.086041.108},
pdf = {../local/Girirajan2009Sequencing.pdf},
file = {Girirajan2009Sequencing.pdf:Girirajan2009Sequencing.pdf:PDF},
institution = {Department of Genome Sciences, Howard Hughes Medical Institute, University
of Washington School of Medicine, Seattle, Washington 98195, USA.},
keywords = {ngs},
owner = {jp},
pii = {gr.086041.108},
pmid = {19029537},
timestamp = {2009.10.09},
url = {http://dx.doi.org/10.1101/gr.086041.108}
}
@article{Girosi1998Equivalence,
author = {Girosi},
title = {An {E}quivalence {B}etween {S}parse {A}pproximation and {S}upport
{V}ector {M}achines.},
journal = {Neural {C}omput},
year = {1998},
volume = {10},
pages = {1455-80},
number = {6},
month = {Jul},
abstract = {This article shows a relationship between two different approximation
techniques: the support vector machines ({SVM}), proposed by {V}.
{V}apnik (1995) and a sparse approximation scheme that resembles
the basis pursuit denoising algorithm ({C}hen, 1995; {C}hen, {D}onoho,
and {S}aunders, 1995). {SVM} is a technique that can be derived from
the structural risk minimization principle ({V}apnik, 1982) and can
be used to estimate the parameters of several different approximation
schemes, including radial basis functions, algebraic and trigonometric
polynomials, {B}-splines, and some forms of multilayer perceptrons.
{B}asis pursuit denoising is a sparse approximation technique in
which a function is reconstructed by using a small number of basis
functions chosen from a large set (the dictionary). {W}e show that
if the data are noiseless, the modified version of basis pursuit
denoising proposed in this article is equivalent to {SVM} in the
following sense: if applied to the same data set, the two techniques
give the same solution, which is obtained by solving the same quadratic
programming problem. {I}n the appendix, we present a derivation of
the {SVM} technique in one framework of regularization theory, rather
than statistical learning theory, establishing a connection between
{SVM}, sparse approximation, and regularization theory.},
keywords = {Algorithms, Automated, Biometry, Computers, DNA, Databases, Factual,
Fungal, Fungal Proteins, GTP-Binding Proteins, Gene Expression, Genes,
Learning, Markov Chains, Models, Neural Networks (Computer), Neurological,
Non-P.H.S., Non-U.S. Gov't, Nucleic Acid Hybridization, Open Reading
Frames, P.H.S., Pattern Recognition, Protein, Protein Structure,
Proteins, Reproducibility of Results, Research Support, Saccharomyces
cerevisiae, Sequence Alignment, Sequence Analysis, Software, Statistical,
Tertiary, U.S. Gov't, 9698353}
}
@article{Girosi1995Regularization,
author = {Girosi, F. and Jones, M. and Poggio, T.},
title = {Regularization {T}heory and {N}eural {N}etworks {A}rchitectures},
journal = {Neural {C}omput.},
year = {1995},
volume = {7},
pages = {219--269},
number = {2},
pdf = {../local/giro95.pdf},
file = {giro95.pdf:local/giro95.pdf:PDF},
subject = {ml},
url = {http://citeseer.nj.nec.com/girosi95regularization.html}
}
@article{Glaser2006Method,
author = {Glaser, F. and Morris, R. J. and Najmanovich, R. J. and Laskowski,
R. A. and Thornton, J. M. },
title = {A method for localizing ligand binding pockets in protein structures.},
journal = {Proteins},
year = {2006},
volume = {62},
pages = {479--488},
number = {2},
month = {February},
abstract = {The accurate identification of ligand binding sites in protein structures
can be valuable in determining protein function. Once the binding
site is known, it becomes easier to perform in silico and experimental
procedures that may allow the ligand type and the protein function
to be determined. For example, binding pocket shape analysis relies
heavily on the correct localization of the ligand binding site. We
have developed SURFNET-ConSurf, a modular, two-stage method for identifying
the location and shape of potential ligand binding pockets in protein
structures. In the first stage, the SURFNET program identifies clefts
in the protein surface that are potential binding sites. In the second
stage, these clefts are trimmed in size by cutting away regions distant
from highly conserved residues, as defined by the ConSurf-HSSP database.
The largest clefts that remain tend to be those where ligands bind.
To test the approach, we analyzed a nonredundant set of 244 protein
structures from the PDB and found that SURFNET-ConSurf identifies
a ligand binding pocket in 75\% of them. The trimming procedure reduces
the original cleft volumes by 30\% on average, while still encompassing
an average 87\% of the ligand volume. From the analysis of the results
we conclude that for those cases in which the ligands are found in
large, highly conserved clefts, the combined SURFNET-ConSurf method
gives pockets that are a better match to the ligand shape and location.
We also show that this approach works better for enzymes than for
nonenzyme proteins.},
address = {European Bioinformatics Institute, European Molecular Biology Laboratory,
Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom.
fabian@ebi.ac.uk},
citeulike-article-id = {472870},
doi = {http://dx.doi.org/10.1002/prot.20769},
issn = {1097-0134},
keywords = {ligand-volume, protein-ligand, surface},
posted-at = {2006-01-20 20:31:25},
priority = {2},
url = {http://dx.doi.org/10.1002/prot.20769}
}
@article{Glenisson2004TXTGate:,
author = {Glenisson, P. and Coessens, B. and Van Vooren, S. and Mathys, J.
and Moreau, Y. and De Moor, B.},
title = {TXTGate: profiling gene groups with text-based information.},
journal = {Genome Biol},
year = {2004},
volume = {5},
pages = {R43},
number = {6},
abstract = {We implemented a framework called TXTGate that combines literature
indices of selected public biological resources in a flexible text-mining
system designed towards the analysis of groups of genes. By means
of tailored vocabularies, term- as well as gene-centric views are
offered on selected textual fields and MEDLINE abstracts used in
LocusLink and the Saccharomyces Genome Database. Subclustering and
links to external resources allow for in-depth analysis of the resulting
term profiles.},
doi = {10.1186/gb-2004-5-6-r43},
institution = {Departement Elektrotechniek (ESAT), Faculteit Toegepaste Wetenschappen,
Katholieke Universiteit Leuven, Kasteelpark Arenberg 10, 3001 Heverlee
(Leuven), Belgium.},
keywords = {Animals; Cluster Analysis; Databases, Genetic; Disease Models, Animal;
Gene Expression Profiling; Gene Expression Regulation, Fungal; Gene
Expression Regulation, Neoplastic; Genes, Fungal; Genes, Neoplasm;
Genome, Fungal; Genome, Human; Humans; Information Storage and Retrieval;
MEDLINE; Mice; Saccharomyces; Salivary Gland Neoplasms; Vocabulary},
owner = {fantine},
pii = {gb-2004-5-6-r43},
pmid = {15186494},
timestamp = {2010.10.21},
url = {http://dx.doi.org/10.1186/gb-2004-5-6-r43}
}
@article{Glotsos2004Automated,
author = {Dimitris Glotsos and Panagiota Spyridonos and Dionisis Cavouras and
Panagiota Ravazoula and Petroula-Arampantoni Dadioti and George Nikiforidis},
title = {Automated segmentation of routinely hematoxylin-eosin-stained microscopic
images by combining support vector machine clustering and active
contour models.},
journal = {Anal {Q}uant {C}ytol {H}istol},
year = {2004},
volume = {26},
pages = {331-40},
number = {6},
month = {Dec},
abstract = {O{BJECTIVE}: {T}o develop a method for the automated segmentation
of images of routinely hematoxylin-eosin ({H}-{E})-stained microscopic
sections to guarantee correct results in computer-assisted microscopy.
{STUDY} {DESIGN}: {C}linical material was composed 50 {H}-{E}-stained
biopsies of astrocytomas and 50 {H}-{E}-stained biopsies of urinary
bladder cancer. {T}he basic idea was to use a support vector machine
clustering ({SVMC}) algorithm to provide gross segmentation of regions
holding nuclei and subsequently to refine nuclear boundary detection
with active contours. {T}he initialization coordinates of the active
contour model were defined using a {SVMC} pixel-based classification
algorithm that discriminated nuclear regions from the surrounding
tissue. {S}tarting from the boundaries of these regions, the snake
fired and propagated until converging to nuclear boundaries. {RESULTS}:
{T}he method was validated for 2 different types of {H}-{E}-stained
images. {R}esults were evaluated by 2 histopathologists. {O}n average,
94\% of nuclei were correctly delineated. {CONCLUSION}: {T}he proposed
algorithm could be of value in computer-based systems for automated
interpretation of microscopic images.},
keywords = {Adenosinetriphosphatase, Adolescent, Adult, Algorithms, Amino Acid
Sequence, Amino Acids, Animals, Astrocytoma, Automated, Automation,
Base Sequence, Bayes Theorem, Biological, Biopsy, Bladder Neoplasms,
Breast Neoplasms, Carbohydrate Conformation, Carbohydrate Sequence,
Cattle, Cell Cycle Proteins, Cell Nucleus, Computational Biology,
Computer Simulation, Computer-Assisted, Crystallography, DNA, Databases,
Diagnosis, Differential, Eosine Yellowish-(YS), Exoribonucleases,
Factual, False Negative Reactions, False Positive Reactions, Female,
Gene Expression, Gene Expression Profiling, Genes, Genetic, Genetic
Techniques, Genetic Vectors, Genome, Hematoxylin, Histocompatibility
Antigens Class I, Human, Humans, Image Interpretation, Image Processing,
Introns, Least-Squares Analysis, MHC Class I, Major Histocompatibility
Complex, Markov Chains, Messenger, Mice, Middle Aged, Models, Molecular
Structure, Monosaccharides, Multigene Family, Mutation, Neoplasms,
Neural Networks (Computer), Non-P.H.S., Non-U.S. Gov't, Nonparametric,
Nucleotidyltransferases, Observer Variation, Oligonucleotide Array
Sequence Analysis, P.H.S., Pattern Recognition, Peptides, Phenotype,
Phylogeny, Plants, Poly A, Polysaccharides, Predictive Value of Tests,
Protein, Protein Biosynthesis, Protein Kinase Inhibitors, Protein
Structure, Proteins, RNA, RNA Helicases, RNA Splicing, Rats, Reproducibility
of Results, Research Support, Retrospective Studies, Saccharomyces
cerevisiae, Saccharomyces cerevisiae Proteins, Secondary, Sensitivity
and Specificity, Sequence Alignment, Software, Species Specificity,
Staining and Labeling, Statistics, Theoretical, Transcription, U.S.
Gov't, Ultrasonography, X-Ray, 15678615}
}
@article{Glotsos2004Computer-based,
author = {Dimitris Glotsos and Panagiota Spyridonos and Panagiotis Petalas
and Dionisis Cavouras and Panagiota Ravazoula and Petroula-Arampatoni
Dadioti and Ioanna Lekka and George Nikiforidis},
title = {Computer-based malignancy grading of astrocytomas employing a support
vector machine classifier, the {WHO} grading system and the regular
hematoxylin-eosin diagnostic staining procedure.},
journal = {Anal {Q}uant {C}ytol {H}istol},
year = {2004},
volume = {26},
pages = {77-83},
number = {2},
month = {Apr},
abstract = {O{BJECTIVE}: {T}o investigate and develop an automated technique for
astrocytoma malignancy grading compatible with the clinical routine.
{STUDY} {DESIGN}: {O}ne hundred forty biopsies of astrocytomas were
collected from 2 hospitals. {T}he degree of tumor malignancy was
defined as low or high according to the {W}orld {H}ealth {O}rganization
grading system. {F}rom each biopsy, images were digitized and segmented
to isolate nuclei from background tissue. {M}orphologic and textural
nuclear features were quantified to encode tumor malignancy. {E}ach
case was represented by a 40-dimensional feature vector. {A}n exhaustive
search procedure in feature space was utilized to determine the best
feature combination that resulted in the smallest classification
error. {L}ow and high grade tumors were discriminated using support
vector machines ({SVM}s). {T}o evaluate the system performance, all
available data were split randomly into training and test sets. {RESULTS}:
{T}he best vector combination consisted of 3 textural and 2 morphologic
features. {L}ow and high grade cases were discriminated with an accuracy
of 90.7\% and 88.9\%, respectively, using an {SVM} classifier with
polynomial kernel of degree 2. {CONCLUSION}: {T}he proposed methodology
was based on standards that are common in daily clinical practice
and might be used in parallel with conventional grading as a second-opinion
tool to reduce subjectivity in the classification of astrocytomas.},
keywords = {Amino Acids, Antibodies, Artificial Intelligence, Astrocytoma, Biological,
Biopsy, Brain, Brain Mapping, Brain Neoplasms, Calibration, Comparative
Study, Computational Biology, Computer-Assisted, Cysteine, Cystine,
Electrodes, Electroencephalography, Eosine Yellowish-(YS), Evoked
Potentials, Female, Hematoxylin, Horseradish Peroxidase, Humans,
Image Processing, Imagery (Psychotherapy), Imagination, Laterality,
Male, Monoclonal, Movement, Neoplasms, Non-P.H.S., Non-U.S. Gov't,
P.H.S., Perception, Principal Component Analysis, Protein, Protein
Array Analysis, Proteins, Research Support, Sensitivity and Specificity,
Sequence Analysis, Software, Tumor Markers, U.S. Gov't, User-Computer
Interface, World Health Organization, 15131894}
}
@article{Glotsos2005Automated,
author = {Dimitris Glotsos and Jussi Tohka and Panagiota Ravazoula and Dionisis
Cavouras and George Nikiforidis},
title = {Automated diagnosis of brain tumours astrocytomas using probabilistic
neural network clustering and support vector machines.},
journal = {Int {J} {N}eural {S}yst},
year = {2005},
volume = {15},
pages = {1-11},
number = {1-2},
abstract = {A computer-aided diagnosis system was developed for assisting brain
astrocytomas malignancy grading. {M}icroscopy images from 140 astrocytic
biopsies were digitized and cell nuclei were automatically segmented
using a {P}robabilistic {N}eural {N}etwork pixel-based clustering
algorithm. {A} decision tree classification scheme was constructed
to discriminate low, intermediate and high-grade tumours by analyzing
nuclear features extracted from segmented nuclei with a {S}upport
{V}ector {M}achine classifier. {N}uclei were segmented with an average
accuracy of 86.5\%. {L}ow, intermediate, and high-grade tumours were
identified with 95\%, 88.3\%, and 91\% accuracies respectively. {T}he
proposed algorithm could be used as a second opinion tool for the
histopathologists.},
pii = {S0129065705000013}
}
@book{Godsil2000Algebraic,
title = {Algebraic graph theory},
publisher = {Springer-Verlag},
year = {2000},
author = {C. Godsil and G. Royle}
}
@article{Goffeau1996Life,
author = {A. Goffeau and B.G. Barrell and H. Bussey and R.W. Davis and B. Dujon
and H. Feldmann and F. Galibert and J.D. Hoheisel and C. Jacq and
M. Johnston and E.J. Louis and H.W. Mewes and Y. Murakami and P.
Philippsen and H. Tettelin and S. G. Oliver},
title = {Life with 6000 genes},
journal = {Science},
year = {1996},
volume = {274},
pages = {546--567},
month = {October},
doi = {10.1126/science.274.5287.546},
pdf = {../local/Goffeau1996Life.pdf},
file = {Goffeau1996Life.pdf:local/Goffeau1996Life.pdf:PDF},
subject = {bio},
url = {http://www.sciencemag.org/cgi/content/abstract/274/5287/546}
}
@article{Goh2007human,
author = {Goh, K.-O. and Cusick, M. E. and Valle, D. and Childs, B. and Vidal,
M. and Barab{\'a}si, A.-L.},
title = {The human disease network},
journal = {Proc. Natl. Acad. Sci. U. S. A.},
year = {2007},
volume = {104},
pages = {8685--8690},
number = {21},
month = {May},
abstract = {A network of disorders and disease genes linked by known disorder-gene
associations offers a platform to explore in a single graph-theoretic
framework all known phenotype and disease gene associations, indicating
the common genetic origin of many diseases. Genes associated with
similar disorders show both higher likelihood of physical interactions
between their products and higher expression profiling similarity
for their transcripts, supporting the existence of distinct disease-specific
functional modules. We find that essential human genes are likely
to encode hub proteins and are expressed widely in most tissues.
This suggests that disease genes also would play a central role in
the human interactome. In contrast, we find that the vast majority
of disease genes are nonessential and show no tendency to encode
hub proteins, and their expression pattern indicates that they are
localized in the functional periphery of the network. A selection-based
model explains the observed difference between essential and disease
genes and also suggests that diseases caused by somatic mutations
should not be peripheral, a prediction we confirm for cancer genes.},
doi = {10.1073/pnas.0701361104},
pdf = {../local/Goh2007human.pdf},
file = {Goh2007human.pdf:Goh2007human.pdf:PDF},
institution = {Center for Complex Network Research and Department of Physics, University
of Notre Dame, Notre Dame, IN 46556, USA.},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {0701361104},
pmid = {17502601},
timestamp = {2011.09.23},
url = {http://dx.doi.org/10.1073/pnas.0701361104}
}
@article{Gold2006Fold,
author = {Gold, N.D. and Jackson, R.M.},
title = {Fold independent structural comparisons of protein-ligand binding
sites for exploring functional relationships.},
journal = {J. Mol. Biol.},
year = {2006},
volume = {355},
pages = {1112--1124},
number = {5},
month = {Feb},
abstract = {The rapid growth in protein structural data and the emergence of structural
genomics projects have increased the need for automatic structure
analysis and tools for function prediction. Small molecule recognition
is critical to the function of many proteins; therefore, determination
of ligand binding site similarity is important for understanding
ligand interactions and may allow their functional classification.
Here, we present a binding sites database (SitesBase) that given
a known protein-ligand binding site allows rapid retrieval of other
binding sites with similar structure independent of overall sequence
or fold similarity. However, each match is also annotated with sequence
similarity and fold information to aid interpretation of structure
and functional similarity. Similarity in ligand binding sites can
indicate common binding modes and recognition of similar molecules,
allowing potential inference of function for an uncharacterised protein
or providing additional evidence of common function where sequence
or fold similarity is already known. Alternatively, the resource
can provide valuable information for detailed studies of molecular
recognition including structure-based ligand design and in understanding
ligand cross-reactivity. Here, we show examples of atomic similarity
between superfamily or more distant fold relatives as well as between
seemingly unrelated proteins. Assignment of unclassified proteins
to structural superfamiles is also undertaken and in most cases substantiates
assignments made using sequence similarity. Correct assignment is
also possible where sequence similarity fails to find significant
matches, illustrating the potential use of binding site comparisons
for newly determined proteins.},
keywords = {geometric hashing, SitesBase, structural genomics, 3D structure comparison},
owner = {vero},
pmid = {16359705},
timestamp = {2009.02.04}
}
@article{Gold2006SitesBase,
author = {Gold, N.D. and Jackson, R.M.},
title = {SitesBase: a database for structure-based protein-ligand binding
site comparisons.},
journal = {Nucleic Acids Res.},
year = {2006},
volume = {34},
pages = {D231--D234},
month = {Jan},
abstract = {There are many components which govern the function of a protein within
a cell. Here, we focus on the molecular recognition of small molecules
and the prediction of common recognition by similarity between protein-ligand
binding sites. SitesBase is an easily accessible database which is
simple to use and holds information about structural similarities
between known ligand binding sites found in the Protein Data Bank.
These similarities are presented to the wider community enabling
full analysis of molecular recognition and potentially protein structure-function
relationships. SitesBase is accessible at http://www.bioinformatics.leeds.ac.uk/sb.},
owner = {vero},
pmid = {16381853},
timestamp = {2009.02.04}
}
@article{Gold1996graduated,
author = {Gold, S. and Rangarajan, A.},
title = {A graduated assignment algorithm for graph matching},
journal = {IEEE Trans. Pattern Anal. Mach. Intell.},
year = {1996},
volume = {18},
pages = {377--388},
number = {4},
month = {April},
abstract = {A graduated assignment algorithm for graph matching is presented which
is fast and accurate even in the presence of high noise. By combining
graduated nonconvexity, two-way (assignment) constraints, and sparsity,
large improvements in accuracy and speed are achieved. Its low order
computational complexity [O(lm), where l and m are the number of
links in the two graphs] and robustness in the presence of noise
offer advantages over traditional combinatorial approaches. The algorithm,
not restricted to any special class of graph, is applied to subgraph
isomorphism, weighted graph matching, and attributed relational graph
matching. To illustrate the performance of the algorithm, attributed
relational graphs derived from objects are matched. Then, results
from twenty-five thousand experiments conducted on 100 node random
graphs of varying types (graphs with only zero-one links, weighted
graphs, and graphs with node attributes and multiple link types)
are reported. No comparable results have been reported by any other
graph matching algorithm before in the research literature. Twenty-five
hundred control experiments are conducted using a relaxation labeling
algorithm and large improvements in accuracy are demonstrated.},
doi = {10.1109/34.491619},
pdf = {../local/Gold1996graduated.pdf},
file = {Gold1996graduated.pdf:Gold1996graduated.pdf:PDF},
owner = {jp},
timestamp = {2008.10.05},
url = {http://dx.doi.org/10.1109/34.491619}
}
@article{Goldbaum2002Comparing,
author = {Michael H Goldbaum and Pamela A Sample and Kwokleung Chan and Julia
Williams and Te-Won Lee and Eytan Blumenthal and Christopher A Girkin
and Linda M Zangwill and Christopher Bowd and Terrence Sejnowski
and Robert N Weinreb},
title = {Comparing machine learning classifiers for diagnosing glaucoma from
standard automated perimetry.},
journal = {Invest {O}phthalmol {V}is {S}ci},
year = {2002},
volume = {43},
pages = {162-9},
number = {1},
month = {Jan},
abstract = {P{URPOSE}: {T}o determine which machine learning classifier learns
best to interpret standard automated perimetry ({SAP}) and to compare
the best of the machine classifiers with the global indices of {STATPAC}
2 and with experts in glaucoma. {METHODS}: {M}ultilayer perceptrons
({MLP}), support vector machines ({SVM}), mixture of {G}aussian ({M}o{G}),
and mixture of generalized {G}aussian ({MGG}) classifiers were trained
and tested by cross validation on the numerical plot of absolute
sensitivity plus age of 189 normal eyes and 156 glaucomatous eyes,
designated as such by the appearance of the optic nerve. {T}he authors
compared performance of these classifiers with the global indices
of {STATPAC}, using the area under the {ROC} curve. {T}wo human experts
were judged against the machine classifiers and the global indices
by plotting their sensitivity-specificity pairs. {RESULTS}: {M}o{G}
had the greatest area under the {ROC} curve of the machine classifiers.
{P}attern {SD} ({PSD}) and corrected {PSD} ({CPSD}) had the largest
areas under the curve of the global indices. {M}o{G} had significantly
greater {ROC} area than {PSD} and {CPSD}. {H}uman experts were not
better at classifying visual fields than the machine classifiers
or the global indices. {CONCLUSIONS}: {M}o{G}, using the entire visual
field and age for input, interpreted {SAP} better than the global
indices of {STATPAC}. {M}achine classifiers may augment the global
indices of {STATPAC}.}
}
@techreport{Goldfarb2000What,
author = {Goldfarb, L. and Golubitsky, O. and Korkin, D.},
title = {What is a structural representation?},
institution = {University of New Brunswick},
year = {2000},
note = {Technical report TR00-137},
url = {http://www.cs.unb.ca/profs/goldfarb/struct.ps}
}
@article{Goldstein2009Common,
author = {Goldstein, D. B.},
title = {Common genetic variation and human traits.},
journal = {N. Engl. J. Med.},
year = {2009},
volume = {360},
pages = {1696--1698},
number = {17},
month = {Apr},
doi = {10.1056/NEJMp0806284},
pdf = {../local/Goldstein2009Common.pdf},
file = {Goldstein2009Common.pdf:Goldstein2009Common.pdf:PDF},
institution = {Center for Human Genome Variation, Institute for Genome Sciences
and Policy, Duke University, Durham, NC, USA.},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {NEJMp0806284},
pmid = {19369660},
timestamp = {2010.10.12},
url = {http://dx.doi.org/10.1056/NEJMp0806284}
}
@article{Golland2005Detection,
author = {Polina Golland and W. Eric L Grimson and Martha E Shenton and Ron
Kikinis},
title = {Detection and analysis of statistical differences in anatomical shape.},
journal = {Med {I}mage {A}nal},
year = {2005},
volume = {9},
pages = {69-86},
number = {1},
month = {Feb},
abstract = {We present a computational framework for image-based analysis and
interpretation of statistical differences in anatomical shape between
populations. {A}pplications of such analysis include understanding
developmental and anatomical aspects of disorders when comparing
patients versus normal controls, studying morphological changes caused
by aging, or even differences in normal anatomy, for example, differences
between genders. {O}nce a quantitative description of organ shape
is extracted from input images, the problem of identifying differences
between the two groups can be reduced to one of the classical questions
in machine learning of constructing a classifier function for assigning
new examples to one of the two groups while making as few misclassifications
as possible. {T}he resulting classifier must be interpreted in terms
of shape differences between the two groups back in the image domain.
{W}e demonstrate a novel approach to such interpretation that allows
us to argue about the identified shape differences in anatomically
meaningful terms of organ deformation. {G}iven a classifier function
in the feature space, we derive a deformation that corresponds to
the differences between the two classes while ignoring shape variability
within each class. {B}ased on this approach, we present a system
for statistical shape analysis using distance transforms for shape
representation and the support vector machines learning algorithm
for the optimal classifier estimation and demonstrate it on artificially
generated data sets, as well as real medical studies.},
doi = {10.1016/j.media.2004.07.003},
keywords = {Algorithms, Amino Acid, Artificial Intelligence, Ascomycota, Automated,
Base Sequence, Chromosome Mapping, Codon, Colonic Neoplasms, Comparative
Study, Computer-Assisted, Crystallography, DNA, DNA Primers, Databases,
Diagnostic Imaging, Gene Expression Profiling, Hordeum, Host-Parasite
Relations, Humans, Image Interpretation, Informatics, Kinetics, Magnetic
Resonance Spectroscopy, Models, Nanotechnology, Non-P.H.S., Non-U.S.
Gov't, Oligonucleotide Array Sequence Analysis, P.H.S., Pattern Recognition,
Plant, Plants, Predictive Value of Tests, Protein, Research Support,
Selection (Genetics), Sequence Alignment, Sequence Analysis, Sequence
Homology, Skin, Software, Statistical, Theoretical, Thermodynamics,
U.S. Gov't, Viral Proteins, X-Ray, 15581813},
pii = {S1361-8415(04)00059-3},
url = {http://dx.doi.org/10.1016/j.media.2004.07.003}
}
@book{Golub1996Matrix,
title = {Matrix computations (3rd ed.)},
publisher = {Johns Hopkins University Press},
year = {1996},
author = {G. H. Golub and C. F. Van Loan},
address = {Baltimore, MD, USA},
isbn = {0-8018-5414-8}
}
@article{Golub2010Counterpoint,
author = {Golub, T.},
title = {Counterpoint: Data first.},
journal = {Nature},
year = {2010},
volume = {464},
pages = {679},
number = {7289},
month = {Apr},
doi = {10.1038/464679a},
institution = {Cancer Program at the Broad Institute, Cambridge, Massachusetts 02142,
USA. golub@broadinstitute.org},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {464679a},
pmid = {20360719},
timestamp = {2010.10.07},
url = {http://dx.doi.org/10.1038/464679a}
}
@article{Golub1999Molecular,
author = {Golub, T. R. and Slonim, D. K. and Tamayo, P. and Huard, C. and Gaasenbeek,
M. and Mesirov, J. P. and Coller, H. and Loh, M. L. and Downing,
J. R. and Caligiuri, M. A. and Bloomfield, C. D. and Lander, E. S.},
title = {Molecular classification of cancer: class discovery and class prediction
by gene expression monitoring},
journal = {Science},
year = {1999},
volume = {286},
pages = {531--537},
abstract = {Although cancer classification has improved over the past 30 years,
there has been no general approach for identifying new cancer classes
(class discovery) or for assigning tumors to known classes (class
prediction). Here, a generic approach to cancer classification based
on gene expression monitoring by DNA microarrays is described and
applied to human acute leukemias as a test case. A class discovery
procedure automatically discovered the distinction between acute
myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL) without
previous knowledge of these classes. An automatically derived class
predictor was able to determine the class of new leukemia cases.
The results demonstrate the feasibility of cancer classification
based solely on gene expression moni- toring and suggest a general
strategy for discovering and predicting cancer classes for other
types of cancer, independent of previous biological knowledge.},
doi = {10.1126/science.286.5439.531},
pdf = {../local/Golub1999Molecular.pdf},
file = {Golub1999Molecular.pdf:Golub1999Molecular.pdf:PDF},
keywords = {csbcbook, csbcbook-ch3, csbcbook-ch4},
subject = {microarray},
url = {http://dx.doi.org/10.1126/science.286.5439.531}
}
@article{Gomez2003Learning,
author = {Gomez, S. M. and Noble, W. S. and Rzhetsky, A.},
title = {Learning to predict protein-protein interactions from protein sequences},
journal = {Bioinformatics},
year = {2003},
volume = {19},
pages = {1875-1881},
number = {15},
abstract = {In order to understand the molecular machinery of the cell, we need
to know about the multitude of protein-protein interactions that
allow the cell to function. {H}igh-throughput technologies provide
some data about these interactions, but so far that data is fairly
noisy. {T}herefore, computational techniques for predicting protein-protein
interactions could be of significant value. {O}ne approach to predicting
interactions in silico is to produce from first principles a detailed
model of a candidate interaction. {W}e take an alternative approach,
employing a relatively simple model that learns dynamically from
a large collection of data. {I}n this work, we describe an attraction-repulsion
model, in which the interaction between a pair of proteins is represented
as the sum of attractive and repulsive forces associated with small,
domain- or motif-sized features along the length of each protein.
{T}he model is discriminative, learning simultaneously from known
interactions and from pairs of proteins that are known (or suspected)
not to interact. {T}he model is efficient to compute and scales well
to very large collections of data. {I}n a cross-validated comparison
using known yeast interactions, the attraction-repulsion method performs
better than several competing techniques.},
pdf = {../local/Gomez2003Learning.pdf},
file = {Gomez2003Learning.pdf:local/Gomez2003Learning.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://bioinformatics.oupjournals.org/cgi/content/abstract/19/15/1875}
}
@article{Gong2004Picking,
author = {Gong, D. and Ferrell, J. E.},
title = {Picking a winner: new mechanistic insights into the design of effective
si{RNA}s.},
journal = {Trends {B}iotechnol.},
year = {2004},
volume = {22},
pages = {451-4},
number = {9},
month = {Sep},
abstract = {Recent work has shown that the efficacy of a small interfering {RNA}
(si{RNA}) for silencing gene expression is a function of how easy
it is to unwind the si{RNA} from the 5'-antisense end. {B}ased on
these insights, one group has designed an algorithm that substantially
improves the odds of picking an effective si{RNA}, and two groups
have shown that 'forked' or 'frayed' si{RNA}s, which should be easier
to unwind from the 5'-antisense end, are more effective than conventional
si{RNA}s. {T}hese strategies represent important steps towards the
rational design of effective si{RNA}s.},
doi = {10.1016/j.tibtech.2004.07.008},
keywords = {sirna},
pii = {S0167-7799(04)00201-X},
url = {http://dx.doi.org/10.1016/j.tibtech.2004.07.008}
}
@article{Gonzalez2009Highlighting,
author = {Gonz\'alez, I. and D\'ejean, S. and Martin, P. G. P. and Gonçalves,
O. and Besse, P. and Baccini, A.},
title = {Highlighting relationships between heterogeneous biological data
through graphical displays based on regularized canonical correlation
analysis.},
journal = {J Biol Syst},
year = {2009},
volume = {17},
pages = {173--199},
owner = {jp},
timestamp = {2012.02.29}
}
@article{Good1993Rapid,
author = {A.C. Good and W.G. Richards},
title = {Rapid {E}valuation of {M}olecular {S}hape {S}imilarity {U}sing {G}aussian
{F}unctions},
journal = {J Chem Inf Comput Sci},
year = {1993},
volume = {33},
pages = {112-116},
owner = {mahe},
timestamp = {2006.09.01}
}
@book{Gordon1999Classification,
title = {Classification},
publisher = {Chapman \& Hall/CRC},
year = {1999},
author = {Gordon, A. D.},
owner = {jp},
timestamp = {2011.12.29}
}
@article{Gordon2003Sequence,
author = {Gordon, L. and Chervonenkis, A. Y. and Gammerman, A. J. and Shahmuradov,
I. A. and Solovyev, V. V.},
title = {Sequence alignment kernel for recognition of promoter regions},
journal = {Bioinformatics},
year = {2003},
volume = {19},
pages = {1964-1971},
number = {15},
abstract = {In this paper we propose a new method for recognition of prokaryotic
promoter regions with startpoints of transcription. {T}he method
is based on {S}equence {A}lignment {K}ernel, a function reflecting
the quantitative measure of match between two sequences. {T}his kernel
function is further used in {D}ual {SVM}, which performs the recognition.
{S}everal recognition methods have been trained and tested on positive
data set, consisting of 669 {sigma}70-promoter regions with known
transcription startpoints of {E}scherichia coli and two negative
data sets of 709 examples each, taken from coding and non-coding
regions of the same genome. {T}he results show that our method performs
well and achieves 16.5% average error rate on positive & coding negative
data and 18.6% average error rate on positive & non-coding negative
data. {A}vailability:{T}he demo version of our method is accessible
from our website http://mendel.cs.rhul.ac.uk/},
pdf = {../local/Gordon2003Sequence.pdf},
file = {Gordon2003Sequence.pdf:local/Gordon2003Sequence.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://bioinformatics.oupjournals.org/cgi/content/abstract/19/15/1964}
}
@article{Goto1998LIGAND:,
author = {S. Goto and T. Nishioka and M. Kanehisa},
title = {L{IGAND}: chemical database for enzyme reactions},
journal = {Bioinformatics},
year = {1998},
volume = {14},
pages = {591--599},
pdf = {../local/goto98.pdf},
file = {goto98.pdf:local/goto98.pdf:PDF},
subject = {bionet},
url = {http://bioinformatics.oupjournals.org/cgi/reprint/14/7/591}
}
@article{Goto2002LIGAND:,
author = {S. Goto and Y. Okuno and M. Hattori and T. Nishioka and M. Kanehisa},
title = {L{IGAND}: database of chemical compounds and reactions in biological
pathways},
journal = {Nucleic {A}cids {R}es.},
year = {2002},
volume = {30},
pages = {402--404},
pdf = {../local/goto02.pdf},
file = {goto02.pdf:local/goto02.pdf:PDF},
subject = {bionet},
url = {http://nar.oupjournals.org/cgi/content/full/30/1/402}
}
@article{Gotusso1957Fortran,
author = {L. Gotusso and A. T. Santolini},
title = {A Fortran IV quasi decision algorithm for the P-equivalence of two
matrices},
journal = {Calcolo},
year = {1957},
volume = {5},
pages = {17--35}
}
@article{Gower1971general,
author = {Gower, J. C.},
title = {A general coefficient of similarity and some of its properties},
journal = {Biometrics},
year = {1971},
volume = {27},
pages = {857--871},
number = {4},
pdf = {../local/Gower1971general.pdf},
file = {Gower1971general.pdf:Gower1971general.pdf:PDF},
owner = {jp},
timestamp = {2013.01.31}
}
@article{Graumann2004Applicability,
author = {Johannes Graumann and Leslie A Dunipace and Jae Hong Seol and W.
Hayes McDonald and John R Yates and Barbara J Wold and Raymond J
Deshaies},
title = {Applicability of tandem affinity purification {M}ud{PIT} to pathway
proteomics in yeast.},
journal = {Mol {C}ell {P}roteomics},
year = {2004},
volume = {3},
pages = {226-37},
number = {3},
month = {Mar},
abstract = {A combined multidimensional chromatography-mass spectrometry approach
known as "{M}ud{PIT}" enables rapid identification of proteins that
interact with a tagged bait while bypassing some of the problems
associated with analysis of polypeptides excised from {SDS}-polyacrylamide
gels. {H}owever, the reproducibility, success rate, and applicability
of {M}ud{PIT} to the rapid characterization of dozens of proteins
have not been reported. {W}e show here that {M}ud{PIT} reproducibly
identified bona fide partners for budding yeast {G}cn5p. {A}dditionally,
we successfully applied {M}ud{PIT} to rapidly screen through a collection
of tagged polypeptides to identify new protein interactions. {T}wenty-five
proteins involved in transcription and progression through mitosis
were modified with a new tandem affinity purification ({TAP}) tag.
{TAP}-{M}ud{PIT} analysis of 22 yeast strains that expressed these
tagged proteins uncovered known or likely interacting partners for
21 of the baits, a figure that compares favorably with traditional
approaches. {T}he proteins identified here comprised 102 previously
known and 279 potential physical interactions. {E}ven for the intensively
studied {S}wi2p/{S}nf2p, the catalytic subunit of the {S}wi/{S}nf
chromatin remodeling complex, our analysis uncovered a new interacting
protein, {R}tt102p. {R}eciprocal tagging and {TAP}-{M}ud{PIT} analysis
of {R}tt102p revealed subunits of both the {S}wi/{S}nf and {RSC}
complexes, identifying {R}tt102p as a common interactor with, and
possible integral component of, these chromatin remodeling machines.
{O}ur experience indicates it is feasible for an investigator working
with a single ion trap instrument in a conventional molecular/cellular
biology laboratory to carry out proteomic characterization of a pathway,
organelle, or process (i.e. "pathway proteomics") by systematic application
of {TAP}-{M}ud{PIT}.},
doi = {10.1074/mcp.M300099-MCP200},
pdf = {../local/Graumann2004Applicability.pdf},
file = {Graumann2004Applicability.pdf:local/Graumann2004Applicability.pdf:PDF},
keywords = {Affinity Labels, Comparative Study, Electrospray Ionization, Genetic,
Mass, Mitosis, Non-P.H.S., Non-U.S. Gov't, P.H.S., Protein Interaction
Mapping, Proteome, Proteomics, Research Support, Saccharomyces cerevisiae,
Saccharomyces cerevisiae Proteins, Signal Transduction, Spectrometry,
Transcription, U.S. Gov't, 14660704},
owner = {vert},
pii = {M300099-MCP200},
url = {http://dx.doi.org/10.1074/mcp.M300099-MCP200}
}
@article{Green1978Conjoint,
author = {Green, Paul E. and Srinivasan, V.},
title = {Conjoint Analysis in Consumer Research: Issues and Outlook},
journal = {The Journal of Consumer Research},
year = {1978},
volume = {5},
pages = {103--123},
number = {2},
abstract = {Since 1971 conjoint analysis has been applied to a wide variety of
problems in consumer research. This paper discusses various issues
involved in implementing conjoint analysis and describes some new
technical developments and application areas for the methodology.},
citeulike-article-id = {5239458},
citeulike-linkout-0 = {http://www.jstor.org/stable/2489001},
keywords = {conjoint\_analysis},
posted-at = {2009-07-23 16:00:51},
priority = {0},
url = {http://www.jstor.org/stable/2489001}
}
@article{Greenshtein2004Persistence,
author = {Greenshtein, E. and Ritov, Y.},
title = {Persistence in high-dimensional linear predictor selection and the
virtue of overparametrization},
journal = {Bernoulli},
year = {2004},
volume = {10},
pages = {971--988},
number = {6},
abstract = {Let $Z^i=(Y^i,X_1^i,\dots,X_m^i)$, $i=1,\dots,n$, be independent and
identically distributed random vectors, $Z^i \sim F, \;F \in {\cal
F}$. It is desired to predict Y by $\sum \beta_j X_j$, where $(\beta_1,\dots,\beta_m)
\in B^n \subseteq \R^m, under a prediction loss. Suppose that $m=n^\alpha$,
$\alpha>1$, that is, there are many more explanatory variables than
observations. We consider sets Bn restricted by the maximal number
of non-zero coefficients of their members, or by their l1 radius.
We study the following asymptotic question: how 'large' may the set
Bn be, so that it is still possible to select empirically a predictor
whose risk under F is close to that of the best predictor in the
set? Sharp bounds for orders of magnitudes are given under various
assumptions on ${\cal F}$. Algorithmic complexity of the ensuing
procedures is also studied. The main message of this paper and the
implications of the orders derived are that under various sparsity
assumptions on the optimal predictor there is 'asymptotically no
harm' in introducing many more explanatory variables than observations.
Furthermore, such practice can be beneficial in comparison with a
procedure that screens in advance a small subset of explanatory variables.
Another main result is that 'lasso' procedures, that is, optimization
under l1 constraints, could be efficient in finding optimal sparse
predictors in high dimensions.},
doi = {10.3150/bj/1106314846},
pdf = {../local/Greenshtein2004Persistence.pdf},
file = {Greenshtein2004Persistence.pdf:local/Greenshtein2004Persistence.pdf:PDF},
owner = {jp},
timestamp = {2008.12.09},
url = {http://dx.doi.org/10.3150/bj/1106314846}
}
@article{Gribskov1990Profile,
author = {Gribskov, M. and L{\"u}thy, R. and and Eisenberg, D.},
title = {Profile {A}nalysis},
journal = {Methods in {E}nzymology},
year = {1990},
volume = {183},
pages = {146--159}
}
@article{Gribskov1996Use,
author = {Gribskov, M. and Robinson, N. L.},
title = {Use of receiver operating characteristic ({ROC}) analysis to evaluate
sequence matching},
journal = {Comput. {C}hem.},
year = {1996},
volume = {20},
pages = {25--33},
number = {1}
}
@article{Grimson2007MicroRNA,
author = {Andrew Grimson and Kyle Kai-How Farh and Wendy K Johnston and Philip
Garrett-Engele and Lee P Lim and David P Bartel},
title = {MicroRNA targeting specificity in mammals: determinants beyond seed
pairing.},
journal = {Mol Cell},
year = {2007},
volume = {27},
pages = {91--105},
number = {1},
month = {Jul},
abstract = {Mammalian microRNAs (miRNAs) pair to 3'UTRs of mRNAs to direct their
posttranscriptional repression. Important for target recognition
are approximately 7 nt sites that match the seed region of the miRNA.
However, these seed matches are not always sufficient for repression,
indicating that other characteristics help specify targeting. By
combining computational and experimental approaches, we uncovered
five general features of site context that boost site efficacy: AU-rich
nucleotide composition near the site, proximity to sites for coexpressed
miRNAs (which leads to cooperative action), proximity to residues
pairing to miRNA nucleotides 13-16, positioning within the 3'UTR
at least 15 nt from the stop codon, and positioning away from the
center of long UTRs. A model combining these context determinants
quantitatively predicts site performance both for exogenously added
miRNAs and for endogenous miRNA-message interactions. Because it
predicts site efficacy without recourse to evolutionary conservation,
the model also identifies effective nonconserved sites and siRNA
off-targets.},
doi = {10.1016/j.molcel.2007.06.017},
pdf = {../local/Grimson2007MicroRNA.pdf},
file = {Grimson2007MicroRNA.pdf:Grimson2007MicroRNA.pdf:PDF},
institution = {Howard Hughes Medical Institute, Massachusetts Institute of Technology,
Cambridge, MA 02139, USA.},
keywords = {sirna},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {S1097-2765(07)00407-8},
pmid = {17612493},
timestamp = {2009.10.28},
url = {http://dx.doi.org/10.1016/j.molcel.2007.06.017}
}
@article{Groebe1988Characterization,
author = {Groebe, D. R. and Uhlenbeck, O. C.},
title = {Characterization of {RNA} hairpin loop stability.},
journal = {Nucleic {A}cids {R}es.},
year = {1988},
volume = {16},
pages = {11725-35},
number = {24},
month = {Dec},
abstract = {Fifteen {RNA} hairpins that share the same stem sequence and have
homopolymer loops of {A}, {C} and {U} residues which vary in length
from three to nine nucleotides were synthesized and their thermal
stabilities determined. {T}m varies as a function of loop size but
is almost independent of loop composition. {L}oops of four or five
nucleotides are found to be the most stable loop size. {T}his is
consistent with the observation that four-membered loops are the
most prevalent loop size in 16{S}-like {RNA}s. {T}he contribution
of each loop to hairpin stability was calculated by subtracting the
known contribution of the helical stem. {T}hese data should be useful
for predicting the stability of other hairpins.}
}
@article{Gross2000Identification,
author = {C. Gross and M. Kelleher and V.R. Iyer and P.O. Brown and D.R. Winge},
title = {Identification of the copper regulon in {S}accharomyces cerevisiae
by {DNA} microarrays},
journal = {J. {B}iol. {C}hem.},
year = {2000},
volume = {275},
pages = {32310--32316},
number = {41},
pdf = {../local/gros00.pdf},
file = {gros00.pdf:local/gros00.pdf:PDF},
subject = {microarray},
url = {http://www.jbc.org/cgi/content/full/275/41/32310}
}
@inproceedings{Grundy1998Family-based,
author = {Grundy, W. N.},
title = {Family-based {H}omology {D}etection via {P}airwise {S}equence {C}omparison},
booktitle = {Proceedings of the {S}econd {A}nnual {I}nternational {C}onference
on {C}omputational {M}olecular {B}iology, {M}arch 22-25},
year = {1998},
pages = {94--100},
pdf = {../local/grun98.pdf},
file = {grun98.pdf:local/grun98.pdf:PDF},
subject = {biocasp},
url = {http://www.cs.columbia.edu/~bgrundy/papers/compare.html}
}
@article{Guba2006Chemogenomics,
author = {Guba, W.},
title = {Chemogenomics strategies for G-protein coupled receptor hit finding.},
journal = {Ernst Schering Res Found Workshop},
year = {2006},
volume = {58},
pages = {21--29},
abstract = {Targeting protein superfamilies via chemogenomics is based on a similarity
clustering of gene sequences and molecular structures of ligands.
Both target and ligand clusters are linked by generating binding
affinity profiles of chemotypes vs a target panel. The application
of this multidimensional similarity paradigm will be described in
the context of Lead Generation to identify novel chemical hit classes
for G-protein coupled receptors.},
doi = {10.1007/3-540-37635-6_2},
keywords = {chemogenomics},
owner = {laurent},
pmid = {16708996},
timestamp = {2007.07.30}
}
@article{Guedj2011refined,
author = {Guedj, M. and Marisa, L. and {de Reynies}, A. and Orsetti, B. and
Schiappa, R. and Bibeau, F. and Macgrogan, G. and Lerebours, F. and
Finetti, P. and Longy, M. and Bertheau, P. and Bertrand, F. and Bonnet,
F. and Martin, A. L. and Feugeas, J. P. and Bièche, I. and Lehmann-Che,
J. and Lidereau, R. and Birnbaum, D. and Bertucci, F. and {de Thé},
H. and Theillet, C.},
title = {A refined molecular taxonomy of breast cancer.},
journal = {Oncogene},
year = {2011},
month = {Jul},
abstract = {The current histoclinical breast cancer classification is simple but
imprecise. Several molecular classifications of breast cancers based
on expression profiling have been proposed as alternatives. However,
their reliability and clinical utility have been repeatedly questioned,
notably because most of them were derived from relatively small initial
patient populations. We analyzed the transcriptomes of 537 breast
tumors using three unsupervised classification methods. A core subset
of 355 tumors was assigned to six clusters by all three methods.
These six subgroups overlapped with previously defined molecular
classes of breast cancer, but also showed important differences,
notably the absence of an ERBB2 subgroup and the division of the
large luminal ER+ group into four subgroups, two of them being highly
proliferative. Of the six subgroups, four were ER+/PR+/AR+, one was
ER-/PR-/AR+ and one was triple negative (AR-/ER-/PR-). ERBB2-amplified
tumors were split between the ER-/PR-/AR+ subgroup and the highly
proliferative ER+ LumC subgroup. Importantly, each of these six molecular
subgroups showed specific copy-number alterations. Gene expression
changes were correlated to specific signaling pathways. Each of these
six subgroups showed very significant differences in tumor grade,
metastatic sites, relapse-free survival or response to chemotherapy.
All these findings were validated on large external datasets including
more than 3000 tumors. Our data thus indicate that these six molecular
subgroups represent well-defined clinico-biological entities of breast
cancer. Their identification should facilitate the detection of novel
prognostic factors or therapeutical targets in breast cancer.Oncogene
advance online publication, 25 July 2011; doi:10.1038/onc.2011.301.},
doi = {10.1038/onc.2011.301},
pdf = {../local/Guedj2011refined.pdf},
file = {Guedj2011refined.pdf:Guedj2011refined.pdf:PDF},
institution = {Ligue Nationale Contre le Cancer, Cartes d'Identité des Tumeurs program,
Paris, France.},
language = {eng},
medline-pst = {aheadofprint},
owner = {jp},
pii = {onc2011301},
pmid = {21785460},
timestamp = {2011.12.01},
url = {http://dx.doi.org/10.1038/onc.2011.301}
}
@article{Guelzim2002Topological,
author = {Guelzim, N. and Bottani, S. and Bourgine, P. and K{\'e}p{\`e}s, F.},
title = {Topological and causal structure of the yeast transcriptional regulatory
network},
journal = {Nat. {G}enet.},
year = {2002},
volume = {31},
pages = {60--63},
pdf = {../local/guel02.pdf},
file = {guel02.pdf:local/guel02.pdf:PDF},
subject = {bionet},
url = {http://www.nature.com/cgi-taf/DynaPage.taf?file=/ng/journal/v31/n1/full/ng873.html}
}
@article{Guermeur2002Combining,
author = {Guermeur, Y.},
title = {Combining {D}iscriminant {M}odels with {N}ew {M}ulti-{C}lass {SVM}s},
journal = {Pattern {A}nal. {A}ppl.},
year = {2002},
volume = {5},
pages = {168-179},
number = {2},
abstract = {The idea of performing model combination, instead of model selection,
has a long theoretical background in statistics. {H}owever, making
use of theoretical results is ordinarily subject to the satisfaction
of strong hypotheses (weak error correlation, availability of large
training sets, possibility to rerun the training procedure an arbitrary
number of times, etc.). {I}n contrast, the practitioner is frequently
faced with the problem of combining a given set of pre-trained classifiers,
with highly correlated errors, using only a small training sample.
{O}verfitting is then the main risk, which cannot be overcome but
with a strict complexity control of the combiner selected. {T}his
suggests that {SVM}s should be well suited for these difficult situations.
{I}nvestigating this idea, we introduce a family of multi-class {SVM}s
and assess them as ensemble methods on a real-world problem. {T}his
task, protein secondary structure prediction, is an open problem
in biocomputing for which model combination appears to be an issue
of central importance. {E}xperimental evidence highlights the gain
in quality resulting from combining some of the most widely used
prediction methods with our {SVM}s rather than with the ensemble
methods traditionally used in the field. {T}he gain increases when
the outputs of the combiners are post-processed with a {DP} algorithm.},
doi = {10.1007/s100440200015},
pdf = {../local/Guermeur2002Combining.pdf},
file = {Guermeur2002Combining.pdf:local/Guermeur2002Combining.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://dx.doi.org/10.1007/s100440200015}
}
@incollection{Guermeur2004kernel,
author = {Guermeur, Y. and Lifschitz, A. and Vert, R.},
title = {A kernel for protein secondary structure prediction},
booktitle = {Kernel {M}ethods in {C}omputational {B}iology},
publisher = {MIT Press},
year = {2004},
editor = {Sch{\"o}lkopf, B. and Tsuda, K. and Vert, J.P.},
pages = {193-206},
keywords = {biosvm},
owner = {vert}
}
@article{Guermeur2004Combining,
author = {Guermeur, Y. and Pollastri, G. and Elisseeff, A. and Zelus, D. and
Paugam-Moisy, H. and Baldi, P.},
title = {Combining protein secondary structure prediction models with ensemble
methods of optimal complexity},
journal = {Neurocomputing},
year = {2004},
volume = {56},
pages = {305-327},
abstract = {Many sophisticated methods are currently available to perform protein
secondary structure prediction. {S}ince they are frequently based
on different principles, and different knowledge sources, significant
benefits can be expected from combining them. {H}owever, the choice
of an appropriate combiner appears to be an issue in its own right.
{T}he first difficulty to overcome when combining prediction methods
is overfitting. {T}his is the reason why we investigate the implementation
of {S}upport {V}ector {M}achines to perform the task. {A} family
of multi-class {SVM}s is introduced. {T}wo of these machines are
used to combine some of the current best protein secondary structure
prediction methods. {T}heir performance is consistently superior
to the performance of the ensemble methods traditionally used in
the field. {T}hey also outperform the decomposition approaches based
on bi-class {SVM}s. {F}urthermore, initial experimental evidence
suggests that their outputs could be processed by the biologist to
perform higher-level treatments.},
doi = {10.1016/j.neucom.2003.10.004},
pdf = {../local/Guermeur2004Combining.pdf},
file = {Guermeur2004Combining.pdf:local/Guermeur2004Combining.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://dx.doi.org/10.1016/j.neucom.2003.10.004}
}
@article{Guiot2007Morphological,
author = {Caterina Guiot and Pier P Delsanto and Thomas S Deisboeck},
title = {Morphological instability and cancer invasion: a `splashing water
drop' analogy.},
journal = {Theor. Biol. Med. Model.},
year = {2007},
volume = {4},
pages = {4},
abstract = {BACKGROUND: Tissue invasion, one of the hallmarks of cancer, is a
major clinical problem. Recent studies suggest that the process of
invasion is driven at least in part by a set of physical forces that
may be susceptible to mathematical modelling which could have practical
clinical value. MODEL AND CONCLUSION: We present an analogy between
two unrelated instabilities. One is caused by the impact of a drop
of water on a solid surface while the other concerns a tumor that
develops invasive cellular branches into the surrounding host tissue.
In spite of the apparent abstractness of the idea, it yields a very
practical result, i.e. an index that predicts tumor invasion based
on a few measurable parameters. We discuss its application in the
context of experimental data and suggest potential clinical implications.},
doi = {10.1186/1742-4682-4-4},
institution = {Dip. Neuroscience and CNISM, Università di Torino, Italy. caterina.guiot@unito.it},
keywords = {Animals; Biomechanics; Cell Adhesion; Humans; Mathematics; Models,
Biological; Neoplasm Invasiveness; Neoplasms, pathology; Surface
Tension},
language = {eng},
medline-pst = {epublish},
owner = {philippe},
pii = {1742-4682-4-4},
pmid = {17254360},
timestamp = {2011.07.15},
url = {http://dx.doi.org/10.1186/1742-4682-4-4}
}
@article{Gulukota1997Two,
author = {K. Gulukota and J. Sidney and A. Sette and C. DeLisi},
title = {Two complementary methods for predicting peptides binding major histocompatibility
complex molecules.},
journal = {J. Mol. Biol.},
year = {1997},
volume = {267},
pages = {1258--1267},
number = {5},
month = {Apr},
abstract = {Peptides that bind to major histocompatibility complex products (MHC)
are known to exhibit certain sequence motifs which, though common,
are neither necessary nor sufficient for binding: MHCs bind certain
peptides that do not have the characteristic motifs and only about
30\% of the peptides having the required motif, bind. In order to
develop and test more accurate methods we measured the binding affinity
of 463 nonamer peptides to HLA-A2.1. We describe two methods for
predicting whether a given peptide will bind to an MHC and apply
them to these peptides. One method is based on simulating a neural
network and another, called the polynomial method, is based on statistical
parameter estimation assuming independent binding of the side-chains
of residues. We compare these methods with each other and with standard
motif-based methods. The two methods are complementary, and both
are superior to sequence motifs. The neural net is superior to simple
motif searches in eliminating false positives. Its behavior can be
coarsely tuned to the strength of binding desired and it is extendable
in a straightforward fashion to other alleles. The polynomial method,
on the other hand, has high sensitivity and is a superior method
for eliminating false negatives. We discuss the validity of the independent
binding assumption in such predictions.},
doi = {10.1006/jmbi.1997.0937},
keywords = {Artificial Intelligence; Computing Methodologies; HLA-A2 Antigen;
Neural Networks (Computer); Oligopeptides; Protein Binding; Reproducibility
of Results},
owner = {laurent},
pii = {S0022-2836(97)90937-2},
pmid = {9150410},
timestamp = {2007.01.27},
url = {http://dx.doi.org/10.1006/jmbi.1997.0937}
}
@article{Gunderson2004Decoding,
author = {Kevin L Gunderson and Semyon Kruglyak and Michael S Graige and Francisco
Garcia and Bahram G Kermani and Chanfeng Zhao and Diping Che and
Todd Dickinson and Eliza Wickham and Jim Bierle and Dennis Doucet
and Monika Milewski and Robert Yang and Chris Siegmund and Juergen
Haas and Lixin Zhou and Arnold Oliphant and Jian-Bing Fan and Steven
Barnard and Mark S Chee},
title = {Decoding randomly ordered DNA arrays.},
journal = {Genome Res},
year = {2004},
volume = {14},
pages = {870--877},
number = {5},
month = {May},
abstract = {We have developed a simple and efficient algorithm to identify each
member of a large collection of DNA-linked objects through the use
of hybridization, and have applied it to the manufacture of randomly
assembled arrays of beads in wells. Once the algorithm has been used
to determine the identity of each bead, the microarray can be used
in a wide variety of applications, including single nucleotide polymorphism
genotyping and gene expression profiling. The algorithm requires
only a few labels and several sequential hybridizations to identify
thousands of different DNA sequences with great accuracy. We have
decoded tens of thousands of arrays, each with 1520 sequences represented
at approximately 30-fold redundancy by up to approximately 50,000
beads, with a median error rate of <1 x 10(-4) per bead. The approach
makes use of error checking codes and provides, for the first time,
a direct functional quality control of every element of each array
that is manufactured. The algorithm can be applied to any spatially
fixed collection of objects or molecules that are associated with
specific DNA sequences.},
doi = {10.1101/gr.2255804},
institution = {Illumina, Inc., San Diego, California 92121, USA.},
keywords = {Algorithms; Computational Biology, methods; Oligonucleotide Array
Sequence Analysis, methods/trends; Random Allocation; Research Design;
Sequence Analysis, DNA, methods; Silicon Dioxide, chemistry},
language = {eng},
medline-pst = {ppublish},
owner = {philippe},
pii = {2255804},
pmid = {15078854},
timestamp = {2010.08.04},
url = {http://dx.doi.org/10.1101/gr.2255804}
}
@article{Guo2005Learning,
author = {Guodong Guo and Charles R Dyer},
title = {Learning from examples in the small sample case: face expression
recognition.},
journal = {I{EEE} {T}rans {S}yst {M}an {C}ybern {B} {C}ybern},
year = {2005},
volume = {35},
pages = {477-88},
number = {3},
month = {Jun},
abstract = {Example-based learning for computer vision can be difficult when a
large number of examples to represent each pattern or object class
is not available. {I}n such situations, learning from a small number
of samples is of practical value. {T}o study this issue, the task
of face expression recognition with a small number of training images
of each expression is considered. {A} new technique based on linear
programming for both feature selection and classifier training is
introduced. {A} pairwise framework for feature selection, instead
of using all classes simultaneously, is presented. {E}xperimental
results compare the method with three others: a simplified {B}ayes
classifier, support vector machine, and {A}da{B}oost. {F}inally,
each algorithm is analyzed and a new categorization of these algorithms
is given, especially for learning from examples in the small sample
case.}
}
@article{Guo2005Feature,
author = {Hong Guo and Lindsay B Jack and Asoke K Nandi},
title = {Feature generation using genetic programming with application to
fault classification.},
journal = {I{EEE} {T}rans {S}yst {M}an {C}ybern {B} {C}ybern},
year = {2005},
volume = {35},
pages = {89-99},
number = {1},
month = {Feb},
abstract = {One of the major challenges in pattern recognition problems is the
feature extraction process which derives new features from existing
features, or directly from raw data in order to reduce the cost of
computation during the classification process, while improving classifier
efficiency. {M}ost current feature extraction techniques transform
the original pattern vector into a new vector with increased discrimination
capability but lower dimensionality. {T}his is conducted within a
predefined feature space, and thus, has limited searching power.
{G}enetic programming ({GP}) can generate new features from the original
dataset without prior knowledge of the probabilistic distribution.
{I}n this paper, a {GP}-based approach is developed for feature extraction
from raw vibration data recorded from a rotating machine with six
different conditions. {T}he created features are then used as the
inputs to a neural classifier for the identification of six bearing
conditions. {E}xperimental results demonstrate the ability of {GP}
to discover autimatically the different bearing conditions using
features expressed in the form of nonlinear functions. {F}urthermore,
four sets of results--using {GP} extracted features with artificial
neural networks ({ANN}) and support vector machines ({SVM}), as well
as traditional features with {ANN} and {SVM}--have been obtained.
{T}his {GP}-based approach is used for bearing fault classification
for the first time and exhibits superior searching power over other
techniques. {A}dditionaly, it significantly reduces the time for
computation compared with genetic algorithm ({GA}), therefore, makes
a more practical realization of the solution.}
}
@article{Guo2004novel,
author = {Guo, J. and Chen, H. and Sun, Z. and Lin, Y.},
title = {A novel method for protein secondary structure prediction using dual-layer
{SVM} and profiles},
journal = {Proteins},
year = {2004},
volume = {54},
pages = {738-743},
number = {4},
abstract = {A high-performance method was developed for protein secondary structure
prediction based on the dual-layer support vector machine ({SVM})
and position-specific scoring matrices ({PSSM}s). {SVM} is a new
machine learning technology that has been successfully applied in
solving problems in the field of bioinformatics. {T}he {SVM}'s performance
is usually better than that of traditional machine learning approaches.
{T}he performance was further improved by combining {PSSM} profiles
with the {SVM} analysis. {T}he {PSSM}s were generated from {PSI}-{BLAST}
profiles, which contain important evolution information. {T}he final
prediction results were generated from the second {SVM} layer output.
{O}n the {CB}513 data set, the three-state overall per-residue accuracy,
{Q}3, reached 75.2%, while segment overlap ({SOV}) accuracy increased
to 80.0%. {O}n the {CB}396 data set, the {Q}3 of our method reached
74.0% and the {SOV} reached 78.1%. {A} web server utilizing the method
has been constructed and is available at http://www.bioinfo.tsinghua.edu.cn/pmsvm.},
doi = {10.1002/prot.10634Â },
pdf = {../local/Guo2004novel.pdf},
file = {Guo2004novel.pdf:local/Guo2004novel.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://dx.doi.org/10.1002/prot.10634Â }
}
@article{Guo2005novel,
author = {Ting Guo and Yanxin Shi and Zhirong Sun},
title = {A novel statistical ligand-binding site predictor: application to
{ATP}-binding sites.},
journal = {Protein {E}ng {D}es {S}el},
year = {2005},
volume = {18},
pages = {65-70},
number = {2},
month = {Feb},
abstract = {Structural genomics initiatives are leading to rapid growth in newly
determined protein 3{D} structures, the functional characterization
of which may still be inadequate. {A}s an attempt to provide insights
into the possible roles of the emerging proteins whose structures
are available and/or to complement biochemical research, a variety
of computational methods have been developed for the screening and
prediction of ligand-binding sites in raw structural data, including
statistical pattern classification techniques. {I}n this paper, we
report a novel statistical descriptor (the {O}riented {S}hell {M}odel)
for protein ligand-binding sites, which utilizes the distance and
angular position distribution of various structural and physicochemical
features present in immediate proximity to the center of a binding
site. {U}sing the support vector machine ({SVM}) as the classifier,
our model identified 69\% of the {ATP}-binding sites in whole-protein
scanning tests and in eukaryotic proteins the accuracy is particularly
high. {W}e propose that this feature extraction and machine learning
procedure can screen out ligand-binding-capable protein candidates
and can yield valuable biochemical information for individual proteins.},
doi = {10.1093/protein/gzi006},
pdf = {../local/Guo2005novel.pdf},
file = {Guo2005novel.pdf:local/Guo2005novel.pdf:PDF},
keywords = {biosvm},
pii = {gzi006},
url = {http://dx.doi.org/10.1093/protein/gzi006}
}
@article{Guo2007Edge-based,
author = {Guo, Z. and Wang, L. and Li, Y. and Gong, X. and Yao, C. and Ma,
W. and Wang, D. and Li, Y. and Zhu, J. and Zhang, M. and Yang, D.
and Rao, S. and Wan, J.},
title = {Edge-based scoring and searching method for identifying condition-responsive
protein-protein interaction sub-network},
journal = {Bioinformatics},
year = {2007},
volume = {23},
pages = {2121--2128},
number = {16},
month = {Aug},
abstract = {Current high-throughput protein-protein interaction (PPI) data do
not provide information about the condition(s) under which the interactions
occur. Thus, the identification of condition-responsive PPI sub-networks
is of great importance for investigating how a living cell adapts
to changing environments.In this article, we propose a novel edge-based
scoring and searching approach to extract a PPI sub-network responsive
to conditions related to some investigated gene expression profiles.
Using this approach, what we constructed is a sub-network connected
by the selected edges (interactions), instead of only a set of vertices
(proteins) as in previous works. Furthermore, we suggest a systematic
approach to evaluate the biological relevance of the identified responsive
sub-network by its ability of capturing condition-relevant functional
modules. We apply the proposed method to analyze a human prostate
cancer dataset and a yeast cell cycle dataset. The results demonstrate
that the edge-based method is able to efficiently capture relevant
protein interaction behaviors under the investigated conditions.Supplementary
data are available at Bioinformatics online.},
doi = {10.1093/bioinformatics/btm294},
pdf = {../local/Guo2007Edge-based.pdf},
file = {Guo2007Edge-based.pdf:Guo2007Edge-based.pdf:PDF},
institution = {Department of Bioinformatics, Bio-pharmaceutical Key Laboratory of
Heilongjiang Province-Incubator of State Key Laboratory, Harbin Medical
University, Harbin 150086, China. guoz@ems.hrbmu.edu.cn},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {btm294},
pmid = {17545181},
timestamp = {2011.10.03},
url = {http://dx.doi.org/10.1093/bioinformatics/btm294}
}
@article{Guo2005Towards,
author = {Guo, Z. and Zhang, T. and Li, X. and Wang, Q. and Xu, J. and Yu,
H. and Zhu, J. and Wang, H. and Wang, C. and Topol, E. J. and Wang,
Q. and Rao, S.},
title = {Towards precise classification of cancers based on robust gene functional
expression profiles.},
journal = {BMC Bioinformatics},
year = {2005},
volume = {6},
pages = {58},
abstract = {Development of robust and efficient methods for analyzing and interpreting
high dimension gene expression profiles continues to be a focus in
computational biology. The accumulated experiment evidence supports
the assumption that genes express and perform their functions in
modular fashions in cells. Therefore, there is an open space for
development of the timely and relevant computational algorithms that
use robust functional expression profiles towards precise classification
of complex human diseases at the modular level.Inspired by the insight
that genes act as a module to carry out a highly integrated cellular
function, we thus define a low dimension functional expression profile
for data reduction. After annotating each individual gene to functional
categories defined in a proper gene function classification system
such as Gene Ontology applied in this study, we identify those functional
categories enriched with differentially expressed genes. For each
functional category or functional module, we compute a summary measure
(s) for the raw expression values of the annotated genes to capture
the overall activity level of the module. In this way, we can treat
the gene expressions within a functional module as an integrative
data point to replace the multiple values of individual genes. We
compare the classification performance of decision trees based on
functional expression profiles with the conventional gene expression
profiles using four publicly available datasets, which indicates
that precise classification of tumour types and improved interpretation
can be achieved with the reduced functional expression profiles.This
modular approach is demonstrated to be a powerful alternative approach
to analyzing high dimension microarray data and is robust to high
measurement noise and intrinsic biological variance inherent in microarray
data. Furthermore, efficient integration with current biological
knowledge has facilitated the interpretation of the underlying molecular
mechanisms for complex human diseases at the modular level.},
doi = {10.1186/1471-2105-6-58},
pdf = {../local/Guo2005Towards.pdf},
file = {Guo2005Towards.pdf:Guo2005Towards.pdf:PDF},
institution = {Department of Computer Science, Harbin Institute of Technology, Harbin
150001, China. guoz@ems.hrbmu.edu.cn},
language = {eng},
medline-pst = {epublish},
owner = {jp},
pii = {1471-2105-6-58},
pmid = {15774002},
timestamp = {2011.08.06},
url = {http://dx.doi.org/10.1186/1471-2105-6-58}
}
@inproceedings{Guorong1996Bhattacharyya,
author = {Guorong, X. and Peiqi, C. and Minhui, W.},
title = {Bhattacharyya distance feature selection},
booktitle = {Pattern Recognition, 1996., Proceedings of the 13th International
Conference on},
year = {1996},
volume = {2},
pages = {195--199},
organization = {IEEE}
}
@article{Gururaja2003Multiple,
author = {Gururaja, T. and Li, W. and Noble, W.S. and Payan, D.G. and Anderson,
D.C.},
title = {Multiple functional categories of proteins identified in an in vitro
cellular ubiquitin affinity extract using shotgun peptide sequencing},
journal = {J {P}roteome {R}es},
year = {2003},
volume = {2},
pages = {394-404},
number = {394-404},
abstract = {Using endogenous human cellular ubiquitin system enzymes and added
his-tagged ubiquitin, {ATP}, and an {ATP}-regenerating system, we
labelled cellular proteins with hexahistidine tagged ubiquitin in
vitro. {L}abeling was dependent on {ATP} and the {ATP} recycling
system, on the proteasome inhibitor {MG}132 and the ubiquitin protease
inhibitor ubiquitin aldehyde, and was inhibited by iodoacetamide.
{L}abeled proteins were affinity extracted in quadruplicate and tryptic
peptides identifed by 2{D} capillary {LC}/{MS}/{MS} comb9ined with
{SEQUEST} and {MEDUSA} analyses. {S}upport vector machine analyais
of the mass spectrometry data allowed prediction of correct matches
between mass spectrometry data and peptide sequences. {O}verall,
144 proteins were identified by peptides predicted to be correctly
sequenced, and 113 were identified by at least three peptides or
one or two peptides with at least an 80% chance of being correct.
{I}dentified proteins included 22 proteasome subunits or associated
proteins, 18 {E}1, {E}2 or {E}3 ubiquitin system enzymes or related
proteins, and four ubiquitin domain proteins. {S}eventeen directly
ubiquitinated proteins or proteins associated with the ubiquitin
system were identified. {F}unctional clusters of other proteins included
redox enzymes, proteins associated with endocytosis, cytoskeletal
proteins, {DNA} damage or repair related proteins, calcium binding
proteins, and splicing factor and related proteins, suggesting that
in vitro ubiquitination is not random, and that these functions may
be regulated by the ubiquitin system. {T}his map of cellular ubiquitinated
proteins and their interacting proteins will be useful for further
studies of ubiquitin system function.},
pdf = {../local/Gururaja2003Multiple.pdf},
file = {Gururaja2003Multiple.pdf:local/Gururaja2003Multiple.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert}
}
@article{Gusterson2009Do,
author = {Gusterson, B.},
title = {Do 'basal-like' breast cancers really exist?},
journal = {Nat. Rev. Cancer},
year = {2009},
volume = {9},
pages = {128--134},
number = {2},
month = {Feb},
abstract = {It has been proposed that gene expression profiles will revolutionize
the classification of breast cancer, eventually replacing histopathology
with a more reproducible technology. These new approaches, combined
with a better understanding of the cellular origins of breast cancer,
should enable us to identify patient subgroups for more effective
therapy. However, in such a rapidly advancing field it is essential
that initial and thought-provoking results do not become established
as 'facts' without question. This Opinion addresses some of the negatives
and positives generated by the term 'basal-like' breast cancer, and
questions its existence as an entity.},
doi = {10.1038/nrc2571},
pdf = {../local/Gusterson2009Do.pdf},
file = {Gusterson2009Do.pdf:Gusterson2009Do.pdf:PDF},
institution = {gy), Dumbarton Road, Glasgow G11 6NT, Scotland, UK. B.A.Gusterson@clinmed.gla.ac.uk},
keywords = {breastcancer},
owner = {jp},
pii = {nrc2571},
pmid = {19132008},
timestamp = {2009.02.03},
url = {http://dx.doi.org/10.1038/nrc2571}
}
@article{Gusterson2005Basal,
author = {Gusterson, B. A. and Ross, D. T. and Heath, V. J. and Stein, T.},
title = {Basal cytokeratins and their relationship to the cellular origin
and functional classification of breast cancer},
journal = {Breast Cancer Res.},
year = {2005},
volume = {7},
pages = {143--148},
number = {4},
abstract = {Recent publications have classified breast cancers on the basis of
expression of cytokeratin-5 and -17 at the RNA and protein levels,
and demonstrated the importance of these markers in defining sporadic
tumours with bad prognosis and an association with BRCA1-related
breast cancers. These important observations using different technology
platforms produce a new functional classification of breast carcinoma.
However, it is important in developing hypotheses about the pathogenesis
of this tumour type to review the nomenclature that is being used
to emphasize potential confusion between terminology that defines
clinical subgroups and markers of cell lineage. This article reviews
the lineages in the normal breast in relation to what have become
known as the 'basal-like' carcinomas.},
doi = {10.1186/bcr1041},
pdf = {../local/Gusterson2005Basal.pdf},
file = {Gusterson2005Basal.pdf:Gusterson2005Basal.pdf:PDF},
institution = {Division of Cancer Sciences and Molecular Pathology, Western Infirmary,
University of Glasgow, Glasgow, UK. bag5f@clinmed.gla.ac.uk},
keywords = {breastcancer},
owner = {jp},
pii = {bcr1041},
pmid = {15987465},
timestamp = {2009.02.04},
url = {http://dx.doi.org/10.1186/bcr1041}
}
@inproceedings{Gutin03Traveling,
author = {G. Gutin},
title = {Travelling Salesman and Related Problems},
booktitle = {Handbook of Graph Theory},
year = {2003}
}
@article{Gutin09Generalized,
author = {Gutin, G. and Karapetyan, D.},
title = {A memetic algorithm for the generalized traveling salesman problem},
journal = {Natural Computing},
year = {2009},
abstract = {The generalized traveling salesman problem (GTSP) is an extension
of the well-known traveling salesman problem. In GTSP, we are given
a partition of cities into groups and we are required to find a minimum
length tour that includes exactly one city from each group. The recent
studies on this subject consider different variations of a memetic
algorithm approach to the GTSP. The aim of this paper is to present
a new memetic algorithm for GTSP with a powerful local search procedure.
The experiments show that the proposed algorithm clearly outperforms
all of the known heuristics with respect to both solution quality
and running time. While the other memetic algorithms were designed
only for the symmetric GTSP, our algorithm can solve both symmetric
and asymmetric instances.},
citeulike-article-id = {4004734},
doi = {http://dx.doi.org/10.1007/s11047-009-9111-6},
posted-at = {2009-02-04 01:33:10},
url = {http://dx.doi.org/10.1007/s11047-009-9111-6}
}
@inproceedings{Gutin09Asymmetric,
author = {Gutin, G. and Karapetyan, D. and Krasnogor, N. },
title = {Memetic Algorithm for the Generalized Asymmetric Traveling Salesman
Problem},
booktitle = {NICSO 2007},
year = {2008},
pages = {199-210},
publisher = {Springer Berlin}
}
@article{Guyon2003introduction,
author = {Guyon, I. and Elisseeff, A.},
title = {An introduction to variable and feature selection},
journal = {J. Mach. Learn. Res.},
year = {2003},
volume = {3},
pages = {1157--1182},
pdf = {../local/Guyon2003introduction.pdf},
file = {Guyon2003introduction.pdf:Guyon2003introduction.pdf:PDF},
owner = {jp},
timestamp = {2010.07.01},
url = {http://jmlr.csail.mit.edu/papers/volume3/guyon03a/guyon03a.pdf}
}
@article{Guyon2002Gene,
author = {Guyon, I. and Weston, J. and Barnhill, S. and Vapnik, V.},
title = {Gene selection for cancer classification using support vector machines},
journal = {Mach. Learn.},
year = {2002},
volume = {46},
pages = {389-422},
number = {1/3},
month = {Jan},
abstract = {D{NA} micro-arrays now permit scientists to screen thousands of genes
simultaneously and determine whether those genes are active, hyperactive
or silent in normal or cancerous tissue. {B}ecause these new micro-array
devices generate bewildering amounts of raw data, new analytical
methods must be developed to sort out whether cancer tissues have
distinctive signatures of gene expression over normal tissues or
other types of cancer tissues. {I}n this paper, we address the problem
of selection of a small subset of genes from broad patterns of gene
expression data, recorded on {DNA} micro-arrays. {U}sing available
training examples from cancer and normal patients, we build a classifier
suitable for genetic diagnosis, as well as drug discovery. {P}revious
attempts to address this problem select genes with correlation techniques.
{W}e propose a new method of gene selection utilizing {S}upport {V}ector
{M}achine methods based on {R}ecursive {F}eature {E}limination ({RFE}).
{W}e demonstrate experimentally that the genes selected by our techniques
yield better classification performance and are biologically relevant
to cancer. {I}n contrast with the baseline method, our method eliminates
gene redundancy automatically and yields better and more compact
gene subsets. {I}n patients with leukemia our method discovered 2
genes that yield zero leave-one-out error, while 64 genes are necessary
for the baseline method to get the best result (one leave-one-out
error). {I}n the colon cancer database, using only 4 genes our method
is 98% accurate, while the baseline method is only 86% accurate.},
pdf = {../local/Guyon2002Gene.pdf},
file = {Guyon2002Gene.pdf:local/Guyon2002Gene.pdf:PDF},
keywords = {biosvm},
subject = {biokernel},
url = {http://homepages.nyu.edu/~jaw281/genesel.pdf}
}
@article{Gygi1999Quantitative,
author = {S. P. Gygi and B. Rist and S. A. Gerber and F. Turecek and M. H.
Gelb and R. Aebersold},
title = {Quantitative analysis of complex protein mixtures using isotope-coded
affinity tags.},
journal = {Nat Biotechnol},
year = {1999},
volume = {17},
pages = {994--999},
number = {10},
month = {Oct},
abstract = {We describe an approach for the accurate quantification and concurrent
sequence identification of the individual proteins within complex
mixtures. The method is based on a class of new chemical reagents
termed isotope-coded affinity tags (ICATs) and tandem mass spectrometry.
Using this strategy, we compared protein expression in the yeast
Saccharomyces cerevisiae, using either ethanol or galactose as a
carbon source. The measured differences in protein expression correlated
with known yeast metabolic function under glucose-repressed conditions.
The method is redundant if multiple cysteinyl residues are present,
and the relative quantification is highly accurate because it is
based on stable isotope dilution techniques. The ICAT approach should
provide a widely applicable means to compare quantitatively global
protein expression in cells and tissues.},
doi = {10.1038/13690},
institution = {Department of Molecular Biotechnology, University of Washington,
Box 357730, Seattle WA 98195-7730, USA.},
keywords = {Affinity Labels; Amino Acid Sequence; Chromatography, Liquid; Isotope
Labeling; Mass Spectrometry; Proteins},
owner = {phupe},
pmid = {10504701},
timestamp = {2010.08.19},
url = {http://dx.doi.org/10.1038/13690}
}
@article{Gyorfi1999simple,
author = {Gyorfi, L. and Lugosi, G. and Morvai, G. },
title = {A simple randomized algorithm for sequential prediction of ergodic
time series},
journal = {I{EEE} {T}rans. {I}nform. {T}heory},
year = {1999},
volume = {47},
pages = {2642 - 2650},
number = {5},
month = {Nov},
abstract = {We present a simple randomized procedure for the prediction of a binary
sequence. {T}he algorithm uses ideas from previous developments of
the theory of the prediction of individual sequences. {W}e show that
if the sequence is a realization of a stationary and ergodic random
process then the average number of mistakes converges, almost surely,
to that of the optimum, given by the {B}ayes predictor. {T}he desirable
finite-sample properties of the predictor are illustrated by its
performance for {M}arkov processes. {I}n such cases the predictor
exhibits near-optimal behavior even without knowing the order of
the {M}arkov process. {P}rediction with side information is also
considered},
pdf = {../local/Gyorfi1999simple.pdf},
file = {Gyorfi1999simple.pdf:local/Gyorfi1999simple.pdf:PDF},
keywords = {information-theory},
owner = {vert}
}
@article{Gyorfi1994There,
author = {Gyorfi, L. and Pali, I. and Van der Meulen, E.C.},
title = {There is no universal source code for an infinite source alphabet},
journal = {I{EEE} {T}rans. {I}nform. {T}heory},
year = {1994},
volume = {40},
pages = {267-271},
number = {1},
month = {Jan},
abstract = {Shows that a discrete infinite distribution with finite entropy cannot
be estimated consistently in information divergence. {A}s a corollary
the authors show that there is no universal source code for an infinite
source alphabet over the class of all discrete memoryless sources
with finite entropy },
pdf = {../local/Gyorfi1994There.pdf},
file = {Gyorfi1994There.pdf:local/Gyorfi1994There.pdf:PDF},
keywords = {information-theory},
owner = {vert}
}
@article{Gartner2003Survey,
author = {G{\"a}rtner, T.},
title = {A {S}urvey of {K}ernels for {S}tructured {D}ata},
journal = {SIGKDD Explor. Newsl.},
year = {2003},
volume = {5},
pages = {49-58},
number = {1},
doi = {http://doi.acm.org/10.1145/959242.959248},
keywords = {kernel-theory},
owner = {mahe},
timestamp = {2006.08.09}
}
@misc{Gartner2002Exponential,
author = {G{\"a}rtner, T.},
title = {Exponential and {G}eometric {K}ernels for {G}raphs},
howpublished = {In NIPS {W}orkshop on {U}nreal {D}ata: {P}rinciples of {M}odeling
{N}onvectorial {D}ata},
year = {2002},
owner = {mahe},
timestamp = {2006.08.09}
}
@inproceedings{Gartner2002Multi-Instance,
author = {G{\"a}rtner, T. and Flach, P.A. and Kowalczyk, A. and Smola, A.J.},
title = {Multi-{I}nstance {K}ernels},
booktitle = {Proceedings of the {N}ineteenth {I}nternational {C}onference on {M}achine
{L}earning},
year = {2002},
editor = {C. Sammut and A. Hoffmann},
pages = {179-186},
publisher = {Morgan Kaufmann},
owner = {mahe},
timestamp = {2006.08.09}
}
@inproceedings{Gartner2003graph,
author = {G{\"a}rtner, T. and Flach, P. and Wrobel, S.},
title = {On graph kernels: hardness results and efficient alternatives},
booktitle = {Proceedings of the {S}ixteenth {A}nnual {C}onference on {C}omputational
{L}earning {T}heory and the {S}eventh {A}nnual {W}orkshop on {K}ernel
{M}achines},
year = {2003},
editor = {Sch{\"o}lkopf, B. and Warmuth, M.},
volume = {2777},
series = {Lecture Notes in Computer Science},
pages = {129--143},
address = {Heidelberg},
publisher = {Springer},
abstract = {As most lsquoreal-worldrsquo data is structured, research in kernel
methods has begun investigating kernels for various kinds of structured
data. {O}ne of the most widely used tools for modeling structured
data are graphs. {A}n interesting and important challenge is thus
to investigate kernels on instances that are represented by graphs.
{S}o far, only very specific graphs such as trees and strings have
been considered. {T}his paper investigates kernels on labeled directed
graphs with general structure. {I}t is shown that computing a strictly
positive definite graph kernel is at least as hard as solving the
graph isomorphism problem. {I}t is also shown that computing an inner
product in a feature space indexed by all possible graphs, where
each feature counts the number of subgraphs isomorphic to that graph,
is {NP}-hard. {O}n the other hand, inner products in an alternative
feature space, based on walks in the graph, can be computed in polynomial
time. {S}uch kernels are defined in this paper.},
doi = {10.1007/b12006},
owner = {vert},
timestamp = {2006.01.19},
url = {http://dx.doi.org/10.1007/b12006}
}
@inproceedings{Gartner2003grapha,
author = {G{\"a}rtner, T. and Glach, P. and Wrobel, S.},
title = {On graph kernels: hardness results and efficient alternatives},
booktitle = {Proceedings of COLT / Kernel workshop},
year = {2003},
timestamp = {2007.04.12}
}
@article{Gartner2004Kernels,
author = {G{\"a}rtner, T. and Lloyd, J.W. and Flach, P.A.},
title = {Kernels and Distances for Structured Data},
journal = {Mach. Learn.},
year = {2004},
volume = {57},
pages = {205-232},
number = {3},
abstract = {This paper brings together two strands of machine learning of increasing
importance: kernel methods and highly structured data. We propose
a general method for constructing a kernel following the syntactic
structure of the data, as defined by its type signature in a higher-order
logic. Our main theoretical result is the positive definiteness of
any kernel thus defined. We report encouraging experimental results
on a range of real-world data sets. By converting our kernel to a
distance pseudo-metric for 1-nearest neighbour, we were able to improve
the best accuracy from the literature on the Diterpene data set by
more than 10%.},
doi = {10.1023/B:MACH.0000039777.23772.30},
keywords = {biosvm},
timestamp = {2006.07.11},
url = {http://dx.doi.org/10.1023/B:MACH.0000039777.23772.30}
}
@book{Guner2000Pharmacophore,
title = {Pharmacophore {P}erception, {D}evelopment, and {U}se in {D}rug {D}esign},
publisher = {International University Line},
year = {2000},
author = {G{\"u}ner, O. F.},
volume = {2},
series = {IUL Biotechnology Series}
}
@article{Goektuerk2001statistical,
author = {S. B. Göktürk and C. Tomasi and B. Acar and C. F. Beaulieu and
D. S. Paik and R. B. Jeffrey and J. Yee and S. Napel},
title = {A statistical 3-{D} pattern processing method for computer-aided
detection of polyps in {CT} colonography.},
journal = {I{EEE} {T}rans {M}ed {I}maging},
year = {2001},
volume = {20},
pages = {1251-60},
number = {12},
month = {Dec},
abstract = {Adenomatous polyps in the colon are believed to be the precursor to
colorectal carcinoma, the second leading cause of cancer deaths in
{U}nited {S}tates. {I}n this paper, we propose a new method for computer-aided
detection of polyps in computed tomography ({CT}) colonography (virtual
colonoscopy), a technique in which polyps are imaged along the wall
of the air-inflated, cleansed colon with {X}-ray {CT}. {I}nitial
work with computer aided detection has shown high sensitivity, but
at a cost of too many false positives. {W}e present a statistical
approach that uses support vector machines to distinguish the differentiating
characteristics of polyps and healthy tissue, and uses this information
for the classification of the new cases. {O}ne of the main contributions
of the paper is the new three-dimensional pattern processing approach,
called random orthogonal shape sections method, which combines the
information from many random images to generate reliable signatures
of shape. {T}he input to the proposed system is a collection of volume
data from candidate polyps obtained by a high-sensitivity, low-specificity
system that we developed previously. {T}he results of our ten-fold
cross-validation experiments show that, on the average, the system
increases the specificity from 0.19 (0.35) to 0.69 (0.74) at a sensitivity
level of 1.0 (0.95).}
}
@inproceedings{Gartner03graph,
author = {T. Gärtner and K. Driessens and J. Ramon},
title = {Exponential and geometric kernels for graphs},
booktitle = {Mach. Learn.},
year = {2002},
pages = {146--163},
publisher = {Springer}
}
@article{Goendoer2009Chromosome,
author = {Anita Göndör and Rolf Ohlsson},
title = {Chromosome crosstalk in three dimensions.},
journal = {Nature},
year = {2009},
volume = {461},
pages = {212--217},
number = {7261},
month = {Sep},
abstract = {The genome forms extensive and dynamic physical interactions with
itself in the form of chromosome loops and bridges, thus exploring
the three-dimensional space of the nucleus. It is now possible to
examine these interactions at the molecular level, and we have gained
glimpses of their functional implications. Chromosomal interactions
can contribute to the silencing and activation of genes within the
three-dimensional context of the nuclear architecture. Technical
advances in detecting these interactions contribute to our understanding
of the functional organization of the genome, as well as its adaptive
plasticity in response to environmental changes during development
and disease.},
doi = {10.1038/nature08453},
institution = {Department of Microbiology, Tumor and Cell Biology, Nobels väg 16,
Box 280, Karolinska Institute, SE-171 77 Stockholm, Sweden. anita.gondor@ki.se},
keywords = {Animals; Cell Nucleus, genetics/metabolism; Chromosome Positioning;
Chromosomes, chemistry/genetics/metabolism; Gene Expression Regulation;
Humans; Nucleic Acid Conformation; Transcription, Genetic},
language = {eng},
medline-pst = {ppublish},
owner = {philippe},
pii = {nature08453},
pmid = {19741702},
timestamp = {2010.08.11},
url = {http://dx.doi.org/10.1038/nature08453}
}
@article{Goendoer2008High-resolution,
author = {Anita Göndör and Carole Rougier and Rolf Ohlsson},
title = {High-resolution circular chromosome conformation capture assay.},
journal = {Nat Protoc},
year = {2008},
volume = {3},
pages = {303--313},
number = {2},
abstract = {The pioneering chromosome conformation capture (3C) method provides
the opportunity to study chromosomal folding in the nucleus. It is
based on formaldehyde cross-linking of living cells followed by enzyme
digestion, intramolecular ligation and quantitative (Q)-PCR analysis.
However, 3C requires prior knowledge of the bait and interacting
sequence (termed interactor) rendering it less useful for genome-wide
studies. As several recent reports document, this limitation has
been overcome by exploiting a circular intermediate in a variant
of the 3C method, termed 4C (for circular 3C). The strategic positioning
of primers within the bait enables the identification of unknown
interacting sequences, which form part of the circular DNA. Here,
we describe a protocol for our 4C method, which produces a high-resolution
interaction map potentially suitable for the analysis of cis-regulatory
elements and for comparison with chromatin marks obtained by chromatin
immunoprecipitation (ChIP) on chip at the sites of interaction. Following
optimization of enzyme digestions and amplification conditions, the
protocol can be completed in 2-3 weeks.},
doi = {10.1038/nprot.2007.540},
institution = {Department of Development and Genetics, Uppsala University, Norbyvägen
18A, S-752 36 Uppsala, Sweden. anita.gondor@ebc.uu.se},
keywords = {Chromatin; Chromosomes, Human, Pair 11; DNA; DNA Restriction Enzymes;
DNA, Circular; Formaldehyde; Genetic Techniques; Humans; Nucleic
Acid Conformation},
owner = {phupe},
pii = {nprot.2007.540},
pmid = {18274532},
timestamp = {2010.08.26},
url = {http://dx.doi.org/10.1038/nprot.2007.540}
}
@article{Haasdonk2005Feature,
author = {Bernard Haasdonk},
title = {Feature space interpretation of {SVM}s with indefinite kernels.},
journal = {I{EEE} {T}rans {P}attern {A}nal {M}ach {I}ntell},
year = {2005},
volume = {27},
pages = {482-92},
number = {4},
month = {Apr},
abstract = {Kernel methods are becoming increasingly popular for various kinds
of machine learning tasks, the most famous being the support vector
machine ({SVM}) for classification. {T}he {SVM} is well understood
when using conditionally positive definite (cpd) kernel functions.
{H}owever, in practice, non-cpd kernels arise and demand application
in {SVM}s. {T}he procedure of "plugging" these indefinite kernels
in {SVM}s often yields good empirical classification results. {H}owever,
they are hard to interpret due to missing geometrical and theoretical
understanding. {I}n this paper, we provide a step toward the comprehension
of {SVM} classifiers in these situations. {W}e give a geometric interpretation
of {SVM}s with indefinite kernel functions. {W}e show that such {SVM}s
are optimal hyperplane classifiers not by margin maximization, but
by minimization of distances between convex hulls in pseudo-{E}uclidean
spaces. {B}y this, we obtain a sound framework and motivation for
indefinite {SVM}s. {T}his interpretation is the basis for further
theoretical analysis, e.g., investigating uniqueness, and for the
derivation of practical guidelines like characterizing the suitability
of indefinite {SVM}s.},
doi = {10.1109/TPAMI.2005.78},
pdf = {../local/Haasdonk2005Feature.pdf},
file = {Haasdonk2005Feature.pdf:local/Haasdonk2005Feature.pdf:PDF},
keywords = {Algorithms, Animals, Antibiotics, Antineoplastic, Artificial Intelligence,
Automated, Automatic Data Processing, Butadienes, Chloroplasts, Cluster
Analysis, Comparative Study, Computer Simulation, Computer-Assisted,
Computing Methodologies, Database Management Systems, Databases,
Diagnosis, Disinfectants, Dose-Response Relationship, Drug, Drug
Toxicity, Electrodes, Electroencephalography, Ethylamines, Expert
Systems, Factual, Feedback, Fungicides, Gene Expression Profiling,
Genes, Genetic Markers, Humans, Image Enhancement, Image Interpretation,
Implanted, Industrial, Information Storage and Retrieval, Kidney,
Kidney Tubules, MEDLINE, Male, Mercuric Chloride, Microarray Analysis,
Molecular Biology, Motor Cortex, Movement, Natural Language Processing,
Neural Networks (Computer), Non-P.H.S., Non-U.S. Gov't, Numerical
Analysis, Pattern Recognition, Plant Proteins, Predictive Value of
Tests, Proteins, Proteome, Proximal, Puromycin Aminonucleoside, Rats,
Reproducibility of Results, Research Support, Sensitivity and Specificity,
Signal Processing, Sprague-Dawley, Subcellular Fractions, Terminology,
Therapy, Time Factors, Toxicogenetics, U.S. Gov't, User-Computer
Interface, 15794155},
url = {http://dx.doi.org/10.1109/TPAMI.2005.78}
}
@book{Hadamard1923Lectures,
title = {Lectures on Cauchy's Problem: In Linear Partial Differential Equations},
publisher = {Dover Publications},
year = {1923},
author = {J. Hadamard}
}
@article{Hadamard1902Sur,
author = {J. Hadamard},
title = {Sur les probl\`emes aux D\'eriv\'ees partielles et leur signification
physique},
journal = {Princeton University Bulletin},
year = {1902},
volume = {13},
pages = {49-52}
}
@article{Haferlach2005AML,
author = {Torsten Haferlach and Alexander Kohlmann and Susanne Schnittger and
Martin Dugas and Wolfgang Hiddemann and Wolfgang Kern and Claudia
Schoch},
title = {A{ML} {M}3 and {AML} {M}3 variant each have a distinct gene expression
signature but also share patterns different from other genetically
defined {AML} subtypes.},
journal = {Genes {C}hromosomes {C}ancer},
year = {2005},
volume = {43},
pages = {113-27},
number = {2},
month = {Jun},
abstract = {Acute promyelocytic leukemia ({APL}) with t(15;17) appears in two
phenotypes: {AML} {M}3, with abnormal promyelocytes showing heavy
granulation and bundles of {A}uer rods, and {AML} {M}3 variant ({M}3v),
with non- or hypogranular cytoplasm and a bilobed nucleus. {W}e investigated
the global gene expression profiles of 35 {APL} patients (19 {AML}
{M}3, 16 {AML} {M}3v) by using high-density {DNA}-oligonucleotide
microarrays. {F}irst, an unsupervised approach clearly separated
{APL} samples from other {AML}s characterized genetically as t(8;21)
(n = 35), inv(16) (n = 35), or t(11q23)/{MLL} (n = 35) or as having
a normal karyotype (n = 50). {S}econd, we found genes with functional
relevance for blood coagulation that were differentially expressed
between {APL} and other {AML}s. {F}urthermore, a supervised pairwise
comparison between {M}3 and {M}3v revealed differential expression
of genes that encode for biological functions and pathways such as
granulation and maturation of hematologic cells, explaining morphologic
and clinical differences. {D}iscrimination between {M}3 and {M}3v
based on gene signatures showed a median classification accuracy
of 90\% by use of 10-fold {CV} and support vector machines. {A}dditional
molecular mutations such as {FLT}3-{LM}, which were significantly
more frequent in {M}3v than in {M}3 ({P} < 0.0001), may partly contribute
to the different phenotypes. {H}owever, linear regression analysis
demonstrated that genes differentially expressed between {M}3 and
{M}3v did not correlate with {FLT}3-{LM}.},
doi = {10.1002/gcc.20175},
pdf = {../local/Haferlach2005AML.pdf},
file = {Haferlach2005AML.pdf:local/Haferlach2005AML.pdf:PDF},
keywords = {biosvm microarray},
url = {http://dx.doi.org/10.1002/gcc.20175}
}
@article{Haferlach2005global,
author = {Torsten Haferlach and Alexander Kohlmann and Susanne Schnittger and
Martin Dugas and Wolfgang Hiddemann and Wolfgang Kern and Claudia
Schoch},
title = {A global approach to the diagnosis of leukemia using gene expression
profiling.},
journal = {Blood},
year = {2005},
volume = {106},
pages = {1189-1198},
number = {4},
month = {Aug},
abstract = {Accurate diagnosis and classification of leukemias are the bases for
the appropriate management of patients. {T}he diagnostic accuracy
and efficiency of present methods may be improved by the use of microarrays
for gene expression profiling. {W}e analyzed gene expression profiles
in bone marrow and peripheral blood samples from 937 patients with
all clinically relevant leukemia subtypes (n=892) and non-leukemic
controls (n=45) by {U}133{A} and {B} {G}ene{C}hips ({A}ffymetrix).
{F}or each subgroup differentially expressed genes were calculated.
{C}lass prediction was performed using support vector machines. {P}rediction
accuracies were estimated by 10-fold cross validation and assessed
for robustness in a 100-fold resampling approach using randomly chosen
test-sets consisting of 1/3 of the samples. {A}pplying the top 100
genes of each subgroup an overall prediction accuracy of 95.1\% was
achieved which was confirmed by resampling (median, 93.8\%; 95\%
confidence interval, 91.4\%-95.8\%). {I}n particular, {AML} with
t(15;17), t(8;21), or inv(16), {CLL}, and {P}ro-{B}-{ALL} with t(11q23)
were classified with 100\% sensitivity and 100\% specificity. {A}ccordingly,
cluster analysis completely separated all of the 13 subgroups analyzed.
{G}ene expression profiling can predict all clinically relevant subentities
of leukemia with high accuracy.},
doi = {10.1182/blood-2004-12-4938},
pdf = {../local/Haferlach2005global.pdf},
file = {Haferlach2005global.pdf:local/Haferlach2005global.pdf:PDF},
keywords = {biosvm microarray},
pii = {2004-12-4938},
url = {http://dx.doi.org/10.1182/blood-2004-12-4938}
}
@article{Haibe-Kains2008Comparison,
author = {Haibe-Kains, B. and Desmedt, C. and Piette, F. and Buyse, M. and
Cardoso, F. and Van't Veer, L. and Piccart, M. and Bontempi, G. and
Sotiriou, C.},
title = {Comparison of prognostic gene expression signatures for breast cancer},
journal = {BMC Genomics},
year = {2008},
volume = {9},
pages = {394},
abstract = {BACKGROUND: During the last years, several groups have identified
prognostic gene expression signatures with apparently similar performances.
However, signatures were never compared on an independent population
of untreated breast cancer patients, where risk assessment was computed
using the original algorithms and microarray platforms. RESULTS:
We compared three gene expression signatures, the 70-gene, the 76-gene
and the Gene expression Grade Index (GGI) signatures, in terms of
predicting distant metastasis free survival (DMFS) for the individual
patient. To this end, we used the previously published TRANSBIG independent
validation series of node-negative untreated primary breast cancer
patients. We observed agreement in prediction for 135 of 198 patients
(68\%) when considering the three signatures. When comparing the
signatures two by two, the agreement in prediction was 71\% for the
70- and 76-gene signatures, 76\% for the 76-gene signature and the
GGI, and 88\% for the 70-gene signature and the GGI. The three signatures
had similar capabilities of predicting DMFS and added significant
prognostic information to that provided by the classical parameters.
CONCLUSION: Despite the difference in development of these signatures
and the limited overlap in gene identity, they showed similar prognostic
performance, adding to the growing evidence that these prognostic
signatures are of clinical relevance.},
doi = {10.1186/1471-2164-9-394},
pdf = {../local/Haibe-Kains2008Comparison.pdf},
file = {Haibe-Kains2008Comparison.pdf:Haibe-Kains2008Comparison.pdf:PDF},
institution = {Functional Genomics Unit, Jules Bordet Institute, Université Libre
de Bruxelles, Brussels, Belgium. bhaibeka@ulb.ac.be},
keywords = {breastcancer},
owner = {jp},
pii = {1471-2164-9-394},
pmid = {18717985},
timestamp = {2008.12.09},
url = {http://dx.doi.org/10.1186/1471-2164-9-394}
}
@article{Haibe-Kains2008comparative,
author = {Haibe-Kains, B. and Desmedt, C. and Sotiriou, C. and Bontempi, G.},
title = {A comparative study of survival models for breast cancer prognostication
based on microarray data: does a single gene beat them all?},
journal = {Bioinformatics},
year = {2008},
volume = {24},
pages = {2200--2208},
number = {19},
doi = {10.1093/bioinformatics/btn374},
pdf = {../local/Haibe-Kains2008comparative.pdf},
file = {Haibe-Kains2008comparative.pdf:Haibe-Kains2008comparative.pdf:PDF},
issn = {1367-4803},
owner = {jp},
publisher = {Oxford Univ Press},
timestamp = {2011.01.14},
url = {http://dx.doi.org/10.1093/bioinformatics/btn374}
}
@manual{Haibe-Kains2011genefu,
title = {genefu: Relevant Functions for Gene Expression Analysis, Especially
in Breast Cancer.},
author = {Haibe-Kains, B. and Schroeder, M. and Bontempi, G. and Sotiriou,
C. and Quackenbush, J.},
year = {2011},
note = {R package version 1.4.0},
owner = {jp},
timestamp = {2012.02.27},
url = {http://compbio.dfci.harvard.edu}
}
@article{Haigh2005Small,
author = {J. A Haigh and B. T. Pickup and J. A. Grant and A. Nicholls},
title = {{S}mall molecule shape-fingerprints.},
journal = {J. Chem. Inf. Model.},
year = {2005},
volume = {45},
pages = {673--684},
number = {3},
abstract = {The optimal overlap between two molecular structures is a useful measure
of shape similarity. However, it usually requires significant computation.
This work describes the design of shape-fingerprints: binary bit
strings that encode molecular shape. Standard measures of similarity
between two shape-fingerprints are shown to be an excellent surrogate
for similarity based on volume overlap but several orders of magnitude
faster to compute. Consequently, shape-fingerprints can be used for
clustering of large data sets, evaluating the diversity of compound
libraries, as descriptors in SAR and as a prescreen for exact shape
comparison against large virtual databases. Our results show that
a small set of shapes can be used to build these fingerprints and
that this set can be applied universally.},
doi = {10.1021/ci049651v},
keywords = {15921457},
owner = {mahe},
pmid = {15921457},
timestamp = {2006.08.22},
url = {http://dx.doi.org/10.1021/ci049651v}
}
@article{Hakenberg2005Systematic,
author = {Jörg Hakenberg and Steffen Bickel and Conrad Plake and Ulf Brefeld
and Hagen Zahn and Lukas Faulstich and Ulf Leser and Tobias Scheffer},
title = {Systematic feature evaluation for gene name recognition.},
journal = {B{MC} {B}ioinformatics},
year = {2005},
volume = {6 Suppl 1},
pages = {S9},
abstract = {In task 1{A} of the {B}io{C}re{A}t{I}v{E} evaluation, systems had
to be devised that recognize words and phrases forming gene or protein
names in natural language sentences. {W}e approach this problem by
building a word classification system based on a sliding window approach
with a {S}upport {V}ector {M}achine, combined with a pattern-based
post-processing for the recognition of phrases. {T}he performance
of such a system crucially depends on the type of features chosen
for consideration by the classification method, such as pre- or postfixes,
character n-grams, patterns of capitalization, or classification
of preceding or following words. {W}e present a systematic approach
to evaluate the performance of different feature sets based on recursive
feature elimination, {RFE}. {B}ased on a systematic reduction of
the number of features used by the system, we can quantify the impact
of different feature sets on the results of the word classification
problem. {T}his helps us to identify descriptive features, to learn
about the structure of the problem, and to design systems that are
faster and easier to understand. {W}e observe that the {SVM} is robust
to redundant features. {RFE} improves the performance by 0.7\%, compared
to using the complete set of attributes. {M}oreover, a performance
that is only 2.3\% below this maximum can be obtained using fewer
than 5\% of the features.},
doi = {10.1186/1471-2105-6-S1-S9},
pdf = {../local/Hakenberg2005Systematic.pdf},
file = {Hakenberg2005Systematic.pdf:local/Hakenberg2005Systematic.pdf:PDF},
keywords = {biosvm},
pii = {1471-2105-6-S1-S9},
url = {http://dx.doi.org/10.1186/1471-2105-6-S1-S9}
}
@article{Hakenberg2004Finding,
author = {Hakenberg, J. and Schmeier ,S. and Kowald, A. and Klipp, E. and Leser,
U.},
title = {Finding kinetic parameters using text mining.},
journal = {O{MICS}},
year = {2004},
volume = {8},
pages = {131-152},
number = {2},
abstract = {The mathematical modeling and description of complex biological processes
has become more and more important over the last years. {S}ystems
biology aims at the computational simulation of complex systems,
up to whole cell simulations. {A}n essential part focuses on solving
a large number of parameterized differential equations. {H}owever,
measuring those parameters is an expensive task, and finding them
in the literature is very laborious. {W}e developed a text mining
system that supports researchers in their search for experimentally
obtained parameters for kinetic models. {O}ur system classifies full
text documents regarding the question whether or not they contain
appropriate data using a support vector machine. {W}e evaluated our
approach on a manually tagged corpus of 800 documents and found that
it outperforms keyword searches in abstracts by a factor of five
in terms of precision.},
pdf = {../local/Hakenberg2004Finding.pdf},
file = {Hakenberg2004Finding.pdf:local/Hakenberg2004Finding.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://www.liebertonline.com/doi/abs/10.1089%2F1536231041388366}
}
@article{Haley2004Kinetic,
author = {Haley, B. and Zamore, P. D.},
title = {{K}inetic analysis of the {RNA}i enzyme complex.},
journal = {Nat. Struct. Mol. Biol.},
year = {2004},
volume = {11},
pages = {599--606},
number = {7},
month = {Jul},
abstract = {The siRNA-directed ribonucleoprotein complex, RISC, catalyzes target
RNA cleavage in the RNA interference pathway. Here, we show that
siRNA-programmed RISC is a classical Michaelis-Menten enzyme in the
presence of ATP. In the absence of ATP, the rate of multiple rounds
of catalysis is limited by release of the cleaved products from the
enzyme. Kinetic analysis suggests that different regions of the siRNA
play distinct roles in the cycle of target recognition, cleavage,
and product release. Bases near the siRNA 5' end disproportionately
contribute to target RNA-binding energy, whereas base pairs formed
by the central and 3' regions of the siRNA provide a helical geometry
required for catalysis. Finally, the position of the scissile phosphate
on the target RNA seems to be determined during RISC assembly, before
the siRNA encounters its RNA target.},
doi = {10.1038/nsmb780},
pdf = {../local/Haley2004Kinetic.pdf},
file = {Haley2004Kinetic.pdf:Haley2004Kinetic.pdf:PDF},
keywords = {sirna},
owner = {vert},
pii = {nsmb780},
pmid = {15170178},
timestamp = {2006.04.27},
url = {http://dx.doi.org/10.1038/nsmb780}
}
@article{Hall2004Unravelling,
author = {Hall, J.},
title = {Unravelling the general properties of si{RNA}s: strength in numbers
and lessons from the past.},
journal = {Nat. {R}ev. {G}enet.},
year = {2004},
volume = {5},
pages = {552-7},
number = {7},
month = {Jul},
doi = {10.1038/nrg1382},
pdf = {../local/Hall2004Unravelling.pdf},
file = {Hall2004Unravelling.pdf:local/Hall2004Unravelling.pdf:PDF},
keywords = {sirna},
pii = {nrg1382},
url = {http://dx.doi.org/10.1038/nrg1382}
}
@article{Hall2005comprehensive,
author = {Neil Hall and Marianna Karras and J. Dale Raine and Jane M Carlton
and Taco W A Kooij and Matthew Berriman and Laurence Florens and
Christoph S Janssen and Arnab Pain and Georges K Christophides and
Keith James and Kim Rutherford and Barbara Harris and David Harris
and Carol Churcher and Michael A Quail and Doug Ormond and Jon Doggett
and Holly E Trueman and Jacqui Mendoza and Shelby L Bidwell and Marie-Adele
Rajandream and Daniel J Carucci and John R Yates and Fotis C Kafatos
and Chris J Janse and Bart Barrell and C. Michael R Turner and Andrew
P Waters and Robert E Sinden},
title = {{A} comprehensive survey of the {P}lasmodium life cycle by genomic,
transcriptomic, and proteomic analyses.},
journal = {Science},
year = {2005},
volume = {307},
pages = {82--86},
number = {5706},
month = {Jan},
abstract = {Plasmodium berghei and Plasmodium chabaudi are widely used model malaria
species. Comparison of their genomes, integrated with proteomic and
microarray data, with the genomes of Plasmodium falciparum and Plasmodium
yoelii revealed a conserved core of 4500 Plasmodium genes in the
central regions of the 14 chromosomes and highlighted genes evolving
rapidly because of stage-specific selective pressures. Four strategies
for gene expression are apparent during the parasites' life cycle:
(i) housekeeping; (ii) host-related; (iii) strategy-specific related
to invasion, asexual replication, and sexual development; and (iv)
stage-specific. We observed posttranscriptional gene silencing through
translational repression of messenger RNA during sexual development,
and a 47-base 3' untranslated region motif is implicated in this
process.},
doi = {10.1126/science.1103717},
pdf = {../local/Hall2005comprehensive.pdf},
file = {Hall2005comprehensive.pdf:local/Hall2005comprehensive.pdf:PDF},
keywords = {plasmodium},
pii = {307/5706/82},
pmid = {15637271},
timestamp = {2006.04.13},
url = {http://dx.doi.org/10.1126/science.1103717}
}
@article{Halperin2002Principles,
author = {I. Halperin and B. Ma and H. Wolfson and R. Nussinov},
title = {Principles of docking: {A}n overview of search algorithms and a guide
to scoring functions.},
journal = {Proteins},
year = {2002},
volume = {47},
pages = {409--443},
number = {4},
month = {Jun},
abstract = {The docking field has come of age. {T}he time is ripe to present the
principles of docking, reviewing the current state of the field.
{T}wo reasons are largely responsible for the maturity of the computational
docking area. {F}irst, the early optimism that the very presence
of the "correct" native conformation within the list of predicted
docked conformations signals a near solution to the docking problem,
has been replaced by the stark realization of the extreme difficulty
of the next scoring/ranking step. {S}econd, in the last couple of
years more realistic approaches to handling molecular flexibility
in docking schemes have emerged. {A}s in folding, these derive from
concepts abstracted from statistical mechanics, namely, populations.
{D}ocking and folding are interrelated. {F}rom the purely physical
standpoint, binding and folding are analogous processes, with similar
underlying principles. {C}omputationally, the tools developed for
docking will be tremendously useful for folding. {F}or large, multidomain
proteins, domain docking is probably the only rational way, mimicking
the hierarchical nature of protein folding. {T}he complexity of the
problem is huge. {H}ere we divide the computational docking problem
into its two separate components. {A}s in folding, solving the docking
problem involves efficient search (and matching) algorithms, which
cover the relevant conformational space, and selective scoring functions,
which are both efficient and effectively discriminate between native
and non-native solutions. {I}t is universally recognized that docking
of drugs is immensely important. {H}owever, protein-protein docking
is equally so, relating to recognition, cellular pathways, and macromolecular
assemblies. {P}roteins function when they are bound to other molecules.
{C}onsequently, we present the review from both the computational
and the biological points of view. {A}lthough large, it covers only
partially the extensive body of literature, relating to small (drug)
and to large protein-protein molecule docking, to rigid and to flexible.
{U}nfortunately, when reviewing these, a major difficulty in assessing
the results is the non-uniformity in the formats in which they are
presented in the literature. {C}onsequently, we further propose a
way to rectify it here.},
doi = {10.1002/prot.10115},
keywords = {chemoinformatics},
owner = {mahe},
pmid = {12001221},
timestamp = {2006.02.03},
url = {http://dx.doi.org/10.1002/prot.10115}
}
@article{Hammond20043D,
author = {Peter Hammond and Tim J Hutton and Judith E Allanson and Linda E
Campbell and Raoul C M Hennekam and Sean Holden and Michael A Patton
and Adam Shaw and I. Karen Temple and Matthew Trotter and Kieran
C Murphy and Robin M Winter},
title = {3{D} analysis of facial morphology.},
journal = {Am {J} {M}ed {G}enet {A}},
year = {2004},
volume = {126},
pages = {339-48},
number = {4},
month = {May},
abstract = {Dense surface models can be used to analyze 3{D} facial morphology
by establishing a correspondence of thousands of points across each
3{D} face image. {T}he models provide dramatic visualizations of
3{D} face-shape variation with potential for training physicians
to recognize the key components of particular syndromes. {W}e demonstrate
their use to visualize and recognize shape differences in a collection
of 3{D} face images that includes 280 controls (2 weeks to 56 years
of age), 90 individuals with {N}oonan syndrome ({NS}) (7 months to
56 years), and 60 individuals with velo-cardio-facial syndrome ({VCFS};
3 to 17 years of age). {T}en-fold cross-validation testing of discrimination
between the three groups was carried out on unseen test examples
using five pattern recognition algorithms (nearest mean, {C}5.0 decision
trees, neural networks, logistic regression, and support vector machines).
{F}or discriminating between individuals with {NS} and controls,
the best average sensitivity and specificity levels were 92 and 93\%
for children, 83 and 94\% for adults, and 88 and 94\% for the children
and adults combined. {F}or individuals with {VCFS} and controls,
the best results were 83 and 92\%. {I}n a comparison of individuals
with {NS} and individuals with {VCFS}, a correct identification rate
of 95\% was achieved for both syndromes. {T}his article contains
supplementary material, which may be viewed at the {A}merican {J}ournal
of {M}edical {G}enetics website at http://www.interscience.wiley.com/jpages/0148-7299/suppmat/index.html.},
doi = {10.1002/ajmg.a.20665},
pdf = {../local/Hammond20043D.pdf},
file = {Hammond20043D.pdf:local/Hammond20043D.pdf:PDF},
url = {http://dx.doi.org/10.1002/ajmg.a.20665}
}
@article{Han2004Evidence,
author = {Han, J.-D. J. and Bertin, N. and Hao, T. and Goldberg, D. S. and
Berriz, G. F. and Zhang, L. V. and Dupuy, D. and Walhout, A. J. M.
and Cusick, M. E. and Roth, F. P. and Vidal, M.},
title = {Evidence for dynamically organized modularity in the yeast protein-protein
interaction network},
journal = {Nature},
year = {2004},
volume = {430},
pages = {88--93},
number = {6995},
month = {Jul},
abstract = {In apparently scale-free protein-protein interaction networks, or
'interactome' networks, most proteins interact with few partners,
whereas a small but significant proportion of proteins, the 'hubs',
interact with many partners. Both biological and non-biological scale-free
networks are particularly resistant to random node removal but are
extremely sensitive to the targeted removal of hubs. A link between
the potential scale-free topology of interactome networks and genetic
robustness seems to exist, because knockouts of yeast genes encoding
hubs are approximately threefold more likely to confer lethality
than those of non-hubs. Here we investigate how hubs might contribute
to robustness and other cellular properties for protein-protein interactions
dynamically regulated both in time and in space. We uncovered two
types of hub: 'party' hubs, which interact with most of their partners
simultaneously, and 'date' hubs, which bind their different partners
at different times or locations. Both in silico studies of network
connectivity and genetic interactions described in vivo support a
model of organized modularity in which date hubs organize the proteome,
connecting biological processes--or modules--to each other, whereas
party hubs function inside modules.},
doi = {10.1038/nature02555},
pdf = {../local/Han2004Evidence.pdf},
file = {Han2004Evidence.pdf:Han2004Evidence.pdf:PDF},
institution = {Center for Cancer Systems Biology and Department of Cancer Biology,
Dana-Farber Cancer Institute, and Department of Genetics, Harvard
Medical School, Boston, Massachusetts 02115, USA.},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {nature02555},
pmid = {15190252},
timestamp = {2011.09.27},
url = {http://dx.doi.org/10.1038/nature02555}
}
@article{Han2004Predicting,
author = {Han, L.Y. and Cai, C.Z. and Ji, Z.L. and Cao, Z.W. and Cui, J. and
Chen, Y.Z.},
title = {Predicting functional family of novel enzymes irrespective of sequence
similarity: a statistical learning approach.},
journal = {Nucl. {A}cids {R}es.},
year = {2004},
volume = {32},
pages = {6437-6444},
number = {21},
abstract = {The function of a protein that has no sequence homolog of known function
is difficult to assign on the basis of sequence similarity. {T}he
same problem may arise for homologous proteins of different functions
if one is newly discovered and the other is the only known protein
of similar sequence. {I}t is desirable to explore methods that are
not based on sequence similarity. {O}ne approach is to assign functional
family of a protein to provide useful hint about its function. {S}everal
groups have employed a statistical learning method, support vector
machines ({SVM}s), for predicting protein functional family directly
from sequence irrespective of sequence similarity. {T}hese studies
showed that {SVM} prediction accuracy is at a level useful for functional
family assignment. {B}ut its capability for assignment of distantly
related proteins and homologous proteins of different functions has
not been critically and adequately assessed. {H}ere {SVM} is tested
for functional family assignment of two groups of enzymes. {O}ne
consists of 50 enzymes that have no homolog of known function from
{PSI}-{BLAST} search of protein databases. {T}he other contains eight
pairs of homologous enzymes of different families. {SVM} correctly
assigns 72% of the enzymes in the first group and 62% of the enzyme
pairs in the second group, suggesting that it is potentially useful
for facilitating functional study of novel proteins. {A} web version
of our software, {SVMP}rot, is accessible at http://jing.cz3.nus.edu.sg/cgi-bin/svmprot.cgi.},
doi = {10.1093/nar/gkh984},
pdf = {../local/Han2004Predicting.pdf},
file = {Han2004Predicting.pdf:local/Han2004Predicting.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://dx.doi.org/10.1093/nar/gkh984}
}
@article{Han2005Prediction,
author = {Han, L.Y. and Cai, C.Z. and Ji, Z.L. and Chen, Y.Z.},
title = {Prediction of functional class of novel viral proteins by a statistical
learning method irrespective of sequence similarity},
journal = {Virology},
year = {2005},
volume = {331},
pages = {136-143},
number = {1},
abstract = {The function of a substantial percentage of the putative protein-coding
open reading frames ({ORF}s) in viral genomes is unknown. {A}s their
sequence is not similar to that of proteins of known function, the
function of these {ORF}s cannot be assigned on the basis of sequence
similarity. {M}ethods complement or in combination with sequence
similarity-based approaches are being explored. {T}he web-based software
{SVMP}rot () to some extent assigns protein functional family irrespective
of sequence similarity and has been found to be useful for studying
distantly related proteins [{C}ai, {C}.{Z}., {H}an, {L}.{Y}., {J}i,
{Z}.{L}., {C}hen, {X}., {C}hen, {Y}.{Z}., 2003. {SVM}-{P}rot: web-based
support vector machine software for functional classification of
a protein from its primary sequence. {N}ucleic {A}cids {R}es. 31(13):
3692-3697]. {H}ere 25 novel viral proteins are selected to test the
capability of {SVMP}rot for functional family assignment of viral
proteins whose function cannot be confidently predicted on by sequence
similarity methods at present. {T}hese proteins are without a sequence
homolog in the {S}wissprot database, with its precise function provided
in the literature, and not included in the training sets of {SVMP}rot.
{T}he predicted functional classes of 72% of these proteins match
the literature-described function, which is compared to the overall
accuracy of 87% for {SVMP}rot functional class assignment of 34582
proteins. {T}his suggests that {SVMP}rot to some extent is capable
of functional class assignment irrespective of sequence similarity
and it is potentially useful for facilitating functional study of
novel viral proteins.},
doi = {10.1016/j.virol.2004.10.020},
pdf = {../local/Han2005Prediction.pdf},
file = {Han2005Prediction.pdf:local/Han2005Prediction.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://dx.doi.org/10.1016/j.virol.2004.10.020}
}
@article{Han2004Prediction,
author = {Han, L.Y. and Cai, C.Z. and Lo, S.L. and Chung, M.C. and Chen, Y.Z.},
title = {Prediction of {RNA}-binding proteins from primary sequence by a support
vector machine approach.},
journal = {R{NA}},
year = {2004},
volume = {10},
pages = {355-368},
number = {3},
abstract = {Elucidation of the interaction of proteins with different molecules
is of significance in the understanding of cellular processes. {C}omputational
methods have been developed for the prediction of protein-protein
interactions. {B}ut insufficient attention has been paid to the prediction
of protein-{RNA} interactions, which play central roles in regulating
gene expression and certain {RNA}-mediated enzymatic processes. {T}his
work explored the use of a machine learning method, support vector
machines ({SVM}), for the prediction of {RNA}-binding proteins directly
from their primary sequence. {B}ased on the knowledge of known {RNA}-binding
and non-{RNA}-binding proteins, an {SVM} system was trained to recognize
{RNA}-binding proteins. {A} total of 4011 {RNA}-binding and 9781
non-{RNA}-binding proteins was used to train and test the {SVM} classification
system, and an independent set of 447 {RNA}-binding and 4881 non-{RNA}-binding
proteins was used to evaluate the classification accuracy. {T}esting
results using this independent evaluation set show a prediction accuracy
of 94.1%, 79.3%, and 94.1% for r{RNA}-, m{RNA}-, and t{RNA}-binding
proteins, and 98.7%, 96.5%, and 99.9% for non-r{RNA}-, non-m{RNA}-,
and non-t{RNA}-binding proteins, respectively. {T}he {SVM} classification
system was further tested on a small class of sn{RNA}-binding proteins
with only 60 available sequences. {T}he prediction accuracy is 40.0%
and 99.9% for sn{RNA}-binding and non-sn{RNA}-binding proteins, indicating
a need for a sufficient number of proteins to train {SVM}. {T}he
{SVM} classification systems trained in this work were added to our
{W}eb-based protein functional classification software {SVMP}rot,
at http://jing.cz3.nus.edu.sg/cgi-bin/svmprot.cgi. {O}ur study suggests
the potential of {SVM} as a useful tool for facilitating the prediction
of protein-{RNA} interactions.},
pdf = {../local/Han2004Prediction.pdf},
file = {Han2004Prediction.pdf:local/Han2004Prediction.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://www.rnajournal.org/cgi/content/abstract/10/3/355}
}
@article{Han2008Apoptosis,
author = {Han, L. and Zhao, Y. and Jia, X.},
title = {Mathematical modeling identified c-FLIP as an apoptotic switch in
death receptor induced apoptosis},
journal = {Apoptosis},
year = {2008},
volume = {13},
pages = {1198-204},
number = {10},
abstract = {Apoptosis is an essential process to get rid of injured or unwanted
cells. In this study, we proposed a mathematical modeling for death
receptor mediated apoptosis to investigate the role of c-FLIP in
controlling the balance between apoptosis and survival. In order
to get insight into how NF-kappa B mediated pro-survival pathway
affects the outcome of our modeling, we implemented reduced models
without taking such regulation into consideration. Our simulation
revealed that c-FLIP could act as a pivotal death or life switch
and this switch-like behavior is bistable, irreversible, and robust.
We introduce a new term, probability apoptosis, to delineate the
likelihood in occurrence of apoptosis events. This simulation system
is plausible and may offer several valuable clinical indications
for the abnormal apoptosis related disease, such as cancer.},
keywords = {csbcbook}
}
@article{Han2005Fold,
author = {Sangjo Han and Byung-Chul Lee and Seung Taek Yu and Chan-Seok Jeong
and Soyoung Lee and Dongsup Kim},
title = {Fold recognition by combining profile-profile alignment and support
vector machine.},
journal = {Bioinformatics},
year = {2005},
volume = {21},
pages = {2667-73},
number = {11},
month = {Jun},
abstract = {M{OTIVATION}: {C}urrently, the most accurate fold-recognition method
is to perform profile-profile alignments and estimate the statistical
significances of those alignments by calculating {Z}-score or {E}-value.
{A}lthough this scheme is reliable in recognizing relatively close
homologs related at the family level, it has difficulty in finding
the remote homologs that are related at the superfamily or fold level.
{RESULTS}: {I}n this paper, we present an alternative method to estimate
the significance of the alignments. {T}he alignment between a query
protein and a template of length n in the fold library is transformed
into a feature vector of length n + 1, which is then evaluated by
support vector machine ({SVM}). {T}he output from {SVM} is converted
to a posterior probability that a query sequence is related to a
template, given {SVM} output. {R}esults show that a new method shows
significantly better performance than {PSI}-{BLAST} and profile-profile
alignment with {Z}-score scheme. {W}hile {PSI}-{BLAST} and {Z}-score
scheme detect 16 and 20\% of superfamily-related proteins, respectively,
at 90\% specificity, a new method detects 46\% of these proteins,
resulting in more than 2-fold increase in sensitivity. {M}ore significantly,
at the fold level, a new method can detect 14\% of remotely related
proteins at 90\% specificity, a remarkable result considering the
fact that the other methods can detect almost none at the same level
of specificity.},
doi = {10.1093/bioinformatics/bti384},
pdf = {../local/Han2005Fold.pdf},
file = {Han2005Fold.pdf:local/Han2005Fold.pdf:PDF},
keywords = {biosvm},
pii = {bti384},
url = {http://dx.doi.org/10.1093/bioinformatics/bti384}
}
@inproceedings{Han2010variance,
author = {Han, Y. and Yu, L.},
title = {A variance reduction framework for stable feature selection},
booktitle = {Data Mining (ICDM), 2010 IEEE 10th International Conference on},
year = {2010},
pages = {206--215},
organization = {IEEE}
}
@article{Hanahan2011Hallmarks,
author = {Hanahan, D. and Weinberg, R. A.},
title = {Hallmarks of cancer: the next generation},
journal = {Cell},
year = {2011},
volume = {144},
pages = {646--674},
number = {5},
month = {Mar},
abstract = {The hallmarks of cancer comprise six biological capabilities acquired
during the multistep development of human tumors. The hallmarks constitute
an organizing principle for rationalizing the complexities of neoplastic
disease. They include sustaining proliferative signaling, evading
growth suppressors, resisting cell death, enabling replicative immortality,
inducing angiogenesis, and activating invasion and metastasis. Underlying
these hallmarks are genome instability, which generates the genetic
diversity that expedites their acquisition, and inflammation, which
fosters multiple hallmark functions. Conceptual progress in the last
decade has added two emerging hallmarks of potential generality to
this list-reprogramming of energy metabolism and evading immune destruction.
In addition to cancer cells, tumors exhibit another dimension of
complexity: they contain a repertoire of recruited, ostensibly normal
cells that contribute to the acquisition of hallmark traits by creating
the "tumor microenvironment." Recognition of the widespread applicability
of these concepts will increasingly affect the development of new
means to treat human cancer.},
doi = {10.1016/j.cell.2011.02.013},
pdf = {../local/Hanahan2011Hallmarks.pdf},
file = {Hanahan2011Hallmarks.pdf:Hanahan2011Hallmarks.pdf:PDF},
institution = { Biophysics, UCSF, San Francisco, CA 94158, USA.},
language = {eng},
medline-pst = {ppublish},
owner = {Andrei Zinovyev},
pii = {S0092-8674(11)00127-9},
pmid = {21376230},
timestamp = {2011.04.07},
url = {http://dx.doi.org/10.1016/j.cell.2011.02.013}
}
@article{Hanahan2000hallmarks,
author = {Hanahan, D. and Weinberg, R. A.},
title = {The hallmarks of cancer},
journal = {Cell},
year = {2000},
volume = {100},
pages = {57--70},
doi = {10.1016/S0092-8674(00)81683-9},
pdf = {../local/Hanahan2000hallmarks.pdf},
file = {Hanahan2000hallmarks.pdf:local/Hanahan2000hallmarks.pdf:PDF},
keywords = {csbcbook, csbcbook-mustread},
owner = {jp},
url = {http://dx.doi.org/10.1016/S0092-8674(00)81683-9}
}
@article{Hanash2004Integrated,
author = {Hanash, S.},
title = {Integrated global profiling of cancer},
journal = {Nat. {R}ev. {C}ancer},
year = {2004},
volume = {4},
pages = {638-644},
number = {8},
abstract = {Tumours are complex biological systems. {N}o single type of molecular
approach fully elucidates tumour behaviour, necessitating analysis
at multiple levels encompassing genomics and proteomics. {I}ntegrated
data sets are required to fully determine the contributions of genome
alterations, host factors and environmental exposures to tumour growth
and progression, as well as the consequences of interactions between
malignant or premalignant cells and their microenvironment. {T}he
sheer amount and heterogeneous nature of data that need to be collected
and integrated are daunting, but effort has already begun to address
these obstacles.},
doi = {doi:10.1038/nrc1414},
pdf = {../local/Hanash2004Integrated.pdf},
file = {Hanash2004Integrated.pdf:local/Hanash2004Integrated.pdf:PDF},
url = {http://dx.doi.org/10.1038/nrc1414}
}
@article{Hanisch2002Co-clustering,
author = {D. Hanisch and A. Zien and R. Zimmer and T. Lengauer},
title = {Co-clustering of biological networks and gene expression data},
journal = {Bioinformatics},
year = {2002},
annote = {To appear},
subject = {microarraybionet},
url = {http://cartan.gmd.de/~hanisch/paper/CoClustering.pdf}
}
@article{Hann1999Chemoinformatics,
author = {M. Hann and R. Green},
title = {{C}hemoinformatics--a new name for an old problem?},
journal = {Curr. Opin. Chem. Biol.},
year = {1999},
volume = {3},
pages = {379--383},
number = {4},
month = {Aug},
abstract = {Library chemistry and high-throughput screening require greater use
of chemoinformatics to increase their effectiveness. Recent advances
in chemoinformatics include new molecular descriptors and pharmacophore
techniques, statistical tools and their applications. Visualisation
methods and hardware development are also opening new opportunities.
The advent of a chemically aware web language and cross-platform
working is ensuring that chemoinformatics methods are becoming available
to all chemists in a more appropriate manner. Much time will continue
to be wasted with incompatible file types without internationally
agreed standards.},
doi = {10.1016/S1367-5931(99)80057-X},
keywords = {Chemistry, Computers, Drug Design, Information Science, Software,
10419846},
owner = {mahe},
pii = {S1367-5931(99)80057-X},
pmid = {10419846},
timestamp = {2006.09.05},
url = {http://dx.doi.org/10.1016/S1367-5931(99)80057-X}
}
@article{Hannon2004Unlocking,
author = {Hannon, G. J. and Rossi, J. J.},
title = {Unlocking the potential of the human genome with {RNA} interference.},
journal = {Nature},
year = {2004},
volume = {431},
pages = {371-8},
number = {7006},
month = {Sep},
abstract = {The discovery of {RNA} interference ({RNA}i) may well be one of the
transforming events in biology in the past decade. {RNA}i can result
in gene silencing or even in the expulsion of sequences from the
genome. {H}arnessed as an experimental tool, {RNA}i has revolutionized
approaches to decoding gene function. {I}t also has the potential
to be exploited therapeutically, and clinical trials to test this
possibility are already being planned.},
doi = {10.1038/nature02870},
pdf = {../local/Hannon2004Unlocking.pdf},
file = {Hannon2004Unlocking.pdf:local/Hannon2004Unlocking.pdf:PDF},
keywords = {sirna},
pii = {nature02870},
url = {http://dx.doi.org/10.1038/nature02870}
}
@article{Hansch1964method,
author = {C. Hansch and T. Fujita},
title = {A method for the correlation of biological activity and chemical
structure},
journal = {J. Am. Chem. Soc},
year = {1964},
volume = {86},
pages = {1616-1626},
owner = {mahe},
timestamp = {2006.09.06}
}
@article{Hansch1968Linear,
author = {C. Hansch and J. E. Quinlan and G. L. Lawrence},
title = {Linear free-energy relationship between partition coefficients and
the aqueous solubility of organic liquids},
journal = {J. Org. Chem.},
year = {1968},
volume = {33},
pages = {347 - 350},
owner = {mahe},
timestamp = {2006.09.06}
}
@article{Harborth2003Sequence,
author = {Harborth, J. and Elbashir, S. M. and Vandenburgh, K. and Manninga,
H. and Scaringe, S. A. and Weber, K. and Tuschl, T.},
title = {Sequence, chemical, and structural variation of small interfering
{RNA}s and short hairpin {RNA}s and the effect on mammalian gene
silencing.},
journal = {Antisense {N}ucleic {A}cid. {D}rug. {D}ev.},
year = {2003},
volume = {13},
pages = {83-105},
number = {2},
month = {Apr},
abstract = {Small interfering {RNA}s (si{RNA}s) induce sequence-specific gene
silencing in mammalian cells and guide m{RNA} degradation in the
process of {RNA} interference ({RNA}i). {B}y targeting endogenous
lamin {A}/{C} m{RNA} in human {H}e{L}a or mouse {SW}3{T}3 cells,
we investigated the positional variation of si{RNA}-mediated gene
silencing. {W}e find cell-type-dependent global effects and cell-type-independent
positional effects. {H}e{L}a cells were about 2-fold more responsive
to si{RNA}s than {SW}3{T}3 cells but displayed a very similar pattern
of positional variation of lamin {A}/{C} silencing. {I}n {H}e{L}a
cells, 26 of 44 tested standard 21-nucleotide (nt) si{RNA} duplexes
reduced the protein expression by at least 90\%, and only 2 duplexes
reduced the lamin {A}/{C} proteins to <50\%. {F}luorescent chromophores
did not perturb gene silencing when conjugated to the 5'-end or 3'-end
of the sense si{RNA} strand and the 5'-end of the antisense si{RNA}
strand, but conjugation to the 3'-end of the antisense si{RNA} abolished
gene silencing. {RN}ase-protecting phosphorothioate and 2'-fluoropyrimidine
{RNA} backbone modifications of si{RNA}s did not significantly affect
silencing efficiency, although cytotoxic effects were observed when
every second phosphate of an si{RNA} duplex was replaced by phosphorothioate.
{S}ynthetic {RNA} hairpin loops were subsequently evaluated for lamin
{A}/{C} silencing as a function of stem length and loop composition.
{A}s long as the 5'-end of the guide strand coincided with the 5'-end
of the hairpin {RNA}, 19-29 base pair (bp) hairpins effectively silenced
lamin {A}/{C}, but when the hairpin started with the 5'-end of the
sense strand, only 21-29 bp hairpins were highly active.},
doi = {10.1089/108729003321629638},
keywords = {Adaptor Protein Complex alpha Subunits, Animal, Animals, Antisense,
Apolipoproteins B, Base Sequence, Biological Transport, Blotting,
Catalytic, Cell Line, Cell Membrane, Cell Survival, Chemical, Cholesterol,
Clathrin, Clathrin Heavy Chains, Disease Models, Endocytosis, Epidermal
Growth Factor, Fluorescence, Gene Expression Profiling, Gene Silencing,
Gene Therapy, Hela Cells, Humans, Injections, Intravenous, Jejunum,
Kinetics, Lamin Type A, Liver, Messenger, Metabolic Syndrome X, Mice,
Microscopy, Models, Molecular Sequence Data, NIH 3T3 Cells, Non-U.S.
Gov't, Nucleic Acid, Oligonucleotides, Open Reading Frames, Post-Transcriptional,
Protein Isoforms, Pyrimidines, RNA, RNA Interference, RNA Processing,
RNA Stability, Research Support, Reverse Transcriptase Polymerase
Chain Reaction, Sensitivity and Specificity, Sequence Homology, Small
Interfering, Subcellular Fractions, Swiss 3T3 Cells, Thionucleotides,
Time Factors, Transfection, Transferrin, Transgenic, Tumor, Western,
12804036},
url = {http://dx.doi.org/10.1089/108729003321629638}
}
@phdthesis{Harchaoui2008Methodes,
author = {Harchaoui, Z.},
title = {M\'ethodes \`a noyaux pour la d\'etection},
school = {Telecom ParisTech},
year = {2008},
owner = {jp},
timestamp = {2009.05.01}
}
@inproceedings{Harchaoui2007Image,
author = {Harchaoui, Z. and Bach, F.},
title = {Image Classification with Segmentation Graph Kernels},
booktitle = {2007 IEEE Computer Society Conference on Computer Vision and Pattern
Recognition (CVPR 2007)},
year = {2007},
pages = {1--8},
publisher = {IEEE Computer Society},
abstract = {We propose a family of kernels between images, defined as kernels
between their respective segmentation graphs. The kernels are based
on soft matching of subtree-patterns of the respective graphs, leveraging
the natural structure of images while remaining robust to the associated
segmentation process uncertainty. Indeed, output from morphological
segmentation is often represented by a labelled graph, each vertex
corresponding to a segmented region, with edges joining neighboring
regions. However, such image representations have mostly remained
underused for learning tasks, partly because of the observed instability
of the segmentation process and the inherent hardness of inexact
graph matching with uncertain graphs. Our kernels count common virtual
substructures amongst images, which enables to perform efficient
supervised classification of natural images with a support vector
machine. Moreover, the kernel machinery allows us to take advantage
of recent advances in kernel-based learning: (i) semi-supervised
learning reduces the required number of labelled images, while (ii)
multiple kernel learning algorithms efficiently select the most relevant
similarity measures between images within our family.},
doi = {10.1109/CVPR.2007.383049},
pdf = {../local/Harchaoui2007Image.pdf},
file = {Harchaoui2007Image.pdf:local/Harchaoui2007Image.pdf:PDF},
keywords = {image},
timestamp = {2008.07.29},
url = {http://dx.doi.org/10.1109/CVPR.2007.383049}
}
@incollection{Harchaoui2008Catching,
author = {Harchaoui, Z. and Levy-Leduc, C.},
title = {Catching Change-points with Lasso},
booktitle = {Adv. Neural. Inform. Process Syst.},
publisher = {MIT Press},
year = {2008},
editor = {J.C. Platt and D. Koller and Y. Singer and S. Roweis},
volume = {20},
pages = {617--624},
address = {Cambridge, MA}
}
@article{Harchaoui2010Multiple,
author = {Harchaoui, Z. and Levy-Leduc, C.},
title = {Multiple Change-Point Estimation With a Total Variation Penalty},
journal = {J. Am. Stat. Assoc.},
year = {2010},
volume = {105},
pages = {1480--1493},
number = {492},
doi = {10.1198/jasa.2010.tm09181},
pdf = {../local/Harchaoui2010Multiple.pdf},
file = {Harchaoui2010Multiple.pdf:Harchaoui2010Multiple.pdf:PDF},
owner = {jp},
timestamp = {2012.10.03},
url = {http://dx.doi.org/10.1198/jasa.2010.tm09181}
}
@inproceedings{Harchaoui2009regularized,
author = {Harchaoui, Z. and Vallet, F. and Lung-Yut-Fong, A. and Cappe, O.},
title = {A regularized kernel-based approach to unsupervised audio segmentation},
booktitle = {ICASSP '09: Proceedings of the 2009 IEEE International Conference
on Acoustics, Speech and Signal Processing},
year = {2009},
pages = {1665--1668},
address = {Washington, DC, USA},
publisher = {IEEE Computer Society},
doi = {10.1109/ICASSP.2009.4959921},
pdf = {../local/Harchaoui2009regularized.pdf},
file = {Harchaoui2009regularized.pdf:Harchaoui2009regularized.pdf:PDF},
keywords = {segmentation},
owner = {jp},
timestamp = {2010.06.04},
url = {http://dx.doi.org/10.1109/ICASSP.2009.4959921}
}
@article{Harismendy2009Evaluation,
author = {Harismendy, O. and Ng, P. C. and Strausberg, R. L. and Wang, X. and
Stockwell, T. B. and Beeson, K. Y. and Schork, N. J. and Murray,
S. S. and Topol, E. J. and Levy, S. and Frazer, K. A.},
title = {Evaluation of next generation sequencing platforms for population
targeted sequencing studies.},
journal = {Genome Biol.},
year = {2009},
volume = {10},
pages = {R32},
number = {3},
abstract = {Next generation sequencing (NGS) platforms are currently being utilized
for targeted sequencing of candidate genes or genomic intervals to
perform sequence-based association studies. To evaluate these platforms
for this application, we analyzed human sequence generated by the
Roche 454, Illumina GA, and the ABI SOLiD technologies for the same
260 kb in four individuals.Local sequence characteristics contribute
to systematic variability in sequence coverage (>100-fold difference
in per-base coverage), resulting in patterns for each NGS technology
that are highly correlated between samples. A comparison of the base
calls to 88 kb of overlapping ABI 3730xL Sanger sequence generated
for the same samples showed that the NGS platforms all have high
sensitivity, identifying >95\% of variant sites. At high coverage,
depth base calling errors are systematic, resulting from local sequence
contexts; as the coverage is lowered additional 'random sampling'
errors in base calling occur.Our study provides important insights
into systematic biases and data variability that need to be considered
when utilizing NGS platforms for population targeted sequencing studies.},
doi = {10.1186/gb-2009-10-3-r32},
pdf = {../local/Harismendy2009Evaluation.pdf},
file = {Harismendy2009Evaluation.pdf:Harismendy2009Evaluation.pdf:PDF},
institution = {Scripps Genomic Medicine, Scripps Translational Science Institute,
The Scripps Research Institute, La Jolla, CA 92037, USA. oharis@scripps.edu},
keywords = {ngs},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {gb-2009-10-3-r32},
pmid = {19327155},
timestamp = {2011.10.28},
url = {http://dx.doi.org/10.1186/gb-2009-10-3-r32}
}
@article{Harris2008Single-molecule,
author = {Timothy D Harris and Phillip R Buzby and Hazen Babcock and Eric Beer
and Jayson Bowers and Ido Braslavsky and Marie Causey and Jennifer
Colonell and James Dimeo and J. William Efcavitch and Eldar Giladi
and Jaime Gill and John Healy and Mirna Jarosz and Dan Lapen and
Keith Moulton and Stephen R Quake and Kathleen Steinmann and Edward
Thayer and Anastasia Tyurina and Rebecca Ward and Howard Weiss and
Zheng Xie},
title = {Single-molecule DNA sequencing of a viral genome.},
journal = {Science},
year = {2008},
volume = {320},
pages = {106--109},
number = {5872},
month = {Apr},
abstract = {The full promise of human genomics will be realized only when the
genomes of thousands of individuals can be sequenced for comparative
analysis. A reference sequence enables the use of short read length.
We report an amplification-free method for determining the nucleotide
sequence of more than 280,000 individual DNA molecules simultaneously.
A DNA polymerase adds labeled nucleotides to surface-immobilized
primer-template duplexes in stepwise fashion, and the asynchronous
growth of individual DNA molecules was monitored by fluorescence
imaging. Read lengths of >25 bases and equivalent phred software
program quality scores approaching 30 were achieved. We used this
method to sequence the M13 virus to an average depth of >150x and
with 100\% coverage; thus, we resequenced the M13 genome with high-sensitivity
mutation detection. This demonstrates a strategy for high-throughput
low-cost resequencing.},
doi = {10.1126/science.1150427},
institution = {Helicos BioSciences Corporation, One Kendall Square, Cambridge, MA
02139, USA. tharris@helicosbio.com},
keywords = {Algorithms; Bacteriophage M13; Computational Biology; DNA Primers;
DNA, Viral; Genome, Viral; Mutation; Sequence Alignment; Sequence
Analysis, DNA; Software; Templates, Genetic},
owner = {phupe},
pii = {320/5872/106},
pmid = {18388294},
timestamp = {2010.08.24},
url = {http://dx.doi.org/10.1126/science.1150427}
}
@inproceedings{Hartemink2002Using,
author = {A.J. Hartemink and D.K. Gifford and T.S. Jaakkola and R.A. Young},
title = {Using graphical models and genomic expression data to statistically
validate models of genetic regulatory networks},
booktitle = {Proceedings of the {P}acific {S}ymposium on {B}iocomputing 2002},
year = {2002},
editor = {Russ B. Altman and A. Keith Dunker and Lawrence Hunter and Kevin
Lauerdale and Teri E. Klein},
pages = {422-433},
publisher = {World Scientific},
pdf = {../local/Hartemink2002Using.pdf},
file = {Hartemink2002Using.pdf:local/Hartemink2002Using.pdf:PDF},
url = {http://helix-web.stanford.edu/psb01/abstracts/p422.html}
}
@book{Hartigan1975Clustering,
title = {Clustering algorithms},
publisher = {Wiley},
year = {1975},
author = {Hartigan, J.},
address = {New-York},
owner = {jp},
timestamp = {2011.12.29}
}
@article{Hartigan1987Estimation,
author = {Hartigan, J. A.},
title = {Estimation of a convex density contour in two dimensions},
journal = {J. {A}mer. {S}tatist. {A}ssoc.},
year = {1987},
volume = {82},
pages = {267--270},
number = {397},
pdf = {../local/Hartigan1987Estimation.pdf},
file = {Hartigan1987Estimation.pdf:local/Hartigan1987Estimation.pdf:PDF},
url = {http://links.jstor.org/sici?sici=0162-1459%28198703%2982%3A397%3C267%3AEOACDC%3E2.0.CO%3B2-G}
}
@article{Hartigan1979A,
author = {J. A. Hartigan and M. A. Wong},
title = {A {K}-Means Clustering Algorithm},
journal = {Applied Statistics},
year = {1979},
volume = {28},
pages = {100--108},
entrydate = {20030618},
key = {Hartigan/Wong:79}
}
@article{Hartwell1999a,
author = {L. H. Hartwell and J. J. Hopfield and S. Leibler and A. W. Murray},
title = {From molecular to modular cell biology.},
journal = {Nature},
year = {1999},
volume = {402},
pages = {C47--C52},
number = {6761 Suppl},
month = {Dec},
abstract = {Cellular functions, such as signal transmission, are carried out by
'modules' made up of many species of interacting molecules. Understanding
how modules work has depended on combining phenomenological analysis
with molecular studies. General principles that govern the structure
and behaviour of modules may be discovered with help from synthetic
sciences such as engineering and computer science, from stronger
interactions between experiment and theory in cell biology, and from
an appreciation of evolutionary constraints.},
doi = {10.1038/35011540},
institution = {Fred Hutchinson Cancer Center, Seattle, Washington 98109, USA.},
keywords = {Action Potentials; Biological Evolution; Forecasting; Models, Biological;
Molecular Biology, trends},
language = {eng},
medline-pst = {ppublish},
owner = {Andrei Zinovyev},
pmid = {10591225},
timestamp = {2011.04.08},
url = {http://dx.doi.org/10.1038/35011540}
}
@book{Hastie1999Generalized,
title = {Generalized Additive Models},
publisher = {Chapman and Hall},
year = {1999},
author = {Hastie, T. and Tibshirani, R.},
address = {London, UK}
}
@book{Hastie2001elements,
title = {The elements of statistical learning: data mining, inference, and
prediction},
publisher = {Springer},
year = {2001},
author = {Hastie, T. and Tibshirani, R. and Friedman, J.}
}
@article{Haury2010Increasing,
author = {Haury, A.C. and Jacob, L. and Vert, J.P.},
title = {Increasing stability and interpretability of gene expression signatures},
journal = {arXiv preprint arXiv:1001.3109},
year = {2010}
}
@article{Haury2012TIGRESS,
author = {Haury, A.C. and Mordelet, F. and Vera-Licona, P. and Vert, J.P.},
title = {TIGRESS: trustful inference of gene regulation using stability selection},
journal = {arXiv preprint arXiv:1205.1181},
year = {2012}
}
@article{Haury2011influence,
author = {Haury, A.-C. and Gestraud, P. and Vert, J.-P.},
title = {The influence of feature selection methods on accuracy, stability
and interpretability of molecular signatures.},
journal = {PLoS One},
year = {2011},
volume = {6},
pages = {e28210},
number = {12},
abstract = {Biomarker discovery from high-dimensional data is a crucial problem
with enormous applications in biology and medicine. It is also extremely
challenging from a statistical viewpoint, but surprisingly few studies
have investigated the relative strengths and weaknesses of the plethora
of existing feature selection methods. In this study we compare 32
feature selection methods on 4 public gene expression datasets for
breast cancer prognosis, in terms of predictive performance, stability
and functional interpretability of the signatures they produce. We
observe that the feature selection method has a significant influence
on the accuracy, stability and interpretability of signatures. Surprisingly,
complex wrapper and embedded methods generally do not outperform
simple univariate feature selection methods, and ensemble feature
selection has generally no positive effect. Overall a simple Student's
t-test seems to provide the best results.},
doi = {10.1371/journal.pone.0028210},
pdf = {../local/Haury2011influence.pdf},
file = {Haury2011influence.pdf:Haury2011influence.pdf:PDF},
institution = {Mines ParisTech, Centre for Computational Biology, Fontainebleau,
France. anne-claire.haury@mines-paristech.fr},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {PONE-D-11-13151},
pmid = {22205940},
timestamp = {2012.03.04},
url = {http://dx.doi.org/10.1371/journal.pone.0028210}
}
@inproceedings{Haury2010stability,
author = {Haury, A.-C. and Vert, J-P.},
title = {On the stability and interpretability of prognosis signatures in
breast cancer},
booktitle = {Proceedings of the Fourth International Workshop on Machine Learning
in Systems Biology (MLSB10)},
year = {2010},
note = {To appear.},
owner = {jp},
timestamp = {2010.10.12}
}
@techreport{Haussler1999Convolution,
author = {Haussler, D.},
title = {Convolution {K}ernels on {D}iscrete {S}tructures},
institution = {UC Santa Cruz},
year = {1999},
number = {UCSC-CRL-99-10},
abstract = {We introduce a new method of constructing kernels on sets whose elements
are discrete structures like strings, trees and graphs. {T}he method
can be applied iteratively to build a kernel on a infinite set from
kernels involving generators of the set. {T}he family of kernels
generated generalizes the family of radial basis kernels. {I}t can
also be used to define kernels in the form of joint {G}ibbs probability
distributions. {K}ernels can be built from hidden {M}arkov random
fields, generalized regular expressions, pair-{HMM}s, or {ANOVA}
decompositions. {U}ses of the method lead to open problems involving
the theory of infinitely divisible positive definite functions. {F}undamentals
of this theory and the theory of reproducing kernel {H}ilbert spaces
are reviewed and applied in establishing the validity of the method.},
pdf = {../local/Haussler1999Convolution.pdf},
file = {Haussler1999Convolution.pdf:local/Haussler1999Convolution.pdf:PDF},
keywords = {biosvm},
subject = {kernel}
}
@article{Haussler1997general,
author = {Haussler, D.},
title = {A general minimax result for relative entropy},
journal = {I{EEE} {T}rans. {I}nform. {T}heory},
year = {1997},
volume = {43},
pages = {1276-1280},
number = {4},
month = {Jul},
abstract = {Suppose nature picks a probability measure {P}&thetas; on a complete
separable metric space {X} at random from a measurable set {P} ?={{P}&thetas;:&thetas;??}.
{T}hen, without knowing &thetas;, a statistician picks a measure
{Q} on {S}. {F}inally, the statistician suffers a loss {D}({P}0∥{Q}),
the relative entropy between {P}&thetas; and {Q}. {W}e show that
the minimax and maximin values of this game are always equal, and
there is always a minimax strategy in the closure of the set of all
{B}ayes strategies. {T}his generalizes previous results of {G}allager(1979),
and {D}avisson and {L}eon-{G}arcia (1980) },
pdf = {../local/Haussler1997general.pdf},
file = {Haussler1997general.pdf:local/Haussler1997general.pdf:PDF},
owner = {vert}
}
@article{Haverkamp2000potential,
author = {Haverkamp, W. and Breithardt, G. and Camm, A. J. and Janse, M. J.
and Rosen, M. R. and Antzelevitch, C. and Escande, D. and Franz,
M. and Malik, M. and Moss, A. and Shah, R.},
title = {{T}he potential for {QT} prolongation and proarrhythmia by non-antiarrhythmic
drugs: clinical and regulatory implications. {R}eport on a policy
conference of the {E}uropean {S}ociety of {C}ardiology.},
journal = {Eur. Heart J.},
year = {2000},
volume = {21},
pages = {1216--1231},
number = {15},
month = {Aug},
doi = {10.1053/euhj.2000.2249},
keywords = {herg},
pii = {S0195668X00922498},
pmid = {10924311},
timestamp = {2006.10.05},
url = {http://dx.doi.org/10.1053/euhj.2000.2249}
}
@article{Hawkins1997Analysis,
author = {D.M. Hawkins and S.S. Young and A. Rusinko},
title = {Analysis of a large structure-activity data set using recursive partitioning},
journal = {Quantitative Structure-Activity Relationships},
year = {1997},
volume = {16},
pages = {296-302},
owner = {mahe},
timestamp = {2006.09.06}
}
@article{He2000Alternating,
author = {He, BS and Yang, H. and Wang, SL},
title = {Alternating direction method with self-adaptive penalty parameters
for monotone variational inequalities},
journal = {Journal of Optimization Theory and applications},
year = {2000},
volume = {106},
pages = {337--356},
number = {2},
publisher = {Springer}
}
@article{He2006Why,
author = {He, X. and Zhang, J.},
title = {Why do hubs tend to be essential in protein networks?},
journal = {PLoS Genet},
year = {2006},
volume = {2},
pages = {e88},
number = {6},
month = {Jun},
abstract = {The protein-protein interaction (PPI) network has a small number of
highly connected protein nodes (known as hubs) and many poorly connected
nodes. Genome-wide studies show that deletion of a hub protein is
more likely to be lethal than deletion of a non-hub protein, a phenomenon
known as the centrality-lethality rule. This rule is widely believed
to reflect the special importance of hubs in organizing the network,
which in turn suggests the biological significance of network architectures,
a key notion of systems biology. Despite the popularity of this explanation,
the underlying cause of the centrality-lethality rule has never been
critically examined. We here propose the concept of essential PPIs,
which are PPIs that are indispensable for the survival or reproduction
of an organism. Our network analysis suggests that the centrality-lethality
rule is unrelated to the network architecture, but is explained by
the simple fact that hubs have large numbers of PPIs, therefore high
probabilities of engaging in essential PPIs. We estimate that approximately
3\% of PPIs are essential in the yeast, accounting for approximately
43\% of essential genes. As expected, essential PPIs are evolutionarily
more conserved than nonessential PPIs. Considering the role of essential
PPIs in determining gene essentiality, we find the yeast PPI network
functionally more robust than random networks, yet far less robust
than the potential optimum. These and other findings provide new
perspectives on the biological relevance of network structure and
robustness.},
doi = {10.1371/journal.pgen.0020088},
institution = {Department of Ecology and Evolutionary Biology, University of Michigan,
Ann Arbor, Michigan, USA.},
language = {eng},
medline-pst = {ppublish},
owner = {Andrei Zinovyev},
pmid = {16751849},
timestamp = {2011.04.07},
url = {http://dx.doi.org/10.1371/journal.pgen.0020088}
}
@article{He2010Stable,
author = {He, Z. and Yu, W.},
title = {Stable feature selection for biomarker discovery},
journal = {arXiv preprint arXiv:1001.0887},
year = {2010}
}
@incollection{Heckerman1999tutorial,
author = {Heckerman, D.},
title = {A tutorial on learning with {B}ayesian networks},
booktitle = {Learning in graphical models},
publisher = {MIT Press},
year = {1999},
editor = {Jordan, M.},
pages = {301--354},
address = {Cambridge, MA, USA},
pdf = {../local/Heckerman1999tutorial.pdf},
file = {Heckerman1999tutorial.pdf:local/Heckerman1999tutorial.pdf:PDF},
keywords = {biogm},
owner = {vert},
timestamp = {2006.01.18}
}
@article{Heckerman2000Dependency,
author = {Heckerman, D. and Chickering, D. M. and Meek, C. and Rounthwaite,
R. and Kadie, C.},
title = {Dependency Networks for Inference, Collaborative Filtering, and Data
Visualization},
journal = {J. Mach. Learn. Res.},
year = {2000},
volume = {1},
pages = {49--75}
}
@article{Heckerman2007Leveraging,
author = {Heckerman, D. and Kadie, D. and Listgarten, J.},
title = {Leveraging information across {HLA} alleles/supertypes improves epitope
prediction.},
journal = {J. Comput. Biol.},
year = {2007},
volume = {14},
pages = {736--746},
number = {6},
abstract = {We present a model for predicting HLA class I restricted CTL epitopes.
In contrast to almost all other work in this area, we train a single
model on epitopes from all HLA alleles and supertypes, yet retain
the ability to make epitope predictions for specific HLA alleles.
We are therefore able to leverage data across all HLA alleles and/or
their supertypes, automatically learning what information should
be shared and also how to combine allele-specific, supertype-specific,
and global information in a principled way. We show that this leveraging
can improve prediction of epitopes having HLA alleles with known
supertypes, and dramatically increases our ability to predict epitopes
having alleles which do not fall into any of the known supertypes.
Our model, which is based on logistic regression, is simple to implement
and understand, is solved by finding a single global maximum, and
is more accurate (to our knowledge) than any other model.},
doi = {10.1089/cmb.2007.R013},
owner = {vert},
pmid = {17691891},
timestamp = {2008.10.29},
url = {http://dx.doi.org/10.1089/cmb.2007.R013}
}
@book{Hedges1985Statistical,
title = {Statistical methods for meta-analysis},
publisher = {Academic Press},
year = {1985},
author = {Hedges, L. V. and Olkin, I.},
owner = {jp},
timestamp = {2011.09.21}
}
@article{Heiner2004Biosystems,
author = {Heiner, M. and Koch, I. and Will, J.},
title = {Model validation of biological pathways using Petri nets--demonstrated
for apoptosis},
journal = {Biosystems},
year = {2004},
volume = {75},
pages = {15--28},
number = {1-3},
abstract = {This paper demonstrates the first steps of a new integrating methodology
to develop and analyse models of biological pathways in a systematic
manner using well established Petri net technologies. The whole approach
comprises step-wise modelling, animation, model validation as well
as qualitative and quantitative analysis for behaviour prediction.
In this paper, the first phase is addressed how to develop and validate
a qualitative model, which might be extended afterwards to a quantitative
model. The example used in this paper is devoted to apoptosis, the
genetically programmed cell death. Apoptosis is an essential part
of normal physiology for most metazoan species. Disturbances in the
apoptotic process could lead to several diseases. The signal transduction
pathway of apoptosis includes highly complex mechanisms to control
and execute programmed cell death. This paper explains how to model
and validate this pathway using qualitative Petri nets. The results
provide a mathematically unique and valid model enabling the confirmation
of known properties as well as new insights in this pathway.},
keywords = {csbcbook}
}
@inproceedings{Helden2001Application,
author = {van Helden, J. and Gilbert, D. and Wernisch, L. and Schroeder, M.
and Wodak, S. J.},
title = {Application of Regulatory Sequence Analysis and Metabolic Network
Analysis to the Interpretation of Gene Expression Data},
booktitle = {JOBIM '00: Selected papers from the First International Conference
on Computational Biology, Biology, Informatics, and Mathematics},
year = {2001},
pages = {147--164},
address = {London, UK},
publisher = {Springer-Verlag},
timestamp = {2006.11.21}
}
@article{Helma2004Data,
author = {Helma, C. and Cramer, T. and Kramer, S. and De Raedt, L.},
title = {Data mining and machine learning techniques for the identification
of mutagenicity inducing substructures and structure activity relationships
of noncongeneric compounds},
journal = {J. Chem. Inf. Comput. Sci.},
year = {2004},
volume = {44},
pages = {1402-11},
number = {4},
abstract = {This paper explores the utility of data mining and machine learning
algorithms for the induction of mutagenicity structure-activity relationships
({SAR}s) from noncongeneric data sets. {W}e compare (i) a newly developed
algorithm ({MOLFEA}) for the generation of descriptors (molecular
fragments) for noncongeneric compounds with traditional {SAR} approaches
(molecular properties) and (ii) different machine learning algorithms
for the induction of {SAR}s from these descriptors. {I}n addition
we investigate the optimal parameter settings for these programs
and give an exemplary interpretation of the derived models. {T}he
predictive accuracies of models using {MOLFEA} derived descriptors
is approximately 10-15\%age points higher than those using molecular
properties alone. {U}sing both types of descriptors together does
not improve the derived models. {F}rom the applied machine learning
techniques the rule learner {PART} and support vector machines gave
the best results, although the differences between the learning algorithms
are only marginal. {W}e were able to achieve predictive accuracies
up to 78\% for 10-fold cross-validation. {T}he resulting models are
relatively easy to interpret and usable for predictive as well as
for explanatory purposes.},
doi = {10.1021/ci034254q},
pdf = {../local/Helma2004Data.pdf},
file = {Helma2004Data.pdf:local/Helma2004Data.pdf:PDF},
keywords = {biosvm chemoinformatics},
url = {http://dx.doi.org/10.1021/ci034254q}
}
@article{Helmbold1997Predicting,
author = {Helmbold, D. P. and Schapire, R. E.},
title = {Predicting {N}early {A}s {W}ell {A}s the {B}est {P}runing of a {D}ecision
{T}ree},
journal = {Machine {L}earning},
year = {1997},
volume = {27},
pages = {51--68},
number = {1},
pdf = {../local/helm97.pdf},
file = {helm97.pdf:local/helm97.pdf:PDF},
subject = {ml},
url = {http://www.research.att.com/~schapire/papers/HelmboldSc95.ps.Z}
}
@incollection{Hendrix2005Phosphodiesterase,
author = {Martin Hendrix and Christopher Kallus},
title = {Phosphodiesterase Inhibitors: A Chemogenomic View},
booktitle = {Chemogenomics in Drug Discovery},
publisher = {Wiley-VCH},
year = {2005},
chapter = {9},
pages = {243-288}
}
@article{Henikoff1992Amino,
author = {Henikoff, S. and Henikoff, J. G.},
title = {Amino acid substitution matrices from protein blocks.},
journal = {Proc. Natl. Acad. Sci. USA},
year = {1992},
volume = {89},
pages = {10915--10919},
number = {22},
month = {Nov},
abstract = {Methods for alignment of protein sequences typically measure similarity
by using a substitution matrix with scores for all possible exchanges
of one amino acid with another. The most widely used matrices are
based on the Dayhoff model of evolutionary rates. Using a different
approach, we have derived substitution matrices from about 2000 blocks
of aligned sequence segments characterizing more than 500 groups
of related proteins. This led to marked improvements in alignments
and in searches using queries from each of the groups.},
keywords = {Algorithms; Amino Acid Sequence; Animals; Caenorhabditis elegans;
Drosophila; Lod Score; Mathematics; Molecular Sequence Data; Probability;
Proteins; Sequence Homology, Amino Acid; Software},
owner = {laurent},
pmid = {1438297},
timestamp = {2008.01.15}
}
@article{Hershkovits1997On,
author = {Hershkovits, Y. and Ziv, J. },
title = {On fixed-database universal data compression with limited memory},
journal = {I{EEE} {T}rans. {I}nform. {T}heory},
year = {1997},
volume = {43},
pages = {1966-1976},
number = {6},
month = {Nov},
abstract = {The amount of fixed side information required for lossless data compression
is discussed. {N}onasymptotic coding and converse theorems are derived
for data-compression algorithms with fixed statistical side information
(?training sequence?) that is not large enough so as to yield the
ultimate compression, namely, the entropy of the source },
pdf = {../local/Hershkovits1997On.pdf},
file = {Hershkovits1997On.pdf:local/Hershkovits1997On.pdf:PDF},
keywords = {information-theory},
owner = {vert}
}
@article{Hert2004Comparison,
author = {Hert, J. and Willett, P. and Wilton, D. J. and Acklin, P. and Azzaoui,
K. and Jacoby, E. and Schuffenhauer, A.},
title = {Comparison of fingerprint-based methods for virtual screening using
multiple bioactive reference structures.},
journal = {J Chem Inf Comput Sci},
year = {2004},
volume = {44},
pages = {1177--1185},
number = {3},
abstract = {Fingerprint-based similarity searching is widely used for virtual
screening when only a single bioactive reference structure is available.
This paper reviews three distinct ways of carrying out such searches
when multiple bioactive reference structures are available: merging
the individual fingerprints into a single combined fingerprint; applying
data fusion to the similarity rankings resulting from individual
similarity searches; and approximations to substructural analysis.
Extended searches on the MDL Drug Data Report database suggest that
fusing similarity scores is the most effective general approach,
with the best individual results coming from the binary kernel discrimination
technique.},
comment = {Slides available at http://cisrg.shef.ac.uk/shef2004/talks/PWillett.pdf},
doi = {10.1021/ci034231b},
institution = {Krebs Institute for Biomolecular Research and Department of Information
Studies, University of Sheffield, Western Bank, Sheffield S10 2TN,
UK.},
keywords = {chemoinformatics, PUlearning},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pmid = {15154787},
timestamp = {2010.04.01},
url = {http://dx.doi.org/10.1021/ci034231b}
}
@article{Hertz2007Identifying,
author = {Tomer Hertz and Chen Yanover},
title = {Identifying HLA supertypes by learning distance functions.},
journal = {Bioinformatics},
year = {2007},
volume = {23},
pages = {e148--e155},
number = {2},
month = {Jan},
abstract = {MOTIVATION: The development of epitope-based vaccines crucially relies
on the ability to classify Human Leukocyte Antigen (HLA) molecules
into sets that have similar peptide binding specificities, termed
supertypes. In their seminal work, Sette and Sidney defined nine
HLA class I supertypes and claimed that these provide an almost perfect
coverage of the entire repertoire of HLA class I molecules. HLA alleles
are highly polymorphic and polygenic and therefore experimentally
classifying each of these molecules to supertypes is at present an
impossible task. Recently, a number of computational methods have
been proposed for this task. These methods are based on defining
protein similarity measures, derived from analysis of binding peptides
or from analysis of the proteins themselves. RESULTS: In this paper
we define both peptide derived and protein derived similarity measures,
which are based on learning distance functions. The peptide derived
measure is defined using a peptide-peptide distance function, which
is learned using information about known binding and non-binding
peptides. The protein derived similarity measure is defined using
a protein-protein distance function, which is learned using information
about alleles previously classified to supertypes by Sette and Sidney
(1999). We compare the classification obtained by these two complimentary
methods to previously suggested classification methods. In general,
our results are in excellent agreement with the classifications suggested
by Sette and Sidney (1999) and with those reported by Buus et al.
(2004). The main important advantage of our proposed distance-based
approach is that it makes use of two different and important immunological
sources of information-HLA alleles and peptides that are known to
bind or not bind to these alleles. Since each of our distance measures
is trained using a different source of information, their combination
can provide a more confident classification of alleles to supertypes.},
doi = {10.1093/Bioinformatics/btl324},
owner = {laurent},
pii = {23/2/e148},
pmid = {17237084},
timestamp = {2007.01.27},
url = {http://dx.doi.org/10.1093/Bioinformatics/btl324}
}
@article{Hertz2006PepDist,
author = {Hertz, T. and Yanover, C.},
title = {{P}ep{D}ist: a new framework for protein-peptide binding prediction
based on learning peptide distance functions.},
journal = {BMC Bioinformatics},
year = {2006},
volume = {7 Suppl 1},
pages = {S3},
abstract = {BACKGROUND: Many different aspects of cellular signalling, trafficking
and targeting mechanisms are mediated by interactions between proteins
and peptides. Representative examples are MHC-peptide complexes in
the immune system. Developing computational methods for protein-peptide
binding prediction is therefore an important task with applications
to vaccine and drug design. METHODS: Previous learning approaches
address the binding prediction problem using traditional margin based
binary classifiers. In this paper we propose PepDist: a novel approach
for predicting binding affinity. Our approach is based on learning
peptide-peptide distance functions. Moreover, we suggest to learn
a single peptide-peptide distance function over an entire family
of proteins (e.g. MHC class I). This distance function can be used
to compute the affinity of a novel peptide to any of the proteins
in the given family. In order to learn these peptide-peptide distance
functions, we formalize the problem as a semi-supervised learning
problem with partial information in the form of equivalence constraints.
Specifically, we propose to use DistBoost, which is a semi-supervised
distance learning algorithm. RESULTS: We compare our method to various
state-of-the-art binding prediction algorithms on MHC class I and
MHC class II datasets. In almost all cases, our method outperforms
all of its competitors. One of the major advantages of our novel
approach is that it can also learn an affinity function over proteins
for which only small amounts of labeled peptides exist. In these
cases, our method's performance gain, when compared to other computational
methods, is even more pronounced. We have recently uploaded the PepDist
webserver which provides binding prediction of peptides to 35 different
MHC class I alleles. The webserver which can be found at http://www.pepdist.cs.huji.ac.il
is powered by a prediction engine which was trained using the framework
presented in this paper. CONCLUSION: The results obtained suggest
that learning a single distance function over an entire family of
proteins achieves higher prediction accuracy than learning a set
of binary classifiers for each of the proteins separately. We also
show the importance of obtaining information on experimentally determined
non-binders. Learning with real non-binders generalizes better than
learning with randomly generated peptides that are assumed to be
non-binders. This suggests that information about non-binding peptides
should also be published and made publicly available.},
doi = {10.1186/1471-2105-7-S1-S3},
keywords = {immunoinformatics},
pii = {1471-2105-7-S1-S3},
pmid = {16723006},
timestamp = {2007.01.25},
url = {http://dx.doi.org/10.1186/1471-2105-7-S1-S3}
}
@inproceedings{Heskes2000Empirical,
author = {Tom Heskes},
title = {Empirical Bayes for Learning to Learn},
booktitle = {ICML '00: Proceedings of the Seventeenth International Conference
on Machine Learning},
year = {2000},
pages = {367--374},
address = {San Francisco, CA, USA},
publisher = {Morgan Kaufmann Publishers Inc.},
isbn = {1-55860-707-2}
}
@article{Hess2006Pharmacogenomic,
author = {Hess, K. R. and Anderson, K. and Symmans, W. F. and Valero, V. and
Ibrahim, N. and Mejia, J. A. and Booser, D. and Theriault, R. L.
and Buzdar, A. U. and Dempsey, P. J. and Rouzier, R. and Sneige,
N. and Ross, J. S. and Vidaurre, T. and G\'omez, H. L. and Hortobagyi,
G. N. and Pusztai, L.},
title = {Pharmacogenomic predictor of sensitivity to preoperative chemotherapy
with paclitaxel and fluorouracil, doxorubicin, and cyclophosphamide
in breast cancer.},
journal = {J Clin Oncol},
year = {2006},
volume = {24},
pages = {4236--4244},
number = {26},
month = {Sep},
abstract = {We developed a multigene predictor of pathologic complete response
(pCR) to preoperative weekly paclitaxel and fluorouracil-doxorubicin-cyclophosphamide
(T/FAC) chemotherapy and assessed its predictive accuracy on independent
cases.One hundred thirty-three patients with stage I-III breast cancer
were included. Pretreatment gene expression profiling was performed
with oligonecleotide microarrays on fine-needle aspiration specimens.
We developed predictors of pCR from 82 cases and assessed accuracy
on 51 independent cases.Overall pCR rate was 26\% in both cohorts.
In the training set, 56 probes were identified as differentially
expressed between pCR versus residual disease, at a false discovery
rate of 1\%. We examined the performance of 780 distinct classifiers
(set of genes + prediction algorithm) in full cross-validation. Many
predictors performed equally well. A nominally best 30-probe set
Diagonal Linear Discriminant Analysis classifier was selected for
independent validation. It showed significantly higher sensitivity
(92\% v 61\%) than a clinical predictor including age, grade, and
estrogen receptor status. The negative predictive value (96\% v 86\%)
and area under the curve (0.877 v 0.811) were nominally better but
not statistically significant. The combination of genomic and clinical
information yielded a predictor not significantly different from
the genomic predictor alone. In 31 samples, RNA was hybridized in
replicate with resulting predictions that were 97\% concordant.A
30-probe set pharmacogenomic predictor predicted pCR to T/FAC chemotherapy
with high sensitivity and negative predictive value. This test correctly
identified all but one of the patients who achieved pCR (12 of 13
patients) and all but one of those who were predicted to have residual
disease had residual cancer (27 of 28 patients).},
doi = {10.1200/JCO.2006.05.6861},
pdf = {../local/Hess2006Pharmacogenomic.pdf},
file = {Hess2006Pharmacogenomic.pdf:Hess2006Pharmacogenomic.pdf:PDF},
institution = {Department of Biostatistics and Applied Mathematics, The University
of Texas M.D. Anderson Cancer Center, Houston, TX 77230-1439, USA.},
keywords = {breastcancer},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {JCO.2006.05.6861},
pmid = {16896004},
timestamp = {2011.11.18},
url = {http://dx.doi.org/10.1200/JCO.2006.05.6861}
}
@article{Hill2006G-protein-coupled,
author = {Hill, S. J.},
title = {{G}-protein-coupled receptors: past, present and future.},
journal = {Br. J. Pharmacol.},
year = {2006},
volume = {147 Suppl 1},
pages = {S27--S37},
month = {Jan},
abstract = {The G-protein-coupled receptor (GPCR) family represents the largest
and most versatile group of cell surface receptors. Drugs active
at these receptors have therapeutic actions across a wide range of
human diseases ranging from allergic rhinitis to pain, hypertension
and schizophrenia. This review provides a brief historical overview
of the properties and signalling characteristics of this important
family of receptors.},
doi = {10.1038/sj.bjp.0706455},
keywords = {chemogenomics},
owner = {laurent},
pii = {0706455},
pmid = {16402114},
timestamp = {2008.01.16},
url = {http://dx.doi.org/10.1038/sj.bjp.0706455}
}
@article{Hindsgaul1999Carbohydrate,
author = {Hindsgaul, O.},
title = {Carbohydrate chemistry. {S}ugars out in the open.},
journal = {Nature},
year = {1999},
volume = {399},
pages = {644-5},
number = {6737},
month = {Jun},
doi = {10.1038/21335},
pdf = {../local/Hindsgaul1999Carbohydrate.pdf},
file = {Hindsgaul1999Carbohydrate.pdf:local/Hindsgaul1999Carbohydrate.pdf:PDF},
keywords = {glycans},
url = {http://dx.doi.org/10.1038/21335}
}
@article{Hinton2006Unsupervised,
author = {Hinton, Geoffrey and Osindero, Simon and Welling, Max and Teh, Yee-Whye},
title = {Unsupervised discovery of nonlinear structure using contrastive backpropagation.},
journal = {Cogn Sci},
year = {2006},
volume = {30},
pages = {725--731},
number = {4},
month = {Jul},
abstract = {We describe a way of modeling high-dimensional data vectors by using
an unsupervised, nonlinear, multilayer neural network in which the
activity of each neuron-like unit makes an additive contribution
to a global energy score that indicates how surprised the network
is by the data vector. The connection weights that determine how
the activity of each unit depends on the activities in earlier layers
are learned by minimizing the energy assigned to data vectors that
are actually observed and maximizing the energy assigned to "confabulations"
that are generated by perturbing an observed data vector in a direction
that decreases its energy under the current model.},
doi = {10.1207/s15516709cog0000_76},
institution = {Department of Computer Science, University of Toronto.},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pmid = {21702832},
timestamp = {2013.03.29},
url = {http://dx.doi.org/10.1207/s15516709cog0000_76}
}
@article{Hinton2006fast,
author = {Hinton, G. E. and Osindero, S. and Teh, Y.-W.},
title = {A fast learning algorithm for deep belief nets.},
journal = {Neural Comput},
year = {2006},
volume = {18},
pages = {1527--1554},
number = {7},
month = {Jul},
abstract = {We show how to use "complementary priors" to eliminate the explaining-away
effects that make inference difficult in densely connected belief
nets that have many hidden layers. Using complementary priors, we
derive a fast, greedy algorithm that can learn deep, directed belief
networks one layer at a time, provided the top two layers form an
undirected associative memory. The fast, greedy algorithm is used
to initialize a slower learning procedure that fine-tunes the weights
using a contrastive version of the wake-sleep algorithm. After fine-tuning,
a network with three hidden layers forms a very good generative model
of the joint distribution of handwritten digit images and their labels.
This generative model gives better digit classification than the
best discriminative learning algorithms. The low-dimensional manifolds
on which the digits lie are modeled by long ravines in the free-energy
landscape of the top-level associative memory, and it is easy to
explore these ravines by using the directed connections to display
what the associative memory has in mind.},
doi = {10.1162/neco.2006.18.7.1527},
institution = {Department of Computer Science, University of Toronto, Canada. hinton@cs.toronto.edu},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pmid = {16764513},
timestamp = {2013.03.29},
url = {http://dx.doi.org/10.1162/neco.2006.18.7.1527}
}
@article{Hizukuri2004Extraction,
author = {Yoshiyuki Hizukuri and Yoshihiro Yamanishi and Kosuke Hashimoto and
Minoru Kanehisa},
title = {Extraction of species-specific glycan substructures.},
journal = {Genome {I}nform {S}er {W}orkshop {G}enome {I}nform},
year = {2004},
volume = {15},
pages = {69-81},
number = {1},
abstract = {Glycans, which are carbohydrate sugar chains attached to some lipids
or proteins, have a huge variety of structures and play a key role
in cell communication, protein interaction and immunity. {T}he availability
of a number of glycan structures stored in the {KEGG}/{GLYCAN} database
makes it possible for us to conduct a large-scale comparative research
of glycans. {I}n this paper, we present a novel approach to compare
glycan structures and extract characteristic glycan substructures
of certain organisms. {I}n the algorithm we developed a new similarity
measure of glycan structures taking into account of several biological
aspects of glycan synthesis and glycosyltransferases, and we confirmed
the validity of our similarity measure by conducting experiments
on its ability to classify glycans between organisms in the framework
of a support vector machine. {F}inally, our method successfully extracted
a set of candidates of substructrues which are characteristic to
human, rat, mouse, bovine, pig, chicken, yeast, wheat and sycamore,
respectively. {W}e confirmed that the characteristic substructures
extracted by our method correspond to the substructures which are
known as the species-specific sugar chain of gamma-glutamyltranspeptidases
in the kidney.},
pdf = {../local/Hizukuri2004Extraction.pdf},
file = {Hizukuri2004Extraction.pdf:local/Hizukuri2004Extraction.pdf:PDF},
keywords = {Amino Acid Sequence, Animals, Carbohydrate Conformation, Carbohydrate
Sequence, Cattle, Computer Simulation, Databases, Genes, Histocompatibility
Antigens Class I, Humans, Least-Squares Analysis, MHC Class I, Major
Histocompatibility Complex, Mice, Monosaccharides, Non-U.S. Gov't,
Peptides, Phylogeny, Plants, Polysaccharides, Protein, Rats, Research
Support, Saccharomyces cerevisiae, Species Specificity, 15712111},
url = {http://www.jsbi.org/journal/IBSB04/IBSB04F018.html}
}
@article{Hizukuri2005Extraction,
author = {Hizukuri, Y. and Yamanishi, Y. and Nakamura, O. and Yagi, F. and
Goto, S. and Kanehisa, M.},
title = {Extraction of leukemia specific glycan motifs in humans by computational
glycomics.},
journal = {Carbohydr. {R}es.},
year = {2005},
volume = {340},
pages = {2270-8},
number = {14},
month = {Oct},
abstract = {There have been almost no standard methods for conducting computational
analyses on glycan structures in comparison to {DNA} and proteins.
{I}n this paper, we present a novel method for extracting functional
motifs from glycan structures using the {KEGG}/{GLYCAN} database.
{F}irst, we developed a new similarity measure for comparing glycan
structures taking into account the characteristic mechanisms of glycan
biosynthesis, and we tested its ability to classify glycans of different
blood components in the framework of support vector machines ({SVM}s).
{T}he results show that our method can successfully classify glycans
from four types of human blood components: leukemic cells, erythrocyte,
serum, and plasma. {N}ext, we extracted characteristic functional
motifs of glycans considered to be specific to each blood component.
{W}e predicted the substructure alpha-d-{N}eup5{A}c-(2-->3)-beta-d-{G}alp-(1-->4)-d-{G}lcp{NA}c
as a leukemia specific glycan motif. {B}ased on the fact that the
{A}grocybe cylindracea galectin ({ACG}) specifically binds to the
same substructure, we conducted an experiment using cell agglutination
assay and confirmed that this fungal lectin specifically recognized
human leukemic cells.},
doi = {10.1016/j.carres.2005.07.012},
keywords = {glycans},
pii = {S0008-6215(05)00355-1},
url = {http://dx.doi.org/10.1016/j.carres.2005.07.012}
}
@article{Ho2002Systematic,
author = {Yuen Ho and Albrecht Gruhler and Adrian Heilbut and Gary D Bader
and Lynda Moore and Sally-Lin Adams and Anna Millar and Paul Taylor
and Keiryn Bennett and Kelly Boutilier and Lingyun Yang and Cheryl
Wolting and Ian Donaldson and Søren Schandorff and Juanita Shewnarane
and Mai Vo and Joanne Taggart and Marilyn Goudreault and Brenda Muskat
and Cris Alfarano and Danielle Dewar and Zhen Lin and Katerina Michalickova
and Andrew R Willems and Holly Sassi and Peter A Nielsen and Karina
J Rasmussen and Jens R Andersen and Lene E Johansen and Lykke H Hansen
and Hans Jespersen and Alexandre Podtelejnikov and Eva Nielsen and
Janne Crawford and Vibeke Poulsen and Birgitte D Sørensen and Jesper
Matthiesen and Ronald C Hendrickson and Frank Gleeson and Tony Pawson
and Michael F Moran and Daniel Durocher and Matthias Mann and Christopher
W V Hogue and Daniel Figeys and Mike Tyers},
title = {Systematic identification of protein complexes in {S}accharomyces
cerevisiae by mass spectrometry.},
journal = {Nature},
year = {2002},
volume = {415},
pages = {180-3},
number = {6868},
month = {Jan},
abstract = {The recent abundance of genome sequence data has brought an urgent
need for systematic proteomics to decipher the encoded protein networks
that dictate cellular function. {T}o date, generation of large-scale
protein-protein interaction maps has relied on the yeast two-hybrid
system, which detects binary interactions through activation of reporter
gene expression. {W}ith the advent of ultrasensitive mass spectrometric
protein identification methods, it is feasible to identify directly
protein complexes on a proteome-wide scale. {H}ere we report, using
the budding yeast {S}accharomyces cerevisiae as a test case, an example
of this approach, which we term high-throughput mass spectrometric
protein complex identification ({HMS}-{PCI}). {B}eginning with 10\%
of predicted yeast proteins as baits, we detected 3,617 associated
proteins covering 25\% of the yeast proteome. {N}umerous protein
complexes were identified, including many new interactions in various
signalling pathways and in the {DNA} damage response. {C}omparison
of the {HMS}-{PCI} data set with interactions reported in the literature
revealed an average threefold higher success rate in detection of
known complexes compared with large-scale two-hybrid studies. {G}iven
the high degree of connectivity observed in this study, even partial
{HMS}-{PCI} coverage of complex proteomes, including that of humans,
should allow comprehensive identification of cellular networks.},
doi = {10.1038/415180a},
pdf = {../local/ho02.pdf},
file = {ho02.pdf:local/ho02.pdf:PDF},
keywords = {Affinity Labels, Amino Acid Sequence, Animals, Cell Cycle Proteins,
Cloning, Comparative Study, DNA, DNA Damage, DNA Repair, Electrospray
Ionization, Fungal, Genetic, Humans, Macromolecular Substances, Mass,
Mitosis, Molecular, Molecular Sequence Data, Non-P.H.S., Non-U.S.
Gov't, P.H.S., Phosphoric Monoester Hydrolases, Protein Binding,
Protein Interaction Mapping, Protein Kinases, Proteome, Proteomics,
Research Support, Ribonucleoproteins, Ribosomes, Saccharomyces cerevisiae,
Saccharomyces cerevisiae Proteins, Sequence Alignment, Signal Transduction,
Spectrometry, Spectrum Analysis, Transcription, U.S. Gov't, 11805813},
owner = {vert},
pii = {415180a},
url = {http://dx.doi.org/10.1038/415180a}
}
@inproceedings{Hoang08Moses,
author = {Hoang, H. and Koehn, P.},
title = {Design of the {Moses} Decoder for Statistical Machine Translation},
booktitle = {ACL 2008 Software workshop},
year = {2008},
pages = {58--65},
address = {Columbus, Ohio},
month = {June},
publisher = {ACL},
citeulike-article-id = {2905903},
keywords = {moses, smt},
posted-at = {2008-06-18 18:53:32},
priority = {0},
url = {http://www.aclweb.org/anthology/W/W08/W08-0510}
}
@incollection{Hochreiter2004Gene,
author = {Hochreiter, S. and Obermayer, K.},
title = {Gene selection for microarray data},
booktitle = {Kernel {M}ethods in {C}omputational {B}iology},
publisher = {MIT Press},
year = {2004},
editor = {Schölkopf, B. and Tsuda, K. and Vert, J.P.},
pages = {319-355},
pdf = {../local/heterogeneous.pdf:http\://cg.ensmp.fr/~vert/publi/04kmcbbook/heterogeneous.pdf:PDF;heterogeneous.pdf:http\},
file = {heterogeneous.pdf:http\://cg.ensmp.fr/~vert/publi/04kmcbbook/heterogeneous.pdf:PDF;heterogeneous.pdf:http\://cg.ensmp.fr/~vert/publi/04kmcbbook/heterogeneous.pdf:PDF},
keywords = {biosvm},
owner = {vert}
}
@article{Hockstein2004Diagnosis,
author = {Neil G Hockstein and Erica R Thaler and Drew Torigian and Wallace
T Miller and Olivia Deffenderfer and C. William Hanson},
title = {Diagnosis of pneumonia with an electronic nose: correlation of vapor
signature with chest computed tomography scan findings.},
journal = {Laryngoscope},
year = {2004},
volume = {114},
pages = {1701-5},
number = {10},
month = {Oct},
abstract = {O{BJECTIVES}/{HYPOTHESIS}: {T}he electronic nose is a sensor of volatile
molecules that is useful in the analysis of expired gases. {T}he
device is well suited to testing the breath of patients receiving
mechanical ventilation and is a potential diagnostic adjunct that
can aid in the detection of patients with ventilator-associated pneumonia.
{STUDY} {DESIGN}: {A} prospective study. {METHODS}: {W}e performed
a prospective study of patients receiving mechanical ventilation
in a surgical intensive care unit who underwent chest computed tomography
({CT}) scanning. {A} single attending radiologist reviewed the chest
{CT} scans, and imaging features were recorded on a standardized
form. {W}ithin 48 hours of chest {CT} scan, five sets of exhaled
gas were sampled from the expiratory limb of the ventilator circuit.
{T}he gases were assayed with a commercially available electronic
nose. {B}oth linear and nonlinear analyses were performed to identify
correlations between imaging features and the assayed gas signatures.
{RESULTS}: {T}wenty-five patients were identified, 13 of whom were
diagnosed with pneumonia by {CT} scan. {S}upport vector machine analysis
was performed in two separate analyses. {I}n the first analysis,
in which a training set was identical to a prediction set, the accuracy
of prediction results was greater than 91.6\%. {I}n the second analysis,
in which the training set and the prediction set were different,
the accuracy of prediction results was at least 80\%, with higher
accuracy depending on the specific parameters and models being used.
{CONCLUSION}: {T}he electronic nose is a new technology that continues
to show promise as a potential diagnostic adjunct in the diagnosis
of pneumonia and other infectious diseases.},
pii = {00005537-200410000-00005}
}
@techreport{Hoefling2009path,
author = {Hoefling, H.},
title = {A path algorithm for the {F}used {L}asso {S}ignal {A}pproximator},
institution = {arXiv},
year = {2009},
number = {0910.0526v1},
month = {Oct.},
pdf = {../local/Hoefling2009path.pdf},
file = {Hoefling2009path.pdf:Hoefling2009path.pdf:PDF},
keywords = {segmentation},
owner = {jp},
timestamp = {2010.05.31}
}
@article{Hoerl1962Application,
author = {A. E. Hoerl},
title = {Application of ridge regression analysis to regression problems},
journal = {{C}hemical {E}ngineering {P}rogress},
year = {1962},
volume = {58},
pages = {54-59}
}
@article{Hoerl1982Citation,
author = {A. E. Hoerl and R. W. Kennard},
title = {Citation Classic - Ridge regression~: biased estimation for nonorthogonal
problems},
journal = {CC/Eng. Tech. Appl. Sci.},
year = {1982},
volume = {35},
pages = {18-18}
}
@article{Hoerl1970Ridge,
author = {A. E. Hoerl and R. W. Kennard},
title = {Ridge regression~: biased estimation for nonorthogonal problems},
journal = {Technometrics},
year = {1970},
volume = {12},
pages = {55-67},
number = {1}
}
@article{Hoffmann2010new,
author = {Hoffmann, Brice and Zaslavskiy, Mikhail and Vert, Jean-Philippe and
Stoven, Veronique},
title = {A new protein binding pocket similarity measure based on comparison
of clouds of atoms in 3D: application to ligand prediction},
journal = {BMC Bioinformatics},
year = {2010},
volume = {11},
pages = {99},
number = {1},
abstract = {BACKGROUND:Predicting which molecules can bind to a given binding
site of a protein with known 3D structure is important to decipher
the protein function, and useful in drug design. A classical assumption
in structural biology is that proteins with similar 3D structures
have related molecular functions, and therefore may bind similar
ligands. However, proteins that do not display any overall sequence
or structure similarity may also bind similar ligands if they contain
similar binding sites. Quantitatively assessing the similarity between
binding sites may therefore be useful to propose new ligands for
a given pocket, based on those known for similar pockets.RESULTS:We
propose a new method to quantify the similarity between binding pockets,
and explore its relevance for ligand prediction. We represent each
pocket by a cloud of atoms, and assess the similarity between two
pockets by aligning their atoms in the 3D space and comparing the
resulting configurations with a convolution kernel. Pocket alignment
and comparison is possible even when the corresponding proteins share
no sequence or overall structure similarities. In order to predict
ligands for a given target pocket, we compare it to an ensemble of
pockets with known ligands to identify the most similar pockets.
We discuss two criteria to evaluate the performance of a binding
pocket similarity measure in the context of ligand prediction, namely,
area under ROC curve (AUC scores) and classification based scores.
We show that the latter is better suited to evaluate the methods
with respect to ligand prediction, and demonstrate the relevance
of our new binding site similarity compared to existing similarity
measures.CONCLUSIONS:This study demonstrates the relevance of the
proposed method to identify ligands binding to known binding pockets.
We also provide a new benchmark for future work in this field. The
new method and the benchmark are available at http://cbio.ensmp.fr/paris},
doi = {10.1186/1471-2105-11-99},
issn = {1471-2105},
pubmedid = {20175916},
url = {http://www.biomedcentral.com/1471-2105/11/99}
}
@article{Hofmann2005Concept-based,
author = {Oliver Hofmann and Dietmar Schomburg},
title = {Concept-based annotation of enzyme classes.},
journal = {Bioinformatics},
year = {2005},
volume = {21},
pages = {2059-66},
number = {9},
month = {May},
abstract = {M{OTIVATION}: {G}iven the explosive growth of biomedical data as well
as the literature describing results and findings, it is getting
increasingly difficult to keep up to date with new information. {K}eeping
databases synchronized with current knowledge is a time-consuming
and expensive task-one which can be alleviated by automatically gathering
findings from the literature using linguistic approaches. {W}e describe
a method to automatically annotate enzyme classes with disease-related
information extracted from the biomedical literature for inclusion
in such a database. {RESULTS}: {E}nzyme names for the 3901 enzyme
classes in the {BRENDA} database, a repository for quantitative and
qualitative enzyme information, were identified in more than 100,000
abstracts retrieved from the {P}ub{M}ed literature database. {P}hrases
in the abstracts were assigned to concepts from the {U}nified {M}edical
{L}anguage {S}ystem ({UMLS}) utilizing the {M}eta{M}ap program, allowing
for the identification of disease-related concepts by their semantic
fields in the {UMLS} ontology. {A}ssignments between enzyme classes
and diseases were created based on their co-occurrence within a single
sentence. {F}alse positives could be removed by a variety of filters
including minimum number of co-occurrences, removal of sentences
containing a negation and the classification of sentences based on
their semantic fields by a {S}upport {V}ector {M}achine. {V}erification
of the assignments with a manually annotated set of 1500 sentences
yielded favorable results of 92\% precision at 50\% recall, sufficient
for inclusion in a high-quality database. {AVAILABILITY}: {S}ource
code is available from the author upon request. {SUPPLEMENTARY} {INFORMATION}:
ftp.uni-koeln.de/institute/biochemie/pub/brenda/info/disease{S}upp.pdf.},
doi = {10.1093/bioinformatics/bti284},
pdf = {../local/Hofmann2005Concept-based.pdf},
file = {Hofmann2005Concept-based.pdf:local/Hofmann2005Concept-based.pdf:PDF},
keywords = {biosvm},
pii = {bti284},
url = {http://dx.doi.org/10.1093/bioinformatics/bti284}
}
@article{Hoheisel2006Microarray,
author = {Hoheisel, J. D.},
title = {Microarray technology: beyond transcript profiling and genotype analysis},
journal = {Nat Rev Genet},
year = {2006},
volume = {7},
pages = {200--210},
number = {3},
month = {Mar},
abstract = {Understanding complex functional mechanisms requires the global and
parallel analysis of different cellular processes. DNA microarrays
have become synonymous with this kind of study and, in many cases,
are the obvious platform to achieve this aim. They have already made
important contributions, most notably to gene-expression studies,
although the true potential of this technology is far greater. Whereas
some assays, such as transcript profiling and genotyping, are becoming
routine, others are still in the early phases of development, and
new areas of application, such as genome-wide epigenetic analysis
and on-chip synthesis, continue to emerge.},
doi = {10.1038/nrg1809},
pdf = {../local/Hoheisel2006Microarray.pdf},
file = {Hoheisel2006Microarray.pdf:Hoheisel2006Microarray.pdf:PDF},
institution = {Division of Functional Genome Analysis, Deutsches Krebsforschungszentrum,
Im Neuenheimer Feld 580, 69120 Heidelberg, Germany. J.Hoheisel@dkfz.de},
keywords = {csbcbook, csbcbook-ch2},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {nrg1809},
pmid = {16485019},
timestamp = {2009.10.18},
url = {http://dx.doi.org/10.1038/nrg1809}
}
@article{Hohlbein2010Surfing,
author = {Johannes Hohlbein and Kristofer Gryte and Mike Heilemann and Achillefs
N Kapanidis},
title = {Surfing on a new wave of single-molecule fluorescence methods.},
journal = {Phys Biol},
year = {2010},
volume = {7},
pages = {031001},
number = {3},
abstract = {Single-molecule fluorescence microscopy is currently one of the most
popular methods in the single-molecule toolbox. In this review, we
discuss recent advances in fluorescence instrumentation and assays:
these methods are characterized by a substantial increase in complexity
of the instrumentation or biological samples involved. Specifically,
we describe new multi-laser and multi-colour fluorescence spectroscopy
and imaging techniques, super-resolution microscopy imaging and the
development of instruments that combine fluorescence detection with
other single-molecule methods such as force spectroscopy. We also
highlight two pivotal developments in basic and applied biosciences:
the new information available from detection of single molecules
in single biological cells and exciting developments in fluorescence-based
single-molecule DNA sequencing.},
doi = {10.1088/1478-3975/7/3/031001},
institution = {Department of Physics, Biological Physics Research Group, University
of Oxford, Oxford, UK. j.hohlbein1@physics.ox.ac.uk},
owner = {phupe},
pii = {S1478-3975(10)53547-7},
pmid = {20686191},
timestamp = {2010.08.20},
url = {http://dx.doi.org/10.1088/1478-3975/7/3/031001}
}
@article{Holen2002Positional,
author = {Holen, T. and Amarzguioui, M. and Wiiger, M. T. and Babaie, E. and
Prydz, H.},
title = {{P}ositional effects of short interfering {RNA}s targeting the human
coagulation trigger {T}issue {F}actor.},
journal = {Nucleic Acids Res.},
year = {2002},
volume = {30},
pages = {1757--1766},
number = {8},
month = {Apr},
abstract = {Chemically synthesised 21-23 bp double-stranded short interfering
RNAs (siRNA) can induce sequence-specific post-transcriptional gene
silencing, in a process termed RNA interference (RNAi). In the present
study, several siRNAs synthesised against different sites on the
same target mRNA (human Tissue Factor) demonstrated striking differences
in silencing efficiency. Only a few of the siRNAs resulted in a significant
reduction in expression, suggesting that accessible siRNA target
sites may be rare in some human mRNAs. Blocking of the 3'-OH with
FITC did not reduce the effect on target mRNA. Mutations in the siRNAs
relative to target mRNA sequence gradually reduced, but did not abolish
mRNA depletion. Inactive siRNAs competed reversibly with active siRNAs
in a sequence-independent manner. Several lines of evidence suggest
the existence of a near equilibrium kinetic balance between mRNA
production and siRNA-mediated mRNA depletion. The silencing effect
was transient, with the level of mRNA recovering fully within 4-5
days, suggesting absence of a propagative system for RNAi in humans.
Finally, we observed 3' mRNA cleavage fragments resulting from the
action of the most effective siRNAs. The depletion rate-dependent
appearance of these fragments argues for the existence of a two-step
mRNA degradation mechanism.},
keywords = {sirna},
owner = {vert},
pmid = {11937629},
timestamp = {2006.03.28}
}
@inproceedings{Holford2005Visual,
author = {Holford, N.},
title = {{VPC}, the visual predictive check -- superiority to standard diagnostic
({R}orschach) plots},
booktitle = {PAGE 14 (http://www. page-meeting.org/?abstract=738)},
year = {2005},
owner = {kb},
timestamp = {2011.04.18},
url = {http://www. page-meeting.org/?abstract=738}
}
@article{Holliday1997Using,
author = {J. D. Holliday and P. Willett},
title = {Using a genetic algorithm to identify common structural features
in sets of ligands.},
journal = {J. {M}ol. {G}raph. {M}odel.},
year = {1997},
volume = {15},
pages = {221--232},
number = {4},
month = {Aug},
abstract = {This article describes a program for pharmacophore mapping, called
{MPHIL} ({M}apping {P}harmacophores in {L}igands). {G}iven as input
a set of molecules that exhibit some common biological activity,
{MPHIL} identifies the smallest 3{D} pattern of pharmacophore points
that has at least m (a user-defined parameter) points in common with
each of the input molecules. {T}he program thus differs from existing
programs for pharmacophore mapping in that it does not require all
of the molecules to share exactly the same pattern of points, although
it will find such a common pattern if it does, indeed, exist. {MPHIL}
uses a genetic algorithm ({GA}) approach in which an initial, and
very rapid, {GA} is used to suggest possible combinations of points
that are then processed by the second {GA} to yield the final 3{D}
pattern.},
keywords = {chemoinformatics},
owner = {mahe},
pii = {S1093326397000806},
pmid = {9524931},
timestamp = {2006.02.03}
}
@article{Holm1979simple,
author = {Holm, S.},
title = {A simple sequentially rejective multiple test procedure},
journal = {Scandinavian Journal of Statistics},
year = {1979},
volume = {6},
pages = {65--70},
number = {2},
owner = {jp},
timestamp = {2012.03.07}
}
@phdthesis{Holst1996Topological,
author = {H. van der Holst},
title = {Topological and spectral graph characterizations},
school = {Universiteit van Amsterdam},
year = {1996},
subject = {net}
}
@article{Homouz20133D,
author = {Homouz, D. and Kudlicki, A. S.},
title = {The {3D} Organization of the Yeast Genome Correlates with Co-Expression
and Reflects Functional Relations between Genes},
journal = {PLoS ONE},
year = {2013},
volume = {8},
pages = {e54699},
number = {1},
month = {01},
abstract = {The spatial organization of eukaryotic genomes is thought to play
an important role in regulating gene expression. The recent advances
in experimental methods including chromatin capture techniques, as
well as the large amounts of accumulated gene expression data allow
studying the relationship between spatial organization of the genome
and co-expression of protein-coding genes. To analyse this genome-wide
relationship at a single gene resolution, we combined the interchromosomal
DNA contacts in the yeast genome measured by Duan et al. with a comprehensive
collection of 1,496 gene expression datasets. We find significant
enhancement of co-expression among genes with contact links. The
co-expression is most prominent when two gene loci fall within 1,000
base pairs from the observed contact. We also demonstrate an enrichment
of inter-chromosomal links between functionally related genes, which
suggests that the non random nature of the genome organization serves
to facilitate coordinated transcription in groups of genes.
},
doi = {10.1371/journal.pone.0054699},
pdf = {../local/Homouz20133D.pdf},
file = {Homouz20133D.pdf:Homouz20133D.pdf:PDF},
keywords = {hic, ngs},
owner = {nelle},
publisher = {Public Library of Science},
timestamp = {2013.03.30},
url = {http://dx.doi.org/10.1371/journal.pone.0054699}
}
@article{Honeyman1998Neural,
author = {Honeyman, M. C. and Brusic, V. and Stone, N. L. and Harrison, L.
C.},
title = {{N}eural network-based prediction of candidate {T}-cell epitopes.},
journal = {Nat. Biotechnol.},
year = {1998},
volume = {16},
pages = {966--969},
number = {10},
month = {Oct},
abstract = {Activation of T cells requires recognition by T-cell receptors of
specific peptides bound to major histocompatibility complex (MHC)
molecules on the surface of either antigen-presenting or target cells.
These peptides, T-cell epitopes, have potential therapeutic applications,
such as for use as vaccines. Their identification, however, usually
requires that multiple overlapping synthetic peptides encompassing
a protein antigen be assayed, which in humans, is limited by volume
of donor blood. T-cell epitopes are a subset of peptides that bind
to MHC molecules. We use an artificial neural network (ANN) model
trained to predict peptides that bind to the MHC class II molecule
HLA-DR4(*0401). Binding prediction facilitates identification of
T-cell epitopes in tyrosine phosphatase IA-2, an autoantigen in DR4-associated
type1 diabetes. Synthetic peptides encompassing IA-2 were tested
experimentally for DR4 binding and T-cell proliferation in humans
at risk for diabetes. ANN-based binding prediction was sensitive
and specific, and reduced the number of peptides required for T-cell
assay by more than half, with only a minor loss of epitopes. This
strategy could expedite identification of candidate T-cell epitopes
in diverse diseases.},
doi = {10.1038/nbt1098-966},
keywords = {immunoinformatics},
pmid = {9788355},
timestamp = {2007.01.25},
url = {http://dx.doi.org/10.1038/nbt1098-966}
}
@article{Hood2004Systems,
author = {Hood, L. and Heath, J. R. and Phelps, M. E. and Lin, B.},
title = {Systems biology and new technologies enable predictive and preventative
medicine},
journal = {Science},
year = {2004},
volume = {306},
pages = {640--643},
number = {5696},
month = {Oct},
abstract = {Systems approaches to disease are grounded in the idea that disease-perturbed
protein and gene regulatory networks differ from their normal counterparts;
we have been pursuing the possibility that these differences may
be reflected by multiparameter measurements of the blood. Such concepts
are transforming current diagnostic and therapeutic approaches to
medicine and, together with new technologies, will enable a predictive
and preventive medicine that will lead to personalized medicine.},
doi = {10.1126/science.1104635},
institution = {Institute for Systems Biology, Seattle, WA, USA. lhood@systemsbiology.org},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {306/5696/640},
pmid = {15499008},
timestamp = {2011.01.21},
url = {http://dx.doi.org/10.1126/science.1104635}
}
@article{Hopkins2002druggable,
author = {Hopkins, A. L. and Groom, C. R.},
title = {The druggable genome},
journal = {Nat. Rev. Drug Discov.},
year = {2002},
volume = {1},
pages = {727--730},
number = {9},
month = {Sep},
abstract = {An assessment of the number of molecular targets that represent an
opportunity for therapeutic intervention is crucial to the development
of post-genomic research strategies within the pharmaceutical industry.
Now that we know the size of the human genome, it is interesting
to consider just how many molecular targets this opportunity represents.
We start from the position that we understand the properties that
are required for a good drug, and therefore must be able to understand
what makes a good drug target.},
doi = {10.1038/nrd892},
keywords = {chemogenomics},
owner = {laurent},
pii = {nrd892},
pmid = {12209152},
timestamp = {2007.09.22},
url = {http://dx.doi.org/10.1038/nrd892}
}
@article{Hormozdiari2009Combinatorial,
author = {Fereydoun Hormozdiari and Can Alkan and Evan E Eichler and S. Cenk
Sahinalp},
title = {Combinatorial algorithms for structural variation detection in high-throughput
sequenced genomes.},
journal = {Genome Res.},
year = {2009},
volume = {19},
pages = {1270--1278},
number = {7},
month = {Jul},
abstract = {Recent studies show that along with single nucleotide polymorphisms
and small indels, larger structural variants among human individuals
are common. The Human Genome Structural Variation Project aims to
identify and classify deletions, insertions, and inversions (>5 Kbp)
in a small number of normal individuals with a fosmid-based paired-end
sequencing approach using traditional sequencing technologies. The
realization of new ultra-high-throughput sequencing platforms now
makes it feasible to detect the full spectrum of genomic variation
among many individual genomes, including cancer patients and others
suffering from diseases of genomic origin. Unfortunately, existing
algorithms for identifying structural variation (SV) among individuals
have not been designed to handle the short read lengths and the errors
implied by the "next-gen" sequencing (NGS) technologies. In this
paper, we give combinatorial formulations for the SV detection between
a reference genome sequence and a next-gen-based, paired-end, whole
genome shotgun-sequenced individual. We describe efficient algorithms
for each of the formulations we give, which all turn out to be fast
and quite reliable; they are also applicable to all next-gen sequencing
methods (Illumina, 454 Life Sciences [Roche], ABI SOLiD, etc.) and
traditional capillary sequencing technology. We apply our algorithms
to identify SV among individual genomes very recently sequenced by
Illumina technology.},
doi = {10.1101/gr.088633.108},
pdf = {../local/Hormozdiari2009Combinatorial.pdf},
file = {Hormozdiari2009Combinatorial.pdf:Hormozdiari2009Combinatorial.pdf:PDF},
institution = {School of Computing Science, Simon Fraser University, Burnaby, British
Columbia, Canada V5A 1S6.},
keywords = {ngs},
owner = {jp},
pii = {gr.088633.108},
pmid = {19447966},
timestamp = {2009.10.09},
url = {http://dx.doi.org/10.1101/gr.088633.108}
}
@article{Horn2004Dynamic,
author = {David Horn and Gideon Dror and Brigitte Quenet},
title = {Dynamic proximity of spatio-temporal sequences.},
journal = {I{EEE} {T}rans {N}eural {N}etw},
year = {2004},
volume = {15},
pages = {1002-8},
number = {5},
month = {Sep},
abstract = {Recurrent networks can generate spatio-temporal neural sequences of
very large cycles, having an apparent random behavior. {N}onetheless
a proximity measure between these sequences may be defined through
comparison of the synaptic weight matrices that generate them. {F}ollowing
the dynamic neural filter ({DNF}) formalism we demonstrate this concept
by comparing teacher and student recurrent networks of binary neurons.
{W}e show that large sequences, providing a training set well exceeding
the {C}over limit, allow for good determination of the synaptic matrices.
{A}lternatively, assuming the matrices to be known, very fast determination
of the biases can be achieved. {T}hus, a spatio-temporal sequence
may be regarded as spatio-temporal encoding of the bias vector. {W}e
introduce a linear support vector machine ({SVM}) variant of the
{DNF} in order to specify an optimal weight matrix. {T}his approach
allows us to deal with noise. {S}patio-temporal sequences generated
by different {DNF}s with the same number of neurons may be compared
by calculating correlations of the synaptic matrices of the reconstructed
{DNF}s. {O}ther types of spatio-temporal sequences need the introduction
of hidden neurons, and/or the use of a kernel variant of the {SVM}
approach. {T}he latter is being defined as a recurrent support vector
network ({RSVN}).}
}
@article{Horn2003GPCRDB,
author = {Horn, F. and Bettler, E. and Oliveira, L. and Campagne, F. and Cohen,
F. E. and Vriend, G.},
title = {{GPCRDB} information system for {G} protein-coupled receptors},
journal = {Nucl. Acids Res.},
year = {2003},
volume = {31},
pages = {294-297},
number = {1},
abstract = {The GPCRDB is a molecular class-specific information system that collects,
combines, validates and disseminates heterogeneous data on G protein-coupled
receptors (GPCRs). The database stores data on sequences, ligand
binding constants and mutations. The system also provides computationally
derived data such as sequence alignments, homology models, and a
series of query and visualization tools. The GPCRDB is updated automatically
once every 4-5 months and is freely accessible at http://www.gpcr.org/7tm/.},
doi = {10.1093/nar/gkg103},
eprint = {http://nar.oxfordjournals.org/cgi/reprint/31/1/294.pdf},
keywords = {chemogenomics},
url = {http://nar.oxfordjournals.org/cgi/content/abstract/31/1/294}
}
@article{Hornberg2006Cancer,
author = {Hornberg, J. J. and Bruggeman, F. J. and Westerhoff, H. V. and Lankelma,
J.},
title = {Cancer: a Systems Biology disease.},
journal = {Biosystems},
year = {2006},
volume = {83},
pages = {81--90},
number = {2-3},
abstract = {Cancer research has focused on the identification of molecular differences
between cancerous and healthy cells. The emerging picture is overwhelmingly
complex. Molecules out of many parallel signal transduction pathways
are involved. Their activities appear to be controlled by multiple
factors. The action of regulatory circuits, cross-talk between pathways
and the non-linear reaction kinetics of biochemical processes complicate
the understanding and prediction of the outcome of intracellular
signaling. In addition, interactions between tumor and other cell
types give rise to a complex supra-cellular communication network.
If cancer is such a complex system, how can one ever predict the
effect of a mutation in a particular gene on a functionality of the
entire system? And, how should one go about identifying drug targets?
Here, we argue that one aspect is to recognize, where the essence
resides, i.e. recognize cancer as a Systems Biology disease. Then,
more cancer biologists could become systems biologists aiming to
provide answers to some of the above systemic questions. To this
aim, they should integrate the available knowledge stemming from
quantitative experimental results through mathematical models. Models
that have contributed to the understanding of complex biological
systems are discussed. We show that the architecture of a signaling
network is important for determining the site at which an oncologist
should intervene. Finally, we discuss the possibility of applying
network-based drug design to cancer treatment and how rationalized
therapies, such as the application of kinase inhibitors, may benefit
from Systems Biology.},
doi = {10.1016/j.biosystems.2005.05.014},
pdf = {../local/Hornberg2006Cancer.pdf},
file = {Hornberg2006Cancer.pdf:Hornberg2006Cancer.pdf:PDF},
institution = {Cell Biology, BioCentrum Amsterdam, Faculty of Earth and Life Sciences,
Vrije Universiteit, De Boelelaan 1085, 1081 HV Amsterdam, The Netherlands.
jorrit.hornberg@falw.vu.nl},
keywords = {csbcbook, csbcbook-mustread},
owner = {jp},
pii = {S0303-2647(05)00117-6},
pmid = {16426740},
timestamp = {2009.10.11},
url = {http://dx.doi.org/10.1016/j.biosystems.2005.05.014}
}
@article{Hornberger2012Clinical,
author = {Hornberger, J. and Alvarado, M.D. and Rebecca, C. and Gutierrez,
H.R. and Tiffany, M.Y. and Gradishar, W.J.},
title = {Clinical Validity/Utility, Change in Practice Patterns, and Economic
Implications of Risk Stratifiers to Predict Outcomes for Early-Stage
Breast Cancer: A Systematic Review},
journal = {Journal of the National Cancer Institute},
year = {2012},
volume = {104},
pages = {1068--1079},
number = {14},
publisher = {Oxford University Press}
}
@article{Horner2009Bioinformatics,
author = {Horner, D. S. and Pavesi, G. and Castrignan{\`o}, T. and De Meo,
P. D. and Liuni, S. and Sammeth, M. and Picardi, E. and Pesole, G.},
title = {Bioinformatics approaches for genomics and post genomics applications
of next-generation sequencing.},
journal = {Brief Bioinform},
year = {2009},
month = {Oct},
abstract = {Technical advances such as the development of molecular cloning, Sanger
sequencing, PCR and oligonucleotide microarrays are key to our current
capacity to sequence, annotate and study complete organismal genomes.
Recent years have seen the development of a variety of so-called
'next-generation' sequencing platforms, with several others anticipated
to become available shortly. The previously unimaginable scale and
economy of these methods, coupled with their enthusiastic uptake
by the scientific community and the potential for further improvements
in accuracy and read length, suggest that these technologies are
destined to make a huge and ongoing impact upon genomic and post-genomic
biology. However, like the analysis of microarray data and the assembly
and annotation of complete genome sequences from conventional sequencing
data, the management and analysis of next-generation sequencing data
requires (and indeed has already driven) the development of informatics
tools able to assemble, map, and interpret huge quantities of relatively
or extremely short nucleotide sequence data. Here we provide a broad
overview of bioinformatics approaches that have been introduced for
several genomics and functional genomics applications of next-generation
sequencing.},
doi = {10.1093/bib/bbp046},
pdf = {../local/Horner2009Bioinformatics.pdf},
file = {Horner2009Bioinformatics.pdf:Horner2009Bioinformatics.pdf:PDF},
keywords = {ngs},
language = {eng},
medline-pst = {aheadofprint},
owner = {jp},
pii = {bbp046},
pmid = {19864250},
timestamp = {2010.01.07},
url = {http://dx.doi.org/10.1093/bib/bbp046}
}
@article{Horvath2003Neighborhooda,
author = {Horvath, D. and Jeandenans, C.},
title = {Neighborhood behavior of in silico structural spaces with respect
to in vitro activity spaces--a benchmark for neighborhood behavior
assessment of different in silico similarity metrics.},
journal = {J. Chem. Inf. Comput. Sci.},
year = {2003},
volume = {43},
pages = {691--698},
number = {2},
abstract = {In a previous work, we have introduced Neighborhood Behavior (NB)
criteria for calculated molecular similarity metrics, based on the
analysis of in vitro activity spaces that simultaneously monitor
the responses of a compound with respect to an entire panel of biologically
relevant receptors and enzymes. Now, these novel NB criteria will
be used as a benchmark for the comparison of different in silico
molecular similarity metrics, addressing the following topics: (1)
the relative performance of 2D vs 3D descriptors, (2) the importance
of the similarity scoring function for a given descriptor set, and
(3) binary or Fuzzy Pharmacophore Fingerprints-can they capture the
similarity of the spatial distribution of pharmacophoric groups despite
different molecular connectivity? It was found that fuzzy pharmacophore
descriptors (FBPA) displayed an optimal NB and, unlike their binary
counterparts, were successful in evidencing pharmacophore pattern
similarity independently of topological similarity. Topological FBPA,
identical to the former except for the use of topological instead
of 3D atom pair distances, display a somehow weaker, but significant
NB. Metrics based on "classical" global 2D and 3D molecular descriptors
and a Dice scoring function also performed well. The choice of the
similarity scoring function is therefore as important as the choice
of the appropriate molecular descriptors.},
doi = {10.1021/ci025635r},
pdf = {../local/Horvath2003Neighborhooda.pdf},
file = {Horvath2003Neighborhooda.pdf:Horvath2003Neighborhooda.pdf:PDF},
keywords = {chemoinformatics},
owner = {vert},
pmid = {12653539},
timestamp = {2006.11.24},
url = {http://dx.doi.org/10.1021/ci025635r}
}
@inproceedings{Horvath2004Cyclic,
author = {T. Horv{\'a}th and T. G{\"a}rtner and S. Wrobel},
title = {Cyclic pattern kernels for predictive graph mining},
booktitle = {Proceedings of the tenth ACM SIGKDD international conference on Knowledge
discovery and data mining},
year = {2004},
pages = {158-167},
address = {New York, NY, USA},
publisher = {ACM Press},
doi = {http://doi.acm.org/10.1145/1014052.1014072},
keywords = {chemoinformatics kernel-theory},
owner = {mahe},
timestamp = {2006.08.02}
}
@article{Hotelling1936Relation,
author = {H. Hotelling},
title = {Relation between two sets of variates},
journal = {Biometrika},
year = {1936},
volume = {28},
pages = {322-377},
pdf = {../local/Hotelling1936Relation.pdf},
file = {Hotelling1936Relation.pdf:local/Hotelling1936Relation.pdf:PDF},
url = {http://links.jstor.org/sici?sici=0006-3444%28193612%2928%3A3%2F4%3C321%3ARBTSOV%3E2.0.CO%3B2-F}
}
@article{Hou2004Remote,
author = {Hou, Y. and Hsu, W. and Lee, M. L. and Bystroff, C.},
title = {Remote homolog detection using local sequence-structure correlations.},
journal = {Proteins},
year = {2004},
volume = {57},
pages = {518-530},
number = {3},
abstract = {Remote homology detection refers to the detection of structural homology
in proteins when there is little or no sequence similarity. {I}n
this article, we present a remote homolog detection method called
{SVM}-{HMMSTR} that overcomes the reliance on detectable sequence
similarity by transforming the sequences into strings of hidden {M}arkov
states that represent local folding motif patterns. {T}hese state
strings are transformed into fixed-dimension feature vectors for
input to a support vector machine. {T}wo sets of features are defined:
an order-independent feature set that captures the amino acid and
local structure composition; and an order-dependent feature set that
captures the sequential ordering of the local structures. {T}ests
using the {S}tructural {C}lassification of {P}roteins ({SCOP}) 1.53
data set show that the {SVM}-{HMMSTR} gives a significant improvement
over several current methods.},
doi = {10.1002/prot.20221},
pdf = {../local/Hou2004Remote.pdf},
file = {Hou2004Remote.pdf:local/Hou2004Remote.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert}
}
@article{Hou2003Efficient,
author = {Hou, Y. and Hsu, W. and Lee, M. L. and Bystroff, C.},
title = {Efficient remote homology detection using local structure},
journal = {Bioinformatics},
year = {2003},
volume = {19},
pages = {2294-2301},
number = {17},
abstract = {Motivation: {T}he function of an unknown biological sequence can often
be accurately inferred if we are able to map this unknown sequence
to its corresponding homologous family. {A}t present, discriminative
methods such as {SVM}-{F}isher and {SVM}-pairwise, which combine
support vector machine ({SVM}) and sequence similarity, are recognized
as the most accurate methods, with {SVM}-pairwise being the most
accurate. {H}owever, these methods typically encode sequence information
into their feature vectors and ignore the structure information.
{T}hey are also computationally inefficient. {B}ased on these observations,
we present an alternative method for {SVM}-based protein classification.
{O}ur proposed method, {SVM}-{I}-sites, utilizes structure similarity
for remote homology detection. {R}esult: {W}e run experiments on
the {S}tructural {C}lassification of {P}roteins 1.53 data set. {T}he
results show that {SVM}-{I}-sites is more efficient than {SVM}-pairwise.
{F}urther, we find that {SVM}-{I}-sites outperforms sequence-based
methods such as {PSI}-{BLAST}, {SAM}, and {SVM}-{F}isher while achieving
a comparable performance with {SVM}-pairwise. {A}vailability: {I}-sites
server is accessible through the web at http://www.bioinfo.rpi.edu.
{P}rograms are available upon request for academics. {L}icensing
agreements are available for commercial interests. {T}he framework
of encoding local structure into feature vector is available upon
request.},
pdf = {../local/Hou2003Efficient.pdf},
file = {Hou2003Efficient.pdf:local/Hou2003Efficient.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://bioinformatics.oupjournals.org/cgi/content/abstract/19/17/2294}
}
@article{Hoyer2004Non-negative,
author = {Hoyer, P. O.},
title = {Non-negative Matrix Factorization with sparseness constraints.},
journal = {J. Mach. Learn. Res.},
year = {2004},
volume = {5},
pages = {1457--1469},
pdf = {../local/Hoyer2004Non-negative.pdf},
file = {Hoyer2004Non-negative.pdf:Hoyer2004Non-negative.pdf:PDF},
owner = {jp},
timestamp = {2012.02.28}
}
@article{Hsieh2004library,
author = {Hsieh, A. C. and Bo, R. and Manola, J. and Vazquez, F. and Bare,
O. and Khvorova, A. and Scaringe, S. and Sellers, W. R.},
title = {A library of si{RNA} duplexes targeting the phosphoinositide 3-kinase
pathway: determinants of gene silencing for use in cell-based screens.},
journal = {Nucleic {A}cids {R}es.},
year = {2004},
volume = {32},
pages = {893-901},
number = {3},
abstract = {Gene silencing through {RNA} interference ({RNA}i) has been established
as a means of conducting reverse genetic studies. {I}n order to better
understand the determinants of short interfering {RNA} (si{RNA})
knockdown for use in high-throughput cell-based screens, 148 si{RNA}
duplexes targeting 30 genes within the {PI}3{K} pathway were selected
and synthesized. {T}he extent of {RNA} knockdown was measured for
22 genes by quantitative real-time {PCR}. {A}nalysis of the parameters
correlating with effective knockdown showed that (i) duplexes targeting
the middle of the coding sequence silenced significantly poorer,
(ii) silencing by duplexes targeting the 3'{UTR} was comparable with
duplexes targeting the coding sequence, (iii) pooling of four or
five duplexes per gene was remarkably efficient in knocking down
gene expression and (iv) among duplexes that achieved a >70\% knockdown
of the m{RNA} there were strong nucleotide preferences at specific
positions, most notably positions 11 ({G} or {C}) and 19 ({T}) of
the si{RNA} duplex. {F}inally, in a proof-of-principle pathway-wide
cell-based genetic screen, conducted to detect negative genetic regulators
of {A}kt {S}473 phosphorylation, both known negative regulators of
this phosphorylation, {PTEN} and {PDK}1, were found. {T}hese data
help to lay the foundation for genome-wide si{RNA} screens in mammalian
cells.},
doi = {10.1093/nar/gkh238},
pdf = {../local/Hsieh2004library.pdf},
file = {Hsieh2004library.pdf:local/Hsieh2004library.pdf:PDF},
keywords = {sirna},
pii = {32/3/893},
url = {http://dx.doi.org/10.1093/nar/gkh238}
}
@article{Hu2004Developing,
author = {Hu, C. and Li, X. and Liang, J.},
title = {Developing optimal non-linear scoring function for protein design},
journal = {Bioinformatics},
year = {2004},
volume = {20},
pages = {3080-3098},
number = {17},
abstract = {Motivation. {P}rotein design aims to identify sequences compatible
with a given protein fold but incompatible to any alternative folds.
{T}o select the correct sequences and to guide the search process,
a design scoring function is critically important. {S}uch a scoring
function should be able to characterize the global fitness landscape
of many proteins simultaneously. {R}esults: {T}o find optimal design
scoring functions, we introduce two geometric views and propose a
formulation using a mixture of non-linear {G}aussian kernel functions.
{W}e aim to solve a simplified protein sequence design problem. {O}ur
goal is to distinguish each native sequence for a major portion of
representative protein structures from a large number of alternative
decoy sequences, each a fragment from proteins of different folds.
{O}ur scoring function discriminates perfectly a set of 440 native
proteins from 14 million sequence decoys. {W}e show that no linear
scoring function can succeed in this task. {I}n a blind test of unrelated
proteins, our scoring function misclassfies only 13 native proteins
out of 194. {T}his compares favorably with about three-four times
more misclassifications when optimal linear functions reported in
the literature are used. {W}e also discuss how to develop protein
folding scoring function. {A}vailability: {A}vailable on request
from the authors.},
doi = {10.1093/bioinformatics/bth369},
pdf = {../local/Hu2004Developing},
file = {Hu2004Developing:local/Hu2004Developing:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://bioinformatics.oupjournals.org/cgi/content/abstract/20/17/3080}
}
@article{Hu2004Improved,
author = {Hu, H.J. and Pan, Y. and Harrison, R. and Tai, P.C.},
title = {Improved protein secondary structure prediction using support vector
machine with a new encoding scheme and an advanced tertiary classifier},
journal = {I{EEE} {T}rans. {N}anobioscience},
year = {2004},
volume = {3},
pages = {265-271},
number = {4},
abstract = {Prediction of protein secondary structures is an important problem
in bioinformatics and has many applications. {T}he recent trend of
secondary structure prediction studies is mostly based on the neural
network or the support vector machine ({SVM}). {T}he {SVM} method
is a comparatively new learning system which has mostly been used
in pattern recognition problems. {I}n this study, {SVM} is used as
a machine learning tool for the prediction of secondary structure
and several encoding schemes, including orthogonal matrix, hydrophobicity
matrix, {BLOSUM}62 substitution matrix, and combined matrix of these,
are applied and optimized to improve the prediction accuracy. {A}lso,
the optimal window length for six {SVM} binary classifiers is established
by testing different window sizes and our new encoding scheme is
tested based on this optimal window size via sevenfold cross validation
tests. {T}he results show 2% increase in the accuracy of the binary
classifiers when compared with the instances in which the classical
orthogonal matrix is used. {F}inally, to combine the results of the
six {SVM} binary classifiers, a new tertiary classifier which combines
the results of one-versus-one binary classifiers is introduced and
the performance is compared with those of existing tertiary classifiers.
{A}ccording to the results, the {Q}3 prediction accuracy of new tertiary
classifier reaches 78.8% and this is better than the best result
reported in the literature.},
pdf = {../local/Hu2004Improved.pdf},
file = {Hu2004Improved.pdf:local/Hu2004Improved.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert}
}
@article{Hu2012HiCNorm,
author = {Hu, Ming and Deng, Ke and Selvaraj, Siddarth and Qin, Zhaohui and
Ren, Bing and Liu, Jun S.},
title = {{HiCNorm: removing biases in Hi-C data via Poisson regression}},
journal = {Bioinformatics},
year = {2012},
volume = {28},
pages = {3131--3133},
number = {23},
month = dec,
abstract = {{Summary: We propose a parametric model, HiCNorm, to remove systematic
biases in the raw Hi-C contact maps, resulting in a simple, fast,
yet accurate normalization procedure. Compared with the existing
Hi-C normalization method developed by Yaffe and Tanay, HiCNorm has
fewer parameters, runs >1000 times faster and achieves higher reproducibility.Availability:
Freely available on the web at: http://www.people.fas.harvard.edu/â¼junliu/HiCNorm/.Contact:
jliu@stat.harvard.eduSupplementary information: Supplementary data
are available at Bioinformatics online.}},
day = {1},
doi = {10.1093/bioinformatics/bts570},
issn = {1460-2059},
keywords = {hi-c},
pmid = {23023982},
posted-at = {2012-12-05 22:45:25},
priority = {2},
publisher = {Oxford University Press},
url = {http://dx.doi.org/10.1093/bioinformatics/bts570}
}
@article{Hu2009Genetic,
author = {Xiaolan Hu and Howard M Stern and Lin Ge and Carol O'Brien and Lauren
Haydu and Cynthia D Honchell and Peter M Haverty and Brock A Peters
and Thomas D Wu and Lukas C Amler and John Chant and David Stokoe
and Mark R Lackner and Guy Cavet},
title = {Genetic alterations and oncogenic pathways associated with breast
cancer subtypes.},
journal = {Mol Cancer Res},
year = {2009},
volume = {7},
pages = {511--522},
number = {4},
month = {Apr},
abstract = {Breast cancers can be divided into subtypes with important implications
for prognosis and treatment. We set out to characterize the genetic
alterations observed in different breast cancer subtypes and to identify
specific candidate genes and pathways associated with subtype biology.
mRNA expression levels of estrogen receptor, progesterone receptor,
and HER2 were shown to predict marker status determined by immunohistochemistry
and to be effective at assigning samples to subtypes. HER2(+) cancers
were shown to have the greatest frequency of high-level amplification
(independent of the ERBB2 amplicon itself), but triple-negative cancers
had the highest overall frequencies of copy gain. Triple-negative
cancers also were shown to have more frequent loss of phosphatase
and tensin homologue and mutation of RB1, which may contribute to
genomic instability. We identified and validated seven regions of
copy number alteration associated with different subtypes, and used
integrative bioinformatics analysis to identify candidate oncogenes
and tumor suppressors, including ERBB2, GRB7, MYST2, PPM1D, CCND1,
HDAC2, FOXA1, and RASA1. We tested the candidate oncogene MYST2 and
showed that it enhances the anchorage-independent growth of breast
cancer cells. The genome-wide and region-specific differences between
subtypes suggest the differential activation of oncogenic pathways.},
doi = {10.1158/1541-7786.MCR-08-0107},
pdf = {../local/Hu2009Genetic.pdf},
file = {Hu2009Genetic.pdf:Hu2009Genetic.pdf:PDF},
institution = {Department of Bioinformatics, Genentech, Inc., South San Francisco,
CA, USA.},
language = {eng},
medline-pst = {ppublish},
owner = {philippe},
pii = {7/4/511},
pmid = {19372580},
timestamp = {2010.08.05},
url = {http://dx.doi.org/10.1158/1541-7786.MCR-08-0107}
}
@article{Hu2006molecular,
author = {Zhiyuan Hu and Cheng Fan and Daniel S Oh and J. S. Marron and Xiaping
He and Bahjat F Qaqish and Chad Livasy and Lisa A Carey and Evangeline
Reynolds and Lynn Dressler and Andrew Nobel and Joel Parker and Matthew
G Ewend and Lynda R Sawyer and Junyuan Wu and Yudong Liu and Rita
Nanda and Maria Tretiakova and Alejandra Ruiz Orrico and Donna Dreher
and Juan P Palazzo and Laurent Perreard and Edward Nelson and Mary
Mone and Heidi Hansen and Michael Mullins and John F Quackenbush
and Matthew J Ellis and Olufunmilayo I Olopade and Philip S Bernard
and Charles M Perou},
title = {The molecular portraits of breast tumors are conserved across microarray
platforms.},
journal = {BMC Genomics},
year = {2006},
volume = {7},
pages = {96},
abstract = {Validation of a novel gene expression signature in independent data
sets is a critical step in the development of a clinically useful
test for cancer patient risk-stratification. However, validation
is often unconvincing because the size of the test set is typically
small. To overcome this problem we used publicly available breast
cancer gene expression data sets and a novel approach to data fusion,
in order to validate a new breast tumor intrinsic list.A 105-tumor
training set containing 26 sample pairs was used to derive a new
breast tumor intrinsic gene list. This intrinsic list contained 1300
genes and a proliferation signature that was not present in previous
breast intrinsic gene sets. We tested this list as a survival predictor
on a data set of 311 tumors compiled from three independent microarray
studies that were fused into a single data set using Distance Weighted
Discrimination. When the new intrinsic gene set was used to hierarchically
cluster this combined test set, tumors were grouped into LumA, LumB,
Basal-like, HER2+/ER-, and Normal Breast-like tumor subtypes that
we demonstrated in previous datasets. These subtypes were associated
with significant differences in Relapse-Free and Overall Survival.
Multivariate Cox analysis of the combined test set showed that the
intrinsic subtype classifications added significant prognostic information
that was independent of standard clinical predictors. From the combined
test set, we developed an objective and unchanging classifier based
upon five intrinsic subtype mean expression profiles (i.e. centroids),
which is designed for single sample predictions (SSP). The SSP approach
was applied to two additional independent data sets and consistently
predicted survival in both systemically treated and untreated patient
groups.This study validates the "breast tumor intrinsic" subtype
classification as an objective means of tumor classification that
should be translated into a clinical assay for further retrospective
and prospective validation. In addition, our method of combining
existing data sets can be used to robustly validate the potential
clinical value of any new gene expression profile.},
doi = {10.1186/1471-2164-7-96},
institution = {Lineberger Comprehensive Cancer Center, University of North Carolina,
Chapel Hill, NC 27599, USA. zhiyuan_hu@med.unc.edu},
keywords = {Breast Neoplasms, genetics; Cluster Analysis; Conserved Sequence,
genetics; Female; Gene Expression Regulation, Neoplastic, genetics;
Genes, Neoplasm, genetics; Genetic Predisposition to Disease; Humans;
Oligonucleotide Array Sequence Analysis, methods; Predictive Value
of Tests; Reproducibility of Results; Research Design; Sample Size;
Survival Analysis},
language = {eng},
medline-pst = {epublish},
owner = {jp},
pii = {1471-2164-7-96},
pmid = {16643655},
timestamp = {2012.02.29},
url = {http://dx.doi.org/10.1186/1471-2164-7-96}
}
@article{Hua2005JImmunol,
author = {Hua, F. and Cornejo, M. G. and Cardone, M. H. and Stokes, C. L. and
Lauffenburger, D. A.},
title = {Effects of Bcl-2 levels on Fas signaling-induced caspase-3 activation:
molecular genetic tests of computational model predictions},
journal = {J Immunol},
year = {2005},
volume = {175},
pages = {985--95},
number = {2},
abstract = {Fas-induced apoptosis is a critical process for normal immune system
development and function. Although many molecular components in the
Fas signaling pathway have been identified, a systematic understanding
of how they work together to determine network dynamics and apoptosis
itself has remained elusive. To address this, we generated a computational
model for interpreting and predicting effects of pathway component
properties. The model integrates current information concerning the
signaling network downstream of Fas activation, through both type
I and type II pathways, until activation of caspase-3. Unknown parameter
values in the model were estimated using experimental data obtained
from human Jurkat T cells. To elucidate critical signaling network
properties, we examined the effects of altering the level of Bcl-2
on the kinetics of caspase-3 activation, using both overexpression
and knockdown in the model and experimentally. Overexpression was
used to distinguish among alternative hypotheses for inhibitory binding
interactions of Bcl-2 with various components in the mitochondrial
pathway. In comparing model simulations with experimental results,
we find the best agreement when Bcl-2 blocks the release of cytochrome
c by binding to both Bax and truncated Bid instead of Bax, truncated
Bid, or Bid alone. Moreover, although Bcl-2 overexpression strongly
reduces caspase-3 activation, Bcl-2 knockdown has a negligible effect,
demonstrating a general model finding that varying the expression
levels of signal molecules frequently has asymmetric effects on the
outcome. Finally, we demonstrate that the relative dominance of type
I vs type II pathways can be switched by varying particular signaling
component levels without changing network structure.},
keywords = {csbcbook}
}
@article{Hua2005Optimal,
author = {Hua, J. and Xiong, Z. and Lowey, J. and Suh, E. and Dougherty, E.
R.},
title = {Optimal number of features as a function of sample size for various
classification rules},
journal = {Bioinformatics},
year = {2005},
volume = {21},
pages = {1509-1515},
number = {8},
month = {Apr},
note = {To appear},
abstract = {Motivation: {G}iven the joint feature-label distribution, increasing
the number of features always results in decreased classification
error; however, this is not the case when a classifier is designed
via a classification rule from sample data. {T}ypically (but not
always), for fixed sample size, the error of a designed classifier
decreases and then increases as the number of features grows. {T}he
potential downside of using too many features is most critical for
small samples, which are commonplace for gene-expression-based classifiers
for phenotype discrimination. {F}or fixed sample size and feature-label
distribution, the issue is to find an optimal number of features.{R}esults:
{S}ince only in rare cases is there a known distribution of the error
as a function of the number of features and sample size, this study
employs simulation for various feature-label distributions and classification
rules, and across a wide range of sample and feature-set sizes. {T}o
achieve the desired end, finding the optimal number of features as
a function of sample size, it employs massively parallel computation.
{S}even classifiers are treated: 3-nearest-neighbor, {G}aussian kernel,
linear support vector machine, polynomial support vector machine,
perceptron, regular histogram and linear discriminant analysis. {T}hree
{G}aussian-based models are considered: linear, nonlinear and bimodal.
{I}n addition, real patient data from a large breast-cancer study
is considered. {T}o mitigate the combinatorial search for finding
optimal feature sets, and to model the situation in which subsets
of genes are co-regulated and correlation is internal to these subsets,
we assume that the covariance matrix of the features is blocked,
with each block corresponding to a group of correlated features.
{A}ltogether there is a large number of error surfaces for the many
cases. {T}hese are provided in full on a companion web-site, which
is meant to serve as resource for those working with small-sample
classification.{A}vailability: {F}or the companion web-site, please
visit http://public.tgen.org/tamu/ofs/.},
doi = {10.1093/bioinformatics/bti171},
pdf = {../local/Hua2005Optimal.pdf},
file = {Hua2005Optimal.pdf:local/Hua2005Optimal.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://bioinformatics.oupjournals.org/cgi/content/abstract/bti171v1}
}
@article{Hua2001Novel,
author = {Hua, S. and Sun, Z.},
title = {A {N}ovel {M}ethod of {P}rotein {S}econdary {S}tructure {P}rediction
with {H}igh {S}egment {O}verlap {M}easure: {S}upport {V}ector {M}achine
{A}pproach},
journal = {J. {M}ol. {B}iol.},
year = {2001},
volume = {308},
pages = {397--407},
number = {2},
month = {April},
doi = {10.1006/jmbi.2001.4580},
pdf = {../local/Hua2001Novel.pdf},
file = {Hua2001Novel.pdf:local/Hua2001Novel.pdf:PDF},
keywords = {biosvm},
subject = {biokernel}
}
@article{Hua2001Support,
author = {Hua, S. and Sun, Z.},
title = {Support vector machine approach for protein subcellular localization
prediction},
journal = {Bioinformatics},
year = {2001},
volume = {17},
pages = {721-728},
number = {8},
abstract = {Motivation: {S}ubcellular localization is a key functional characteristic
of proteins. {A} fully automatic and reliable prediction system for
protein subcellular localization is needed, especially for the analysis
of large-scale genome sequences. {R}esults: {I}n this paper, {S}upport
{V}ector {M}achine has been introduced to predict the subcellular
localization of proteins from their amino acid compositions. {T}he
total prediction accuracies reach 91.4% for three subcellular locations
in prokaryotic organisms and 79.4% for four locations in eukaryotic
organisms. {P}redictions by our approach are robust to errors in
the protein {N}-terminal sequences. {T}his new approach provides
superior prediction performance compared with existing algorithms
based on amino acid composition and can be a complementary method
to other existing methods based on sorting signals. {A}vailability:
{A} web server implementing the prediction method is available at
http://www.bioinfo.tsinghua.edu.cn/{S}ub{L}oc/. {C}ontact: sunzhr@mail.tsinghua.edu.cn;
huasj00@mails.tsinghua.edu.cn {S}upplementary information: {S}upplementary
material is available at http://www.bioinfo.tsinghua.edu.cn/{S}ub{L}oc},
pdf = {../local/Hua2001Support.pdf},
file = {Hua2001Support.pdf:local/Hua2001Support.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://bioinformatics.oupjournals.org/cgi/content/abstract/17/8/721}
}
@inproceedings{Huan2004Accurate,
author = {Huan, J. and Wang, W. and Washington, A. and Prins, J. and Shah,
R. and Tropsha, A.},
title = {Accurate classification of protein structural families using coherent
subgraph analysis.},
booktitle = {Proceedings of the {P}acific {S}ymposium on {B}iocomputing 2002},
year = {2004},
pages = {411-422},
abstract = {Protein structural annotation and classification is an important problem
in bioinformatics. {W}e report on the development of an efficient
subgraph mining technique and its application to finding characteristic
substructural patterns within protein structural families. {I}n our
method, protein structures are represented by graphs where the nodes
are residues and the edges connect residues found within certain
distance from each other. {A}pplication of subgraph mining to proteins
is challenging for a number reasons: (1) protein graphs are large
and complex, (2) current protein databases are large and continue
to grow rapidly, and (3) only a small fraction of the frequent subgraphs
among the huge pool of all possible subgraphs could be significant
in the context of protein classification. {T}o address these challenges,
we have developed an information theoretic model called coherent
subgraph mining. {F}rom information theory, the entropy of a random
variable {X} measures the information content carried by {X} and
the {M}utual {I}nformation ({MI}) between two random variables {X}
and {Y} measures the correlation between {X} and {Y}. {W}e define
a subgraph {X} as coherent if it is strongly correlated with every
sufficiently large sub-subgraph {Y} embedded in it. {B}ased on the
{MI} metric, we have designed a search scheme that only reports coherent
subgraphs. {T}o determine the significance of coherent protein subgraphs,
we have conducted an experimental study in which all coherent subgraphs
were identified in several protein structural families annotated
in the {SCOP} database ({M}urzin et al, 1995). {T}he {S}upport {V}ector
{M}achine algorithm was used to classify proteins from different
families under the binary classification scheme. {W}e find that this
approach identifies spatial motifs unique to individual {SCOP} families
and affords excellent discrimination between families.},
pdf = {../local/Huan2004Accurate.pdf},
file = {Huan2004Accurate.pdf:local/Huan2004Accurate.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert}
}
@article{Huang2006Ligsite,
author = {Bingding Huang and Michael Schroeder},
title = {LIGSITEcsc: predicting ligand binding sites using the Connolly surface
and degree of conservation.},
journal = {BMC Struct Biol},
year = {2006},
volume = {6},
pages = {19},
abstract = {BACKGROUND: Identifying pockets on protein surfaces is of great importance
for many structure-based drug design applications and protein-ligand
docking algorithms. Over the last ten years, many geometric methods
for the prediction of ligand-binding sites have been developed. RESULTS:
We present LIGSITEcsc, an extension and implementation of the LIGSITE
algorithm. LIGSITEcsc is based on the notion of surface-solvent-surface
events and the degree of conservation of the involved surface residues.
We compare our algorithm to four other approaches, LIGSITE, CAST,
PASS, and SURFNET, and evaluate all on a dataset of 48 unbound/bound
structures and 210 bound-structures. LIGSITEcsc performs slightly
better than the other tools and achieves a success rate of 71\% and
75\%, respectively. CONCLUSION: The use of the Connolly surface leads
to slight improvements, the prediction re-ranking by conservation
to significant improvements of the binding site predictions. A web
server for LIGSITEcsc and its source code is available at scoppi.biotec.tu-dresden.de/pocket},
doi = {10.1186/1472-6807-6-19},
institution = {atics Group, Biotechnological Center, Technical University Dresden,
Germany. bingding.huang@biotec.tu-dresden.de},
keywords = {Algorithms; Binding Sites; Databases, Protein; Ligands; Models, Molecular;
Proteins, chemistry},
owner = {bricehoffmann},
pii = {1472-6807-6-19},
pmid = {16995956},
timestamp = {2009.02.13},
url = {http://dx.doi.org/10.1186/1472-6807-6-19}
}
@article{Bild2003,
author = {Huang, E. and Ishida, S. and Pittman, J. and Dressman, H. and Bild,
A. and Kloos, M. and D'Amico, M. and Pestell, R. G. and West, M.
and Nevins, J. R.},
title = {Gene expression phenotypic models that predict the activity of oncogenic
pathways},
journal = {Nat {G}enet},
year = {2003},
volume = {34},
pages = {226-30},
number = {2},
abstract = {High-density {DNA} microarrays measure expression of large numbers
of genes in one assay. {T}he ability to find underlying structure
in complex gene expression data sets and rigorously test association
of that structure with biological conditions is essential to developing
multi-faceted views of the gene activity that defines cellular phenotype.
{W}e sought to connect features of gene expression data with biological
hypotheses by integrating 'metagene' patterns from {DNA} microarray
experiments in the characterization and prediction of oncogenic phenotypes.
{W}e applied these techniques to the analysis of regulatory pathways
controlled by the genes {HRAS} ({H}arvey rat sarcoma viral oncogene
homolog), {MYC} (myelocytomatosis viral oncogene homolog) and {E}2{F}1,
{E}2{F}2 and {E}2{F}3 (encoding {E}2{F} transcription factors 1,
2 and 3, respectively). {T}he phenotypic models accurately predict
the activity of these pathways in the context of normal cell proliferation.
{M}oreover, the metagene models trained with gene expression patterns
evoked by ectopic production of {M}yc or {R}as proteins in primary
tissue culture cells properly predict the activity of in vivo tumor
models that result from deregulation of the {MYC} or {HRAS} pathways.
{W}e conclude that these gene expression phenotypes have the potential
to characterize the complex genetic alterations that typify the neoplastic
state, whether in vitro or in vivo, in a way that truly reflects
the complexity of the regulatory pathways that are affected.},
keywords = {Animals *Cell Cycle Proteins *DNA-Binding Proteins E2F Transcription
Factors E2F1 Transcription Factor E2F2 Transcription Factor E2F3
Transcription Factor Female *Gene Expression Gene Expression Profiling
Gene Expression Regulation, Neoplastic Genes, myc Genes, ras Mammary
Neoplasms, Experimental/genetics Mice Mice, Transgenic *Models, Genetic
Oligonucleotide Array Sequence Analysis *Oncogenes Phenotype Transcription
Factors/genetics}
}
@article{Huang2004novel,
author = {Jian-qiang Huang and Xiang-xian Chen and Le-yu Wang},
title = {A novel method for tracking pedestrians from real-time video.},
journal = {J {Z}hejiang {U}niv {S}ci},
year = {2004},
volume = {5},
pages = {99-105},
number = {1},
month = {Jan},
abstract = {This novel method of {P}edestrian {T}racking using {S}upport {V}ector
({PTSV}) proposed for a video surveillance instrument combines the
{S}upport {V}ector {M}achine ({SVM}) classifier into an optic-flow
based tracker. {T}he traditional method using optical flow tracks
objects by minimizing an intensity difference function between successive
frames, while {PTSV} tracks objects by maximizing the {SVM} classification
score. {A}s the {SVM} classifier for object and non-object is pre-trained,
there is need only to classify an image block as object or non-object
without having to compare the pixel region of the tracked object
in the previous frame. {T}o account for large motions between successive
frames we build pyramids from the support vectors and use a coarse-to-fine
scan in the classification stage. {T}o accelerate the training of
{SVM}, a {S}equential {M}inimal {O}ptimization {M}ethod ({SMO}) is
adopted. {T}he results of using a kernel-{PTSV} for pedestrian tracking
from real time video are shown at the end. {C}omparative experimental
results showed that {PTSV} improves the reliability of tracking compared
to that of traditional tracking method using optical flow.}
}
@article{Huang2005Support,
author = {Jing Huang and Feng Shi},
title = {Support vector machines for predicting apoptosis proteins types.},
journal = {Acta {B}iotheor.},
year = {2005},
volume = {53},
pages = {39-47},
number = {1},
abstract = {Apoptosis proteins have a central role in the development and homeostasis
of an organism. {T}hese proteins are very important for understanding
the mechanism of programmed cell death, and their function is related
to their types. {A}ccording to the classification scheme by {Z}hou
and {D}octor (2003), the apoptosis proteins are categorized into
the following four types: (1) cytoplasmic protein; (2) plasma membrane-bound
protein; (3) mitochondrial inner and outer proteins; (4) other proteins.
{A} powerful learning machine, the {S}upport {V}ector {M}achine,
is applied for predicting the type of a given apoptosis protein by
incorporating the sqrt-amino acid composition effect. {H}igh success
rates were obtained by the re-substitute test (98/98 = 100 \%) and
the jackknife test (89/98 = 90.8\%).},
doi = {10.1007/s10441-005-7002-5},
pdf = {../local/Huang2005Support.pdf},
file = {Huang2005Support.pdf:local/Huang2005Support.pdf:PDF},
keywords = {biosvm},
url = {http://dx.doi.org/10.1007/s10441-005-7002-5}
}
@article{Huang2010benefit,
author = {Huang, J. and Zhang, T.},
title = {The benefit of group sparsity},
journal = {Ann. Stat.},
year = {2010},
volume = {38},
pages = {1978--2004},
number = {4},
abstract = {This paper develops a theory for group Lasso using a concept called
strong group sparsity. Our result shows that group Lasso is superior
to standard Lasso for strongly group-sparse signals. This provides
a convincing theoretical justification for using group sparse regularization
when the underlying group structure is consistent with the data.
Moreover, the theory predicts some limitations of the group Lasso
formulation that are confirmed by simulation studies.},
doi = {10.1214/09-AOS778},
owner = {jp},
timestamp = {2011.04.21},
url = {http://dx.doi.org/10.1214/09-AOS778}
}
@inproceedings{Huang2009Learning,
author = {Huang, J. and Zhang, T. and Metaxas, D.},
title = {Learning with structured sparsity},
booktitle = {Proceedings of the 26th Annual International Conference on Machine
Learning},
year = {2009},
pages = {417--424},
organization = {ACM}
}
@article{Huang2004Boosting,
author = {Huang, K. and Murphy, R.F.},
title = {Boosting accuracy of automated classification of fluorescence microscope
images for location proteomics},
journal = {B{MC} {B}ioinformatics},
year = {2004},
volume = {5},
pages = {78},
number = {78},
abstract = {Background {D}etailed knowledge of the subcellular location of each
expressed protein is critical to a full understanding of its function.
{F}luorescence microscopy, in combination with methods for fluorescent
tagging, is the most suitable current method for proteome-wide determination
of subcellular location. {P}revious work has shown that neural network
classifiers can distinguish all major protein subcellular location
patterns in both 2{D} and 3{D} fluorescence microscope images. {B}uilding
on these results, we evaluate here new classifiers and features to
improve the recognition of protein subcellular location patterns
in both 2{D} and 3{D} fluorescence microscope images. {R}esults {W}e
report here a thorough comparison of the performance on this problem
of eight different state-of-the-art classification methods, including
neural networks, support vector machines with linear, polynomial,
radial basis, and exponential radial basis kernel functions, and
ensemble methods such as {A}da{B}oost, {B}agging, and {M}ixtures-of-{E}xperts.
{T}en-fold cross validation was used to evaluate each classifier
with various parameters on different {S}ubcellular {L}ocation {F}eature
sets representing both 2{D} and 3{D} fluorescence microscope images,
including new feature sets incorporating features derived from {G}abor
and {D}aubechies wavelet transforms. {A}fter optimal parameters were
chosen for each of the eight classifiers, optimal majority-voting
ensemble classifiers were formed for each feature set. {C}omparison
of results for each image for all eight classifiers permits estimation
of the lower bound classification error rate for each subcellular
pattern, which we interpret to reflect the fraction of cells whose
patterns are distorted by mitosis, cell death or acquisition errors.
{O}verall, we obtained statistically significant improvements in
classification accuracy over the best previously published results,
with the overall error rate being reduced by one-third to one-half
and with the average accuracy for single 2{D} images being higher
than 90% for the first time. {I}n particular, the classification
accuracy for the easily confused endomembrane compartments (endoplasmic
reticulum, {G}olgi, endosomes, lysosomes) was improved by 5?15%.
{W}e achieved further improvements when classification was conducted
on image sets rather than on individual cell images. {C}onclusions
{T}he availability of accurate, fast, automated classification systems
for protein location patterns in conjunction with high throughput
fluorescence microscope imaging techniques enables a new subfield
of proteomics, location proteomics. {T}he accuracy and sensitivity
of this approach represents an important alternative to low-resolution
assignments by curation or sequence-based prediction.},
doi = {10.1186/1471-2105-5-78},
pdf = {../local/Huang2004Boosting.pdf},
file = {Huang2004Boosting.pdf:local/Huang2004Boosting.pdf:PDF},
owner = {vert},
url = {http://www.biomedcentral.com/1471-2105/5/78}
}
@article{Huang2005CTKPred,
author = {Huang, N. and Chen, H. and Sun, Z.},
title = {C{TKP}red: an {SVM}-based method for the prediction and classification
of the cytokine superfamily.},
journal = {Protein {E}ng. {D}es. {S}el.},
year = {2005},
month = {Jun},
abstract = {Cell proliferation, differentiation and death are controlled by a
multitude of cell-cell signals and loss of this control has devastating
consequences. {P}rominent among these regulatory signals is the cytokine
superfamily, which has crucial functions in the development, differentiation
and regulation of immune cells. {I}n this study, a support vector
machine ({SVM})-based method was developed for predicting families
and subfamilies of cytokines using dipeptide composition. {T}he taxonomy
of the cytokine superfamily with which our method complies was described
in the {C}ytokine {F}amily c{DNA} {D}atabase (db{CFC}) and the dataset
used in this study for training and testing was obtained from the
db{CFC} and {S}tructural {C}lassification of {P}roteins ({SCOP}).
{T}he method classified cytokines and non-cytokines with an accuracy
of 92.5\% by 7-fold cross-validation. {T}he method is further able
to predict seven major classes of cytokine with an overall accuracy
of 94.7\%. {A} server for recognition and classification of cytokines
based on multi-class {SVM}s has been set up at http://bioinfo.tsinghua.edu.cn/~huangni/{CTKP}red/.},
doi = {10.1093/protein/gzi041},
pdf = {../local/Huang2005CTKPred.pdf},
file = {Huang2005CTKPred.pdf:local/Huang2005CTKPred.pdf:PDF},
keywords = {biosvm},
pii = {gzi041},
url = {http://dx.doi.org/10.1093/protein/gzi041}
}
@article{Huang2005Computation,
author = {Shao-Wei Huang and Jenn-Kang Hwang},
title = {Computation of conformational entropy from protein sequences using
the machine-learning method--application to the study of the relationship
between structural conservation and local structural stability.},
journal = {Proteins},
year = {2005},
volume = {59},
pages = {802-9},
number = {4},
month = {Jun},
abstract = {A complete protein sequence can usually determine a unique conformation;
however, the situation is different for shorter subsequences--some
of them are able to adopt unique conformations, independent of context;
while others assume diverse conformations in different contexts.
{T}he conformations of subsequences are determined by the interplay
between local and nonlocal interactions. {A} quantitative measure
of such structural conservation or variability will be useful in
the understanding of the sequence-structure relationship. {I}n this
report, we developed an approach using the support vector machine
method to compute the conformational variability directly from sequences,
which is referred to as the sequence structural entropy. {A}s a practical
application, we studied the relationship between sequence structural
entropy and the hydrogen exchange for a set of well-studied proteins.
{W}e found that the slowest exchange cores usually comprise amino
acids of the lowest sequence structural entropy. {O}ur results indicate
that structural conservation is closely related to the local structural
stability. {T}his relationship may have interesting implications
in the protein folding processes, and may be useful in the study
of the sequence-structure relationship.},
doi = {10.1002/prot.20462},
pdf = {../local/Huang2005Computation.pdf},
file = {Huang2005Computation.pdf:local/Huang2005Computation.pdf:PDF},
keywords = {biosvm},
url = {http://dx.doi.org/10.1002/prot.20462}
}
@article{Huang2005Gene,
author = {Huang, T. M. and Kecman, V.},
title = {Gene extraction for cancer diagnosis by support vector machines-{A}n
improvement.},
journal = {Artif. {I}ntell. {M}ed.},
year = {2005},
month = {Jul},
abstract = {O{BJECTIVE}:: {T}o improve the performance of gene extraction for
cancer diagnosis by recursive feature elimination with support vector
machines ({RFE}-{SVM}s): {A} cancer diagnosis by using the {DNA}
microarray data faces many challenges the most serious one being
the presence of thousands of genes and only several dozens (at the
best) of patient's samples. {T}hus, making any kind of classification
in high-dimensional spaces from a limited number of data is both
an extremely difficult and a prone to an error procedure. {T}he improved
{RFE}-{SVM}s is introduced and used here for an elimination of less
relevant genes and just for a reduction of the overall number of
genes used in a medical diagnostic. {METHODS}:: {T}he paper shows
why and how the, usually neglected, penalty parameter {C} and some
standard data preprocessing techniques (normalizing and scaling)
influence classification results and the gene selection of {RFE}-{SVM}s.
{T}he gene selected by {RFE}-{SVM}s is compared with eight other
gene selection algorithms implemented in the {R}ankgene software
to investigate whether there is any consensus among the algorithms,
so the scope of finding the right set of genes can be reduced. {RESULTS}::
{T}he improved {RFE}-{SVM}s is applied on the two benchmarking colon
and lymphoma cancer data sets with various {C} parameters and different
standard preprocessing techniques. {H}ere, decreasing {C} leads to
the smaller diagnosis error in comparisons to other known methods
applied to the benchmarking data sets. {W}ith an appropriate parameter
{C} and with a proper preprocessing procedure, the reduction in a
diagnosis error is as high as 36\%. {CONCLUSIONS}:: {T}he results
suggest that with a properly chosen parameter {C}, the extracted
genes and the constructed classifier will ensure less overfitting
of the training data leading to an increased accuracy in selecting
relevant genes. {F}inally, comparison in gene ranking obtained by
different algorithms shows that there is a significant consensus
among the various algorithms as to which set of genes is relevant.},
doi = {10.1016/j.artmed.2005.01.006},
pdf = {../local/Huang2005Gene.pdf},
file = {Huang2005Gene.pdf:local/Huang2005Gene.pdf:PDF},
keywords = {biosvm},
pii = {S0933-3657(05)00051-5},
url = {http://dx.doi.org/10.1016/j.artmed.2005.01.006}
}
@article{Huang2005Supporta,
author = {Yu-Len Huang and Dar-Ren Chen},
title = {Support vector machines in sonography: application to decision making
in the diagnosis of breast cancer.},
journal = {Clin {I}maging},
year = {2005},
volume = {29},
pages = {179-84},
number = {3},
abstract = {We evaluated a series of pathologically proven breast tumors using
the support vector machine ({SVM}) in the differential diagnosis
of solid breast tumors. {T}his study evaluated two ultrasonic image
databases, i.e., {DB}1 and {DB}2. {T}he {DB}1 contained 140 ultrasonic
images of solid breast nodules (52 malignant and 88 benign). {T}he
{DB}2 contained 250 ultrasonic images of solid breast nodules (35
malignant and 215 benign). {T}he physician-located regions of interest
({ROI}) of sonography and textual features were utilized to classify
breast tumors. {A}n {SVM} classifier using interpixel textual features
classified the tumor as benign or malignant. {T}he receiver operating
characteristic ({ROC}) area index for the proposed system on the
{DB}1 and the {DB}2 are 0.9695+/-0.0150 and 0.9552+/-0.0161, respectively.
{T}he proposed system differentiates solid breast nodules with a
relatively high accuracy and helps inexperienced operators avoid
misdiagnosis. {T}he main advantage in the proposed system is that
the training procedure of {SVM} was very fast and stable. {T}he training
and diagnosis procedure of the proposed system is almost 700 times
faster than that of multilayer perception neural networks ({MLP}s).
{W}ith the growth of the database, new ultrasonic images can be collected
and used as reference cases while performing diagnoses. {T}his study
reduces the training and diagnosis time dramatically.},
doi = {10.1016/j.clinimag.2004.08.002},
pii = {S0899-7071(04)00170-6},
url = {http://dx.doi.org/10.1016/j.clinimag.2004.08.002}
}
@article{Huber1964Robust,
author = {Huber, P. J.},
title = {Robust Estimation of a Location Parameter},
journal = {Ann. Math. Statist.},
year = {1964},
volume = {35},
pages = {73--101},
number = {1},
doi = {doi:10.1214/aoms/1177703732},
pdf = {../local/Huber1964Robust.pdf},
file = {Huber1964Robust.pdf:Huber1964Robust.pdf:PDF},
owner = {jp},
timestamp = {2011.07.23},
url = {http://dx.doi.org/doi:10.1214/aoms/1177703732}
}
@article{Hudis2007Trastuzumab,
author = {Hudis, C.A.},
title = {Trastuzumab--mechanism of action and use in clinical practice.},
journal = {N. Engl. J. Med.},
year = {2007},
volume = {357},
pages = {39--51},
number = {1},
month = {Jul},
doi = {10.1056/NEJMra043186},
pdf = {../local/Hudis2007Trastuzumab.pdf},
file = {Hudis2007Trastuzumab.pdf:Hudis2007Trastuzumab.pdf:PDF},
institution = {Breast Cancer Medicine Service, Solid Tumor Division, Department
of Medicine, Memorial Sloan-Kettering Cancer Center, New York, USA.
hudisc@mskcc.org},
keywords = {csbcbook},
owner = {jp},
pii = {357/1/39},
pmid = {17611206},
timestamp = {2009.10.09},
url = {http://dx.doi.org/10.1056/NEJMra043186}
}
@mastersthesis{Hue2004Semi-supervised,
author = {Hue, M.},
title = {Semi-supervised learning for protein structure prediction},
school = {Ecole des Mines de Paris},
year = {2004},
owner = {vert}
}
@inproceedings{Hue2010learning,
author = {Hue, M. and Vert, J-P.},
title = {On learning with kernels for unordered pairs},
booktitle = {Proceedings of the 27th International Conference on Machine Learning
(ICML-10), June 21-24, 2010, Haifa, Israel},
year = {2010},
editor = {F{\"u}rnkranz, J. and Joachims, T.},
pages = {463--470},
publisher = {Omnipress},
owner = {jp},
timestamp = {2010.10.14},
url = {http://www.icml2010.org/papers/520.pdf}
}
@article{Huebert2006Genome-wide,
author = {Dana J Huebert and Michael Kamal and Aisling O'Donovan and Bradley
E Bernstein},
title = {Genome-wide analysis of histone modifications by ChIP-on-chip.},
journal = {Methods},
year = {2006},
volume = {40},
pages = {365--369},
number = {4},
month = {Dec},
abstract = {Post-translational modifications to histone proteins regulate the
packaging of genomic DNA into chromatin, gene activity and other
functions of the genome. They are understood to play key roles in
embryonic development and disease pathogenesis. Recent advances in
technology have made it possible to analyze chromatin structure genome-wide
in mammalian cells. Global patterns of histone modifications can
be observed using a technique called ChIP-on-chip, which combines
the specificity of chromatin immunoprecipitation with the unbiased,
high-throughput capabilities of microarrays. The resulting maps provide
insight into the functions of, and relationships between, different
modifications. Here, we provide validated ChIP-on-chip methods for
analyzing histone modification patterns at genome-scale in mammalian
cells.},
doi = {10.1016/j.ymeth.2006.07.032},
institution = {Molecular Pathology Unit and Center for Cancer Research, Massachusetts
General Hospital, Charlestown, MA 02129, USA.},
keywords = {Animals; Chromatin Immunoprecipitation; Chromosomes, Mammalian; Genomics;
Histone Code; Histones; Oligonucleotide Array Sequence Analysis;
Protein Processing, Post-Translational},
owner = {phupe},
pii = {S1046-2023(06)00227-1},
pmid = {17101450},
timestamp = {2010.08.09},
url = {http://dx.doi.org/10.1016/j.ymeth.2006.07.032}
}
@article{Huesken2005Design,
author = {Huesken, D. and Lange, J. and Mickanin, C. and Weiler, J. and Asselbergs,
F. and Warner, J. and Meloon, B. and Engel, S. and Rosenberg, A.
and Cohen, D. and Labow, M. and Reinhardt, M. and Natt, F. and Hall,
J.},
title = {Design of a genome-wide si{RNA} library using an artificial neural
network.},
journal = {Nat. {B}iotechnol.},
year = {2005},
volume = {23},
pages = {995-1001},
number = {8},
month = {Aug},
abstract = {The largest gene knock-down experiments performed to date have used
multiple short interfering/short hairpin (si/sh){RNA}s per gene1,
2, 3. {T}o overcome this burden for design of a genome-wide si{RNA}
library, we used the {S}tuttgart {N}eural {N}et {S}imulator to train
algorithms on a data set of 2,182 randomly selected si{RNA}s targeted
to 34 m{RNA} species, assayed through a high-throughput fluorescent
reporter gene system. {T}he algorithm, ({BIOPRED}si), reliably predicted
activity of 249 si{RNA}s of an independent test set ({P}earson coefficient
r = 0.66) and si{RNA}s targeting endogenous genes at m{RNA} and protein
levels. {N}eural networks trained on a complementary 21-nucleotide
(nt) guide sequence were superior to those trained on a 19-nt sequence.
{BIOPRED}si was used in the design of a genome-wide si{RNA} collection
with two potent si{RNA}s per gene. {W}hen this collection of 50,000
si{RNA}s was used to identify genes involved in the cellular response
to hypoxia, two of the most potent hits were the key hypoxia transcription
factors {HIF}1{A} and {ARNT}.},
doi = {10.1038/nbt1118},
pdf = {../local/Huesken2005Design.pdf},
file = {Huesken2005Design.pdf:local/Huesken2005Design.pdf:PDF},
keywords = {sirna},
url = {http://dx.doi.org/10.1038/nbt1118}
}
@article{Hughey1996Hidden,
author = {Hughey, R. and Krogh, A.},
title = {Hidden {M}arkov models for sequence analysis: {E}xtension and analysis
of the basic method},
journal = {C{ABIOS}},
year = {1996},
volume = {12(2)},
pages = {95--107}
}
@article{Huh2003Global,
author = {Huh, W.-K. and Falvo, J. V. and Gerke, L. C. and Carroll, A. S. and
Howson, R. W. and Weissman, J. S. and O'Shea, E. K.},
title = {{G}lobal analysis of protein localization in budding yeast.},
journal = {Nature},
year = {2003},
volume = {425},
pages = {686--691},
number = {6959},
month = {Oct},
abstract = {A fundamental goal of cell biology is to define the functions of proteins
in the context of compartments that organize them in the cellular
environment. Here we describe the construction and analysis of a
collection of yeast strains expressing full-length, chromosomally
tagged green fluorescent protein fusion proteins. We classify these
proteins, representing 75\% of the yeast proteome, into 22 distinct
subcellular localization categories, and provide localization information
for 70\% of previously unlocalized proteins. Analysis of this high-resolution,
high-coverage localization data set in the context of transcriptional,
genetic, and protein-protein interaction data helps reveal the logic
of transcriptional co-regulation, and provides a comprehensive view
of interactions within and between organelles in eukaryotic cells.},
doi = {10.1038/nature02026},
pdf = {../local/Huh2003Global.pdf},
file = {Huh2003Global.pdf:Huh2003Global.pdf:PDF},
pii = {nature02026},
pmid = {14562083},
timestamp = {2007.02.01},
url = {http://dx.doi.org/10.1038/nature02026}
}
@article{Humphrey1996VMD,
author = {Humphrey, W. and Dalke, A. and Schulten, K.},
title = {{VMD}: visual molecular dynamics.},
journal = {J. Mol. Graph.},
year = {1996},
volume = {14},
pages = {33--8, 27-8},
number = {1},
month = {Feb},
abstract = {VMD is a molecular graphics program designed for the display and analysis
of molecular assemblies, in particular biopolymers such as proteins
and nucleic acids. VMD can simultaneously display any number of structures
using a wide variety of rendering styles and coloring methods. Molecules
are displayed as one or more "representations," in which each representation
embodies a particular rendering method and coloring scheme for a
selected subset of atoms. The atoms displayed in each representation
are chosen using an extensive atom selection syntax, which includes
Boolean operators and regular expressions. VMD provides a complete
graphical user interface for program control, as well as a text interface
using the Tcl embeddable parser to allow for complex scripts with
variable substitution, control loops, and function calls. Full session
logging is supported, which produces a VMD command script for later
playback. High-resolution raster images of displayed molecules may
be produced by generating input scripts for use by a number of photorealistic
image-rendering applications. VMD has also been expressly designed
with the ability to animate molecular dynamics (MD) simulation trajectories,
imported either from files or from a direct connection to a running
MD simulation. VMD is the visualization component of MDScope, a set
of tools for interactive problem solving in structural biology, which
also includes the parallel MD program NAMD, and the MDCOMM software
used to connect the visualization and simulation programs. VMD is
written in C++, using an object-oriented design; the program, including
source code and extensive documentation, is freely available via
anonymous ftp and through the World Wide Web.},
owner = {laurent},
pii = {0263785596000185},
pmid = {8744570},
timestamp = {2008.01.16}
}
@article{Huppi2005Defining,
author = {Huppi, K. and Martin, S. E. and Caplen, N. J.},
title = {Defining and assaying {RNA}i in mammalian cells.},
journal = {Mol. {C}ell},
year = {2005},
volume = {17},
pages = {1-10},
number = {1},
month = {Jan},
abstract = {The investigation of protein function through the inhibition of activity
has been critical to our understanding of many normal and abnormal
biological processes. {U}ntil recently, functional inhibition in
biological systems has been induced using a variety of approaches
including small molecule antagonists, antibodies, aptamers, ribozymes,
antisense oligonucleotides or transcripts, morpholinos, dominant-negative
mutants, and knockout transgenic animals. {A}lthough all of these
approaches have made substantial advances in our understanding of
the function of many proteins, a lack of specificity or restricted
applicability has limited their utility. {R}ecently, exploitation
of the naturally occurring posttranscriptional gene silencing mechanism
triggered by double-stranded {RNA} (ds{RNA}), termed {RNA} interference
({RNA}i), has gained much favor as an alternative means for analyzing
gene function. {A}spects of the basic biology of {RNA}i, its application
as a functional genomics tool, and its potential as a therapeutic
approach have been extensively reviewed ({H}annon and {R}ossi, 2004;
{M}eister and {T}uschl, 2004); however, there has been only limited
discussion as to how to design and validate an individual {RNA}i
effector molecule and how to interpret {RNA}i data overall, particularly
with reference to experimentation in mammalian cells. {T}his perspective
will aim to consider some of the issues encountered when conducting
and interpreting {RNA}i experiments in mammalian cells.},
doi = {10.1016/j.molcel.2004.12.017},
keywords = {sirna},
pii = {S1097276504008032},
url = {http://dx.doi.org/10.1016/j.molcel.2004.12.017}
}
@article{Hupe2004Analysis,
author = {Hup{\'e}, P. and Stransky, N. and Thiery, J.-P. and Radvanyi, F.
and Barillot, E.},
title = {Analysis of array {CGH} data: from signal ratio to gain and loss
of DNA regions},
journal = {Bioinformatics},
year = {2004},
volume = {20},
pages = {3413--3422},
number = {18},
month = {Dec},
abstract = {MOTIVATION: Genomic DNA regions are frequently lost or gained during
tumor progression. Array Comparative Genomic Hybridization (array
CGH) technology makes it possible to assess these changes in DNA
in cancers, by comparison with a normal reference. The identification
of systematically deleted or amplified genomic regions in a set of
tumors enables biologists to identify genes involved in cancer progression
because tumor suppressor genes are thought to be located in lost
genomic regions and oncogenes, in gained regions. Array CGH profiles
should also improve the classification of tumors. The achievement
of these goals requires a methodology for detecting the breakpoints
delimiting altered regions in genomic patterns and assigning a status
(normal, gained or lost) to each chromosomal region. RESULTS: We
have developed a methodology for the automatic detection of breakpoints
from array CGH profile, and the assignment of a status to each chromosomal
region. The breakpoint detection step is based on the Adaptive Weights
Smoothing (AWS) procedure and provides highly convincing results:
our algorithm detects 97, 100 and 94\% of breakpoints in simulated
data, karyotyping results and manually analyzed profiles, respectively.
The percentage of correctly assigned statuses ranges from 98.9 to
99.8\% for simulated data and is 100\% for karyotyping results. Our
algorithm also outperforms other solutions on a public reference
dataset. AVAILABILITY: The R package GLAD (Gain and Loss Analysis
of DNA) is available upon request.},
doi = {10.1093/bioinformatics/bth418},
pdf = {../local/Hupe2004Analysis.pdf},
file = {Hupe2004Analysis.pdf:Hupe2004Analysis.pdf:PDF},
institution = {>},
keywords = {cgh},
owner = {jp},
pii = {bth418},
pmid = {15381628},
timestamp = {2008.12.09},
url = {http://dx.doi.org/10.1093/bioinformatics/bth418}
}
@article{Hutter2004Prediction,
author = {Hutter, B. and Schaab, C. and Albrecht, S. and Borgmann, M. and Brunner,
N. A. and Freiberg, C. and Ziegelbauer, K. and Rock, C. O. and Ivanov,
I. and Loferer, H.},
title = {Prediction of {M}echanisms of {A}ction of {A}ntibacterial {C}ompounds
by {G}ene {E}xpression {P}rofiling},
journal = {Antimicrob. {A}gents {C}hemother.},
year = {2004},
volume = {48},
pages = {2838-2844},
number = {8},
month = {Aug},
abstract = {We have generated a database of expression profiles carrying the transcriptional
responses of the model organism {B}acillus subtilis following treatment
with 37 well-characterized antibacterial compounds of different classes.
{T}he database was used to build a predictor for the assignment of
the mechanisms of action ({M}o{A}s) of antibacterial compounds by
the use of support vector machines. {T}his predictor was able to
correctly classify the {M}o{A} class for most compounds tested. {F}urthermore,
we provide evidence that the in vivo {M}o{A} of hexachlorophene does
not match the {M}o{A} predicted from in vitro data, a situation frequently
faced in drug discovery. {A} database of this kind may facilitate
the prioritization of novel antibacterial entities in drug discovery
programs. {P}otential applications and limitations are discussed.},
doi = {10.1128/AAC.48.8.2838-2844.2004},
eprint = {http://aac.asm.org/cgi/reprint/48/8/2838.pdf},
pdf = {../local/Hutter2004Prediction.pdf},
file = {Hutter2004Prediction.pdf:local/Hutter2004Prediction.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://dx.doi.org/10.1128/AAC.48.8.2838-2844.2004}
}
@article{Huynh-Thu2010Inferring,
author = {Huynh-Thu, V. A. and Irrthum, A. and Wehenkel, L. and Geurts, P.},
title = {Inferring regulatory networks from expression data using tree-based
methods},
journal = {PLoS One},
year = {2010},
volume = {5},
pages = {e12776},
number = {9},
abstract = {One of the pressing open problems of computational systems biology
is the elucidation of the topology of genetic regulatory networks
(GRNs) using high throughput genomic data, in particular microarray
gene expression data. The Dialogue for Reverse Engineering Assessments
and Methods (DREAM) challenge aims to evaluate the success of GRN
inference algorithms on benchmarks of simulated data. In this article,
we present GENIE3, a new algorithm for the inference of GRNs that
was best performer in the DREAM4 In Silico Multifactorial challenge.
GENIE3 decomposes the prediction of a regulatory network between
p genes into p different regression problems. In each of the regression
problems, the expression pattern of one of the genes (target gene)
is predicted from the expression patterns of all the other genes
(input genes), using tree-based ensemble methods Random Forests or
Extra-Trees. The importance of an input gene in the prediction of
the target gene expression pattern is taken as an indication of a
putative regulatory link. Putative regulatory links are then aggregated
over all genes to provide a ranking of interactions from which the
whole network is reconstructed. In addition to performing well on
the DREAM4 In Silico Multifactorial challenge simulated data, we
show that GENIE3 compares favorably with existing algorithms to decipher
the genetic regulatory network of Escherichia coli. It doesn't make
any assumption about the nature of gene regulation, can deal with
combinatorial and non-linear interactions, produces directed GRNs,
and is fast and scalable. In conclusion, we propose a new algorithm
for GRN inference that performs well on both synthetic and real gene
expression data. The algorithm, based on feature selection with tree-based
ensemble methods, is simple and generic, making it adaptable to other
types of genomic data and interactions.},
doi = {10.1371/journal.pone.0012776},
pdf = {../local/Huynh-Thu2010Inferring.pdf},
file = {Huynh-Thu2010Inferring.pdf:Huynh-Thu2010Inferring.pdf:PDF},
institution = {Department of Electrical Engineering and Computer Science, Systems
and Modeling, University of Liège, Liège, Belgium. vahuynh@ulg.ac.be},
language = {eng},
medline-pst = {epublish},
owner = {jp},
pmid = {20927193},
timestamp = {2010.12.16},
url = {http://dx.doi.org/10.1371/journal.pone.0012776}
}
@inproceedings{Hwang2010Heterogeneous,
author = {Hwang, T. and Kuang, R.},
title = {A Heterogeneous Label Propagation Algorithm for Disease Gene Discovery},
booktitle = {Proceedings of the SIAM International Conference on Data Mining,
SDM 2010, April 29 - May 1, 2010, Columbus, Ohio, USA},
year = {2010},
pages = {583--594},
bibsource = {DBLP, http://dblp.uni-trier.de},
ee = {http://www.siam.org/proceedings/datamining/2010/dm10_051_hwangt.pdf},
owner = {mordelet},
timestamp = {2010.10.01}
}
@article{Hyman2002Impact,
author = {Hyman, Elizabeth and Kauraniemi, Päivikki and Hautaniemi, Sampsa
and Wolf, Maija and Mousses, Spyro and Rozenblum, Ester and Ringnér,
Markus and Sauter, Guido and Monni, Outi and Elkahloun, Abdel and
Kallioniemi, Olli-P. and Kallioniemi, Anne},
title = {Impact of DNA amplification on gene expression patterns in breast
cancer.},
journal = {Cancer Res},
year = {2002},
volume = {62},
pages = {6240--6245},
number = {21},
month = {Nov},
abstract = {Genetic changes underlie tumor progression and may lead to cancer-specific
expression of critical genes. Over 1100 publications have described
the use of comparative genomic hybridization (CGH) to analyze the
pattern of copy number alterations in cancer, but very few of the
genes affected are known. Here, we performed high-resolution CGH
analysis on cDNA microarrays in breast cancer and directly compared
copy number and mRNA expression levels of 13,824 genes to quantitate
the impact of genomic changes on gene expression. We identified and
mapped the boundaries of 24 independent amplicons, ranging in size
from 0.2 to 12 Mb. Throughout the genome, both high- and low-level
copy number changes had a substantial impact on gene expression,
with 44\% of the highly amplified genes showing overexpression and
10.5\% of the highly overexpressed genes being amplified. Statistical
analysis with random permutation tests identified 270 genes whose
expression levels across 14 samples were systematically attributable
to gene amplification. These included most previously described amplified
genes in breast cancer and many novel targets for genomic alterations,
including the HOXB7 gene, the presence of which in a novel amplicon
at 17q21.3 was validated in 10.2\% of primary breast cancers and
associated with poor patient prognosis. In conclusion, CGH on cDNA
microarrays revealed hundreds of novel genes whose overexpression
is attributable to gene amplification. These genes may provide insights
to the clonal evolution and progression of breast cancer and highlight
promising therapeutic targets.},
institution = {Howard Hughes Medical Institute-NIH Research Scholar, Bethesda, Maryland
20892, USA.},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pmid = {12414653},
timestamp = {2012.02.29}
}
@article{Iafrate2004Detection,
author = {A. John Iafrate and Lars Feuk and Miguel N Rivera and Marc L Listewnik
and Patricia K Donahoe and Ying Qi and Stephen W Scherer and Charles
Lee},
title = {Detection of large-scale variation in the human genome},
journal = {Nat. Genet.},
year = {2004},
volume = {36},
pages = {949--951},
number = {9},
month = {Sep},
abstract = {We identified 255 loci across the human genome that contain genomic
imbalances among unrelated individuals. Twenty-four variants are
present in > 10\% of the individuals that we examined. Half of these
regions overlap with genes, and many coincide with segmental duplications
or gaps in the human genome assembly. This previously unappreciated
heterogeneity may underlie certain human phenotypic variation and
susceptibility to disease and argues for a more dynamic human genome
structure.},
doi = {10.1038/ng1416},
pdf = {../local/Iafrate2004Detection.pdf},
file = {Iafrate2004Detection.pdf:Iafrate2004Detection.pdf:PDF},
institution = {Department of Pathology, Brigham and Women's Hospital, 20 Shattuck
St., Thorn 6-28, Boston, Massachusetts 02115, USA.},
keywords = {cgh, csbcbook, csbcbook-ch2},
owner = {jp},
pii = {ng1416},
pmid = {9},
timestamp = {2009.02.08},
url = {http://dx.doi.org/10.1038/ng1416}
}
@article{Ideker2002Discovering,
author = {Ideker, T. and Ozier, O. and Schwikowski, B. and Siegel, A. F.},
title = {Discovering regulatory and signalling circuits in molecular interaction
networks.},
journal = {Bioinformatics},
year = {2002},
volume = {18 Suppl 1},
pages = {S233--S240},
abstract = {MOTIVATION: In model organisms such as yeast, large databases of protein-protein
and protein-DNA interactions have become an extremely important resource
for the study of protein function, evolution, and gene regulatory
dynamics. In this paper we demonstrate that by integrating these
interactions with widely-available mRNA expression data, it is possible
to generate concrete hypotheses for the underlying mechanisms governing
the observed changes in gene expression. To perform this integration
systematically and at large scale, we introduce an approach for screening
a molecular interaction network to identify active subnetworks, i.e.,
connected regions of the network that show significant changes in
expression over particular subsets of conditions. The method we present
here combines a rigorous statistical measure for scoring subnetworks
with a search algorithm for identifying subnetworks with high score.
RESULTS: We evaluated our procedure on a small network of 332 genes
and 362 interactions and a large network of 4160 genes containing
all 7462 protein-protein and protein-DNA interactions in the yeast
public databases. In the case of the small network, we identified
five significant subnetworks that covered 41 out of 77 (53\%) of
all significant changes in expression. Both network analyses returned
several top-scoring subnetworks with good correspondence to known
regulatory mechanisms in the literature. These results demonstrate
how large-scale genomic approaches may be used to uncover signalling
and regulatory pathways in a systematic, integrative fashion.},
pdf = {../local/Ideker2002Discovering.pdf},
file = {Ideker2002Discovering.pdf:Ideker2002Discovering.pdf:PDF},
institution = {Whitehead Institute for Biomedical Research, Cambridge, MA 02142,
USA Institute for Systems Biology, Seattle, WA 98103, USA. trey@wi.mit.edu},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pmid = {12169552},
timestamp = {2011.09.24}
}
@article{Ideker2008Protein,
author = {Ideker, T. and Sharan, R.},
title = {Protein networks in disease.},
journal = {Genome Res},
year = {2008},
volume = {18},
pages = {644--652},
number = {4},
month = {Apr},
abstract = {During a decade of proof-of-principle analysis in model organisms,
protein networks have been used to further the study of molecular
evolution, to gain insight into the robustness of cells to perturbation,
and for assignment of new protein functions. Following these analyses,
and with the recent rise of protein interaction measurements in mammals,
protein networks are increasingly serving as tools to unravel the
molecular basis of disease. We review promising applications of protein
networks to disease in four major areas: identifying new disease
genes; the study of their network properties; identifying disease-related
subnetworks; and network-based disease classification. Applications
in infectious disease, personalized medicine, and pharmacology are
also forthcoming as the available protein network information improves
in quality and coverage.},
doi = {10.1101/gr.071852.107},
pdf = {../local/Ideker2008Protein.pdf},
file = {Ideker2008Protein.pdf:Ideker2008Protein.pdf:PDF},
institution = {Department of Bioengineering, University of California at San Diego,
La Jolla, California 92093, USA.},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {18/4/644},
pmid = {18381899},
timestamp = {2012.03.09},
url = {http://dx.doi.org/10.1101/gr.071852.107}
}
@article{Ifantis2003nonlinear,
author = {A. Ifantis and S. Papadimitriou},
title = {The nonlinear predictability of the electrotelluric field variations
data analyzed with support vector machines as an earthquake precursor.},
journal = {Int {J} {N}eural {S}yst},
year = {2003},
volume = {13},
pages = {315-32},
number = {5},
month = {Oct},
abstract = {This work investigates the nonlinear predictability of the {E}lectro
{T}elluric {F}ield ({ETF}) variations data in order to develop new
intelligent tools for the difficult task of earthquake prediction.
{S}upport {V}ector {M}achines trained on a signal window have been
used to predict the next sample. {W}e observe a significant increase
at this short-term unpredictability of the {ETF} signal at about
two weeks time period before the major earthquakes that took place
in regions near the recording devices. {T}he unpredictability increase
can be attributed to a quick time variation of the dynamics that
produce the {ETF} signal due to the earthquake generation process.
{T}hus, this increase can be taken into advantage for signaling for
an increased possibility of a large earthquake within the next few
days in the neighboring region of the recording station.},
keywords = {Air Pollutants, Aircraft, Algorithms, Artificial Intelligence, Automated,
Base Composition, Comparative Study, Computational Biology, Computer
Simulation, Computer-Assisted, Computing Methodologies, Cytosine,
Data Interpretation, Databases, Enhancer Elements (Genetics), Environmental
Monitoring, Ethanol, Exons, Fourier Transform Infrared, Genetic,
Guanine, Humans, Image Interpretation, Natural Disasters, Non-P.H.S.,
Non-U.S. Gov't, Nonlinear Dynamics, Online Systems, P.H.S., Pattern
Recognition, Photography, Probability, Pyrimidines, RNA Precursors,
RNA Splice Sites, RNA Splicing, Radiation, Reproducibility of Results,
Research Support, Sensitivity and Specificity, Signal Processing,
Spectroscopy, Statistical, Subtraction Technique, Thermodynamics,
Time Factors, U.S. Gov't, Untranslated Regions, Video Recording,
Walking, 14652873},
pii = {S0129065703001674}
}
@article{Iizuka2003Oligonucleotide,
author = {Norio Iizuka and Masaaki Oka and Hisafumi Yamada-Okabe and Minekatsu
Nishida and Yoshitaka Maeda and Naohide Mori and Takashi Takao and
Takao Tamesa and Akira Tangoku and Hisahiro Tabuchi and Kenji Hamada
and Hironobu Nakayama and Hideo Ishitsuka and Takanobu Miyamoto and
Akira Hirabayashi and Shunji Uchimura and Yoshihiko Hamamoto},
title = {Oligonucleotide microarray for prediction of early intrahepatic recurrence
of hepatocellular carcinoma after curative resection.},
journal = {Lancet},
year = {2003},
volume = {361},
pages = {923-9},
number = {9361},
month = {Mar},
abstract = {B{ACKGROUND}: {H}epatocellular carcinoma has a poor prognosis because
of the high intrahepatic recurrence rate. {T}here are technological
limitations to traditional methods such as {TNM} staging for accurate
prediction of recurrence, suggesting that new techniques are needed.
{METHODS}: {W}e investigated m{RNA} expression profiles in tissue
specimens from a training set, comprising 33 patients with hepatocellular
carcinoma, with high-density oligonucleotide microarrays representing
about 6000 genes. {W}e used this training set in a supervised learning
manner to construct a predictive system, consisting of 12 genes,
with the {F}isher linear classifier. {W}e then compared the predictive
performance of our system with that of a predictive system with a
support vector machine ({SVM}-based system) on a blinded set of samples
from 27 newly enrolled patients. {FINDINGS}: {E}arly intrahepatic
recurrence within 1 year after curative surgery occurred in 12 (36\%)
and eight (30\%) patients in the training and blinded sets, respectively.
{O}ur system correctly predicted early intrahepatic recurrence or
non-recurrence in 25 (93\%) of 27 samples in the blinded set and
had a positive predictive value of 88\% and a negative predictive
value of 95\%. {B}y contrast, the {SVM}-based system predicted early
intrahepatic recurrence or non-recurrence correctly in only 16 (60\%)
individuals in the blinded set, and the result yielded a positive
predictive value of only 38\% and a negative predictive value of
79\%. {INTERPRETATION}: {O}ur system predicted early intrahepatic
recurrence or non-recurrence for patients with hepatocellular carcinoma
much more accurately than the {SVM}-based system, suggesting that
our system could serve as a new method for characterising the metastatic
potential of hepatocellular carcinoma.},
doi = {http://dx.doi.org/10.1016/S0140-6736(03)12775-4},
pdf = {../local/Iizuka2003Oligonucleotide.pdf},
file = {Iizuka2003Oligonucleotide.pdf:local/Iizuka2003Oligonucleotide.pdf:PDF},
pii = {S0140673603127754},
url = {http://10.1016/S0140-6736(03)12775-4}
}
@article{Ikeda2005asymptotic,
author = {Kazushi Ikeda and Tsutomu Aoishi},
title = {An asymptotic statistical analysis of support vector machines with
soft margins.},
journal = {Neural {N}etw},
year = {2005},
volume = {18},
pages = {251-9},
number = {3},
month = {Apr},
abstract = {The generalization properties of support vector machines ({SVM}s)
are examined. {F}rom a geometrical point of view, the estimated parameter
of an {SVM} is the one nearest the origin in the convex hull formed
with given examples. {S}ince introducing soft margins is equivalent
to reducing the convex hull of the examples, an {SVM} with soft margins
has a different learning curve from the original. {I}n this paper
we derive the asymptotic average generalization error of {SVM}s with
soft margins in simple cases, that is, only when the dimension of
inputs is one, and quantitatively show that soft margins increase
the generalization error.},
doi = {10.1016/j.neunet.2004.11.008},
pdf = {../local/Ikeda2005asymptotic.pdf},
file = {Ikeda2005asymptotic.pdf:local/Ikeda2005asymptotic.pdf:PDF},
keywords = {Apoptosis, Gene Expression Profiling, Humans, Neoplasms, Non-U.S.
Gov't, Oligonucleotide Array Sequence Analysis, Polymerase Chain
Reaction, Proteins, Research Support, Subcellular Fractions, Unknown
Primary, 15896573},
pii = {S0893-6080(05)00021-3},
url = {http://dx.doi.org/10.1016/j.neunet.2004.11.008}
}
@article{Ikeda2005[Tongue,
author = {Naoya Ikeda and Takashi Uozumi},
title = {[{T}ongue diagnosis support system]},
journal = {Hokkaido {I}gaku {Z}asshi},
year = {2005},
volume = {80},
pages = {269-77},
number = {3},
month = {May},
abstract = {Tongue diagnosis is one of the most important diagnostic methods in
{O}riental {M}edical {S}cience ({OMS}). {T}his diagnosis is painless
and non-invasive method. {H}owever, it is not easy to cultivate skillful
doctors. {A}s one of the reasons, definition of tongue color is rather
subjective and sensuous measure and color isn't related to quantitative
physical value. {I}t is, therefore, necessary to associate tongue
color with physical numerical value. {T}here are two problems to
overcome the issue. 1) {I}t is necessary for diagnosis to extract
a region for diagnosis from entire picture because a tongue picture
consists of two regions, a tongue and a background. 2) {A}ssociate
tongue color with physical numerical value. {F}or extracting tongue
region, we used {P}rogressive {L}ive{W}ire method that is an {A}ctive
{C}ontour {M}odel. {A}nd, for associating tongue color with physical
measurement, we propose a hierarchical method. {W}e use static rule
and support vector machine for clustering colors. {T}he performance
of developed system is improved compared with an early developed
one. {I}n addition, the developed system did not make a critical
incorrect discernment that causes incorrect choice about inspection
in the layer of rule base. {I}n this research average color appraisal
is done from the region of 37 points. {B}ut, color judgment in the
literature with the judgment by the eye of the human, has always
done average judgment with not to limit, there is also a possibility
some weight attaching being done. {T}herefore, from either one enabling
the mass data and the comparison with the group of specialists is
necessary as an appraisal.},
keywords = {Color, Computer-Assisted, Diagnosis, English Abstract, Expert Systems,
Humans, Tongue, 15960161}
}
@article{Imoto2002Estimation,
author = {Imoto, S. and Goto, T. and Miyano, S.},
title = {Estimation of genetic networks and functional structures between
genes by using {B}ayesian networks and nonparametric regression.},
journal = {Pac. {S}ymp. {B}iocomput.},
year = {2002},
pages = {175--186},
abstract = {We propose a new method for constructing genetic network from gene
expression data by using {B}ayesian networks. {W}e use nonparametric
regression for capturing nonlinear relationships between genes and
derive a new criterion for choosing the network in general situations.
{I}n a theoretical sense, our proposed theory and methodology include
previous methods based on {B}ayes approach. {W}e applied the proposed
method to the {S}. cerevisiae cell cycle data and showed the effectiveness
of our method by comparing with previous methods.},
pdf = {../local/Imoto2002Estimation.pdf},
file = {Imoto2002Estimation.pdf:local/Imoto2002Estimation.pdf:PDF},
keywords = {biogm},
owner = {vert},
pmid = {11928473},
timestamp = {2006.02.16},
url = {http://helix-web.stanford.edu/psb02/imoto.pdf}
}
@article{Imoto2003Bayesian,
author = {Imoto, S. and Kim, S. and Goto, T. and Miyano, S. and Aburatani,
S. and Tashiro, K. and Kuhara, S.},
title = {Bayesian network and nonparametric heteroscedastic regression for
nonlinear modeling of genetic network.},
journal = {J. {B}ioinform. {C}omput. {B}iol.},
year = {2003},
volume = {1},
pages = {231--252},
number = {2},
month = {Jul},
abstract = {We propose a new statistical method for constructing a genetic network
from microarray gene expression data by using a {B}ayesian network.
{A}n essential point of {B}ayesian network construction is the estimation
of the conditional distribution of each random variable. {W}e consider
fitting nonparametric regression models with heterogeneous error
variances to the microarray gene expression data to capture the nonlinear
structures between genes. {S}electing the optimal graph, which gives
the best representation of the system among genes, is still a problem
to be solved. {W}e theoretically derive a new graph selection criterion
from {B}ayes approach in general situations. {T}he proposed method
includes previous methods based on {B}ayesian networks. {W}e demonstrate
the effectiveness of the proposed method through the analysis of
{S}accharomyces cerevisiae gene expression data newly obtained by
disrupting 100 genes.},
doi = {10.1142/S0219720003000071},
pdf = {../local/Imoto2003Bayesian.pdf},
file = {Imoto2003Bayesian.pdf:local/Imoto2003Bayesian.pdf:PDF},
keywords = {biogm},
owner = {vert},
pii = {S0219720003000071},
pmid = {15290771},
timestamp = {2006.02.16},
url = {http://dx.doi.org/10.1142/S0219720003000071}
}
@article{Imoto2002Bayesian,
author = {Imoto, S. and Sunyong, K. and Goto, T. and Aburatani, S. and Tashiro,
K. and Kuhara, S. and Miyano, S.},
title = {Bayesian network and nonparametric heteroscedastic regression for
nonlinear modeling of genetic network.},
journal = {Proc. {IEEE} {C}omput. {S}oc. {B}ioinform. {C}onf.},
year = {2002},
volume = {1},
pages = {219--227},
abstract = {We propose a new statistical method for constructing genetic network
from microarray gene expression data by using a {B}ayesian network.
{A}n essential point of {B}ayesian network construction is in the
estimation of the conditional distribution of each random variable.
{W}e consider fitting nonparametric regression models with heterogeneous
error variances to the microarray gene expression data to capture
the nonlinear structures between genes. {A} problem still remains
to be solved in selecting an optimal graph, which gives the best
representation of the system among genes. {W}e theoretically derive
a new graph selection criterion from {B}ayes approach in general
situations. {T}he proposed method includes previous methods based
on {B}ayesian networks. {W}e demonstrate the effectiveness of the
proposed method through the analysis of {S}accharomyces cerevisiae
gene expression data newly obtained by disrupting 100 genes.},
doi = {10.1109/CSB.2002.1039344},
pdf = {../local/Imoto2002Bayesian.pdf},
file = {Imoto2002Bayesian.pdf:local/Imoto2002Bayesian.pdf:PDF},
keywords = {biogm},
owner = {vert},
pmid = {15838138},
timestamp = {2006.02.16},
url = {http://dx.doi.org/10.1109/CSB.2002.1039344}
}
@article{Inokuchi2003Complete,
author = {Inokuchi, A. and Washio, T. and Motoda, H.},
title = {Complete mining of frequent patterns from graphs: mining graph data},
journal = {Mach. Learn.},
year = {2003},
volume = {50},
pages = {321-354},
number = {3},
timestamp = {2006.08.03}
}
@article{Consortium2003International,
author = {{International HapMap Consortium}},
title = {The International HapMap Project.},
journal = {Nature},
year = {2003},
volume = {426},
pages = {789--796},
number = {6968},
month = {Dec},
abstract = {The goal of the International HapMap Project is to determine the common
patterns of DNA sequence variation in the human genome and to make
this information freely available in the public domain. An international
consortium is developing a map of these patterns across the genome
by determining the genotypes of one million or more sequence variants,
their frequencies and the degree of association between them, in
DNA samples from populations with ancestry from parts of Africa,
Asia and Europe. The HapMap will allow the discovery of sequence
variants that affect common disease, will facilitate development
of diagnostic tools, and will enhance our ability to choose targets
for therapeutic intervention.},
keywords = {Base Sequence; Continental Population Groups, genetics; DNA, genetics;
Gene Frequency; Genetic Variation, genetics; Genome, Human; Genomics,
methods; Haplotypes, genetics; Humans; International Cooperation;
Polymorphism, Single Nucleotide, genetics; Public Sector},
language = {eng},
medline-pst = {ppublish},
owner = {philippe},
pmid = {14685227},
timestamp = {2010.08.01}
}
@article{Consortium2001Physical,
author = {{International Human Genome Mapping Consortium}},
title = {A physical map of the human genome},
journal = {Nature},
year = {2001},
volume = {409},
owner = {philippe},
timestamp = {2010.07.28}
}
@article{Consortium2004Finishing,
author = {{International Human Genome Sequencing Consortium}},
title = {Finishing the euchromatic sequence of the human genome},
journal = {Nature},
year = {2004},
volume = {431},
owner = {philippe},
timestamp = {2010.07.28}
}
@article{Consortium2001Initial,
author = {{International Human Genome Sequencing Consortium}},
title = {Initial sequencing and analysis of the human genome},
journal = {Nature},
year = {2001},
volume = {409},
pages = {860-921},
number = {6822},
month = {Feb},
abstract = {The human genome holds an extraordinary trove of information about
human development, physiology, medicine and evolution. {H}ere we
report the results of an international collaboration to produce and
make freely available a draft sequence of the human genome. {W}e
also present an initial analysis of the data, describing some of
the insights that can be gleaned from the sequence.},
doi = {10.1038/35057062},
pdf = {../local/Consortium2001Initial.pdf},
file = {Consortium2001Initial.pdf:local/Consortium2001Initial.pdf:PDF},
keywords = {genomics bio},
owner = {vert},
url = {http://dx.doi.org/10.1038/35057062 }
}
@article{Ioannidis2005most,
author = {Ioannidis, J.P.A.},
title = {Why most published research findings are false},
journal = {PLoS medicine},
year = {2005},
volume = {2},
pages = {e124},
number = {8},
publisher = {Public Library of Science}
}
@article{Ioannidis2005Microarrays,
author = {Ioannidis, J. P. A.},
title = {Microarrays and molecular research: noise discovery?},
journal = {Lancet},
year = {2005},
volume = {365},
pages = {454},
number = {9458},
pdf = {../local/Ioannidis2005Microarrays.pdf},
file = {Ioannidis2005Microarrays.pdf:Ioannidis2005Microarrays.pdf:PDF},
keywords = {microarray},
owner = {jp},
timestamp = {2011.01.12}
}
@article{Ioannidis2009Repeatability,
author = {John P A Ioannidis and David B Allison and Catherine A Ball and Issa
Coulibaly and Xiangqin Cui and Aedín C Culhane and Mario Falchi and
Cesare Furlanello and Laurence Game and Giuseppe Jurman and Jon Mangion
and Tapan Mehta and Michael Nitzberg and Grier P Page and Enrico
Petretto and Vera van Noort},
title = {Repeatability of published microarray gene expression analyses.},
journal = {Nat Genet},
year = {2009},
volume = {41},
pages = {149--155},
number = {2},
month = {Feb},
abstract = {Given the complexity of microarray-based gene expression studies,
guidelines encourage transparent design and public data availability.
Several journals require public data deposition and several public
databases exist. However, not all data are publicly available, and
even when available, it is unknown whether the published results
are reproducible by independent scientists. Here we evaluated the
replication of data analyses in 18 articles on microarray-based gene
expression profiling published in Nature Genetics in 2005-2006. One
table or figure from each article was independently evaluated by
two teams of analysts. We reproduced two analyses in principle and
six partially or with some discrepancies; ten could not be reproduced.
The main reason for failure to reproduce was data unavailability,
and discrepancies were mostly due to incomplete data annotation or
specification of data processing and analysis. Repeatability of published
microarray studies is apparently limited. More strict publication
rules enforcing public data availability and explicit description
of data processing and analysis should be considered.},
doi = {10.1038/ng.295},
institution = {Clinical and Molecular Epidemiology Unit, Department of Hygiene and
Epidemiology, University of Ioannina School of Medicine, Ioannina
45110, Greece. jioannid@cc.uoi.gr},
keywords = {Animals; Data Interpretation, Statistical; Databases, Genetic; Gene
Expression Profiling, standards; Genome-Wide Association Study, standards;
Humans; Oligonucleotide Array Sequence Analysis, standards; Peer
Review, Research; Publications, standards; Reproducibility of Results},
language = {eng},
medline-pst = {ppublish},
owner = {phupe},
pii = {ng.295},
pmid = {19174838},
timestamp = {2011.04.08},
url = {http://dx.doi.org/10.1038/ng.295}
}
@article{Irizarry2003Exploration,
author = {Irizarry, R. A. and Hobbs, B. and Collin, F. and Beazer-Barclay,
Y. D. and Antonellis, K. J. and Scherf, U. and Speed, T. P.},
title = {Exploration, normalization, and summaries of high density oligonucleotide
array probe level datas},
journal = {Biostatistics},
year = {2003},
volume = {4},
pages = {249--264},
number = {2},
month = {Apr},
abstract = {In this paper we report exploratory analyses of high-density oligonucleotide
array data from the Affymetrix GeneChip system with the objective
of improving upon currently used measures of gene expression. Our
analyses make use of three data sets: a small experimental study
consisting of five MGU74A mouse GeneChip arrays, part of the data
from an extensive spike-in study conducted by Gene Logic and Wyeth's
Genetics Institute involving 95 HG-U95A human GeneChip arrays; and
part of a dilution study conducted by Gene Logic involving 75 HG-U95A
GeneChip arrays. We display some familiar features of the perfect
match and mismatch probe (PM and MM) values of these data, and examine
the variance-mean relationship with probe-level data from probes
believed to be defective, and so delivering noise only. We explain
why we need to normalize the arrays to one another using probe level
intensities. We then examine the behavior of the PM and MM using
spike-in data and assess three commonly used summary measures: Affymetrix's
(i) average difference (AvDiff) and (ii) MAS 5.0 signal, and (iii)
the Li and Wong multiplicative model-based expression index (MBEI).
The exploratory data analyses of the probe level data motivate a
new summary measure that is a robust multi-array average (RMA) of
background-adjusted, normalized, and log-transformed PM values. We
evaluate the four expression summary measures using the dilution
study data, assessing their behavior in terms of bias, variance and
(for MBEI and RMA) model fit. Finally, we evaluate the algorithms
in terms of their ability to detect known levels of differential
expression using the spike-in data. We conclude that there is no
obvious downside to using RMA and attaching a standard error (SE)
to this quantity using a linear model which removes probe-specific
affinities.},
doi = {10.1093/biostatistics/4.2.249},
pdf = {../local/Irizarry2003Exploration.pdf},
file = {Irizarry2003Exploration.pdf:Irizarry2003Exploration.pdf:PDF},
institution = {Department of Biostatistics, Johns Hopkins University, Baltimore,
MD 21205, USA. rafa@jhu.edu},
keywords = {csbcbook, csbcbook-ch2},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {4/2/249},
pmid = {12925520},
timestamp = {2009.10.18},
url = {http://dx.doi.org/10.1093/biostatistics/4.2.249}
}
@article{Irizarry2008Comprehensive,
author = {Rafael A Irizarry and Christine Ladd-Acosta and Benilton Carvalho
and Hao Wu and Sheri A Brandenburg and Jeffrey A Jeddeloh and Bo
Wen and Andrew P Feinberg},
title = {Comprehensive high-throughput arrays for relative methylation (CHARM).},
journal = {Genome Res},
year = {2008},
volume = {18},
pages = {780--790},
number = {5},
month = {May},
abstract = {This study was originally conceived to test in a rigorous way the
specificity of three major approaches to high-throughput array-based
DNA methylation analysis: (1) MeDIP, or methylated DNA immunoprecipitation,
an example of antibody-mediated methyl-specific fractionation; (2)
HELP, or HpaII tiny fragment enrichment by ligation-mediated PCR,
an example of differential amplification of methylated DNA; and (3)
fractionation by McrBC, an enzyme that cuts most methylated DNA.
These results were validated using 1466 Illumina methylation probes
on the GoldenGate methylation assay and further resolved discrepancies
among the methods through quantitative methylation pyrosequencing
analysis. While all three methods provide useful information, there
were significant limitations to each, specifically bias toward CpG
islands in MeDIP, relatively incomplete coverage in HELP, and location
imprecision in McrBC. However, we found that with an original array
design strategy using tiling arrays and statistical procedures that
average information from neighboring genomic locations, much improved
specificity and sensitivity could be achieved, e.g., approximately
100\% sensitivity at 90\% specificity with McrBC. We term this approach
"comprehensive high-throughput arrays for relative methylation" (CHARM).
While this approach was applied to McrBC analysis, the array design
and computational algorithms are fractionation method-independent
and make this a simple, general, relatively inexpensive tool suitable
for genome-wide analysis, and in which individual samples can be
assayed reliably at very high density, allowing locus-level genome-wide
epigenetic discrimination of individuals, not just groups of samples.
Furthermore, unlike the other approaches, CHARM is highly quantitative,
a substantial advantage in application to the study of human disease.},
doi = {10.1101/gr.7301508},
institution = {Department of Biostatistics, Johns Hopkins Bloomberg School of Public
Health, Baltimore, Maryland 21205, USA. rafa@jhu.edu},
keywords = {Bias (Epidemiology); CpG Islands; DNA Methylation; Genome, Human;
Genomics; Humans; Oligonucleotide Array Sequence Analysis; Reference
Standards; Reproducibility of Results; Sensitivity and Specificity},
owner = {phupe},
pii = {gr.7301508},
pmid = {18316654},
timestamp = {2010.08.10},
url = {http://dx.doi.org/10.1101/gr.7301508}
}
@article{Irizarry2006Comparison,
author = {Rafael A Irizarry and Zhijin Wu and Harris A Jaffee},
title = {Comparison of Affymetrix GeneChip expression measures.},
journal = {Bioinformatics},
year = {2006},
volume = {22},
pages = {789--794},
number = {7},
month = {Apr},
abstract = {MOTIVATION: In the Affymetrix GeneChip system, preprocessing occurs
before one obtains expression level measurements. Because the number
of competing preprocessing methods was large and growing we developed
a benchmark to help users identify the best method for their application.
A webtool was made available for developers to benchmark their procedures.
At the time of writing over 50 methods had been submitted. RESULTS:
We benchmarked 31 probe set algorithms using a U95A dataset of spike
in controls. Using this dataset, we found that background correction,
one of the main steps in preprocessing, has the largest effect on
performance. In particular, background correction appears to improve
accuracy but, in general, worsen precision. The benchmark results
put this balance in perspective. Furthermore, we have improved some
of the original benchmark metrics to provide more detailed information
regarding precision and accuracy. A handful of methods stand out
as providing the best balance using spike-in data with the older
U95A array, although different experiments on more current arrays
may benchmark differently. AVAILABILITY: The affycomp package, now
version 1.5.2, continues to be available as part of the Bioconductor
project (http://www.bioconductor.org). The webtool continues to be
available at http://affycomp.biostat.jhsph.edu CONTACT: rafa@jhu.edu
SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics
online.},
doi = {10.1093/bioinformatics/btk046},
institution = {Department of Biostatistics, Johns Hopkins University, 615 N. Wolfe
Street, Baltimore, MD 21205, USA. rafa@jhu.edu},
keywords = {Algorithms; Benchmarking; Gene Expression Profiling, methods; Oligonucleotide
Array Sequence Analysis, instrumentation/methods; Reproducibility
of Results; Software},
language = {eng},
medline-pst = {ppublish},
owner = {philippe},
pii = {btk046},
pmid = {16410320},
timestamp = {2010.08.04},
url = {http://dx.doi.org/10.1093/bioinformatics/btk046}
}
@article{Irwin2005ZINC,
author = {Irwin, J. J. and Shoichet, B. K.},
title = {Z{INC}--a free database of commercially available compounds for virtual
screening.},
journal = {J {C}hem {I}nf {M}odel},
year = {2005},
volume = {45},
pages = {177-82},
number = {1},
abstract = {A critical barrier to entry into structure-based virtual screening
is the lack of a suitable, easy to access database of purchasable
compounds. {W}e have therefore prepared a library of 727,842 molecules,
each with 3{D} structure, using catalogs of compounds from vendors
(the size of this library continues to grow). {T}he molecules have
been assigned biologically relevant protonation states and are annotated
with properties such as molecular weight, calculated {L}og{P}, and
number of rotatable bonds. {E}ach molecule in the library contains
vendor and purchasing information and is ready for docking using
a number of popular docking programs. {W}ithin certain limits, the
molecules are prepared in multiple protonation states and multiple
tautomeric forms. {I}n one format, multiple conformations are available
for the molecules. {T}his database is available for free download
(http://zinc.docking.org) in several common file formats including
{SMILES}, mol2, 3{D} {SDF}, and {DOCK} flexibase format. {A} {W}eb-based
query tool incorporating a molecular drawing interface enables the
database to be searched and browsed and subsets to be created. {U}sers
can process their own molecules by uploading them to a server. {O}ur
hope is that this database will bring virtual screening libraries
to a wide community of structural biologists and medicinal chemists.},
doi = {10.1021/ci049714+},
pdf = {../local/Irwin2005ZINC.pdf},
file = {Irwin2005ZINC.pdf:local/Irwin2005ZINC.pdf:PDF},
keywords = {Databases, Digital, Drug Design, Factual, Libraries, Molecular Conformation,
Molecular Structure, P.H.S., Research Support, U.S. Gov't, 15667143},
url = {http://dx.doi.org/10.1021/ci049714+}
}
@article{Ishkanian2004tiling,
author = {Ishkanian, A. S. and Malloff, C. A. and Watson, S. K. and DeLeeuw,
R. J. and Chi, B. and Coe, B. P. and Snijders, A. and Albertson,
D. G. and Pinkel, D. and Marra, M. A. and Ling, V. and MacAulay,
C. and Lam, W. L.},
title = {A tiling resolution {DNA} microarray with complete coverage of the
human genome},
journal = {Nat. Genet.},
year = {2004},
volume = {36},
pages = {299--303},
number = {3},
month = {Mar},
abstract = {We constructed a tiling resolution array consisting of 32,433 overlapping
BAC clones covering the entire human genome. This increases our ability
to identify genetic alterations and their boundaries throughout the
genome in a single comparative genomic hybridization (CGH) experiment.
At this tiling resolution, we identified minute DNA alterations not
previously reported. These alterations include microamplifications
and deletions containing oncogenes, tumor-suppressor genes and new
genes that may be associated with multiple tumor types. Our findings
show the need to move beyond conventional marker-based genome comparison
approaches, that rely on inference of continuity between interval
markers. Our submegabase resolution tiling set for array CGH (SMRT
array) allows comprehensive assessment of genomic integrity and thereby
the identification of new genes associated with disease.},
doi = {10.1038/ng1307},
pdf = {../local/Ishkanian2004tiling.pdf},
file = {Ishkanian2004tiling.pdf:Ishkanian2004tiling.pdf:PDF},
institution = {British Columbia Cancer Research Centre, 601 West 10th Avenue, Vancouver,
British Columbia V5Z 1L3, Canada.},
keywords = {csbcbook, microarray},
owner = {jp},
pii = {ng1307},
pmid = {14981516},
timestamp = {2009.10.08},
url = {http://dx.doi.org/10.1038/ng1307}
}
@article{Ito2001comprehensive,
author = {Ito, T. and Chiba, T. and Ozawa, R. and Yoshida, M. and Hattori,
M. and Sakaki, Y.},
title = {A comprehensive two-hybrid analysis to explore the yeast protein
interactome},
journal = {Proc. {N}atl. {A}cad. {S}ci. {USA}},
year = {2001},
volume = {98},
pages = {4569--4574},
number = {8},
pdf = {../local/ito01.pdf},
file = {ito01.pdf:local/ito01.pdf:PDF},
subject = {bionet},
url = {http://www.pnas.org/cgi/content/full/98/8/4569}
}
@article{Ito2000Toward,
author = {Ito, T. and Tashiro, K. and Muta, S. and Ozawa, R. and Chiba, T.
and Nishizawa, M. and Yamamoto, K. and Kuhara, S. and Sakaki, Y.},
title = {Toward a protein-protein interaction map of the budding yeast: {A}
comprehensive system to examine two-hybrid interactions in all possible
combinations between the yeast proteins},
journal = {Proc. {N}atl. {A}cad. {S}ci. {USA}},
year = {2000},
volume = {93},
pages = {1143--1147},
number = {3},
pdf = {../local/ito00.pdf},
file = {ito00.pdf:local/ito00.pdf:PDF},
subject = {bionet},
url = {http://www.pnas.org/cgi/content/full/97/3/1143}
}
@inproceedings{Ivanciuc2007Applications,
author = {Ivanciuc, O.},
title = {Applications of Support Vector Machines in Chemistry},
booktitle = {Reviews in Computational Chemistry},
year = {2007},
editor = {Lipkowitz, K. B. and Cundari, T. R.},
volume = {23},
pages = {291--400},
address = {Weiheim},
publisher = {Wiley-VCH},
owner = {vert},
timestamp = {2007.08.02}
}
@book{Ivanov1976theory,
title = {The theory of approximate methods and their application to the numerical
solution of singular integral equations},
publisher = {Nordhoff International},
year = {1976},
author = {V.V. Ivanov},
address = {Leiden},
subject = {ml}
}
@article{Ivshina2006Genetic,
author = {Ivshina, A.V. and George, J. and Senko, O. and Mow, B. and Putti,
T.C. and Smeds, J. and Lindahl, T. and Pawitan, Y. and Hall, P. and
Nordgren, H. and others},
title = {Genetic reclassification of histologic grade delineates new clinical
subtypes of breast cancer},
journal = {Cancer research},
year = {2006},
volume = {66},
pages = {10292--10301},
number = {21},
publisher = {AACR}
}
@article{Iwamoto2010Predicting,
author = {Iwamoto, T. and Pusztai, L. and others},
title = {Predicting prognosis of breast cancer with gene signatures: are we
lost in a sea of data?},
journal = {Genome medicine},
year = {2010},
volume = {2},
pages = {81},
number = {11},
publisher = {BioMed Central Ltd}
}
@article{Natraj2005Three,
author = {Iyer, N. and Jayanti, S. and Lou, K. and Kalyanaraman, Y. and Ramani,
K. },
title = {Three-dimensional shape searching: state-of-the-art review and future
trends},
journal = {Computer-Aided Design},
year = {2005},
volume = {37},
pages = {509--530},
number = {5},
month = {April},
abstract = {Three-dimensional shape searching is a problem of current interest
in several different fields. Most techniques have been developed
for a particular domain and reduce a shape into a simpler shape representation.
The techniques developed for a particular domain will also find applications
in other domains.We classify and compare various 3D shape searching
techniques based on their shape representations. A brief description
of each technique is provided followed by a detailed survey of the
state-of-the-art. The paper concludes by identifying gaps in current
shape search techniques and identifies directions for future research.},
booktitle = {Geometric Modeling and Processing 2004},
citeulike-article-id = {670915},
doi = {http://dx.doi.org/10.1016/j.cad.2004.07.002},
keywords = {3d-feature-extraction, cad, feature-extraction, object-modeling, object-representation,
object-retrieval, pattern-recognition, search-benchmark, survey},
posted-at = {2006-05-26 08:28:52},
priority = {2},
url = {http://dx.doi.org/10.1016/j.cad.2004.07.002}
}
@article{Jaakkola2000Discriminative,
author = {Jaakkola, T. and Diekhans, M. and Haussler, D.},
title = {A {D}iscriminative {F}ramework for {D}etecting {R}emote {P}rotein
{H}omologies},
journal = {J. {C}omput. {B}iol.},
year = {2000},
volume = {7},
pages = {95--114},
number = {1,2},
pdf = {../local/jaak00.pdf},
file = {jaak00.pdf:local/jaak00.pdf:PDF},
keywords = {biosvm},
subject = {biokernelcasp},
url = {http://www.cse.ucsc.edu/research/compbio/discriminative/Jaakola2-1998.ps}
}
@inproceedings{Jaakkola1999Maximum,
author = {Tommi Jaakkola and Marina Meila and Tony Jebara},
title = {Maximum Entropy Discrimination},
booktitle = {Adv. {N}eural {I}nform. {P}rocess. {S}yst.},
year = {1999},
volume = {12},
publisher = {MIT Press, Cambridge, MA}
}
@inproceedings{Jaakkola1999Using,
author = {Jaakkola, T. S. and Diekhans, M. and Haussler, D.},
title = {Using the {F}isher {K}ernel {M}ethod to {D}etect {R}emote {P}rotein
{H}omologies},
booktitle = {Proceedings of the {S}eventh {I}nternational {C}onference on {I}ntelligent
{S}ystems for {M}olecular {B}iology},
year = {1999},
pages = {149--158},
publisher = {AAAI Press},
keywords = {biosvm},
owner = {jeanphilippevert}
}
@inproceedings{Jaakkola1999Exploiting,
author = {Jaakkola, T. S. and Haussler, D.},
title = {Exploiting generative models in discriminative classifiers},
booktitle = {Proc. of {T}enth {C}onference on {A}dvances in {N}eural {I}nformation
{P}rocessing {S}ystems},
year = {1999},
pdf = {../local/jaak99.pdf},
file = {jaak99.pdf:local/jaak99.pdf:PDF},
keywords = {biosvm},
subject = {kernel},
url = {http://www.cse.ucsc.edu/research/ml/papers/Jaakola.ps}
}
@inproceedings{Jaakkola1999Probabilistic,
author = {Jaakkola, T. S. and Haussler, D.},
title = {Probabilistic kernel regression models},
booktitle = {Proceedings of the 1999 {C}onference on {AI} and {S}tatistics},
year = {1999},
publisher = {Morgan Kaufmann},
pdf = {../local/jaak99b.pdf},
file = {jaak99b.pdf:local/jaak99b.pdf:PDF},
subject = {kernel},
url = {http://alpha-bits.ai.mit.edu/people/tommi/publications/probker.ps.gz}
}
@article{Jablonka2002changing,
author = {Jablonka, A. and Lamb, M. J.},
title = {The changing concept of epigenetics},
journal = {Ann N Y Acad Sci},
year = {2002},
volume = {981},
pages = {82--96},
month = {Dec},
abstract = {We discuss the changing use of epigenetics, a term coined by Conrad
Waddington in the 1940s, and how the epigenetic approach to development
differs from the genetic approach. Originally, epigenetics referred
to the study of the way genes and their products bring the phenotype
into being. Today, it is primarily concerned with the mechanisms
through which cells become committed to a particular form or function
and through which that functional or structural state is then transmitted
in cell lineages. We argue that modern epigenetics is important not
only because it has practical significance for medicine, agriculture,
and species conservation, but also because it has implications for
the way in which we should view heredity and evolution. In particular,
recognizing that there are epigenetic inheritance systems through
which non-DNA variations can be transmitted in cell and organismal
lineages broadens the concept of heredity and challenges the widely
accepted gene-centered neo-Darwinian version of Darwinism.},
institution = {Cohn Institute for the History and Philosophy of Science and Ideas,
Tel Aviv University, Tel Aviv 69978, Israel. jablonka@post.tau.ac.il},
keywords = {csbcbook},
owner = {jp},
pmid = {12547675},
timestamp = {2009.10.11}
}
@article{Jackson2003Expression,
author = {Jackson, A. L. and Bartz, S. R. and Schelter, J. and Kobayashi, S.
V. and Burchard, J. and Mao, M. and Li, B. and Cavet, G. and Linsley,
P. S.},
title = {Expression profiling reveals off-target gene regulation by {RNA}i.},
journal = {Nat. {B}iotechnol.},
year = {2003},
volume = {21},
pages = {635-7},
number = {6},
month = {Jun},
abstract = {R{NA} interference is thought to require near-identity between the
small interfering {RNA} (si{RNA}) and its cognate m{RNA}. {H}ere,
we used gene expression profiling to characterize the specificity
of gene silencing by si{RNA}s in cultured human cells. {T}ranscript
profiles revealed si{RNA}-specific rather than target-specific signatures,
including direct silencing of nontargeted genes containing as few
as eleven contiguous nucleotides of identity to the si{RNA}. {T}hese
results demonstrate that si{RNA}s may cross-react with targets of
limited sequence similarity.},
doi = {10.1038/nbt831},
keywords = {sirna},
pii = {nbt831},
url = {http://dx.doi.org/10.1038/nbt831}
}
@article{Jackson2004Noise,
author = {Jackson, A. L. and Linsley, P. S.},
title = {Noise amidst the silence: off-target effects of si{RNA}s?},
journal = {Trends {G}enet.},
year = {2004},
volume = {20},
pages = {521-4},
number = {11},
month = {Nov},
abstract = {R{NA} interference ({RNA}i), mediated by short interfering {RNA}s
(si{RNA}s), is widely used to silence gene expression and to define
gene function in mammalian cells. {I}nitially, this gene silencing
via transcript degradation was believed to be exquisitely specific,
requiring near-identity between the si{RNA} and the target m{RNA}.
{H}owever, several recent reports have suggested that non-specific
effects can be induced by si{RNA}s, both at the level of m{RNA} and
protein. {T}hese findings suggest that si{RNA}s can regulate the
expression of unintended targets, and argue for further experiments
on the mechanism and extent of off-target gene regulation(s). {I}n
the meantime, caution is warranted in interpreting gene function
and phenotypes resulting from {RNA}i experiments.},
doi = {10.1016/j.tig.2004.08.006},
keywords = {sirna},
pii = {S0168-9525(04)00240-9},
url = {http://dx.doi.org/10.1016/j.tig.2004.08.006}
}
@incollection{Jacob2009Clustered,
author = {Jacob, L. and Bach, F. and Vert, J.-P.},
title = {Clustered Multi-Task Learning: A Convex Formulation},
booktitle = {Advances in Neural Information Processing Systems 21},
publisher = {MIT Press},
year = {2009},
pages = {745--752},
url = {http://books.nips.cc/papers/files/nips21/NIPS2008\_0680.pdf}
}
@techreport{Jacob2008VirtualOLD,
author = {Jacob, L. and Hoffmann, B. and Stoven, B. and Vert, J.-P.},
title = {Virtual screening of {GPCR}s: an \textit{in silico} chemogenomics
approach},
institution = {Arxiv},
year = {2008},
number = {0801.4301},
location = {Mines ParisTech},
timestamp = {2008.01.24}
}
@article{Jacob2008Virtual,
author = {Jacob, L. and Hoffmann, B. and Stoven, V. and Vert, J.-P.},
title = {Virtual screening of {GPCR}s: an {\it in silico} chemogenomics approach},
journal = {BMC Bioinformatics},
year = {2008},
volume = {9},
pages = {363},
doi = {10.1186/1471-2105-9-363},
pdf = {../local/Jacob2008Virtual.pdf},
file = {Jacob2008Virtual.pdf:Jacob2008Virtual.pdf:PDF},
keywords = {chemogenomics},
url = {http://dx.doi.org/10.1186/1471-2105-9-363}
}
@inproceedings{Jacob2009Group,
author = {Jacob, L. and Obozinski, G. and Vert, J.-P.},
title = {Group lasso with overlap and graph lasso},
booktitle = {ICML '09: Proceedings of the 26th Annual International Conference
on Machine Learning},
year = {2009},
pages = {433--440},
address = {New York, NY, USA},
publisher = {ACM},
doi = {http://doi.acm.org/10.1145/1553374.1553431},
pdf = {../local/Jacob2009Group.pdf},
file = {Jacob2009Group.pdf:Jacob2009Group.pdf:PDF},
isbn = {978-1-60558-516-1},
location = {Montreal, Quebec, Canada}
}
@article{Jacob2008Efficient,
author = {Jacob, L. and Vert, J.-P.},
title = {Efficient peptide-{MHC}-{I} binding prediction for alleles with few
known binders.},
journal = {Bioinformatics},
year = {2008},
volume = {24},
pages = {358--366},
number = {3},
month = {Feb},
abstract = {MOTIVATION: In silico methods for the prediction of antigenic peptides
binding to MHC class I molecules play an increasingly important role
in the identification of T-cell epitopes. Statistical and machine
learning methods in particular are widely used to score candidate
binders based on their similarity with known binders and non-binders.
The genes coding for the MHC molecules, however, are highly polymorphic,
and statistical methods have difficulties building models for alleles
with few known binders. In this context, recent work has demonstrated
the utility of leveraging information across alleles to improve the
performance of the prediction. RESULTS: We design a support vector
machine algorithm that is able to learn peptide-MHC-I binding models
for many alleles simultaneously, by sharing binding information across
alleles. The sharing of information is controlled by a user-defined
measure of similarity between alleles. We show that this similarity
can be defined in terms of supertypes, or more directly by comparing
key residues known to play a role in the peptide-MHC binding. We
illustrate the potential of this approach on various benchmark experiments
where it outperforms other state-of-the-art methods. AVAILABILITY:
The method is implemented on a web server: http://cbio.ensmp.fr/kiss.
All data and codes are freely and publicly available from the authors.},
doi = {10.1093/bioinformatics/btm611},
pdf = {../local/Jacob2008Efficient.pdf},
file = {Jacob2008Efficient.pdf:Jacob2008Efficient.pdf:PDF},
keywords = {chemogenomics immunoinformatics},
owner = {laurent},
pii = {btm611},
pmid = {18083718},
timestamp = {2008.03.27},
url = {http://dx.doi.org/10.1093/bioinformatics/btm611}
}
@article{Jacob2008Protein,
author = {Jacob, L. and Vert, J.-P.},
title = {Protein-ligand interaction prediction: an improved chemogenomics
approach},
journal = {Bioinformatics},
year = {2008},
volume = {24},
pages = {2149--2156},
number = {19},
doi = {10.1093/bioinformatics/btn409},
pdf = {../local/Jacob2008Protein.pdf},
file = {Jacob2008Protein.pdf:Jacob2008Protein.pdf:PDF},
keywords = {chemogenomics},
url = {http://bioinformatics.oxfordjournals.org/cgi/reprint/btn409}
}
@techreport{Jacob2007Kernel,
author = {Jacob, L. and Vert, J.-P.},
title = {Kernel methods for in silico chemogenomics},
institution = {arXiv},
year = {2007},
number = {0709.3931v1},
keywords = {chemogenomics},
timestamp = {2007.10.25},
url = {http://fr.arxiv.org/abs/0709.3931}
}
@techreport{Jacob2006Epitope,
author = {Jacob, L. and Vert, J.-P.},
title = {Epitope prediction improved by multitask support vector machines},
institution = {arXiv},
year = {2006},
number = {arXiv:q-bio/0702008v1},
owner = {jacob}
}
@article{Jacoby20067,
author = {Edgar Jacoby and Rochdi Bouhelal and Marc Gerspacher and Klaus Seuwen},
title = {The 7 TM G-protein-coupled receptor target family.},
journal = {ChemMedChem},
year = {2006},
volume = {1},
pages = {761--782},
number = {8},
month = {Aug},
abstract = {Chemical biology approaches have a long history in the exploration
of the G-protein-coupled receptor (GPCR) family, which represents
the largest and most important group of targets for therapeutics.
The analysis of the human genome revealed a significant number of
new members with unknown physiological function which are today the
focus of many reverse pharmacology drug-discovery programs. As the
seven hydrophobic transmembrane segments are a defining common structural
feature of these receptors, and as signaling through heterotrimeric
G proteins is not demonstrated in all cases, these proteins are also
referred to as seven transmembrane (7 TM) or serpentine receptors.
This review summarizes important historic milestones of GPCR research,
from the beginning, when pharmacology was mainly descriptive, to
the age of modern molecular biology, with the cloning of the first
receptor and now the availability of the entire human GPCR repertoire
at the sequence and protein level. It shows how GPCR-directed drug
discovery was initially based on the careful testing of a few specifically
made chemical compounds and is today pursued with modern drug-discovery
approaches, including combinatorial library design, structural biology,
molecular informatics, and advanced screening technologies for the
identification of new compounds that activate or inhibit GPCRs specifically.
Such compounds, in conjunction with other new technologies, allow
us to study the role of receptors in physiology and medicine, and
will hopefully result in novel therapies. We also outline how basic
research on the signaling and regulatory mechanisms of GPCRs is advancing,
leading to the discovery of new GPCR-interacting proteins and thus
opening new perspectives for drug development. Practical examples
from GPCR expression studies, HTS (high-throughput screening), and
the design of monoamine-related GPCR-focused combinatorial libraries
illustrate ongoing GPCR chemical biology research. Finally, we outline
future progress that may relate today's discoveries to the development
of new medicines.},
doi = {10.1002/cmdc.200600134},
owner = {laurent},
pmid = {16902930},
timestamp = {2008.01.16},
url = {http://dx.doi.org/10.1002/cmdc.200600134}
}
@article{Jaenisch2003Epigenetic,
author = {Rudolf Jaenisch and Adrian Bird},
title = {Epigenetic regulation of gene expression: how the genome integrates
intrinsic and environmental signals.},
journal = {Nat. Genet.},
year = {2003},
volume = {33 Suppl},
pages = {245--254},
month = {Mar},
abstract = {Cells of a multicellular organism are genetically homogeneous but
structurally and functionally heterogeneous owing to the differential
expression of genes. Many of these differences in gene expression
arise during development and are subsequently retained through mitosis.
Stable alterations of this kind are said to be 'epigenetic', because
they are heritable in the short term but do not involve mutations
of the DNA itself. Research over the past few years has focused on
two molecular mechanisms that mediate epigenetic phenomena: DNA methylation
and histone modifications. Here, we review advances in the understanding
of the mechanism and role of DNA methylation in biological processes.
Epigenetic effects by means of DNA methylation have an important
role in development but can also arise stochastically as animals
age. Identification of proteins that mediate these effects has provided
insight into this complex process and diseases that occur when it
is perturbed. External influences on epigenetic processes are seen
in the effects of diet on long-term diseases such as cancer. Thus,
epigenetic mechanisms seem to allow an organism to respond to the
environment through changes in gene expression. The extent to which
environmental effects can provoke epigenetic responses represents
an exciting area of future research.},
doi = {10.1038/ng1089},
institution = {Whitehead Institute for Biomedical Research and Department of Biology,
Massachusetts Institute of Technology, 9 Cambridge Center, Cambridge,
MA 02142, USA.},
keywords = {Aging; Animals; Cloning, Organism; DNA Methylation; Diet; Dosage Compensation,
Genetic; Gene Expression Regulation, Developmental; Genetic Diseases,
Inborn; Genome; Genomic Imprinting; Humans; Mice; Models, Genetic;
Mutation; Neoplasms; Phenotype; Signal Transduction},
owner = {ljacob},
pii = {ng1089},
pmid = {12610534},
timestamp = {2009.09.14},
url = {http://dx.doi.org/10.1038/ng1089}
}
@article{Jain1999Data,
author = {Jain, A. K. and Murty, M. N. and Flynn, P. J.},
title = {Data clustering: a review},
journal = {ACM Comput. Surv.},
year = {1999},
volume = {31},
pages = {3},
pdf = {../local/Jain1999Data.pdf},
file = {Jain1999Data.pdf:Jain1999Data.pdf:PDF},
owner = {jp},
timestamp = {2011.12.29}
}
@article{Jambon2003New,
author = {Martin Jambon and Anne Imberty and Gilbert Deléage and Christophe
Geourjon},
title = {A new bioinformatic approach to detect common 3D sites in protein
structures.},
journal = {Proteins},
year = {2003},
volume = {52},
pages = {137--145},
number = {2},
month = {Aug},
abstract = {An innovative bioinformatic method has been designed and implemented
to detect similar three-dimensional (3D) sites in proteins. This
approach allows the comparison of protein structures or substructures
and detects local spatial similarities: this method is completely
independent from the amino acid sequence and from the backbone structure.
In contrast to already existing tools, the basis for this method
is a representation of the protein structure by a set of stereochemical
groups that are defined independently from the notion of amino acid.
An efficient heuristic for finding similarities that uses graphs
of triangles of chemical groups to represent the protein structures
has been developed. The implementation of this heuristic constitutes
a software named SuMo (Surfing the Molecules), which allows the dynamic
definition of chemical groups, the selection of sites in the proteins,
and the management and screening of databases. To show the relevance
of this approach, we focused on two extreme examples illustrating
convergent and divergent evolution. In two unrelated serine proteases,
SuMo detects one common site, which corresponds to the catalytic
triad. In the legume lectins family composed of >100 structures that
share similar sequences and folds but may have lost their ability
to bind a carbohydrate molecule, SuMo discriminates between functional
and non-functional lectins with a selectivity of 96\%. The time needed
for searching a given site in a protein structure is typically 0.1
s on a PIII 800MHz/Linux computer; thus, in further studies, SuMo
will be used to screen the PDB.},
doi = {10.1002/prot.10339},
institution = {Institut de Biologie et Chimie des Protéines (IBCP), Lyon, France.},
keywords = {Algorithms; Catalytic Domain; Chymotrypsin, chemistry/genetics; Computational
Biology, methods; Evolution, Molecular; Fabaceae, chemistry; Models,
Molecular; Plant Lectins, chemistry/genetics; Protein Conformation;
Proteins, chemistry; Reproducibility of Results; Subtilisin, chemistry/genetics},
owner = {bricehoffmann},
pmid = {12833538},
timestamp = {2009.02.13},
url = {http://dx.doi.org/10.1002/prot.10339}
}
@book{Jameson1987Summing,
title = {Summing and Nuclear Norms in Banach Space Theory},
publisher = {Cambridge University Press},
year = {1987},
author = {Jameson, G. J. O.},
number = {8},
series = {London Mathematical Society Student Texts},
doi = {10.1017/CBO9780511569166},
pdf = {../local/Jameson1987Summing.pdf},
file = {Jameson1987Summing.pdf:Jameson1987Summing.pdf:PDF},
owner = {jp},
timestamp = {2013.03.29},
url = {http://dx.doi.org/10.1017/CBO9780511569166}
}
@article{Jamieson2006Medicinal,
author = {Jamieson, C. and Moir, E. M. and Rankovic, Z. and Wishart, G.},
title = {{M}edicinal chemistry of h{ERG} optimizations: {H}ighlights and hang-ups.},
journal = {J. Med. Chem.},
year = {2006},
volume = {49},
pages = {5029--5046},
number = {17},
month = {Aug},
doi = {10.1021/jm060379l},
pdf = {../local/Jamieson2006Medicinal.pdf},
file = {Jamieson2006Medicinal.pdf:local/Jamieson2006Medicinal.pdf:PDF},
keywords = {herg},
pmid = {16913693},
timestamp = {2007.02.03},
url = {http://dx.doi.org/10.1021/jm060379l}
}
@article{Janoueix-Lerosey2005Preferential,
author = {Isabelle Janoueix-Lerosey and Philippe Hupé and Zofia Maciorowski
and Philippe La Rosa and Gudrun Schleiermacher and Gaëlle Pierron
and Stéphane Liva and Emmanuel Barillot and Olivier Delattre},
title = {Preferential occurrence of chromosome breakpoints within early replicating
regions in neuroblastoma.},
journal = {Cell Cycle},
year = {2005},
volume = {4},
pages = {1842--1846},
number = {12},
month = {Dec},
abstract = {Neuroblastoma (NB) is a frequent paediatric extra cranial solid tumor
characterized by the occurrence of unbalanced chromosome translocations,
frequently, but not exclusively, involving chromosomes 1 and 17.
We have used a 1 Mb resolution BAC array to further refine the mapping
of breakpoints in NB cell lines. Replication timing profiles were
evaluated in 7 NB cell lines, using DNAs from G1 and S phases flow
sorted nuclei hybridised on the same array. Strikingly, these replication
timing profiles were highly similar between the different NB cell
lines. Furthermore, a significant level of similarity was also observed
between NB cell lines and lymphoblastoid cells. A segmentation analysis
using the Adaptative Weights Smoothing procedure was performed to
determine regions of coordinate replication. More than 50\% of the
breakpoints mapped to early replicating regions, which account for
23.7\% of the total genome. The breakpoints frequency per 10(8) bases
was therefore 10.84 for early replicating regions, whereas it was
only 2.94 for late replicating regions, these difference being highly
significant (p < 10(-4)). This strong association was also observed
when chromosomes 1 and 17, the two most frequent translocation partners
in NB were excluded from the statistical analysis. These results
unambiguously establish a link between unbalanced translocations,
whose most likely mechanism of occurrence relies on break-induced
replication, and early replication of the genome.},
pdf = {../local/Janoueix-Lerosey2005Preferential.pdf},
file = {Janoueix-Lerosey2005Preferential.pdf:Janoueix-Lerosey2005Preferential.pdf:PDF},
institution = {Laboratoire de Pathologie Moléculaire des Cancers, Institut Curie,
Paris, France.},
keywords = {csbcbook, csbcbook-ch2},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {2257},
pmid = {16294040},
timestamp = {2009.10.18},
url = {http://www.landesbioscience.com/journals/cc/article/2257/}
}
@article{Jansen2002Relating,
author = {Jansen, R. and Greenbaum, D. and Gerstein, M.},
title = {Relating whole-genome expression data with protein-protein interactions},
journal = {Genome Res.},
year = {2002},
volume = {12},
pages = {37--46},
number = {1},
month = {Jan},
abstract = {We investigate the relationship of protein-protein interactions with
mRNA expression levels, by integrating a variety of data sources
for yeast. We focus on known protein complexes that have clearly
defined interactions between their subunits. We find that subunits
of the same protein complex show significant coexpression, both in
terms of similarities of absolute mRNA levels and expression profiles,
e.g., we can often see subunits of a complex having correlated patterns
of expression over a time course. We classify the yeast protein complexes
as either permanent or transient, with permanent ones being maintained
through most cellular conditions. We find that, generally, permanent
complexes, such as the ribosome and proteasome, have a particularly
strong relationship with expression, while transient ones do not.
However, we note that several transient complexes, such as the RNA
polymerase II holoenzyme and the replication complex, can be subdivided
into smaller permanent ones, which do have a strong relationship
to gene expression. We also investigated the interactions in aggregated,
genome-wide data sets, such as the comprehensive yeast two-hybrid
experiments, and found them to have only a weak relationship with
gene expression, similar to that of transient complexes. (Further
details on genecensus.org/expression/interactions and bioinfo.mbb.yale.edu/expression/interactions.)},
doi = {10.1101/gr.205602},
pdf = {../local/Jansen2002Relating.pdf},
file = {Jansen2002Relating.pdf:Jansen2002Relating.pdf:PDF},
institution = {Department of Molecular Biophysics, Yale University, New Haven, Connecticut
06520, USA.},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pmid = {11779829},
timestamp = {2011.09.27},
url = {http://dx.doi.org/10.1101/gr.205602}
}
@article{Jansen2003Bayesian,
author = {Jansen, R. and Yu, H. and Greenbaum, D. and Kluger, Y. and Krogan,
N.J. and Chung, S. and Emili, A. and Snyder, M. and Greenblatt, J.F.
and Gerstein, M.},
title = {A {B}ayesian networks approach for predicting protein-protein interactions
from genomic data},
journal = {Science},
year = {2003},
volume = {302},
pages = {449-453},
number = {5644},
abstract = {We have developed an approach using {B}ayesian networks to predict
protein-protein interactions genome-wide in yeast. {O}ur method naturally
weights and combines into reliable predictions genomic features only
weakly associated with interaction (e.g., m{RNA} coexpression, coessentiality,
and colocalization). {I}n addition to de novo predictions, it can
integrate often noisy, experimental interaction data sets. {W}e observe
that at given levels of sensitivity, our predictions are more accurate
than the existing high-throughput experimental data sets. {W}e validate
our predictions with new {TAP}?tagging (tandem affinity purification)
experiments.},
doi = {10.1126/science.1087361},
pdf = {../local/Jansen2003Bayesian.pdf},
file = {Jansen2003Bayesian.pdf:local/Jansen2003Bayesian.pdf:PDF},
keywords = {biogm},
owner = {vert},
url = {http://dx.doi.org/10.1126/science.1087361}
}
@article{Jarzab2005Gene,
author = {Barbara Jarzab and Malgorzata Wiench and Krzysztof Fujarewicz and
Krzysztof Simek and Michal Jarzab and Malgorzata Oczko-Wojciechowska
and Jan Wloch and Agnieszka Czarniecka and Ewa Chmielik and Dariusz
Lange and Agnieszka Pawlaczek and Sylwia Szpak and Elzbieta Gubala
and Andrzej Swierniak},
title = {Gene expression profile of papillary thyroid cancer: sources of variability
and diagnostic implications.},
journal = {Cancer {R}es.},
year = {2005},
volume = {65},
pages = {1587-97},
number = {4},
month = {Feb},
abstract = {The study looked for an optimal set of genes differentiating between
papillary thyroid cancer ({PTC}) and normal thyroid tissue and assessed
the sources of variability in gene expression profiles. {T}he analysis
was done by oligonucleotide microarrays ({G}ene{C}hip {HG}-{U}133{A})
in 50 tissue samples taken intraoperatively from 33 patients (23
{PTC} patients and 10 patients with other thyroid disease). {I}n
the initial group of 16 {PTC} and 16 normal samples, we assessed
the sources of variability in the gene expression profile by singular
value decomposition which specified three major patterns of variability.
{T}he first and the most distinct mode grouped transcripts differentiating
between tumor and normal tissues. {T}wo consecutive modes contained
a large proportion of immunity-related genes. {T}o generate a multigene
classifier for tumor-normal difference, we used support vector machines-based
technique (recursive feature replacement). {I}t included the following
19 genes: {DPP}4, {GJB}3, {ST}14, {SERPINA}1, {LRP}4, {MET}, {EVA}1,
{SPUVE}, {LGALS}3, {HBB}, {MKRN}2, {MRC}2, {IGSF}1, {KIAA}0830, {RXRG},
{P}4{HA}2, {CDH}3, {IL}13{RA}1, and {MTMR}4, and correctly discriminated
17 of 18 additional {PTC}/normal thyroid samples and all 16 samples
published in a previous microarray study. {S}elected novel genes
({LRP}4, {EVA}1, {TMPRSS}4, {QPCT}, and {SLC}34{A}2) were confirmed
by {Q}-{PCR}.{O}ur results prove that the gene expression signal
of {PTC} is easily detectable even when cancer cells do not prevail
over tumor stroma. {W}e indicate and separate the confounding variability
related to the immune response. {F}inally, we propose a potent molecular
classifier able to discriminate between {PTC} and nonmalignant thyroid
in more than 90\% of investigated samples.},
doi = {10.1158/0008-5472.CAN-04-3078},
pdf = {../local/Jarzab2005Gene.pdf},
file = {Jarzab2005Gene.pdf:local/Jarzab2005Gene.pdf:PDF},
keywords = {biosvm},
pii = {65/4/1587},
url = {http://dx.doi.org/10.1158/0008-5472.CAN-04-3078}
}
@inproceedings{Jebara2004Multi-task,
author = {Jebara, Tony},
title = {Multi-task feature and kernel selection for SVMs},
booktitle = {ICML '04: Proceedings of the twenty-first international conference
on Machine learning},
year = {2004},
pages = {55},
address = {New York, NY, USA},
publisher = {ACM},
doi = {http://doi.acm.org/10.1145/1015330.1015426},
isbn = {1-58113-828-5},
location = {Banff, Alberta, Canada}
}
@article{Jebara2004Probability,
author = {Jebara, T. and Kondor, R. and Howard, A.},
title = {Probability {P}roduct {K}ernels},
journal = {J. {M}ach. {L}earn. {R}es.},
year = {2004},
volume = {5},
pages = {819-844},
keywords = {kernel-theory},
owner = {mahe},
timestamp = {2006.08.09},
url = {http://jmlr.csail.mit.edu/papers/v5/jebara04a.html}
}
@techreport{Jenatton2009Structured,
author = {Jenatton, R. and Audibert, J.-Y. and Bach, F.},
title = {Structured variable selection with sparsity-inducing norms},
institution = {arXiv},
year = {2009},
number = {0904.3523},
abstract = {{W}e consider the empirical risk minimization problem for linear supervised
learning, with regularization by structured sparsity-inducing norms.
{T}hese are defined as sums of {E}uclidean norms on certain subsets
of variables, extending the usual $\ell_1$-norm and the group $\ell_1$-norm
by allowing the subsets to overlap. {T}his leads to a specific set
of allowed nonzero patterns for the solutions of such problems. {W}e
first explore the relationship between the groups defining the norm
and the resulting nonzero patterns, providing both forward and backward
algorithms to go back and forth from groups to patterns. {T}his allows
the design of norms adapted to specific prior knowledge expressed
in terms of nonzero patterns. {W}e also present an efficient active
set algorithm, and analyze the consistency of variable selection
for least-squares linear regression in low and high-dimensional settings.},
pdf = {../local/Jenatton2009Structured.pdf},
file = {Jenatton2009Structured.pdf:Jenatton2009Structured.pdf:PDF},
keywords = {variable selection;sparsity; convex optimization;learning theory},
language = {{A}nglais},
pages = {40 },
url = {http://fr.arxiv.org/abs/0904.3523}
}
@article{Jenatton2011Proximal,
author = {Jenatton, R. and Mairal, J. and Obozinski, G. and Bach, F.},
title = {Proximal Methods for Hierarchical Sparse Coding},
journal = {J. Mach. Learn. Res.},
year = {2011},
volume = {12},
pages = {2297--2334},
number = {Jul},
url = {http://jmlr.csail.mit.edu/papers/v12/jenatton11a.html}
}
@article{Jensen2009STRING,
author = {Jensen, L.J. and Kuhn, M. and Stark, M. and Chaffron, S. and Creevey,
C. and Muller, J. and Doerks, T. and Julien, P. and Roth, A. and
Simonovic, M. and Bork, P. and von Mering, C.},
title = {STRING 8--a global view on proteins and their functional interactions
in 630 organisms.},
journal = {Nucleic Acids Res},
year = {2009},
volume = {37},
pages = {D412--D416},
number = {Database issue},
month = {Jan},
abstract = {Functional partnerships between proteins are at the core of complex
cellular phenotypes, and the networks formed by interacting proteins
provide researchers with crucial scaffolds for modeling, data reduction
and annotation. STRING is a database and web resource dedicated to
protein-protein interactions, including both physical and functional
interactions. It weights and integrates information from numerous
sources, including experimental repositories, computational prediction
methods and public text collections, thus acting as a meta-database
that maps all interaction evidence onto a common set of genomes and
proteins. The most important new developments in STRING 8 over previous
releases include a URL-based programming interface, which can be
used to query STRING from other resources, improved interaction prediction
via genomic neighborhood in prokaryotes, and the inclusion of protein
structures. Version 8.0 of STRING covers about 2.5 million proteins
from 630 organisms, providing the most comprehensive view on protein-protein
interactions currently available. STRING can be reached at http://string-db.org/.},
doi = {10.1093/nar/gkn760},
institution = {European Molecular Biology Laboratory, Heidelberg, Germany.},
keywords = {Databases, Protein; Genomics; Multiprotein Complexes; Protein Interaction
Mapping; Proteins; User-Computer Interface},
owner = {fantine},
pii = {gkn760},
pmid = {18940858},
timestamp = {2010.10.21},
url = {http://dx.doi.org/10.1093/nar/gkn760}
}
@article{Jeong2001Lethality,
author = {H. Jeong and S. P. Mason and A.-L. Barab{\'a}si and Z. N. Oltvai},
title = {Lethality and centrality in protein networks},
journal = {Nature},
year = {2001},
volume = {411},
pages = {41--42},
pdf = {../local/jeon01.pdf},
file = {jeon01.pdf:local/jeon01.pdf:PDF},
subject = {bionet},
url = {http://www.nature.com/cgi-taf/DynaPage.taf?file=/nature/journal/v411/n6833/full/411041a0_fs.html&content_filetype=PDF}
}
@article{Jeong2001,
author = {H. Jeong and S. P. Mason and A. L. Barabási and Z. N. Oltvai},
title = {Lethality and centrality in protein networks.},
journal = {Nature},
year = {2001},
volume = {411},
pages = {41--42},
number = {6833},
month = {May},
doi = {10.1038/35075138},
institution = {Department of Physics, University of Notre Dame, Notre Dame, Indiana
46556, USA.},
keywords = {Fungal Proteins, genetics/physiology; Gene Deletion; Protein Binding;
Proteome; Saccharomyces cerevisiae, genetics/physiology; Signal Transduction},
language = {eng},
medline-pst = {ppublish},
owner = {Andrei Zinovyev},
pii = {35075138},
pmid = {11333967},
timestamp = {2011.04.07},
url = {http://dx.doi.org/10.1038/35075138}
}
@article{Jeong2000large-scale,
author = {H. Jeong and B. Tombor and R. Albert and Z. N. Oltvai and A.-L. Barab{\'a}si},
title = {The large-scale organization of metabolic networks},
journal = {Nature},
year = {2000},
volume = {407},
pages = {651--654},
pdf = {../local/jeon00.pdf},
file = {jeon00.pdf:local/jeon00.pdf:PDF},
subject = {bionet},
url = {http://www.nature.com/cgi-taf/DynaPage.taf?file=/nature/journal/v407/n6804/full/407651a0_fs.html&content_filetype=PDF}
}
@article{Jerebko2005Support,
author = {Anna K Jerebko and James D Malley and Marek Franaszek and Ronald
M Summers},
title = {Support vector machines committee classification method for computer-aided
polyp detection in {CT} colonography.},
journal = {Acad {R}adiol},
year = {2005},
volume = {12},
pages = {479-86},
number = {4},
month = {Apr},
abstract = {R{ATIONALE} {AND} {OBJECTIVES}: {A} new classification scheme for
the computer-aided detection of colonic polyps in computed tomographic
colonography is proposed. {MATERIALS} {AND} {METHODS}: {T}he scheme
involves an ensemble of support vector machines ({SVM}s) for classification,
a smoothed leave-one-out ({SLOO}) cross-validation method for obtaining
error estimates, and use of a bootstrap aggregation method for training
and model selection. {O}ur use of an ensemble of {SVM} classifiers
with bagging (bootstrap aggregation), built on different feature
subsets, is intended to improve classification performance compared
with single {SVM}s and reduce the number of false-positive detections.
{T}he bootstrap-based model-selection technique is used for tuning
{SVM} parameters. {I}n our first experiment, two independent data
sets were used: the first, for feature and model selection, and the
second, for testing to evaluate the generalizability of our model.
{I}n the second experiment, the test set that contained higher resolution
data was used for training and testing (using the {SLOO} method)
to compare {SVM} committee and single {SVM} performance. {RESULTS}:
{T}he overall sensitivity on independent test set was 75\%, with
1.5 false-positive detections/study, compared with 76\%-78\% sensitivity
and 4.5 false-positive detections/study estimated using the {SLOO}
method on the training set. {T}he sensitivity of the {SVM} ensemble
retrained on the former test set estimated using the {SLOO} method
was 81\%, which is 7\%-10\% greater than the sensitivity of a single
{SVM}. {T}he number of false-positive detections per study was 2.6,
a 1.5 times reduction compared with a single {SVM}. {CONCLUSION}:
{T}raining an {SVM} ensemble on one data set and testing it on the
independent data has shown that the {SVM} committee classification
method has good generalizability and achieves high sensitivity and
a low false-positive rate. {T}he model selection and improved error
estimation method are effective for computer-aided polyp detection.},
doi = {10.1016/j.acra.2004.04.024},
keywords = {, , 15831422},
pii = {S1076-6332(05)00038-3},
url = {http://dx.doi.org/10.1016/j.acra.2004.04.024}
}
@article{Jia2006Demonstration,
author = {Jia, P. and Shi, T. and Cai, Y. and Li, Y.},
title = {{D}emonstration of two novel methods for predicting functional si{RNA}
efficiency.},
journal = {BMC Bioinformatics},
year = {2006},
volume = {7},
pages = {271},
abstract = {BACKGROUND: siRNAs are small RNAs that serve as sequence determinants
during the gene silencing process called RNA interference (RNAi).
It is well know that siRNA efficiency is crucial in the RNAi pathway,
and the siRNA efficiency for targeting different sites of a specific
gene varies greatly. Therefore, there is high demand for reliable
siRNAs prediction tools and for the design methods able to pick up
high silencing potential siRNAs. RESULTS: In this paper, two systems
have been established for the prediction of functional siRNAs: (1)
a statistical model based on sequence information and (2) a machine
learning model based on three features of siRNA sequences, namely
binary description, thermodynamic profile and nucleotide composition.
Both of the two methods show high performance on the two datasets
we have constructed for training the model. CONCLUSION: Both of the
two methods studied in this paper emphasize the importance of sequence
information for the prediction of functional siRNAs. The way of denoting
a bio-sequence by binary system in mathematical language might be
helpful in other analysis work associated with fixed-length bio-sequence.},
doi = {10.1186/1471-2105-7-271},
pdf = {../local/Jia2006Demonstration.pdf},
file = {Jia2006Demonstration.pdf:local/Jia2006Demonstration.pdf:PDF},
keywords = {sirna},
pii = {1471-2105-7-271},
pmid = {16729898},
timestamp = {2006.10.12},
url = {http://dx.doi.org/10.1186/1471-2105-7-271}
}
@article{Jiang2009Compensatory,
author = {Jiang, D. and Zhou, S. and Chen, Y.-P. P.},
title = {Compensatory ability to null mutation in metabolic networks},
journal = {Biotechnol. Bioeng.},
year = {2009},
volume = {103},
pages = {361--369},
number = {2},
month = {Jun},
abstract = {Robustness is an inherent property of biological system. It is still
a limited understanding of how it is accomplished at the cellular
or molecular level. To this end, this article analyzes the impact
degree of each reaction to others, which is defined as the number
of cascading failures of following and/or forward reactions when
an initial reaction is deleted. By analyzing more than 800 organism's
metabolic networks, it suggests that the reactions with larger impact
degrees are likely essential and the universal reactions should also
be essential. Alternative metabolic pathways compensate null mutations,
which represents that average impact degrees for all organisms are
small. Interestingly, average impact degrees of archaea organisms
are smaller than other two categories of organisms, eukayote and
bacteria, indicating that archaea organisms have strong robustness
to resist the various perturbations during the evolution process.
The results show that scale-free feature and reaction reversibility
contribute to the robustness in metabolic networks. The optimal growth
temperature of organism also relates the robust structure of metabolic
network.},
doi = {10.1002/bit.22237},
pdf = {../local/Jiang2009Compensatory.pdf},
file = {Jiang2009Compensatory.pdf:Jiang2009Compensatory.pdf:PDF},
institution = {Shanghai Key Laboratory of Intelligent Information Processing, Fudan
University, Shanghai, China.},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pmid = {19160379},
timestamp = {2011.11.29},
url = {http://dx.doi.org/10.1002/bit.22237}
}
@article{Jiang2009Statistical,
author = {Jiang, H. and Wong, W. H.},
title = {Statistical inferences for isoform expression in {RNA-Seq}.},
journal = {Bioinformatics},
year = {2009},
volume = {25},
pages = {1026--1032},
number = {8},
month = {Apr},
abstract = {SUMMARY: The development of RNA sequencing (RNA-Seq) makes it possible
for us to measure transcription at an unprecedented precision and
throughput. However, challenges remain in understanding the source
and distribution of the reads, modeling the transcript abundance
and developing efficient computational methods. In this article,
we develop a method to deal with the isoform expression estimation
problem. The count of reads falling into a locus on the genome annotated
with multiple isoforms is modeled as a Poisson variable. The expression
of each individual isoform is estimated by solving a convex optimization
problem and statistical inferences about the parameters are obtained
from the posterior distribution by importance sampling. Our results
show that isoform expression inference in RNA-Seq is possible by
employing appropriate statistical methods.},
doi = {10.1093/bioinformatics/btp113},
pdf = {../local/Jiang2009Statistical.pdf},
file = {Jiang2009Statistical.pdf:Jiang2009Statistical.pdf:PDF},
institution = {Institute for Computational and Mathematical Engineering and Department
of Statistics, Stanford University, Stanford, CA 94305, USA.},
keywords = {ngs, rnaseq},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {btp113},
pmid = {19244387},
timestamp = {2012.03.06},
url = {http://dx.doi.org/10.1093/bioinformatics/btp113}
}
@article{Jiang-Ning2004Cooperativity,
author = {Jiang-Ning, S. and Wei-Jiang, L. and Wen-Bo, X.},
title = {Cooperativity of the oxidization of cysteines in globular proteins.},
journal = {J. {T}heor. {B}iol.},
year = {2004},
volume = {231},
pages = {85-95},
number = {1},
abstract = {Based on the 639 non-homologous proteins with 2910 cysteine-containing
segments of well-resolved three-dimensional structures, a novel approach
has been proposed to predict the disulfide-bonding state of cysteines
in proteins by constructing a two-stage classifier combining a first
global linear discriminator based on their amino acid composition
and a second local support vector machine classifier. {T}he overall
prediction accuracy of this hybrid classifier for the disulfide-bonding
state of cysteines in proteins has scored 84.1% and 80.1%, when measured
on cysteine and protein basis using the rigorous jack-knife procedure,
respectively. {I}t shows that whether cysteines should form disulfide
bonds depends not only on the global structural features of proteins
but also on the local sequence environment of proteins. {T}he result
demonstrates the applicability of this novel method and provides
comparable prediction performance compared with existing methods
for the prediction of the oxidation states of cysteines in proteins.},
doi = {10.1016/j.jtbi.2004.06.002},
pdf = {../local/Jiang-Ning2004Cooperativity.pdf},
file = {Jiang-Ning2004Cooperativity.pdf:local/Jiang-Ning2004Cooperativity.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://dx.doi.org/10.1016/j.jtbi.2004.06.002}
}
@article{Jin2007yeast,
author = {Fulai Jin and Larisa Avramova and Jing Huang and Tony Hazbun},
title = {A yeast two-hybrid smart-pool-array system for protein-interaction
mapping.},
journal = {Nat Methods},
year = {2007},
volume = {4},
pages = {405--407},
number = {5},
month = {May},
abstract = {We present here a new two-hybrid smart pool array (SPA) system in
which, instead of individual activation domain strains, well-designed
activation domain pools are screened in an array format that allows
built-in replication and prey-bait deconvolution. Using this method,
a Saccharomyces cerevisiae genome SPA increases yeast two-hybrid
screening efficiency by an order of magnitude.},
doi = {10.1038/nmeth1042},
institution = {Department of Molecular and Medical Pharmacology, David Geffen School
of Medicine, and the Molecular Biology Institute, University of California,
Los Angeles, California 90095, USA.},
keywords = {Genome, Fungal; Protein Interaction Mapping; Saccharomyces cerevisiae;
Saccharomyces cerevisiae Proteins; Two-Hybrid System Techniques},
owner = {phupe},
pii = {nmeth1042},
pmid = {17450148},
timestamp = {2010.09.01},
url = {http://dx.doi.org/10.1038/nmeth1042}
}
@incollection{Joachims1999Making,
author = {Joachims, T.},
title = {Making large-{S}cale {SVM} {L}earning {P}ractical},
booktitle = {Advances in {K}ernel {M}ethods - {S}upport {V}ector {L}earning},
publisher = {MIT Press},
year = {1999},
editor = {B. Sch{\"o}lkopf and C. Burges and A. Smola},
pages = {169--184},
pdf = {../local/Joachims1999Making.pdf},
file = {Joachims1999Making.pdf:local/Joachims1999Making.pdf:PDF}
}
@book{Joachims2002Learning,
title = {Learning to Classify Text Using Support Vector Machines},
publisher = {Kluwer Academic Publishers},
year = {2002},
author = {T. Joachims},
owner = {mahe},
timestamp = {2006.09.07}
}
@inproceedings{Joachims1999Transductive,
author = {Joachims, T.},
title = {Transductive Inference for Text Classification using Support Vector
Machines},
booktitle = {{ICML '99}: Proceedings of the Sixteenth International Conference
on Machine Learning},
year = {1999},
pages = {200--209},
address = {San Francisco, CA, USA},
publisher = {Morgan Kaufmann Publishers Inc.},
pdf = {../local/Joachims1999Transductive.pdf},
file = {Joachims1999Transductive.pdf:Joachims1999Transductive.pdf:PDF},
isbn = {1-55860-612-2},
keywords = {PUlearning},
owner = {fantine},
timestamp = {2009.06.09}
}
@inproceedings{Joachims97aprobabilistic,
author = {Joachims, T.},
title = {A Probabilistic Analysis of the {Rocchio} Algorithm with {TFIDF}
for Text Categorization},
booktitle = {{ICML '97}: Proceedings of the Fourteenth International Conference
on Machine Learning},
year = {1997},
pages = {143--151},
address = {Nashville, Tennessee, USA},
publisher = {Morgan Kaufmann Publishers Inc.},
owner = {mordelet},
timestamp = {2010.07.16}
}
@article{John2004Human,
author = {Bino John and Anton J Enright and Alexei Aravin and Thomas Tuschl
and Chris Sander and Debora S Marks},
title = {Human MicroRNA targets.},
journal = {PLoS Biol},
year = {2004},
volume = {2},
pages = {e363},
number = {11},
month = {Nov},
abstract = {MicroRNAs (miRNAs) interact with target mRNAs at specific sites to
induce cleavage of the message or inhibit translation. The specific
function of most mammalian miRNAs is unknown. We have predicted target
sites on the 3' untranslated regions of human gene transcripts for
all currently known 218 mammalian miRNAs to facilitate focused experiments.
We report about 2,000 human genes with miRNA target sites conserved
in mammals and about 250 human genes conserved as targets between
mammals and fish. The prediction algorithm optimizes sequence complementarity
using position-specific rules and relies on strict requirements of
interspecies conservation. Experimental support for the validity
of the method comes from known targets and from strong enrichment
of predicted targets in mRNAs associated with the fragile X mental
retardation protein in mammals. This is consistent with the hypothesis
that miRNAs act as sequence-specific adaptors in the interaction
of ribonuclear particles with translationally regulated messages.
Overrepresented groups of targets include mRNAs coding for transcription
factors, components of the miRNA machinery, and other proteins involved
in translational regulation, as well as components of the ubiquitin
machinery, representing novel feedback loops in gene regulation.
Detailed information about target genes, target processes, and open-source
software for target prediction (miRanda) is available at http://www.microrna.org.
Our analysis suggests that miRNA genes, which are about 1\% of all
human genes, regulate protein production for 10\% or more of all
human genes.},
doi = {10.1371/journal.pbio.0020363},
pdf = {../local/John2004Human.pdf},
file = {John2004Human.pdf:John2004Human.pdf:PDF},
institution = {Computational Biology Center, Memorial Sloan-Kettering Cancer Center,
New York, New York, USA.},
keywords = {sirna},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pmid = {15502875},
timestamp = {2009.10.28},
url = {http://dx.doi.org/10.1371/journal.pbio.0020363}
}
@inbook{John1996Stock,
pages = {303--316},
title = {Stock selection using {R}econ},
publisher = {World Scientific},
year = {1996},
editor = {Abu-Mostafa, Y. and Moody, J. and Refenes, P. and Weigend, A.},
author = {John, G. H. and Miller, P. and Kerber, R.},
pdf = {../local/John1996Stock.pdf},
file = {John1996Stock.pdf:John1996Stock.pdf:PDF},
owner = {jp},
timestamp = {2011.04.08}
}
@inproceedings{Johnson02Experimental,
author = {D.S. Johnson and G. Gutin and L.A. McGeoch and A. Yeo and W. Zhang
and A. Zverovich},
title = {Experimental Analysis of Heuristics for the ATSP},
booktitle = {The Travelling Salesman Problem and Its Variations},
year = {2002},
pages = {445--487}
}
@article{Johnson2007Genome-wide,
author = {Johnson, D. S. and Mortazavi, A. and Myers, R. M. and Wold, B.},
title = {Genome-wide mapping of in vivo protein-DNA interactions},
journal = {Science},
year = {2007},
volume = {316},
pages = {1497--1502},
number = {5830},
month = {Jun},
abstract = {In vivo protein-DNA interactions connect each transcription factor
with its direct targets to form a gene network scaffold. To map these
protein-DNA interactions comprehensively across entire mammalian
genomes, we developed a large-scale chromatin immunoprecipitation
assay (ChIPSeq) based on direct ultrahigh-throughput DNA sequencing.
This sequence census method was then used to map in vivo binding
of the neuron-restrictive silencer factor (NRSF; also known as REST,
for repressor element-1 silencing transcription factor) to 1946 locations
in the human genome. The data display sharp resolution of binding
position [+/-50 base pairs (bp)], which facilitated our finding motifs
and allowed us to identify noncanonical NRSF-binding motifs. These
ChIPSeq data also have high sensitivity and specificity [ROC (receiver
operator characteristic) area >/= 0.96] and statistical confidence
(P <10(-4)), properties that were important for inferring new candidate
interactions. These include key transcription factors in the gene
network that regulates pancreatic islet cell development.},
doi = {10.1126/science.1141319},
pdf = {../local/Johnson2007Genome-wide.pdf},
file = {Johnson2007Genome-wide.pdf:Johnson2007Genome-wide.pdf:PDF},
institution = {Department of Genetics, Stanford University School of Medicine, Stanford,
CA, 94305-5120, USA.},
owner = {jp},
pii = {1141319},
pmid = {17540862},
timestamp = {2008.12.09},
url = {http://dx.doi.org/10.1126/science.1141319}
}
@article{Johnson2005Kinomics,
author = {Johnson, S. A. and Hunter, T.},
title = {Kinomics: methods for deciphering the kinome.},
journal = {Nat. Methods},
year = {2005},
volume = {2},
pages = {17--25},
number = {1},
month = {Jan},
abstract = {Phosphorylation by protein kinases is the most widespread and well-studied
signaling mechanism in eukaryotic cells. Phosphorylation can regulate
almost every property of a protein and is involved in all fundamental
cellular processes. Cataloging and understanding protein phosphorylation
is no easy task: many kinases may be expressed in a cell, and one-third
of all intracellular proteins may be phosphorylated, representing
as many as 20,000 distinct phosphoprotein states. Defining the kinase
complement of the human genome, the kinome, has provided an excellent
starting point for understanding the scale of the problem. The kinome
consists of 518 kinases, and every active protein kinase phosphorylates
a distinct set of substrates in a regulated manner. Deciphering the
complex network of phosphorylation-based signaling is necessary for
a thorough and therapeutically applicable understanding of the functioning
of a cell in physiological and pathological states. We review contemporary
techniques for identifying physiological substrates of the protein
kinases and studying phosphorylation in living cells.},
doi = {10.1038/nmeth731},
pdf = {../local/Johnson2005Kinomics.pdf},
file = {Johnson2005Kinomics.pdf:Johnson2005Kinomics.pdf:PDF},
institution = {Molecular and Cell Biology Laboratory, Salk Institute, 10010 North
Torrey Pines Road, La Jolla, California 92037, USA.},
keywords = {csbcbook, csbcbook-ch2},
owner = {jp},
pii = {nmeth731},
pmid = {15789031},
timestamp = {2009.10.13},
url = {http://dx.doi.org/10.1038/nmeth731}
}
@article{Johnson2007Adjusting,
author = {W. Evan Johnson and Cheng Li and Ariel Rabinovic},
title = {Adjusting batch effects in microarray expression data using empirical
Bayes methods.},
journal = {Biostatistics},
year = {2007},
volume = {8},
pages = {118--127},
number = {1},
month = {Jan},
abstract = {Non-biological experimental variation or "batch effects" are commonly
observed across multiple batches of microarray experiments, often
rendering the task of combining data from these batches difficult.
The ability to combine microarray data sets is advantageous to researchers
to increase statistical power to detect biological phenomena from
studies where logistical considerations restrict sample size or in
studies that require the sequential hybridization of arrays. In general,
it is inappropriate to combine data sets without adjusting for batch
effects. Methods have been proposed to filter batch effects from
data, but these are often complicated and require large batch sizes
( > 25) to implement. Because the majority of microarray studies
are conducted using much smaller sample sizes, existing methods are
not sufficient. We propose parametric and non-parametric empirical
Bayes frameworks for adjusting data for batch effects that is robust
to outliers in small sample sizes and performs comparable to existing
methods for large samples. We illustrate our methods using two example
data sets and show that our methods are justifiable, easy to apply,
and useful in practice. Software for our method is freely available
at: http://biosun1.harvard.edu/complab/batch/.},
doi = {10.1093/biostatistics/kxj037},
institution = {Department of Biostatistics and Computational Biology, Dana-Farber
Cancer Institute, Boston, MA, USA.},
keywords = {Bayes Theorem; Data Interpretation, Statistical; Gene Expression Profiling,
methods; Humans; Oligonucleotide Array Sequence Analysis, methods},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {kxj037},
pmid = {16632515},
timestamp = {2012.02.29},
url = {http://dx.doi.org/10.1093/biostatistics/kxj037}
}
@article{Jojic2006Learning,
author = {Jojic, N. and Reyes-Gomez, M. and Heckerman, D. and Kadie, C. and
Schueler-Furman, O.},
title = {{L}earning {MHC} {I}--peptide binding.},
journal = {Bioinformatics},
year = {2006},
volume = {22},
pages = {e227--e235},
number = {14},
month = {Jul},
abstract = {MOTIVATION AND RESULTS: Motivated by the ability of a simple threading
approach to predict MHC I--peptide binding, we developed a new and
improved structure-based model for which parameters can be estimated
from additional sources of data about MHC-peptide binding. In addition
to the known 3D structures of a small number of MHC-peptide complexes
that were used in the original threading approach, we included three
other sources of information on peptide-MHC binding: (1) MHC class
I sequences; (2) known binding energies for a large number of MHC-peptide
complexes; and (3) an even larger binary dataset that contains information
about strong binders (epitopes) and non-binders (peptides that have
a low affinity for a particular MHC molecule). Our model significantly
outperforms the standard threading approach in binding energy prediction.
In our approach, which we call adaptive double threading, the parameters
of the threading model are learnable, and both MHC and peptide sequences
can be threaded onto structures of other alleles. These two properties
make our model appropriate for predicting binding for alleles for
which very little data (if any) is available beyond just their sequence,
including prediction for alleles for which 3D structures are not
available. The ability of our model to generalize beyond the MHC
types for which training data is available also separates our approach
from epitope prediction methods which treat MHC alleles as symbolic
types, rather than biological sequences. We used the trained binding
energy predictor to study viral infections in 246 HIV patients from
the West Australian cohort, and over 1000 sequences in HIV clade
B from Los Alamos National Laboratory database, capturing the course
of HIV evolution over the last 20 years. Finally, we illustrate short-,
medium-, and long-term adaptation of HIV to the human immune system.
AVAILABILITY: http://www.research.microsoft.com/~jojic/hlaBinding.html.},
doi = {10.1093/bioinformatics/btl255},
keywords = {immunoinformatics},
pii = {22/14/e227},
pmid = {16873476},
timestamp = {2007.01.25},
url = {http://dx.doi.org/10.1093/bioinformatics/btl255}
}
@book{Jolliffe1996Principal,
title = {Principal component analysis},
publisher = {Springer-Verlag},
year = {1996},
author = {I.T. Jolliffe},
address = {New-York}
}
@article{Jones2004Molecular,
author = {Jones, C. and Ford, E. and Gillett, C. and Ryder, K. and Merrett,
S. and Reis-Filho, J. S. and Fulford, L. G. and Hanby, A. and Lakhani,
S. R.},
title = {Molecular Cytogenetic Identification of Subgroups of Grade III Invasive
Ductal Breast Carcinomas with Different Clinical Outcomes},
journal = {Clin. Cancer Res.},
year = {2004},
volume = {10},
pages = {5988-5997},
number = {18},
abstract = {Tumor grade is an established indicator of breast cancer outcome,
although considerable heterogeneity exists even within-grade. Around
25% of grade III invasive ductal breast carcinomas are associated
with a "basal" phenotype, and these tumors are reported to be a distinct
subgroup. We have investigated whether this group of breast cancers
has a distinguishing pattern of genetic alterations and which of
these may relate to the different clinical outcome of these patients.
We performed comparative genomic hybridization (CGH) analysis on
43 grade III invasive ductal breast carcinomas positive for basal
cytokeratin 14, as well as 43 grade- and age-matched CK14-negative
controls, all with up to 25 years (median, 7 years) of clinical follow-up.
Significant differences in CGH alterations were seen between the
two groups in terms of mean number of changes (CK14+ve - 6.5, CK14-ve
- 10.3; P = 0.0012) and types of alterations at chromosomes 4q, 7q,
8q, 9p, 13q, 16p, 17p, 17q, 19p, 19q, 20p, 20q and Xp. Supervised
and unsupervised algorithms separated the two groups on CGH data
alone with 76% and 74% accuracy, respectively. Hierarchical clustering
revealed distinct subgroups, one of which contained 18 (42%) of the
CK14+ve tumors. This subgroup had significantly shorter overall survival
(P = 0.0414) than other grade III tumors, regardless of CK14 status,
and was an independent prognostic marker (P = 0.031). These data
provide evidence that the "basal" phenotype on its own does not convey
a poor prognosis. Basal tumors are also heterogeneous with only a
subset, identifiable by pattern of genetic alterations, exhibiting
a shorter overall survival. Robust characterization of this basal
group is necessary if it is to have a major impact on management
of patients with breast cancer.},
doi = {10.1158/1078-0432.CCR-03-0731},
eprint = {http://clincancerres.aacrjournals.org/cgi/reprint/10/18/5988.pdf},
pdf = {../local/Jones2004Molecular.pdf},
file = {Jones2004Molecular.pdf:Jones2004Molecular.pdf:PDF},
keywords = {breastcancer, cgh},
owner = {jp},
timestamp = {2008.12.08},
url = {http://clincancerres.aacrjournals.org/cgi/content/abstract/10/18/5988}
}
@article{Jones1997Development,
author = {G. Jones and P. Willett and R. C. Glen and A. R. Leach and R. Taylor},
title = {{D}evelopment and validation of a genetic algorithm for flexible
docking.},
journal = {J. Mol. Biol.},
year = {1997},
volume = {267},
pages = {727--748},
number = {3},
month = {Apr},
abstract = {Prediction of small molecule binding modes to macromolecules of known
three-dimensional structure is a problem of paramount importance
in rational drug design (the "docking" problem). We report the development
and validation of the program GOLD (Genetic Optimisation for Ligand
Docking). GOLD is an automated ligand docking program that uses a
genetic algorithm to explore the full range of ligand conformational
flexibility with partial flexibility of the protein, and satisfies
the fundamental requirement that the ligand must displace loosely
bound water on binding. Numerous enhancements and modifications have
been applied to the original technique resulting in a substantial
increase in the reliability and the applicability of the algorithm.
The advanced algorithm has been tested on a dataset of 100 complexes
extracted from the Brookhaven Protein DataBank. When used to dock
the ligand back into the binding site, GOLD achieved a 71\% success
rate in identifying the experimental binding mode.},
doi = {10.1006/jmbi.1996.0897},
keywords = {Algorithms, Binding Sites, Computer Simulation, Crystallography, Genetic,
Humans, Ligands, Models, Molecular, NADP, Protein Binding, Protein
Conformation, Proteins, Tetrahydrofolate Dehydrogenase, X-Ray, 9126849},
owner = {mahe},
pii = {97-9},
pmid = {9126849},
timestamp = {2006.09.05},
url = {http://dx.doi.org/10.1006/jmbi.1996.0897}
}
@article{Jones2002DNA,
author = {Peter A Jones},
title = {DNA methylation and cancer.},
journal = {Oncogene},
year = {2002},
volume = {21},
pages = {5358--5360},
number = {35},
month = {Aug},
abstract = {There is tremendous ferment in the field of epigenetics as the relationships
between chromatin structure and DNA methylation patterns become clearer.
Central to this activity is the realization that the 'histone code',
which involves the post-translational modification of histones and
which has important ramifications for chromatin structure, may be
linked to the DNA cytosine methylation pattern. New discoveries have
suggested that histone lysine 9 methylation is implicated in the
spread of heterochromatin in Drosophila and other organisms. Very
recently it has been found that histone lysine 9 methylation is also
necessary for some DNA methylation in Neurospora and plants. There
is therefore the possibility that these two processes are closely
linked, suggesting ways in which DNA methylation patterns may be
established during normal development. Understanding these processes
is fundamental to understanding what goes awry during the process
of aging and carcinogenesis where DNA methylation patterns become
substantially altered and contribute to the malignant phenotype.},
doi = {10.1038/sj.onc.1205597},
institution = {USC/Norris Comprehensive Cancer Center, Department of Urology, Keck
School of Medicine of the University of Southern California, 1441
Eastlake Avenue, MS 8302L, Los Angeles, California, CA 90089-9181.
jones_p@ccnt.hsc.usc.edu},
keywords = {Animals; Chromatin; CpG Islands; DNA Methylation; DNA, Neoplasm; Gene
Expression Regulation; Gene Silencing; Histone-Lysine N-Methyltransferase;
Humans; Neoplasms; Plants; Transcription, Genetic},
owner = {ljacob},
pmid = {12154398},
timestamp = {2009.09.14},
url = {http://dx.doi.org/10.1038/sj.onc.1205597}
}
@article{Jones2002fundamental,
author = {Jones, P. A. and Baylin, S. B.},
title = {The fundamental role of epigenetic events in cancer},
journal = {Nat. Rev. Genet.},
year = {2002},
volume = {3},
pages = {415--428},
number = {6},
month = {Jun},
abstract = {Patterns of DNA methylation and chromatin structure are profoundly
altered in neoplasia and include genome-wide losses of, and regional
gains in, DNA methylation. The recent explosion in our knowledge
of how chromatin organization modulates gene transcription has further
highlighted the importance of epigenetic mechanisms in the initiation
and progression of human cancer. These epigenetic changes -- in particular,
aberrant promoter hypermethylation that is associated with inappropriate
gene silencing -- affect virtually every step in tumour progression.
In this review, we discuss these epigenetic events and the molecular
alterations that might cause them and/or underlie altered gene expression
in cancer.},
doi = {10.1038/nrg816},
pdf = {../local/Jones2002fundamental.pdf},
file = {Jones2002fundamental.pdf:Jones2002fundamental.pdf:PDF},
institution = {USC/Norris Comprehensive Cancer Center, Department of Urology, Keck
School of Medicine, University of Southern California, 1441 Eastlake
Avenue, MS 8302L, Los Angeles, California 90089-9181, USA. jones_p@ccnt.hsc.usc.edu},
owner = {jp},
pii = {nrg816},
pmid = {12042769},
timestamp = {2008.12.09},
url = {http://dx.doi.org/10.1038/nrg816}
}
@article{Jones-Rhoades2004Computational,
author = {Matthew W Jones-Rhoades and David P Bartel},
title = {Computational identification of plant microRNAs and their targets,
including a stress-induced miRNA.},
journal = {Mol Cell},
year = {2004},
volume = {14},
pages = {787--799},
number = {6},
month = {Jun},
abstract = {MicroRNAs (miRNAs) are approximately 21-nucleotide RNAs, some of which
have been shown to play important gene-regulatory roles during plant
development. We developed comparative genomic approaches to systematically
identify both miRNAs and their targets that are conserved in Arabidopsis
thaliana and rice (Oryza sativa). Twenty-three miRNA candidates,
representing seven newly identified gene families, were experimentally
validated in Arabidopsis, bringing the total number of reported miRNA
genes to 92, representing 22 families. Nineteen newly identified
target candidates were confirmed by detecting mRNA fragments diagnostic
of miRNA-directed cleavage in plants. Overall, plant miRNAs have
a strong propensity to target genes controlling development, particularly
those of transcription factors and F-box proteins. However, plant
miRNAs have conserved regulatory functions extending beyond development,
in that they also target superoxide dismutases, laccases, and ATP
sulfurylases. The expression of miR395, the sulfurylase-targeting
miRNA, increases upon sulfate starvation, showing that miRNAs can
be induced by environmental stress.},
doi = {10.1016/j.molcel.2004.05.027},
pdf = {../local/Jones-Rhoades2004Computational.pdf},
file = {Jones-Rhoades2004Computational.pdf:Jones-Rhoades2004Computational.pdf:PDF},
institution = {Whitehead Institute for Biomedical Research and Department of Biology,
Massachusetts Institute of Technology, 9 Cambridge Center, Cambridge,
MA 02142, USA.},
keywords = {sirna},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {S1097276504003284},
pmid = {15200956},
timestamp = {2009.10.28},
url = {http://dx.doi.org/10.1016/j.molcel.2004.05.027}
}
@article{Jong2004Breakpoint,
author = {Jong, K. and Marchiori, E. and Meijer, G. and Vaart, A. V. D. and
Ylstra, B.},
title = {Breakpoint identification and smoothing of array comparative genomic
hybridization data.},
journal = {Bioinformatics},
year = {2004},
volume = {20},
pages = {3636--3637},
number = {18},
month = {Dec},
abstract = {SUMMARY: We describe a tool, called aCGH-Smooth, for the automated
identification of breakpoints and smoothing of microarray comparative
genomic hybridization (array CGH) data. aCGH-Smooth is written in
visual C++, has a user-friendly interface including a visualization
of the results and user-defined parameters adapting the performance
of data smoothing and breakpoint recognition. aCGH-Smooth can handle
array-CGH data generated by all array-CGH platforms: BAC, PAC, cosmid,
cDNA and oligo CGH arrays. The tool has been successfully applied
to real-life data. AVAILABILITY: aCGH-Smooth is free for researchers
at academic and non-profit institutions at http://www.few.vu.nl/~vumarray/.},
doi = {10.1093/bioinformatics/bth355},
pdf = {../local/Jong2004Breakpoint.pdf},
file = {Jong2004Breakpoint.pdf:Jong2004Breakpoint.pdf:PDF},
institution = {>},
keywords = {cgh},
owner = {jp},
pii = {bth355},
pmid = {15201182},
timestamp = {2008.12.09},
url = {http://dx.doi.org/10.1093/bioinformatics/bth355}
}
@article{Jonsson1999Xpose,
author = {E. N. Jonsson and M. O. Karlsson},
title = {{X}pose--an {S}-{PLUS} based population pharmacokinetic/pharmacodynamic
model building aid for {NONMEM}},
journal = {Comput Meth Prog Bio},
year = {1999},
volume = {58},
pages = {51-64},
number = {1}
}
@incollection{Jorgensen2003Sense,
author = {Jorgensen, R. A.},
title = {Sense cosuppression in plants: Past, present, and future},
booktitle = {RNAi: A guide to gene silencing},
publisher = {Cold Spring Harbor Laboratory Press},
year = {2003},
editor = {Hannon, G. J.},
address = {Cold Spring Harbor, NY},
keywords = {sirna},
owner = {vert},
timestamp = {2006.03.29}
}
@article{Jorgensen2004many,
author = {W. L. Jorgensen},
title = {{T}he many roles of computation in drug discovery.},
journal = {Science},
year = {2004},
volume = {303},
pages = {1813--1818},
number = {5665},
month = {Mar},
abstract = {An overview is given on the diverse uses of computational chemistry
in drug discovery. Particular emphasis is placed on virtual screening,
de novo design, evaluation of drug-likeness, and advanced methods
for determining protein-ligand binding.},
doi = {10.1126/science.1096361},
pdf = {../local/Jorgensen2004many.pdf},
file = {Jorgensen2004many.pdf:Jorgensen2004many.pdf:PDF},
keywords = {chemoinformatics},
owner = {mahe},
pii = {303/5665/1813},
pmid = {15031495},
timestamp = {2006.08.15},
url = {http://dx.doi.org/10.1126/science.1096361}
}
@article{Jorissen2005Virtual,
author = {R. N. Jorissen and M. K. Gilson},
title = {Virtual screening of molecular databases using a support vector machine.},
journal = {J {C}hem {I}nf {M}odel},
year = {2005},
volume = {45},
pages = {549-61},
number = {3},
abstract = {The {S}upport {V}ector {M}achine ({SVM}) is an algorithm that derives
a model used for the classification of data into two categories and
which has good generalization properties. {T}his study applies the
{SVM} algorithm to the problem of virtual screening for molecules
with a desired activity. {I}n contrast to typical applications of
the {SVM}, we emphasize not classification but enrichment of actives
by using a modified version of the standard {SVM} function to rank
molecules. {T}he method employs a simple and novel criterion for
picking molecular descriptors and uses cross-validation to select
{SVM} parameters. {T}he resulting method is more effective at enriching
for active compounds with novel chemistries than binary fingerprint-based
methods such as binary kernel discrimination.},
doi = {10.1021/ci049641u},
pdf = {../local/Jorissen2005Virtual.pdf},
file = {Jorissen2005Virtual.pdf:local/Jorissen2005Virtual.pdf:PDF},
keywords = {biosvm},
url = {http://dx.doi.org/10.1021/ci049641u}
}
@article{Reactome2005,
author = {Joshi-Tope, G. and Gillespie, M. and Vastrik, I. and D'Eustachio,
P. and Schmidt, E. and de Bono, B. and Jassal, B. and Gopinath, G.
R. and Wu, G. R. and Matthews, L. and Lewis, S. and Birney, E. and
Stein, L.},
title = {Reactome: a knowledgebase of biological pathways},
journal = {Nucleic {A}cids {R}es},
year = {2005},
volume = {33},
pages = {D428-32},
number = {Database issue},
note = {1362-4962 (Electronic) Journal Article},
abstract = {Reactome, located at http://www.reactome.org is a curated, peer-reviewed
resource of human biological processes. {G}iven the genetic makeup
of an organism, the complete set of possible reactions constitutes
its reactome. {T}he basic unit of the {R}eactome database is a reaction;
reactions are then grouped into causal chains to form pathways. {T}he
{R}eactome data model allows us to represent many diverse processes
in the human system, including the pathways of intermediary metabolism,
regulatory pathways, and signal transduction, and high-level processes,
such as the cell cycle. {R}eactome provides a qualitative framework,
on which quantitative data can be superimposed. {T}ools have been
developed to facilitate custom data entry and annotation by expert
biologists, and to allow visualization and exploration of the finished
dataset as an interactive process map. {A}lthough our primary curational
domain is pathways from {H}omo sapiens, we regularly create electronic
projections of human pathways onto other organisms via putative orthologs,
thus making {R}eactome relevant to model organism research communities.
{T}he database is publicly available under open source terms, which
allows both its content and its software infrastructure to be freely
used and redistributed.},
keywords = {Animals *Databases, Factual Gene Expression Profiling Humans Metabolism
*Physiological Processes Research Support, Non-U.S. Gov't Research
Support, U.S. Gov't, P.H.S. Signal Transduction User-Computer Interface}
}
@article{Jovanovic2010epigenetics,
author = {Jovana Jovanovic and Jo Anders Rønneberg and Jörg Tost and Vessela
Kristensen},
title = {The epigenetics of breast cancer.},
journal = {Mol Oncol},
year = {2010},
volume = {4},
pages = {242--254},
number = {3},
month = {Jun},
abstract = {Epigenetic changes can be defined as stable molecular alterations
of a cellular phenotype such as the gene expression profile of a
cell that are heritable during somatic cell divisions (and sometimes
germ line transmissions) but do not involve changes of the DNA sequence
itself. Epigenetic phenomena are mediated by several molecular mechanisms
comprising histone modifications, polycomb/trithorax protein complexes,
small non-coding or antisense RNAs and DNA methylation. These different
modifications are closely interconnected. Epigenetic regulation is
critical in normal growth and development and closely conditions
the transcriptional potential of genes. Epigenetic mechanisms convey
genomic adaption to an environment thereby ultimately contributing
towards given phenotype. In this review we will describe the various
aspects of epigenetics and in particular DNA methylation in breast
carcinogenesis and their potential application for diagnosis, prognosis
and treatment decision.},
doi = {10.1016/j.molonc.2010.04.002},
institution = {Department for Clinical Molecular Biology (EpiGen), Institute for
Clinical Medicine, Akershus University Hospital, University of Oslo,
Norway.},
keywords = {Breast Neoplasms, diagnosis/genetics/pathology/therapy; Chromatin,
chemistry/metabolism; DNA Methylation; DNA Modification Methylases,
metabolism; DNA, chemistry/metabolism; Epigenesis, Genetic; Female;
Gene Expression Regulation, Neoplastic; Histones, metabolism; Humans;
MicroRNAs, genetics/metabolism; Molecular Structure; Prognosis; Receptors,
Estrogen, genetics/metabolism; Tumor Markers, Biological, metabolism},
language = {eng},
medline-pst = {ppublish},
owner = {philippe},
pii = {S1574-7891(10)00024-4},
pmid = {20627830},
timestamp = {2011.06.04},
url = {http://dx.doi.org/10.1016/j.molonc.2010.04.002}
}
@article{Juditsky2000Functional,
author = {Juditsky, A. and Nemirovski, A.},
title = {Functional {A}ggregation for {N}onparametric {E}stimation},
journal = {Ann. {S}tat.},
year = {2000},
volume = {28},
pages = {681--712},
number = {3},
month = {June},
pdf = {../local/judi00.pdf},
file = {judi00.pdf:local/judi00.pdf:PDF},
subject = {stat},
url = {http://ftp://ftp.irisa.fr/techreports/1996/PI-993.ps.gz}
}
@article{Jonsdottir2005Prediction,
author = {Svava Osk Jónsdóttir and Flemming Steen Jørgensen and Søren Brunak},
title = {Prediction methods and databases within chemoinformatics: emphasis
on drugs and drug candidates.},
journal = {Bioinformatics},
year = {2005},
volume = {21},
pages = {2145--2160},
number = {10},
month = {May},
abstract = {MOTIVATION: To gather information about available databases and chemoinformatics
methods for prediction of properties relevant to the drug discovery
and optimization process. RESULTS: We present an overview of the
most important databases with 2-dimensional and 3-dimensional structural
information about drugs and drug candidates, and of databases with
relevant properties. Access to experimental data and numerical methods
for selecting and utilizing these data is crucial for developing
accurate predictive in silico models. Many interesting predictive
methods for classifying the suitability of chemical compounds as
potential drugs, as well as for predicting their physico-chemical
and ADMET properties have been proposed in recent years. These methods
are discussed, and some possible future directions in this rapidly
developing field are described.},
doi = {10.1093/bioinformatics/bti314},
keywords = {Chemistry, Pharmaceutical; Computational Biology; Databases, Factual;
Drug Design; Models, Chemical; Models, Molecular; Pharmaceutical
Preparations; Structure-Activity Relationship},
owner = {vert},
pii = {bti314},
pmid = {15713739},
timestamp = {2007.08.02},
url = {http://dx.doi.org/10.1093/bioinformatics/bti314}
}
@article{Kueffner2012Inferring,
author = {K\"{u}ffner, R. and Petri, T. and Tavakkolkhah, P. and Windhager,
L. and Zimmer, R},
title = {Inferring gene regulatory networks by {ANOVA}},
journal = {Bioinformatics},
year = {2012},
doi = {10.1093/bioinformatics/bts143},
owner = {anne-clairehaury},
timestamp = {2012.03.23},
url = {http://dx.doi.org/10.1093/bioinformatics/bts143}
}
@article{Kahraman2007Shape,
author = {A. Kahraman and R. J. Morris and R. A. Laskowski and J. M. Thornton},
title = {Shape variation in protein binding pockets and their ligands.},
journal = {J. Mol. Biol.},
year = {2007},
volume = {368},
pages = {283--301},
number = {1},
month = {Apr},
abstract = {A common assumption about the shape of protein binding pockets is
that they are related to the shape of the small ligand molecules
that can bind there. But to what extent is that assumption true?
Here we use a recently developed shape matching method to compare
the shapes of protein binding pockets to the shapes of their ligands.
We find that pockets binding the same ligand show greater variation
in their shapes than can be accounted for by the conformational variability
of the ligand. This suggests that geometrical complementarity in
general is not sufficient to drive molecular recognition. Nevertheless,
we show when considering only shape and size that a significant proportion
of the recognition power of a binding pocket for its ligand resides
in its shape. Additionally, we observe a "buffer zone" or a region
of free space between the ligand and protein, which results in binding
pockets being on average three times larger than the ligand that
they bind.},
doi = {10.1016/j.jmb.2007.01.086},
keywords = {Binding Sites; Computer Simulation; Ligands; Models, Molecular; Models,
Statistical; Protein Binding; Protein Conformation; Protein Folding},
owner = {laurent},
pii = {S0022-2836(07)00164-7},
pmid = {17337005},
timestamp = {2008.07.08},
url = {http://dx.doi.org/10.1016/j.jmb.2007.01.086}
}
@article{Kalatzis2003Support,
author = {I. Kalatzis and D. Pappas and N. Piliouras and D. Cavouras},
title = {Support vector machines based analysis of brain {SPECT} images for
determining cerebral abnormalities in asymptomatic diabetic patients.},
journal = {Med {I}nform {I}nternet {M}ed},
year = {2003},
volume = {28},
pages = {221-30},
number = {3},
month = {Sep},
abstract = {Purpose: {A}n image processing method was developed to investigate
whether brain {SPECT} images of patients with diabetes mellitus type
{II} ({DMII}) and no brain damage differ from those of normal subjects.
{M}aterials and methods: {T}wenty-five {DMII} patients and eight
healthy volunteers underwent brain 99m{T}c-{B}icisate {SPECT} examination.
{A} semi-automatic method, allowing for physician's interaction,
was developed to delineate specific brain regions ({ROI}s) on the
{SPECT} images. {T}wenty-eight features from the grey-level histogram
and the spatial-dependence matrix were computed from numerous small
image-samples collected from each specific {ROI}. {C}lassification
into 'diabetics' and 'non-diabetics' was performed for each {ROI}
separately. {T}he classical least squares-minimum distance ({LSMD})
classifier and the recently developed support vector machines ({SVM})
classifier were used. {S}ystem performance was evaluated by means
of the leave-one-out method; one sample was left out, the classifier
was trained by the rest of the samples, and the left-out sample was
classified. {B}y repeating for all samples, the classifier's performance
could be tested on data not incorporated in its design. {R}esults:
{H}ighest classification accuracies ({LSMD}: 97.8\%, {SVM}: 99.1\%)
were achieved at the right occipital lobule employing two features,
the standard deviation and entropy. {F}or the rest of the {ROI}s
classification accuracies ranged between 84.5 and 98.6\%. {C}onclusion:
{O}ur findings indicate cerebral blood flow disruption in patients
with {DMII}. {T}he proposed system may assist physicians in evaluating
cerebral blood flow in patients with {DMII} undergoing brain {SPECT}.},
doi = {10.1080/14639230310001613449},
pii = {YC711HD68JH0RXQY},
url = {http://dx.doi.org/10.1080/14639230310001613449}
}
@article{Kalatzis2004Design,
author = {I. Kalatzis and N. Piliouras and E. Ventouras and C. C. Papageorgiou
and A. D. Rabavilas and D. Cavouras},
title = {Design and implementation of an {SVM}-based computer classification
system for discriminating depressive patients from healthy controls
using the {P}600 component of {ERP} signals.},
journal = {Comput {M}ethods {P}rograms {B}iomed},
year = {2004},
volume = {75},
pages = {11-22},
number = {1},
month = {Jul},
abstract = {A computer-based classification system has been designed capable of
distinguishing patients with depression from normal controls by event-related
potential ({ERP}) signals using the {P}600 component. {C}linical
material comprised 25 patients with depression and an equal number
of gender and aged-matched healthy controls. {A}ll subjects were
evaluated by a computerized version of the digit span {W}echsler
test. {EEG} activity was recorded and digitized from 15 scalp electrodes
(leads). {S}eventeen features related to the shape of the waveform
were generated and were employed in the design of an optimum support
vector machine ({SVM}) classifier at each lead. {T}he outcomes of
those {SVM} classifiers were selected by a majority-vote engine ({MVE}),
which assigned each subject to either the normal or depressive classes.
{MVE} classification accuracy was 94\% when using all leads and 92\%
or 82\% when using only the right or left scalp leads, respectively.
{T}hese findings support the hypothesis that depression is associated
with dysfunction of right hemisphere mechanisms mediating the processing
of information that assigns a specific response to a specific stimulus,
as those mechanisms are reflected by the {P}600 component of {ERP}s.
{O}ur method may aid the further understanding of the neurophysiology
underlying depression, due to its potentiality to integrate theories
of depression and psychophysiology.},
doi = {10.1016/j.cmpb.2003.09.003},
pdf = {../local/Kalatzis2004Design.pdf},
file = {Kalatzis2004Design.pdf:local/Kalatzis2004Design.pdf:PDF},
pii = {S0169260703001305},
url = {http://dx.doi.org/10.1016/j.cmpb.2003.09.003}
}
@article{Kallioniemi2010DNA,
author = {Anne Kallioniemi},
title = {{DNA} copy number analysis on tissue microarrays.},
journal = {Methods Mol Biol},
year = {2010},
volume = {664},
pages = {127--134},
abstract = {Detection of DNA sequence copy number changes is essential in both
clinical practice and basic research, especially in cancer research.
The combination of fluorescence in situ hybridization (FISH) and
tissue microarray (TMA) technology provides high-throughput means
for the evaluation of genetic aberrations in a large number of tissue
samples. FISH on TMA is technically demanding and several protocols
that include a variety of tissue pretreatment steps have been developed
to improve the success of this methodology. Despite of the technical
difficulties, FISH analysis on TMA has been successfully used not
only to uncover genetic alterations in various malignancies but to
also rapidly establish the clinical significance of such changes.},
doi = {10.1007/978-1-60761-806-5\_13},
institution = {Institute of Medical Technology, University of Tampere and Tampere
University Hospital, Tampere, Finland, anne.kallioniemi@uta.fi.},
language = {eng},
medline-pst = {ppublish},
owner = {philippe},
pmid = {20690059},
timestamp = {2010.08.08},
url = {http://dx.doi.org/10.1007/978-1-60761-806-5_13}
}
@article{Kallioniemi2008CGH,
author = {Kallioniemi, A.},
title = {{CGH} microarrays and cancer.},
journal = {Curr Opin Biotechnol},
year = {2008},
volume = {19},
pages = {36--40},
number = {1},
month = {Feb},
abstract = {Genetic alterations are a key feature of cancer cells and typically
target biological processes and pathways that contribute to cancer
pathogenesis. Array-based comparative genomic hybridization (aCGH)
has provided a wealth of new information on copy number changes in
cancer on a genome-wide level and aCGH data have also been utilized
in cancer classification. More importantly, aCGH analyses have allowed
highly accurate localization of specific genetic alterations that,
for example, are associated with tumor progression, therapy response,
or patient outcome. The genes involved in these aberrations are likely
to contribute to cancer pathogenesis, and the high-resolution mapping
by aCGH greatly facilitates the subsequent identification of these
cancer-associated genes.},
doi = {10.1016/j.copbio.2007.11.004},
pdf = {../local/Kallioniemi2008CGH.pdf},
file = {Kallioniemi2008CGH.pdf:Kallioniemi2008CGH.pdf:PDF},
institution = {Laboratory of Cancer Genetics, Tampere University Hospital and Institute
of Medical Technology, University of Tampere, Biokatu 6, Tampere
FI-33014, Finland. anne.kallioniemi@uta.fi},
keywords = {csbcbook, csbcbook-ch2, cgh},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {S0958-1669(07)00148-6},
pmid = {18162393},
timestamp = {2009.10.18},
url = {http://dx.doi.org/10.1016/j.copbio.2007.11.004}
}
@article{Kallioniemi1992Comparative,
author = {A. Kallioniemi and O. P. Kallioniemi and D. Sudar and D. Rutovitz
and J. W. Gray and F. Waldman and D. Pinkel},
title = {Comparative genomic hybridization for molecular cytogenetic analysis
of solid tumors.},
journal = {Science},
year = {1992},
volume = {258},
pages = {818--821},
number = {5083},
month = {Oct},
abstract = {Comparative genomic hybridization produces a map of DNA sequence copy
number as a function of chromosomal location throughout the entire
genome. Differentially labeled test DNA and normal reference DNA
are hybridized simultaneously to normal chromosome spreads. The hybridization
is detected with two different fluorochromes. Regions of gain or
loss of DNA sequences, such as deletions, duplications, or amplifications,
are seen as changes in the ratio of the intensities of the two fluorochromes
along the target chromosomes. Analysis of tumor cell lines and primary
bladder tumors identified 16 different regions of amplification,
many in loci not previously known to be amplified.},
pdf = {../local/Kallioniemi1992Comparative.pdf},
file = {Kallioniemi1992Comparative.pdf:Kallioniemi1992Comparative.pdf:PDF},
institution = {Department of Laboratory Medicine, University of California, San
Francisco 94143.},
keywords = {csbcbook, csbcbook-ch2, cgh},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pmid = {1359641},
timestamp = {2009.10.18},
url = {http://www.sciencemag.org/cgi/reprint/258/5083/818}
}
@article{Kallioniemi2001Tissue,
author = {O. P. Kallioniemi and U. Wagner and J. Kononen and G. Sauter},
title = {Tissue microarray technology for high-throughput molecular profiling
of cancer.},
journal = {Hum Mol Genet},
year = {2001},
volume = {10},
pages = {657--662},
number = {7},
month = {Apr},
abstract = {Tissue microarray (TMA) technology allows rapid visualization of molecular
targets in thousands of tissue specimens at a time, either at the
DNA, RNA or protein level. The technique facilitates rapid translation
of molecular discoveries to clinical applications. By revealing the
cellular localization, prevalence and clinical significance of candidate
genes, TMAs are ideally suitable for genomics-based diagnostic and
drug target discovery. TMAs have a number of advantages compared
with conventional techniques. The speed of molecular analyses is
increased by more than 100-fold, precious tissues are not destroyed
and a very large number of molecular targets can be analyzed from
consecutive TMA sections. The ability to study archival tissue specimens
is an important advantage as such specimens are usually not applicable
in other high-throughput genomic and proteomic surveys. Construction
and analysis of TMAs can be automated, increasing the throughput
even further. Most of the applications of the TMA technology have
come from the field of cancer research. Examples include analysis
of the frequency of molecular alterations in large tumor materials,
exploration of tumor progression, identification of predictive or
prognostic factors and validation of newly discovered genes as diagnostic
and therapeutic targets.},
institution = {Cancer Genetics Branch, National Human Genome Research Institute,
National Institutes of Health, 49 Convent Drive, Room 4A24, MSC 4465,
Bethesda, MD 20892, USA. okalli@nhgri.nih.gov},
keywords = {Animals; Genetic Techniques; Humans; In Situ Hybridization, methods;
Neoplasms, metabolism/pathology; Oligonucleotide Array Sequence Analysis;
Tissue Distribution},
language = {eng},
medline-pst = {ppublish},
owner = {philippe},
pmid = {11257096},
timestamp = {2010.08.08}
}
@article{Kalousis2007Stability,
author = {Kalousis, A. and Prados, J. and Hilario, M.},
title = {Stability of feature selection algorithms: a study on high-dimensional
spaces},
journal = {Knowledge and information systems},
year = {2007},
volume = {12},
pages = {95--116},
number = {1},
publisher = {Springer}
}
@article{Kam96documentimage,
author = {A. C. Kam and G. E. Kopec},
title = {Document image decoding by heuristic search},
journal = {IEEE Trans. Pattern Anal. Mach. Intell.},
year = {1996},
volume = {18},
pages = {945--950}
}
@article{Kamangar2006Patterns,
author = {Kamangar, F. and Dores, G. M. and Anderson, W. F.},
title = {Patterns of cancer incidence, mortality, and prevalence across five
continents: defining priorities to reduce cancer disparities in different
geographic regions of the world},
journal = {J. Clin. Oncol.},
year = {2006},
volume = {24},
pages = {2137--2150},
number = {14},
month = {May},
abstract = {Efforts to reduce global cancer disparities begin with an understanding
of geographic patterns in cancer incidence, mortality, and prevalence.
Using the GLOBOCAN (2002) and Cancer Incidence in Five Continents
databases, we describe overall cancer incidence, mortality, and prevalence,
age-adjusted temporal trends, and age-specific incidence patterns
in selected geographic regions of the world. For the eight most common
malignancies-cancers of lung, breast, colon and rectum, stomach,
prostate, liver, cervix, and esophagus-the most important risk factors,
cancer prevention and control measures are briefly reviewed. In 2002,
an estimated 11 million new cancer cases and 7 million cancer deaths
were reported worldwide; nearly 25 million persons were living with
cancer. Among the eight most common cancers, global disparities in
cancer incidence, mortality, and prevalence are evident, likely due
to complex interactions of nonmodifiable (ie, genetic susceptibility
and aging) and modifiable risk factors (ie, tobacco, infectious agents,
diet, and physical activity). Indeed, when risk factors among populations
are intertwined with differences in individual behaviors, cultural
beliefs and practices, socioeconomic conditions, and health care
systems, global cancer disparities are inevitable. For the eight
most common cancers, priorities for reducing cancer disparities are
discussed.},
doi = {10.1200/JCO.2005.05.2308},
institution = {gy and Biostatistics Branches, Division of Cancer Epidemiology and
Genetics, National Institutes of Health, Department of Health and
Human Services, Rockville, MD 20852-7244, USA.},
owner = {jp},
pii = {24/14/2137},
pmid = {16682732},
timestamp = {2008.11.26},
url = {http://dx.doi.org/10.1200/JCO.2005.05.2308}
}
@article{Kamath2003Systematic,
author = {Kamath, R. S. and Fraser, A. G. and Dong, Y. and Poulin, G. and Durbin,
R. and Gotta, M. and Kanapin, A. and Le Bot, N. and Moreno, S. and
Sohrmann, M. and Welchman, D. P. and Zipperlen, P and Ahringer, J.},
title = {Systematic functional analysis of the {C}aenorhabditis elegans genome
using {RNA}i},
journal = {Nature},
year = {2003},
volume = {421},
pages = {231-237},
number = {6920},
month = {Jan},
abstract = {A principal challenge currently facing biologists is how to connect
the complete {DNA} sequence of an organism to its development and
behaviour. {L}arge-scale targeted-deletions have been successful
in defining gene functions in the single-celled yeast {S}accharomyces
cerevisiae, but comparable analyses have yet to be performed in an
animal. {H}ere we describe the use of {RNA} interference to inhibit
the function of approximately 86% of the 19,427 predicted genes of
{C}. elegans. {W}e identified mutant phenotypes for 1,722 genes,
about two-thirds of which were not previously associated with a phenotype.
{W}e find that genes of similar functions are clustered in distinct,
multi-megabase regions of individual chromosomes; genes in these
regions tend to share transcriptional profiles. {O}ur resulting data
set and reusable {RNA}i library of 16,757 bacterial clones will facilitate
systematic analyses of the connections among gene sequence, chromosomal
location and gene function in {C}. elegans.},
doi = {10.1038/nature01278},
pdf = {../local/Kamath2003Systematic.pdf},
file = {Kamath2003Systematic.pdf:local/Kamath2003Systematic.pdf:PDF},
owner = {vert},
url = {http://dx.doi.org/10.1038/nature01278}
}
@inproceedings{Kandola2003Learning,
author = {Kandola, J. and Shawe-Taylor, J. and Cristianini, N.},
title = {Learning {S}emantic {S}imilarity},
booktitle = {Advances in {N}eural {I}nformation {P}rocessing {S}ystems 15},
year = {2003},
editor = {Suzanna Becker and Sebastian Thrun and Klaus Obermayer},
publisher = {MIT Press}
}
@techreport{Kandola2002On,
author = {Kandola, J. and Shawe-Taylor, J. and Cristianini, N.},
title = {On the application of diffusion kernel to text data},
institution = {Neurocolt},
year = {2002},
note = {NeuroCOLT Technical Report NC-TR-02-122},
pdf = {../local/kand02.ps.gz},
file = {kand02.ps.gz:local/kand02.ps.gz:PostScript},
subject = {kernel},
url = {http://www.neurocolt.com/abs/2002/abs02122.html}
}
@article{Kanehisa2001Prediction,
author = {Kanehisa, M. },
title = {Prediction of higher order functional networks from genomic data},
journal = {Pharmacogenomics},
year = {2001},
volume = {2},
pages = {373--385},
number = {4},
doi = {10.1517/14622416.2.4.373},
owner = {vert},
url = {http://dx.doi.org/10.1517/14622416.2.4.373}
}
@article{Kanehisa1997database,
author = {M. Kanehisa},
title = {A database for post-genome analysis},
journal = {Trends {G}enet.},
year = {1997},
volume = {13},
pages = {375--376},
doi = {10.1016/S0168-9525(97)01223-7},
pdf = {../local/Kanehisa1997database.pdf},
file = {Kanehisa1997database.pdf:local/Kanehisa1997database.pdf:PDF},
subject = {bionet},
url = {http://dx.doi.org/10.1016/S0168-9525(97)01223-7}
}
@article{Kanehisa2002KEGG,
author = {M. Kanehisa and S. Goto and S. Kawashima and A. Nakaya},
title = {The {KEGG} databases at {G}enome{N}et},
journal = {Nucleic {A}cids {R}es.},
year = {2002},
volume = {30},
pages = {42--46},
pdf = {../local/kane02.pdf},
file = {kane02.pdf:local/kane02.pdf:PDF},
subject = {bionet},
url = {http://nar.oupjournals.org/cgi/content/full/30/1/42}
}
@article{Kanehisa2004KEGG,
author = {Kanehisa, M. and Goto, S. and Kawashima, S. and Okuno, Y. and Hattori,
M.},
title = {The {KEGG} resource for deciphering the genome.},
journal = {Nucleic {A}cids {R}es.},
year = {2004},
volume = {32},
pages = {D277-80},
number = {Database issue},
month = {Jan},
abstract = {A grand challenge in the post-genomic era is a complete computer representation
of the cell and the organism, which will enable computational prediction
of higher-level complexity of cellular processes and organism behavior
from genomic information. {T}oward this end we have been developing
a knowledge-based approach for network prediction, which is to predict,
given a complete set of genes in the genome, the protein interaction
networks that are responsible for various cellular processes. {KEGG}
at http://www.genome.ad.jp/kegg/ is the reference knowledge base
that integrates current knowledge on molecular interaction networks
such as pathways and complexes ({PATHWAY} database), information
about genes and proteins generated by genome projects ({GENES}/{SSDB}/{KO}
databases) and information about biochemical compounds and reactions
({COMPOUND}/{GLYCAN}/{REACTION} databases). {T}hese three types of
database actually represent three graph objects, called the protein
network, the gene universe and the chemical universe. {N}ew efforts
are being made to abstract knowledge, both computationally and manually,
about ortholog clusters in the {KO} ({KEGG} {O}rthology) database,
and to collect and analyze carbohydrate structures in the {GLYCAN}
database.},
doi = {10.1093/nar/gkh063},
keywords = {glycans},
pii = {32/suppl_1/D277},
url = {http://dx.doi.org/10.1093/nar/gkh063}
}
@article{Kaper2004BCI,
author = {Matthias Kaper and Peter Meinicke and Ulf Grossekathoefer and Thomas
Lingner and Helge Ritter},
title = {B{CI} {C}ompetition 2003--{D}ata set {II}b: support vector machines
for the {P}300 speller paradigm.},
journal = {I{EEE} {T}rans {B}iomed {E}ng},
year = {2004},
volume = {51},
pages = {1073-6},
number = {6},
month = {Jun},
abstract = {We propose an approach to analyze data from the {P}300 speller paradigm
using the machine-learning technique support vector machines. {I}n
a conservative classification scheme, we found the correct solution
after five repetitions. {W}hile the classification within the competition
is designed for offline analysis, our approach is also well-suited
for a real-world online solution: {I}t is fast, requires only 10
electrode positions and demands only a small amount of preprocessing.},
keywords = {Algorithms, Animals, Antisense, Artificial Intelligence, Automated,
Autonomic Nervous System, Brain, Cell Line, Child, Cluster Analysis,
Cognition, Comparative Study, Computational Biology, Computer Simulation,
Computer-Assisted, DNA Fingerprinting, Databases, Drug Evaluation,
Electroencephalography, Emotions, Event-Related Potentials, Factual,
Fluorescence, Fuzzy Logic, Gene Silencing, Gene Targeting, Genetic,
Hela Cells, Humans, Imaging, Intracellular Space, Microscopy, Models,
Monitoring, Neoplasms, Neural Networks (Computer), Non-U.S. Gov't,
Oligonucleotides, P.H.S., P300, Pattern Recognition, Peptides, Physiologic,
Preclinical, Predictive Value of Tests, Preschool, Prognosis, Protein
Interaction Mapping, Protein Structure, Proteins, Proteomics, Quantitative
Structure-Activity Relationship, Quaternary, RNA, RNA Interference,
Recognition (Psychology), Reproducibility of Results, Research Support,
Sensitivity and Specificity, Signal Processing, Small Interfering,
Software, Thionucleotides, Three-Dimensional, Tumor, U.S. Gov't,
User-Computer Interface, Word Processing, 15188881}
}
@article{Kapetanovic2004Overview,
author = {Izet M Kapetanovic and Simon Rosenfeld and Grant Izmirlian},
title = {Overview of commonly used bioinformatics methods and their applications.},
journal = {Ann {N} {Y} {A}cad {S}ci},
year = {2004},
volume = {1020},
pages = {10-21},
month = {May},
abstract = {Bioinformatics, in its broad sense, involves application of computer
processes to solve biological problems. {A} wide range of computational
tools are needed to effectively and efficiently process large amounts
of data being generated as a result of recent technological innovations
in biology and medicine. {A} number of computational tools have been
developed or adapted to deal with the experimental riches of complex
and multivariate data and transition from data collection to information
or knowledge. {T}hese include a wide variety of clustering and classification
algorithms, including self-organized maps ({SOM}), artificial neural
networks ({ANN}), support vector machines ({SVM}), fuzzy logic, and
even hyphenated techniques as neuro-fuzzy networks. {T}hese bioinformatics
tools are being evaluated and applied in various medical areas including
early detection, risk assessment, classification, and prognosis of
cancer. {T}he goal of these efforts is to develop and identify bioinformatics
methods with optimal sensitivity, specificity, and predictive capabilities.},
doi = {10.1196/annals.1310.003},
pdf = {../local/Kapetanovic2004Overview.pdf},
file = {Kapetanovic2004Overview.pdf:local/Kapetanovic2004Overview.pdf:PDF},
keywords = {Computational Biology, Fuzzy Logic, Humans, Neoplasms, Neural Networks
(Computer), Prognosis, 15208179},
pii = {1020/1/10},
url = {http://dx.doi.org/10.1196/annals.1310.003}
}
@misc{Kaplunovsky2009Statistics,
author = {Kaplunovsky, A. and Khailenko, V. and Bolshoy, A. and Atambayeva,
S. and Ivashchenko, A.},
title = {Statistics of Exon Lengths in Animals, Plants, Fungi, and Protists},
year = {2009},
journal = {World Academy of Science, Engineering and Technology},
volume = {52}
}
@article{Kapp2006Discovery,
author = {Amy V Kapp and Stefanie S Jeffrey and Anita Langerød and Anne-Lise
Børresen-Dale and Wonshik Han and Dong-Young Noh and Ida R K Bukholm
and Monica Nicolau and Patrick O Brown and Robert Tibshirani},
title = {Discovery and validation of breast cancer subtypes.},
journal = {BMC Genomics},
year = {2006},
volume = {7},
pages = {231},
abstract = {Previous studies demonstrated breast cancer tumor tissue samples could
be classified into different subtypes based upon DNA microarray profiles.
The most recent study presented evidence for the existence of five
different subtypes: normal breast-like, basal, luminal A, luminal
B, and ERBB2+.Based upon the analysis of 599 microarrays (five separate
cDNA microarray datasets) using a novel approach, we present evidence
in support of the most consistently identifiable subtypes of breast
cancer tumor tissue microarrays being: ESR1+/ERBB2-, ESR1-/ERBB2-,
and ERBB2+ (collectively called the ESR1/ERBB2 subtypes). We validate
all three subtypes statistically and show the subtype to which a
sample belongs is a significant predictor of overall survival and
distant-metastasis free probability.As a consequence of the statistical
validation procedure we have a set of centroids which can be applied
to any microarray (indexed by UniGene Cluster ID) to classify it
to one of the ESR1/ERBB2 subtypes. Moreover, the method used to define
the ESR1/ERBB2 subtypes is not specific to the disease. The method
can be used to identify subtypes in any disease for which there are
at least two independent microarray datasets of disease samples.},
doi = {10.1186/1471-2164-7-231},
institution = {Department of Statistics, Stanford University, Stanford, CA, USA.
AKapp@stanford.edu},
keywords = {Algorithms; Breast Neoplasms, classification/genetics/pathology; Female;
Gene Expression Profiling, methods/statistics /&/ numerical data;
Humans; Multivariate Analysis; Oligonucleotide Array Sequence Analysis,
methods/statistics /&/ numerical data; Proportional Hazards Models;
Risk Factors; Survival Analysis},
language = {eng},
medline-pst = {epublish},
owner = {jp},
pii = {1471-2164-7-231},
pmid = {16965636},
timestamp = {2012.02.29},
url = {http://dx.doi.org/10.1186/1471-2164-7-231}
}
@article{Karchin2002Classifying,
author = {Karchin, R. and Karplus, K. and Haussler, D.},
title = {Classifying {G}-protein coupled receptors with support vector machines},
journal = {Bioinformatics},
year = {2002},
volume = {18},
pages = {147--159},
abstract = {Motivation: {T}he enormous amount of protein sequence data uncovered
by genome research has increased the demand for computer software
that can automate the recognition of new proteins. {W}e discuss the
relative merits of various automated methods for recognizing {G}-{P}rotein
{C}oupled {R}eceptors ({GPCR}s), a superfamily of cell membrane proteins.
{GPCR}s are found in a wide range of organisms and are central to
a cellular signalling network that regulates many basic physiological
processes. {T}hey are the focus of a significant amount of current
pharmaceutical research because they play a key role in many diseases.
{H}owever, their tertiary structures remain largely unsolved. {T}he
methods described in this paper use only primary sequence information
to make their predictions. {W}e compare a simple nearest neighbor
approach ({BLAST}), methods based on multiple alignments generated
by a statistical profile {H}idden {M}arkov {M}odel ({HMM}), and methods,
including {S}upport {V}ector {M}achines ({SVM}s), that transform
protein sequences into fixed-length feature vectors. {R}esults: {T}he
last is the most computationally expensive method, but our experiments
show that, for those interested in annotation-quality classification,
the results are worth the effort. {I}n two-fold cross-validation
experiments testing recognition of {GPCR} subfamilies that bind a
specific ligand (such as a histamine molecule), the errors per sequence
at the {M}inimum {E}rror {P}oint ({MEP}) were 13.7% for multi-class
{SVM}s, 17.1% for our {SVM}tree method of hierarchical multi-class
{SVM} classification, 25.5% for {BLAST}, 30% for profile {HMM}s,
and 49% for classification based on nearest neighbor feature vector
{K}ernel {N}earest {N}eighbor (kern{NN}). {T}he percentage of true
positives recognized before the first false positive was 65% for
both {SVM} methods, 13% for {BLAST}, 5% for profile {HMM}s and 4%
for kern{NN}. {A}vailability: {W}e have set up a web server for {GPCR}
subfamily classification based on hierarchical multi-class {SVM}s
at http://www.soe.ucsc.edu/research/compbio/gpcr-subclass. {B}y scanning
predicted peptides found in the human genome with the {SVM}tree server,
we have identified a large number of genes that encode {GPCR}s. {A}
list of our predictions for human {GPCR}s is available at http://www.soe.ucsc.edu/research/compbio/gpcr·hg/class·results.
{W}e also provide suggested subfamily classification for 18 sequences
previously identified as unclassified {C}lass {A} (rhodopsin-like)
{GPCR}s in {GPCRDB} ({H}orn et al. , {N}ucleic {A}cids {R}es. , 26,
277?281, 1998), available at http://www.soe.ucsc.edu/research/compbio/gpcr/class{A}·unclassified/},
comment = {Un papier intéressant sur l'utilisation du Fisher kernel pour classer
les GPCR, une famille de protéines importante pour l'industrie pharmaceutique.},
pdf = {../local/Karchin2002Classifying.pdf},
file = {Karchin2002Classifying.pdf:local/Karchin2002Classifying.pdf:PDF},
keywords = {fisher-kernel sequence-classification biosvm},
subject = {biokernel},
url = {http://bioinformatics.oupjournals.org/cgi/reprint/18/1/147}
}
@article{Karchin2005Improving,
author = {R. Karchin and L. Kelly and A. Sali},
title = {Improving functional annotation of non-synonomous {SNP}s with information
theory.},
journal = {Pac {S}ymp {B}iocomput},
year = {2005},
pages = {397-408},
abstract = {Automated functional annotation of ns{SNP}s requires that amino-acid
residue changes are represented by a set of descriptive features,
such as evolutionary conservation, side-chain volume change, effect
on ligand-binding, and residue structural rigidity. {I}dentifying
the most informative combinations of features is critical to the
success of a computational prediction method. {W}e rank 32 features
according to their mutual information with functional effects of
amino-acid substitutions, as measured by in vivo assays. {I}n addition,
we use a greedy algorithm to identify a subset of highly informative
features. {T}he method is simple to implement and provides a quantitative
measure for selecting the best predictive features given a set of
features that a human expert believes to be informative. {W}e demonstrate
the usefulness of the selected highly informative features by cross-validated
tests of a computational classifier, a support vector machine ({SVM}).
{T}he {SVM}'s classification accuracy is highly correlated with the
ranking of the input features by their mutual information. {T}wo
features describing the solvent accessibility of "wild-type" and
"mutant" amino-acid residues and one evolutionary feature based on
superfamily-level multiple alignments produce comparable overall
accuracy and 6\% fewer false positives than a 32-feature set that
considers physiochemical properties of amino acids, protein electrostatics,
amino-acid residue flexibility, and binding interactions.},
keywords = {biosvm}
}
@article{Karklin2005Classification,
author = {Karklin, Y. and Meraz, R. F. and Holbrook, S.R.},
title = {Classification of non-coding {RNA} using graph representations of
secondary structure.},
journal = {Pac. {S}ymp. {B}iocomput.},
year = {2005},
pages = {4-15},
abstract = {Some genes produce transcripts that function directly in regulatory,
catalytic, or structural roles in the cell. {T}hese non-coding {RNA}s
are prevalent in all living organisms, and methods that aid the understanding
of their functional roles are essential. {RNA} secondary structure,
the pattern of base-pairing, contains the critical information for
determining the three dimensional structure and function of the molecule.
{I}n this work we examine whether the basic geometric and topological
properties of secondary structure are sufficient to distinguish between
{RNA} families in a learning framework. {F}irst, we develop a labeled
dual graph representation of {RNA} secondary structure by adding
biologically meaningful labels to the dual graphs proposed by {G}an
et al [1]. {N}ext, we define a similarity measure directly on the
labeled dual graphs using the recently developed marginalized kernels
[2]. {U}sing this similarity measure, we were able to train {S}upport
{V}ector {M}achine classifiers to distinguish {RNA}s of known families
from random {RNA}s with similar statistics. {F}or 22 of the 25 families
tested, the classifier achieved better than 70\% accuracy, with much
higher accuracy rates for some families. {T}raining a set of classifiers
to automatically assign family labels to {RNA}s using a one vs. all
multi-class scheme also yielded encouraging results. {F}rom these
initial learning experiments, we suggest that the labeled dual graph
representation, together with kernel machine methods, has potential
for use in automated analysis and classification of uncharacterized
{RNA} molecules or efficient genome-wide screens for {RNA} molecules
from existing families.},
keywords = {biosvm},
url = {http://helix-web.stanford.edu/psb05/karklin.pdf}
}
@article{Karklin2005Classificationa,
author = {Yan Karklin and Richard F Meraz and Stephen R Holbrook},
title = {{C}lassification of non-coding {RNA} using graph representations
of secondary structure.},
journal = {Pac Symp Biocomput},
year = {2005},
pages = {4--15},
abstract = {Some genes produce transcripts that function directly in regulatory,
catalytic, or structural roles in the cell. These non-coding RNAs
are prevalent in all living organisms, and methods that aid the understanding
of their functional roles are essential. RNA secondary structure,
the pattern of base-pairing, contains the critical information for
determining the three dimensional structure and function of the molecule.
In this work we examine whether the basic geometric and topological
properties of secondary structure are sufficient to distinguish between
RNA families in a learning framework. First, we develop a labeled
dual graph representation of RNA secondary structure by adding biologically
meaningful labels to the dual graphs proposed by Gan et al [1]. Next,
we define a similarity measure directly on the labeled dual graphs
using the recently developed marginalized kernels [2]. Using this
similarity measure, we were able to train Support Vector Machine
classifiers to distinguish RNAs of known families from random RNAs
with similar statistics. For 22 of the 25 families tested, the classifier
achieved better than 70\% accuracy, with much higher accuracy rates
for some families. Training a set of classifiers to automatically
assign family labels to RNAs using a one vs. all multi-class scheme
also yielded encouraging results. From these initial learning experiments,
we suggest that the labeled dual graph representation, together with
kernel machine methods, has potential for use in automated analysis
and classification of uncharacterized RNA molecules or efficient
genome-wide screens for RNA molecules from existing families.},
keywords = {Base Sequence, Models, Molecular, Non-, Nucleic Acid Conformation,
P.H.S., RNA, Research Support, U.S. Gov't, Untranslated, 15759609},
pmid = {15759609},
timestamp = {2006.08.03}
}
@incollection{Baringhaus2005A,
author = {Karl-Heinz Baringhaus, Gerhard Hessler},
title = {A Chemical Genomics Approach for Ion Channel Modulators},
booktitle = {Chemogenomics in Drug Discovery},
publisher = {Wiley-VCH},
year = {2005},
chapter = {8},
pages = {221-242}
}
@article{Karni2009network-based,
author = {Karni, S. and Soreq, H. and Sharan, R.},
title = {A network-based method for predicting disease-causing genes},
journal = {J. Comput. Biol.},
year = {2009},
volume = {16},
pages = {181--189},
number = {2},
month = {Feb},
abstract = {A fundamental problem in human health is the inference of disease-causing
genes, with important applications to diagnosis and treatment. Previous
work in this direction relied on knowledge of multiple loci associated
with the disease, or causal genes for similar diseases, which limited
its applicability. Here we present a new approach to causal gene
prediction that is based on integrating protein-protein interaction
network data with gene expression data under a condition of interest.
The latter are used to derive a set of disease-related genes which
is assumed to be in close proximity in the network to the causal
genes. Our method applies a set-cover-like heuristic to identify
a small set of genes that best "cover" the disease-related genes.
We perform comprehensive simulations to validate our method and test
its robustness to noise. In addition, we validate our method on real
gene expression data and on gene specific knockouts. Finally, we
apply it to suggest possible genes that are involved in myasthenia
gravis.},
doi = {10.1089/cmb.2008.05TT},
pdf = {../local/Karni2009network-based.pdf},
file = {Karni2009network-based.pdf:Karni2009network-based.pdf:PDF},
institution = {Blavatnik School of Computer Science, Tel-Aviv University, Tel-Aviv,
Israel.},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {10.1089/cmb.2008.05TT},
pmid = {19193144},
timestamp = {2011.09.24},
url = {http://dx.doi.org/10.1089/cmb.2008.05TT}
}
@article{BioCyc2005,
author = {Karp, P. D. and Ouzounis, C. A. and Moore-Kochlacs, C. and Goldovsky,
L. and Kaipa, P. and Ahren, D. and Tsoka, S. and Darzentas, N. and
Kunin, V. and Lopez-Bigas, N.},
title = {Expansion of the {B}io{C}yc collection of pathway/genome databases
to 160 genomes},
journal = {Nucleic {A}cids {R}es},
year = {2005},
volume = {33},
pages = {6083-9},
number = {19},
abstract = {The {B}io{C}yc database collection is a set of 160 pathway/genome
databases ({PGDB}s) for most eukaryotic and prokaryotic species whose
genomes have been completely sequenced to date. {E}ach {PGDB} in
the {B}io{C}yc collection describes the genome and predicted metabolic
network of a single organism, inferred from the {M}eta{C}yc database,
which is a reference source on metabolic pathways from multiple organisms.
{I}n addition, each bacterial {PGDB} includes predicted operons for
the corresponding species. {T}he {B}io{C}yc collection provides a
unique resource for computational systems biology, namely global
and comparative analyses of genomes and metabolic networks, and a
supplement to the {B}io{C}yc resource of curated {PGDB}s. {T}he {O}mics
viewer available through the {B}io{C}yc website allows scientists
to visualize combinations of gene expression, proteomics and metabolomics
data on the metabolic maps of these organisms. {T}his paper discusses
the computational methodology by which the {B}io{C}yc collection
has been expanded, and presents an aggregate analysis of the collection
that includes the range of number of pathways present in these organisms,
and the most frequently observed pathways. {W}e seek scientists to
adopt and curate individual {PGDB}s within the {B}io{C}yc collection.
{O}nly by harnessing the expertise of many scientists we can hope
to produce biological databases, which accurately reflect the depth
and breadth of knowledge that the biomedical research community is
producing.},
keywords = {Animals Computational Biology *Databases, Genetic *Genome Genome,
Archaeal Genome, Bacterial Genomics Humans Metabolism/genetics Research
Support, N.I.H., Extramural Research Support, Non-U.S. Gov't Research
Support, U.S. Gov't, P.H.S.}
}
@article{Karplus1998Hidden,
author = {Karplus, K. and Barrett, C. and Hughey, R.},
title = {Hidden {M}arkov {M}odels for {D}etecting {R}emote {P}rotein {H}omologies},
journal = {Bioinformatics},
year = {1998},
volume = {14},
pages = {846--856},
number = {10},
pdf = {../local/karp98.pdf},
file = {karp98.pdf:local/karp98.pdf:PDF},
subject = {biocasp},
url = {http://www.cse.ucsc.edu/research/compbio/papers/w9824.ps}
}
@article{Karr2012whole,
author = {Karr, J. R. and Sanghvi, J. C. and Macklin, D. N. and Gutschow, M.
V. and Jacobs, J. M. and Bolival, B. and Assad-Garcia, N. and Glass,
J. I. and Covert, M. W.},
title = {A whole-cell computational model predicts phenotype from genotype.},
journal = {Cell},
year = {2012},
volume = {150},
pages = {389--401},
number = {2},
month = {Jul},
abstract = {Understanding how complex phenotypes arise from individual molecules
and their interactions is a primary challenge in biology that computational
approaches are poised to tackle. We report a whole-cell computational
model of the life cycle of the human pathogen Mycoplasma genitalium
that includes all of its molecular components and their interactions.
An integrative approach to modeling that combines diverse mathematics
enabled the simultaneous inclusion of fundamentally different cellular
processes and experimental measurements. Our whole-cell model accounts
for all annotated gene functions and was validated against a broad
range of data. The model provides insights into many previously unobserved
cellular behaviors, including in vivo rates of protein-DNA association
and an inverse relationship between the durations of DNA replication
initiation and replication. In addition, experimental analysis directed
by model predictions identified previously undetected kinetic parameters
and biological functions. We conclude that comprehensive whole-cell
models can be used to facilitate biological discovery.},
doi = {10.1016/j.cell.2012.05.044},
pdf = {../local/Karr2012whole.pdf},
file = {Karr2012whole.pdf:Karr2012whole.pdf:PDF},
institution = {Graduate Program in Biophysics, Stanford University, Stanford, CA
94305, USA.},
keywords = {Phenotype},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {S0092-8674(12)00776-3},
pmid = {22817898},
timestamp = {2012.10.24},
url = {http://dx.doi.org/10.1016/j.cell.2012.05.044}
}
@inproceedings{Kashima2003Marginalized,
author = {Kashima, H. and Tsuda, K. and Inokuchi, A.},
title = {Marginalized {K}ernels between {L}abeled {G}raphs},
booktitle = {Proceedings of the {T}wentieth {I}nternational {C}onference on {M}achine
{L}earning},
year = {2003},
editor = {Faucett, T. and Mishra, N.},
pages = {321-328},
address = {New York, NY, USA},
publisher = {AAAI Press},
pdf = {../local/Kashima2003Marginalized.pdf},
file = {Kashima2003Marginalized.pdf:local/Kashima2003Marginalized.pdf:PDF},
keywords = {biosvm},
owner = {vert}
}
@incollection{Kashima2004Kernels,
author = {Kashima, H. and Tsuda, K. and Inokuchi, A.},
title = {Kernels for graphs},
booktitle = {Kernel {M}ethods in {C}omputational {B}iology},
publisher = {MIT Press},
year = {2004},
editor = {Sch{\"o}lkopf, B. and Tsuda, K. and Vert, J.P.},
pages = {155-170},
address = {The MIT Press, Cambridge, Massachussetts},
keywords = {biosvm chemoinformatics},
owner = {vert}
}
@article{Kass1995Reference,
author = {Kass, R. E. and Wasserman, L.},
title = {A Reference Bayesian Test for Nested Hypotheses and its Relationship
to the Schwarz Criterion},
journal = {J. Am. Stat. Assoc.},
year = {1995},
volume = {90},
pages = {928--934},
number = {431},
doi = {10.2307/2291327},
pdf = {../local/Kass1995Reference.pdf},
file = {Kass1995Reference.pdf:Kass1995Reference.pdf:PDF},
owner = {jp},
timestamp = {2011.12.29},
url = {http://dx.doi.org/10.2307/2291327}
}
@techreport{Kato2001Operator,
author = {Kato, T.},
title = {Operator dynamics in molecular biology},
institution = {I.H.E.S.},
year = {2001},
note = {Technical report IHES/M/01/41},
pdf = {../local/Kato2001Operator.pdf},
file = {Kato2001Operator.pdf:local/Kato2001Operator.pdf:PDF},
url = {http://www.ihes.fr/PREPRINTS/M01/Resu/resu-M01-41.html}
}
@article{Kato2005Selective,
author = {Kato, T. and Tsuda, K. and Asai, K.},
title = {{S}elective integration of multiple biological data for supervised
network inference.},
journal = {Bioinformatics},
year = {2005},
volume = {21},
pages = {2488--2495},
number = {10},
month = {May},
abstract = {MOTIVATION: Inferring networks of proteins from biological data is
a central issue of computational biology. Most network inference
methods, including Bayesian networks, take unsupervised approaches
in which the network is totally unknown in the beginning, and all
the edges have to be predicted. A more realistic supervised framework,
proposed recently, assumes that a substantial part of the network
is known. We propose a new kernel-based method for supervised graph
inference based on multiple types of biological datasets such as
gene expression, phylogenetic profiles and amino acid sequences.
Notably, our method assigns a weight to each type of dataset and
thereby selects informative ones. Data selection is useful for reducing
data collection costs. For example, when a similar network inference
problem must be solved for other organisms, the dataset excluded
by our algorithm need not be collected. RESULTS: First, we formulate
supervised network inference as a kernel matrix completion problem,
where the inference of edges boils down to estimation of missing
entries of a kernel matrix. Then, an expectation-maximization algorithm
is proposed to simultaneously infer the missing entries of the kernel
matrix and the weights of multiple datasets. By introducing the weights,
we can integrate multiple datasets selectively and thereby exclude
irrelevant and noisy datasets. Our approach is favorably tested in
two biological networks: a metabolic network and a protein interaction
network. AVAILABILITY: Software is available on request.},
doi = {10.1093/bioinformatics/bti339},
pii = {bti339},
pmid = {15728114},
timestamp = {2007.02.01},
url = {http://dx.doi.org/10.1093/bioinformatics/bti339}
}
@book{Kay1998Fundamentals,
title = {{F}undamentals of {S}tatistical {S}ignal {P}rocessing},
publisher = {Prentice-Hall},
year = {1998},
author = {Kay, S.M.},
volume = {2},
location = {Englewood Cliffs, NJ},
owner = {kb}
}
@inproceedings{Kazhdan2003Rotation,
author = {Michael Kazhdan and Thomas Funkhouser and Szymon Rusinkiewicz},
title = {Rotation invariant spherical harmonic representation of 3D shape
descriptors},
booktitle = {SGP '03: Proceedings of the 2003 Eurographics/ACM SIGGRAPH symposium
on Geometry processing},
year = {2003},
pages = {156--164},
address = {Aire-la-Ville, Switzerland, Switzerland},
publisher = {Eurographics Association},
isbn = {1-58113-687-0},
location = {Aachen, Germany}
}
@inproceedings{Kearns1993Efficient,
author = {Kearns, M.},
title = {Efficient noise-tolerant learning from statistical queries},
booktitle = {Journal of the ACM},
year = {1993},
pages = {392--401},
owner = {fantine},
timestamp = {2009.07.21},
url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.49.669}
}
@article{Keerthi2003Asymptotic,
author = {S. Sathiya Keerthi and Chih-Jen Lin},
title = {Asymptotic behaviors of support vector machines with {G}aussian kernel.},
journal = {Neural {C}omput},
year = {2003},
volume = {15},
pages = {1667-89},
number = {7},
month = {Jul},
abstract = {Support vector machines ({SVM}s) with the gaussian ({RBF}) kernel
have been popular for practical use. {M}odel selection in this class
of {SVM}s involves two hyperparameters: the penalty parameter {C}
and the kernel width sigma. {T}his letter analyzes the behavior of
the {SVM} classifier when these hyperparameters take very small or
very large values. {O}ur results help in understanding the hyperparameter
space that leads to an efficient heuristic method of searching for
hyperparameter values with small generalization errors. {T}he analysis
also indicates that if complete model selection using the gaussian
kernel has been conducted, there is no need to consider linear {SVM}.},
doi = {10.1162/089976603321891855},
url = {http://dx.doi.org/10.1162/089976603321891855}
}
@article{Keerthi2003SMO,
author = {S. S. Keerthi and S. K. Shevade},
title = {S{MO} algorithm for least-squares {SVM} formulations.},
journal = {Neural {C}omput},
year = {2003},
volume = {15},
pages = {487-507},
number = {2},
month = {Feb},
abstract = {This article extends the well-known {SMO} algorithm of support vector
machines ({SVM}s) to least-squares {SVM} formulations that include
{LS}-{SVM} classification, kernel ridge regression, and a particular
form of regularized kernel {F}isher discriminant. {T}he algorithm
is shown to be asymptotically convergent. {I}t is also extremely
easy to implement. {C}omputational experiments show that the algorithm
is fast and scales efficiently (quadratically) as a function of the
number of examples.},
doi = {10.1162/089976603762553013},
url = {http://dx.doi.org/10.1162/089976603762553013}
}
@article{Kellenberger2004Comparative,
author = {E. Kellenberger and J. Rodrigo and P. Muller and D. Rognan},
title = {Comparative evaluation of eight docking tools for docking and virtual
screening accuracy.},
journal = {Proteins},
year = {2004},
volume = {57},
pages = {225--242},
number = {2},
month = {Nov},
abstract = {Eight docking programs (DOCK, FLEXX, FRED, GLIDE, GOLD, SLIDE, SURFLEX,
and QXP) that can be used for either single-ligand docking or database
screening have been compared for their propensity to recover the
X-ray pose of 100 small-molecular-weight ligands, and for their capacity
to discriminate known inhibitors of an enzyme (thymidine kinase)
from randomly chosen "drug-like" molecules. Interestingly, both properties
are found to be correlated, since the tools showing the best docking
accuracy (GLIDE, GOLD, and SURFLEX) are also the most successful
in ranking known inhibitors in a virtual screening experiment. Moreover,
the current study pinpoints some physicochemical descriptors of either
the ligand or its cognate protein-binding site that generally lead
to docking/scoring inaccuracies.},
doi = {10.1002/prot.20149},
owner = {mahe},
pmid = {15340911},
timestamp = {2006.09.07},
url = {http://dx.doi.org/10.1002/prot.20149}
}
@article{Kellenberger2008How,
author = {Kellenberger, E. and Schalon, C. and Rognan, D.},
title = {How to Measure the Similarity Between Protein Ligand-Binding Sites?},
journal = {Current Computer-Aided Drug Design},
year = {2008},
volume = {4},
pages = {209--220},
number = {3},
month = {Sep.},
abstract = {Quantification of local similarity between protein 3D structures is
a promising tool in computer-aided drug design and prediction of
biological function. Over the last ten years, several computational
methods were proposed, mostly based on geometrical comparisons. This
review summarizes the recent literature and gives an overview of
available programs.
A particular interest is given to the underlying methodologies. Our
analysis points out strengths and weaknesses of the various approaches.
If all described methods work relatively well when two binding sites
obviously resemble each other, scoring potential solutions remains
a difficult issue, especially if the similarity is low. The other
challenging question is the protein flexibility, which is indeed
difficult to evaluate from a static representation. Last, most of
recently developed techniques are fast and can be applied to large
amounts of data.
Examples were carefully chosen to illustrate the wide applicability
domain of the most popular methods: detection of common structural
motifs, identification of secondary targets for a drug-like compound,
comparison of binding sites across a functional family, comparison
of homology models, database screening.},
doi = {10.2174/157340908785747401},
pdf = {../local/Kellenberger2008How.pdf},
file = {Kellenberger2008How.pdf:Kellenberger2008How.pdf:PDF},
keywords = {chemogenomics},
owner = {jp},
timestamp = {2009.10.30},
url = {http://dx.doi.org/10.2174/157340908785747401}
}
@article{Kelley2003Conserved,
author = {Kelley, B.P. and Sharan, R. and Karp, R.M. and Sittler, T. and Root,
D.E. and Stockwell, B.R. and Ideker, T.},
title = {Conserved pathways within bacteria and yeast as revealed by global
protein network alignment.},
journal = {Proc. Natl. Acad. Sci. USA},
year = {2003},
volume = {100},
pages = {11394--11399},
number = {20},
month = {Sep},
abstract = {We implement a strategy for aligning two protein-protein interaction
networks that combines interaction topology and protein sequence
similarity to identify conserved interaction pathways and complexes.
Using this approach we show that the protein-protein interaction
networks of two distantly related species, Saccharomyces cerevisiae
and Helicobacter pylori, harbor a large complement of evolutionarily
conserved pathways, and that a large number of pathways appears to
have duplicated and specialized within yeast. Analysis of these findings
reveals many well characterized interaction pathways as well as many
unanticipated pathways, the significance of which is reinforced by
their presence in the networks of both species.},
doi = {10.1073/pnas.1534710100},
pdf = {../local/Kelley2003Conserved.pdf},
file = {Kelley2003Conserved.pdf:local/Kelley2003Conserved.pdf:PDF},
institution = {Whitehead Institute for Biomedical Research, 9 Cambridge Center,
Cambridge, MA 02142, USA.},
owner = {jp},
pii = {1534710100},
pmid = {14504397},
timestamp = {2008.10.03},
url = {http://dx.doi.org/10.1073/pnas.1534710100}
}
@article{Kelley2004PathBLAST,
author = {Kelley, B.P. and Yuan, B. and Lewitter, F. and Sharan, R. and Stockwell,
B.R. and Ideker, T.},
title = {{PathBLAST}: a tool for alignment of protein interaction networks.},
journal = {Nucleic Acids Res.},
year = {2004},
volume = {32},
pages = {W83--W88},
number = {Web Server issue},
month = {Jul},
abstract = {PathBLAST is a network alignment and search tool for comparing protein
interaction networks across species to identify protein pathways
and complexes that have been conserved by evolution. The basic method
searches for high-scoring alignments between pairs of protein interaction
paths, for which proteins of the first path are paired with putative
orthologs occurring in the same order in the second path. This technique
discriminates between true- and false-positive interactions and allows
for functional annotation of protein interaction pathways based on
similarity to the network of another, well-characterized species.
PathBLAST is now available at http://www.pathblast.org/ as a web-based
query. In this implementation, the user specifies a short protein
interaction path for query against a target protein-protein interaction
network selected from a network database. PathBLAST returns a ranked
list of matching paths from the target network along with a graphical
view of these paths and the overlap among them. Target protein-protein
interaction networks are currently available for Helicobacter pylori,
Saccharomyces cerevisiae, Caenorhabditis elegans and Drosophila melanogaster.
Just as BLAST enables rapid comparison of protein sequences between
genomes, tools such as PathBLAST are enabling comparative genomics
at the network level.},
doi = {10.1093/nar/gkh411},
institution = {Whitehead Institute for Biomedical Research, Cambridge, MA 02142,
USA.},
owner = {jp},
pii = {32/suppl_2/W83},
pmid = {15215356},
timestamp = {2008.10.03},
url = {http://dx.doi.org/10.1093/nar/gkh411}
}
@article{Kelley2005Systematic,
author = {Kelley, R. and Ideker, T.},
title = {{S}ystematic interpretation of genetic interactions using protein
networks.},
journal = {Nat. Biotechnol.},
year = {2005},
volume = {23},
pages = {561--566},
number = {5},
month = {May},
abstract = {Genetic interaction analysis,in which two mutations have a combined
effect not exhibited by either mutation alone, is a powerful and
widespread tool for establishing functional linkages between genes.
In the yeast Saccharomyces cerevisiae, ongoing screens have generated
>4,800 such genetic interaction data. We demonstrate that by combining
these data with information on protein-protein, prote in-DNA or metabolic
networks, it is possible to uncover physical mechanisms behind many
of the observed genetic effects. Using a probabilistic model, we
found that 1,922 genetic interactions are significantly associated
with either between- or within-pathway explanations encoded in the
physical networks, covering approximately 40\% of known genetic interactions.
These models predict new functions for 343 proteins and suggest that
between-pathway explanations are better than within-pathway explanations
at interpreting genetic interactions identified in systematic screens.
This study provides a road map for how genetic and physical interactions
can be integrated to reveal pathway organization and function.},
doi = {10.1038/nbt1096},
pii = {nbt1096},
pmid = {15877074},
timestamp = {2006.11.21},
url = {http://dx.doi.org/10.1038/nbt1096}
}
@article{Kerr2001Experimental,
author = {M. K. Kerr and G. A. Churchill},
title = {Experimental design for gene expression microarrays.},
journal = {Biostatistics},
year = {2001},
volume = {2},
pages = {183--201},
number = {2},
month = {Jun},
abstract = {We examine experimental design issues arising with gene expression
microarray technology. Microarray experiments have multiple sources
of variation, and experimental plans should ensure that effects of
interest are not confounded with ancillary effects. A commonly used
design is shown to violate this principle and to be generally inefficient.
We explore the connection between microarray designs and classical
block design and use a family of ANOVA models as a guide to choosing
a design. We combine principles of good design and A-optimality to
give a general set of recommendations for design with microarrays.
These recommendations are illustrated in detail for one kind of experimental
objective, where we also give the results of a computer search for
good designs.},
doi = {10.1093/biostatistics/2.2.183},
institution = {The Jackson Laboratory, Bar Harbor, ME, USA. garyc@jax.org},
language = {eng},
medline-pst = {ppublish},
owner = {philippe},
pii = {2/2/183},
pmid = {12933549},
timestamp = {2010.08.15},
url = {http://dx.doi.org/10.1093/biostatistics/2.2.183}
}
@article{Kertesz2007role,
author = {Michael Kertesz and Nicola Iovino and Ulrich Unnerstall and Ulrike
Gaul and Eran Segal},
title = {The role of site accessibility in microRNA target recognition.},
journal = {Nat Genet},
year = {2007},
volume = {39},
pages = {1278--1284},
number = {10},
month = {Oct},
abstract = {MicroRNAs are key regulators of gene expression, but the precise mechanisms
underlying their interaction with their mRNA targets are still poorly
understood. Here, we systematically investigate the role of target-site
accessibility, as determined by base-pairing interactions within
the mRNA, in microRNA target recognition. We experimentally show
that mutations diminishing target accessibility substantially reduce
microRNA-mediated translational repression, with effects comparable
to those of mutations that disrupt sequence complementarity. We devise
a parameter-free model for microRNA-target interaction that computes
the difference between the free energy gained from the formation
of the microRNA-target duplex and the energetic cost of unpairing
the target to make it accessible to the microRNA. This model explains
the variability in our experiments, predicts validated targets more
accurately than existing algorithms, and shows that genomes accommodate
site accessibility by preferentially positioning targets in highly
accessible regions. Our study thus demonstrates that target accessibility
is a critical factor in microRNA function.},
doi = {10.1038/ng2135},
pdf = {../local/Kertesz2007role.pdf},
file = {Kertesz2007role.pdf:Kertesz2007role.pdf:PDF},
institution = {omputer Science and Applied Mathematics, Weizmann Institute of Science,
Rehovot 76100, Israel.},
keywords = {sirna},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {ng2135},
pmid = {17893677},
timestamp = {2009.10.28},
url = {http://dx.doi.org/10.1038/ng2135}
}
@inproceedings{Keselman03many-to-many,
author = {Y. Keselman and A. Shokoufandeh and M. F. Demirci and S. Dickinson},
title = {Many-to-Many Graph Matching via Metric Embedding},
booktitle = {CVPR},
year = {2003},
pages = {850--857}
}
@article{Keserue2003Prediction,
author = {Keser{\"u}, G. M.},
title = {{P}rediction of h{ERG} potassium channel affinity by traditional
and hologram q{SAR} methods.},
journal = {Bioorg. Med. Chem. Lett.},
year = {2003},
volume = {13},
pages = {2773--2775},
number = {16},
month = {Aug},
abstract = {Traditional and hologram QSAR (HQSAR) models were developed for the
prediction of hERG potassium channel affinities. The models were
validated on three different test sets including compounds with published
patch-clamp IC(50) data and two subsets from the World Drug Index
(compounds indicated to have ECG modifying adverse effect and drugs
marked to be approved, respectively). Discriminant analysis performed
on the full set of hERG data resulted in a traditional QSAR model
that classified 83\% of actives and 87\% of inactives correctly.
Analysis of our HQSAR model revealed it to be predictive in both
IC(50) and discrimination studies.},
keywords = {chemoinformatics herg},
pii = {S0960894X0300492X},
pmid = {12873512},
timestamp = {2006.10.06}
}
@article{Khan2005Proteome,
author = {Shahid M Khan and Blandine Franke-Fayard and Gunnar R Mair and Edwin
Lasonder and Chris J Janse and Matthias Mann and Andrew P Waters},
title = {{P}roteome analysis of separated male and female gametocytes reveals
novel sex-specific {P}lasmodium biology.},
journal = {Cell},
year = {2005},
volume = {121},
pages = {675--687},
number = {5},
month = {Jun},
abstract = {Gametocytes, the precursor cells of malaria-parasite gametes, circulate
in the blood and are responsible for transmission from host to mosquito
vector. The individual proteomes of male and female gametocytes were
analyzed using mass spectrometry, following separation by flow sorting
of transgenic parasites expressing green fluorescent protein, in
a sex-specific manner. Promoter tagging in transgenic parasites confirmed
the designation of stage and sex specificity of the proteins. The
male proteome contained 36\% (236 of 650) male-specific and the female
proteome 19\% (101 of 541) female-specific proteins, but they share
only 69 proteins, emphasizing the diverged features of the sexes.
Of all the malaria life-cycle stages analyzed, the male gametocyte
has the most distinct proteome, containing many proteins involved
in flagellar-based motility and rapid genome replication. By identification
of gender-specific protein kinases and phosphatases and using targeted
gene disruption of two kinases, new sex-specific regulatory pathways
were defined.},
doi = {10.1016/j.cell.2005.03.027},
pdf = {../local/Khan2005Proteome.pdf},
file = {Khan2005Proteome.pdf:local/Khan2005Proteome.pdf:PDF},
keywords = {plasmodium},
pii = {S0092-8674(05)00299-0},
pmid = {15935749},
timestamp = {2006.04.13},
url = {http://dx.doi.org/10.1016/j.cell.2005.03.027}
}
@article{Kharchenko2006Identifying,
author = {Kharchenko, P. and Chen, L. and Freund, Y. and Vitkup, D. and Church,
G. M.},
title = {{I}dentifying metabolic enzymes with multiple types of association
evidence.},
journal = {BMC Bioinformatics},
year = {2006},
volume = {7},
pages = {177},
abstract = {BACKGROUND: Existing large-scale metabolic models of sequenced organisms
commonly include enzymatic functions which can not be attributed
to any gene in that organism. Existing computational strategies for
identifying such missing genes rely primarily on sequence homology
to known enzyme-encoding genes. RESULTS: We present a novel method
for identifying genes encoding for a specific metabolic function
based on a local structure of metabolic network and multiple types
of functional association evidence, including clustering of genes
on the chromosome, similarity of phylogenetic profiles, gene expression,
protein fusion events and others. Using E. coli and S. cerevisiae
metabolic networks, we illustrate predictive ability of each individual
type of association evidence and show that significantly better predictions
can be obtained based on the combination of all data. In this way
our method is able to predict 60\% of enzyme-encoding genes of E.
coli metabolism within the top 10 (out of 3551) candidates for their
enzymatic function, and as a top candidate within 43\% of the cases.
CONCLUSION: We illustrate that a combination of genome context and
other functional association evidence is effective in predicting
genes encoding metabolic enzymes. Our approach does not rely on direct
sequence homology to known enzyme-encoding genes, and can be used
in conjunction with traditional homology-based metabolic reconstruction
methods. The method can also be used to target orphan metabolic activities.},
doi = {10.1186/1471-2105-7-177},
pii = {1471-2105-7-177},
pmid = {16571130},
timestamp = {2006.11.21},
url = {http://dx.doi.org/10.1186/1471-2105-7-177}
}
@article{Kharchenko2004Filling,
author = {Kharchenko, P. and Vitkup, D. and Church, G. M.},
title = {{F}illing gaps in a metabolic network using expression information.},
journal = {Bioinformatics},
year = {2004},
volume = {20 Suppl 1},
pages = {I178--I185},
month = {Aug},
abstract = {MOTIVATION: The metabolic models of both newly sequenced and well-studied
organisms contain reactions for which the enzymes have not been identified
yet. We present a computational approach for identifying genes encoding
such missing metabolic enzymes in a partially reconstructed metabolic
network. RESULTS: The metabolic expression placement (MEP) method
relies on the coexpression properties of the metabolic network and
is complementary to the sequence homology and genome context methods
that are currently being used to identify missing metabolic genes.
The MEP algorithm predicts over 20\% of all known Saccharomyces cerevisiae
metabolic enzyme-encoding genes within the top 50 out of 5594 candidates
for their enzymatic function, and 70\% of metabolic genes whose expression
level has been significantly perturbed across the conditions of the
expression dataset used. AVAILABILITY: Freely available (in Supplementary
information). SUPPLEMENTARY INFORMATION: Available at the following
URL http://arep.med.harvard.edu/kharchenko/mep/supplements.html},
doi = {10.1093/bioinformatics/bth930},
keywords = {Bacterial, Binding Sites, Biological, Comparative Study, DNA, Energy
Metabolism, Enzyme Induction, Enzymes, Escherichia coli Proteins,
Fungal, Gene Expression Regulation, Genes, Genetic, Genome, Models,
Non-P.H.S., Non-U.S. Gov't, Phylogeny, Promoter Regions (Genetics),
Protein, Research Support, Saccharomyces cerevisiae, Saccharomyces
cerevisiae Proteins, Sequence Analysis, Systems Biology, Transcription
Factors, U.S. Gov't, 15262797},
pii = {20/suppl_1/i178},
pmid = {15262797},
timestamp = {2006.11.21},
url = {http://dx.doi.org/10.1093/bioinformatics/bth930}
}
@article{Khvorova2003Functional,
author = {Khvorova, A. and Reynolds, A. and Jayasena, S.D.},
title = {Functional si{RNA}s and mi{RNA}s exhibit strand bias.},
journal = {Cell},
year = {2003},
volume = {115},
pages = {209-216},
number = {2},
month = {Oct},
abstract = {Both micro{RNA}s (mi{RNA}) and small interfering {RNA}s (si{RNA})
share a common set of cellular proteins ({D}icer and the {RNA}-induced
silencing complex [{RISC}]) to elicit {RNA} interference. {I}n the
following work, a statistical analysis of the internal stability
of published mi{RNA} sequences in the context of mi{RNA} precursor
hairpins revealed enhanced flexibility of mi{RNA} precursors, especially
at the 5?-anti-sense ({AS}) terminal base pair. {T}he same trend
was observed in si{RNA}, with functional duplexes displaying a lower
internal stability (?0.5 kcal/mol) at the 5?-{AS} end than nonfunctional
duplexes. {A}verage internal stability of si{RNA} molecules retrieved
from plant cells after introduction of long {RNA} sequences also
shows this characteristic thermodynamic signature. {T}ogether, these
results suggest that the thermodynamic properties of si{RNA} play
a critical role in determining the molecule's function and longevity,
possibly biasing the steps involved in duplex unwinding and strand
retention by {RISC}.},
doi = {10.1016/S0092-8674(03)00801-8},
pdf = {../local/Khvorova2003Functional.pdf},
file = {Khvorova2003Functional.pdf:local/Khvorova2003Functional.pdf:PDF},
keywords = {sirna},
url = {http://dx.doi.org/10.1016/S0092-8674(03)00801-8}
}
@article{Kieffer1978unified,
author = {Kieffer, J.},
title = {A unified approach to weak universal source coding},
journal = {I{EEE} {T}rans. {I}nform. {T}heory},
year = {1978},
volume = {24},
pages = {674-682},
number = {6},
month = {Nov},
abstract = { {A} new method of constructing a universal sequence of block codes
for coding a class of ergodic sources is given. {W}ith this method,
a weakly universal sequence of codes is constructed for variable-rate
noise. less coding and for fixed- and variable-rate coding with respect
to a fidelity criterion. {I}n this way a unified approach to weak
universal block source coding is obtained. {F}or the noiseless variable-rate
coding and the fixed-rate coding with respect to fidelity criterion,
the assumptions made on the alphabets, distortion measures, and class
of sources are both necessary and sufficient. {F}or fixed-rate coding
with respect to a fidelity criterion, the sample distortion of the
universal code sequence converges in{L}^{l}norm for each source to
the optimum distortion for that source. {F}or both variable-rate
noiseless coding and variable-rate coding with respect to a fidelity
criterion, the sample rate of the universal code sequence converges
in{L}^{1}norm for each source to the optimum rate for that source.
{U}sing this fact, a universal sequence of codes for fixed-rate noiseless
coding is obtained. {S}ome applications to stationary nonergodic
sources are also considered. {T}he results of {D}avisson, {Z}iv,
{N}euhoff, {G}ray, {P}ursley, and {M}ackenthun are extended. },
pdf = {../local/Kieffer1978unified.pdf},
file = {Kieffer1978unified.pdf:local/Kieffer1978unified.pdf:PDF},
keywords = {universal-coding information-theory},
owner = {vert}
}
@article{Kim2008Insights,
author = {Eddo Kim and Amir Goren and Gil Ast},
title = {Insights into the connection between cancer and alternative splicing},
journal = {Trends Genet.},
year = {2008},
volume = {24},
pages = {7-10},
keywords = {csbcbook}
}
@article{Kim2007Sparse,
author = {Hyunsoo Kim and Haesun Park},
title = {Sparse non-negative matrix factorizations via alternating non-negativity-constrained
least squares for microarray data analysis.},
journal = {Bioinformatics},
year = {2007},
volume = {23},
pages = {1495--1502},
number = {12},
month = {Jun},
abstract = {Many practical pattern recognition problems require non-negativity
constraints. For example, pixels in digital images and chemical concentrations
in bioinformatics are non-negative. Sparse non-negative matrix factorizations
(NMFs) are useful when the degree of sparseness in the non-negative
basis matrix or the non-negative coefficient matrix in an NMF needs
to be controlled in approximating high-dimensional data in a lower
dimensional space.In this article, we introduce a novel formulation
of sparse NMF and show how the new formulation leads to a convergent
sparse NMF algorithm via alternating non-negativity-constrained least
squares. We apply our sparse NMF algorithm to cancer-class discovery
and gene expression data analysis and offer biological analysis of
the results obtained. Our experimental results illustrate that the
proposed sparse NMF algorithm often achieves better clustering performance
with shorter computing time compared to other existing NMF algorithms.The
software is available as supplementary material.},
doi = {10.1093/bioinformatics/btm134},
pdf = {../local/Kim2007Sparse.pdf},
file = {Kim2007Sparse.pdf:Kim2007Sparse.pdf:PDF},
institution = {College of Computing, Georgia Institute of Technology, Atlanta, GA
30332, USA. hskim@cc.gatech.edu},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {btm134},
pmid = {17483501},
timestamp = {2012.02.28},
url = {http://dx.doi.org/10.1093/bioinformatics/btm134}
}
@article{Kim2004Predictiona,
author = {Kim, H. and Park, H.},
title = {Prediction of protein relative solvent accessibility with support
vector machines and long-range interaction 3{D} local descriptor},
journal = {Proteins},
year = {2004},
volume = {54},
pages = {557-562},
number = {3},
month = {Feb},
abstract = {The prediction of protein relative solvent accessibility gives us
helpful information for the prediction of tertiary structure of a
protein. {T}he {SVM}psi method, which uses support vector machines
({SVM}s), and the position-specific scoring matrix ({PSSM}) generated
from {PSI}-{BLAST} have been applied to achieve better prediction
accuracy of the relative solvent accessibility. {W}e have introduced
a three-dimensional local descriptor that contains information about
the expected remote contacts by both the long-range interaction matrix
and neighbor sequences. {M}oreover, we applied feature weights to
kernels in {SVM}s in order to consider the degree of significance
that depends on the distance from the specific amino acid. {R}elative
solvent accessibility based on a two state-model, for 25%, 16%, 5%,
and 0% accessibility are predicted at 78.7%, 80.7%, 82.4%, and 87.4%
accuracy, respectively. {T}hree-state prediction results provide
a 64.5% accuracy with 9%; 36% threshold. {T}he support vector machine
approach has successfully been applied for solvent accessibility
prediction by considering long-range interaction and handling unbalanced
data.},
doi = {10.1002/prot.10602},
pdf = {../local/Kim2004Predictiona.pdf},
file = {Kim2004Predictiona.pdf:local/Kim2004Predictiona.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://dx.doi.org/10.1002/prot.10602}
}
@article{Kim2003Protein,
author = {Kim, H. and Park, H.},
title = {Protein secondary structure prediction based on an improved support
vector machines approach},
journal = {Protein {E}ng.},
year = {2003},
volume = {16},
pages = {553-560},
number = {8},
month = {Aug},
abstract = {The prediction of protein secondary structure is an important step
in the prediction of protein tertiary structure. {A} new protein
secondary structure prediction method, {SVM}psi, was developed to
improve the current level of prediction by incorporating new tertiary
classifiers and their jury decision system, and the {PSI}-{BLAST}
{PSSM} profiles. {A}dditionally, efficient methods to handle unbalanced
data and a new optimization strategy for maximizing the {Q}3 measure
were developed. {T}he {SVM}psi produces the highest published {Q}3
and {SOV}94 scores on both the {RS}126 and {CB}513 data sets to date.
{F}or a new {KP}480 set, the prediction accuracy of {SVM}psi was
{Q}3 = 78.5% and {SOV}94 = 82.8%. {M}oreover, the blind test results
for 136 non-redundant protein sequences which do not contain homologues
of training data sets were {Q}3 = 77.2% and {SOV}94 = 81.8%. {T}he
{SVM}psi results in {CASP}5 illustrate that it is another competitive
method to predict protein secondary structure.},
pdf = {../local/Kim2003Protein.pdf},
file = {Kim2003Protein.pdf:local/Kim2003Protein.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://peds.oupjournals.org/cgi/content/abstract/16/8/553}
}
@unpublished{Kim2001Evolving,
author = {J. Kim and P.L. Krapivsky and B. Kahng and S. Redner},
title = {Evolving protein interaction networks},
note = {E-print cond-mat/0203167},
year = {2001},
pdf = {../local/kim02.pdf},
file = {kim02.pdf:local/kim02.pdf:PDF},
subject = {bionetprot},
url = {http://xxx.lanl.gov/abs/cond-mat/0203167}
}
@article{Kim2004Prediction,
author = {Kim, J. H. and Lee, J. and Oh, B. and Kimm, K. and Koh, I.},
title = {Prediction of phosphorylation sites using {SVM}s},
journal = {Bioinformatics},
year = {2004},
volume = {20},
pages = {3179-3184},
number = {17},
abstract = {Motivation: {P}hosphorylation is involved in diverse signal transduction
pathways. {B}y predicting phosphorylation sites and their kinases
from primary protein sequences, we can obtain much valuable information
that can form the basis for further research. {U}sing support vector
machines, we attempted to predict phosphorylation sites and the type
of kinase that acts at each site. {R}esults: {O}ur prediction system
was limited to phosphorylation sites catalyzed by four protein kinase
families and four protein kinase groups. {T}he accuracy of the predictions
ranged from 83 to 95% at the kinase family level, and 76-91% at the
kinase group level. {T}he prediction system used--{P}red{P}hospho--can
be applied to the functional study of proteins, and can help predict
the changes in phosphorylation sites caused by amino acid variations
at intra- and interspecies levels. {A}vailability: {P}red{P}hospho
is available at http://www.ngri.re.kr/proteo/{P}red{P}hospho.htm.
{S}upplementary information: http://www.ngri.re.kr/proteo/supplementary.doc},
doi = {10.1093/bioinformatics/bth382},
pdf = {../local/Kim2004Prediction.pdf},
file = {Kim2004Prediction.pdf:local/Kim2004Prediction.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://bioinformatics.oupjournals.org/cgi/content/abstract/20/17/3179}
}
@inproceedings{Kim2008Robust,
author = {Kim, J. S. and Scott, C.},
title = {Robust kernel density estimation},
booktitle = {Proc. IEEE Int. Conf. Acoustics, Speech and Signal Processing ICASSP
2008},
year = {2008},
pages = {3381--3384},
doi = {10.1109/ICASSP.2008.4518376},
pdf = {../local/Kim2008Robust.pdf},
file = {Kim2008Robust.pdf:Kim2008Robust.pdf:PDF},
keywords = {kernelbook},
owner = {jp},
timestamp = {2011.07.23},
url = {http://dx.doi.org/10.1109/ICASSP.2008.4518376}
}
@article{Kim2012Robust,
author = {Kim, J. S. and Scott, C. D.},
title = {Robust kernel density estimation},
journal = {J. Mach. Learn. Res.},
year = {2012},
volume = {13},
pages = {2529--2565},
pdf = {../local/Kim2012Robust.pdf},
file = {Kim2012Robust.pdf:Kim2012Robust.pdf:PDF},
owner = {jp},
timestamp = {2012.10.19},
url = {http://www.jmlr.org/papers/volume13/kim12b/kim12b.pdf}
}
@article{Kim2004Emotion,
author = {K. H. Kim and S. W. Bang and S. R. Kim},
title = {Emotion recognition system using short-term monitoring of physiological
signals.},
journal = {Med {B}iol {E}ng {C}omput},
year = {2004},
volume = {42},
pages = {419-27},
number = {3},
month = {May},
abstract = {A physiological signal-based emotion recognition system is reported.
{T}he system was developed to operate as a user-independent system,
based on physiological signal databases obtained from multiple subjects.
{T}he input signals were electrocardiogram, skin temperature variation
and electrodermal activity, all of which were acquired without much
discomfort from the body surface, and can reflect the influence of
emotion on the autonomic nervous system. {T}he system consisted of
preprocessing, feature extraction and pattern classification stages.
{P}reprocessing and feature extraction methods were devised so that
emotion-specific characteristics could be extracted from short-segment
signals. {A}lthough the features were carefully extracted, their
distribution formed a classification problem, with large overlap
among clusters and large variance within clusters. {A} support vector
machine was adopted as a pattern classifier to resolve this difficulty.
{C}orrect-classification ratios for 50 subjects were 78.4\% and 61.8\%,
for the recognition of three and four categories, respectively.},
keywords = {Algorithms, Animals, Antisense, Artificial Intelligence, Autonomic
Nervous System, Cell Line, Child, Cluster Analysis, Comparative Study,
Computational Biology, Computer Simulation, Computer-Assisted, DNA
Fingerprinting, Drug Evaluation, Emotions, Fluorescence, Fuzzy Logic,
Gene Silencing, Gene Targeting, Genetic, Hela Cells, Humans, Imaging,
Intracellular Space, Microscopy, Models, Monitoring, Neoplasms, Neural
Networks (Computer), Non-U.S. Gov't, Oligonucleotides, P.H.S., Physiologic,
Preclinical, Preschool, Prognosis, Proteomics, Quantitative Structure-Activity
Relationship, RNA, RNA Interference, Recognition (Psychology), Research
Support, Sensitivity and Specificity, Signal Processing, Small Interfering,
Thionucleotides, Three-Dimensional, Tumor, U.S. Gov't, User-Computer
Interface, 15191089}
}
@article{Kim2009Effects,
author = {Kim, S.Y.},
title = {Effects of sample size on robustness and prediction accuracy of a
prognostic gene signature},
journal = {BMC bioinformatics},
year = {2009},
volume = {10},
pages = {147},
number = {1},
publisher = {BioMed Central Ltd}
}
@article{Kim2004Enhancing,
author = {Sang-Woon Kim and B. John Oommen},
title = {Enhancing prototype reduction schemes with recursion: a method applicable
for "large" data sets.},
journal = {I{EEE} {T}rans {S}yst {M}an {C}ybern {B} {C}ybern},
year = {2004},
volume = {34},
pages = {1384-97},
number = {3},
month = {Jun},
abstract = {Most of the prototype reduction schemes ({PRS}), which have been reported
in the literature, process the data in its entirety to yield a subset
of prototypes that are useful in nearest-neighbor-like classification.
{F}oremost among these are the prototypes for nearest neighbor classifiers,
the vector quantization technique, and the support vector machines.
{T}hese methods suffer from a major disadvantage, namely, that of
the excessive computational burden encountered by processing all
the data. {I}n this paper, we suggest a recursive and computationally
superior mechanism referred to as adaptive recursive partitioning
({ARP})_{PRS}. {R}ather than process all the data using a {PRS},
we propose that the data be recursively subdivided into smaller subsets.
{T}his recursive subdivision can be arbitrary, and need not utilize
any underlying clustering philosophy. {T}he advantage of {ARP}_{PRS}
is that the {PRS} processes subsets of data points that effectively
sample the entire space to yield smaller subsets of prototypes. {T}hese
prototypes are then, in turn, gathered and processed by the {PRS}
to yield more refined prototypes. {I}n this manner, prototypes which
are in the interior of the {V}oronoi spaces, and thus ineffective
in the classification, are eliminated at the subsequent invocations
of the {PRS}. {W}e are unaware of any {PRS} that employs such a recursive
philosophy. {A}lthough we marginally forfeit accuracy in return for
computational efficiency, our experimental results demonstrate that
the proposed recursive mechanism yields classification comparable
to the best reported prototype condensation schemes reported to-date.
{I}ndeed, this is true for both artificial data sets and for samples
involving real-life data sets. {T}he results especially demonstrate
that a fair computational advantage can be obtained by using such
a recursive strategy for "large" data sets, such as those involved
in data mining and text categorization applications.}
}
@article{Kim2005Locally,
author = {Tae-Kyun Kim and Josef Kittler},
title = {Locally linear discriminant analysis for multimodally distributed
classes for face recognition with a single model image.},
journal = {I{EEE} {T}rans {P}attern {A}nal {M}ach {I}ntell},
year = {2005},
volume = {27},
pages = {318-27},
number = {3},
month = {Mar},
abstract = {We present a novel method of nonlinear discriminant analysis involving
a set of locally linear transformations called "{L}ocally {L}inear
{D}iscriminant {A}nalysis ({LLDA})." {T}he underlying idea is that
global nonlinear data structures are locally linear and local structures
can be linearly aligned. {I}nput vectors are projected into each
local feature space by linear transformations found to yield locally
linearly transformed classes that maximize the between-class covariance
while minimizing the within-class covariance. {I}n face recognition,
linear discriminant analysis ({LDA}) has been widely adopted owing
to its efficiency, but it does not capture nonlinear manifolds of
faces which exhibit pose variations. {C}onventional nonlinear classification
methods based on kernels such as generalized discriminant analysis
({GDA}) and support vector machine ({SVM}) have been developed to
overcome the shortcomings of the linear method, but they have the
drawback of high computational cost of classification and overfitting.
{O}ur method is for multiclass nonlinear discrimination and it is
computationally highly efficient as compared to {GDA}. {T}he method
does not suffer from overfitting by virtue of the linear base structure
of the solution. {A} novel gradient-based learning algorithm is proposed
for finding the optimal set of local linear bases. {T}he optimization
does not exhibit a local-maxima problem. {T}he transformation functions
facilitate robust face recognition in a low-dimensional subspace,
under pose variations, using a single model image. {T}he classification
results are given for both synthetic and real face data.}
}
@article{Kim2006Blockwise,
author = {Kim, Y. and Kim, J. and Kim, K.},
title = {Blockwise sparse regression},
journal = {Statistica Sinica},
year = {2006},
volume = {16},
pages = {375--390},
pdf = {../local/Kim2006Blockwise.pdf},
file = {Kim2006Blockwise.pdf:Kim2006Blockwise.pdf:PDF},
keywords = {lasso},
owner = {jp},
timestamp = {2010.06.26}
}
@article{Kimeldorf1971Some,
author = {G. S. Kimeldorf and G. Wahba},
title = {Some results on {T}chebycheffian spline functions},
journal = {J. {M}ath. {A}nal. {A}ppl.},
year = {1971},
volume = {33},
pages = {82-95}
}
@inproceedings{Kin2002Marginalized,
author = {Kin, T. and Tsuda, K. and Asai, K.},
title = {Marginalized kernels for {RNA} sequence data analysis},
booktitle = {Genome {I}nformatics 2002},
year = {2002},
editor = {Lathtop, R.H. and Nakai, K. and Miyano, S. and Takagi, T. and Kanehisa,
M.},
pages = {112-122},
publisher = {Universal Academic Press},
abstract = {We present novel kernels that measure similarity of two {RNA} sequences,
taking account of their secondary structures. {T}wo types of kernels
are presented. {O}ne is for {RNA} sequences with known secondary
structures, the other for those without known secondary structures.
{T}he latter employs stochastic context-free grammar ({SCFG}) for
estimating the secondary structure. {W}e call the latter the {\it
marginalized count kernel} ({MCK}). {W}e show computational experiments
for {MCK} using 74 sets of human t{RNA} sequence data: (i) kernel
principal component analysis ({PCA}) for visualizing t{RNA} similarities,
(ii) supervised classification with support vector machines ({SVM}s).
{B}oth types of experiment show promising results for {MCK}s.},
pdf = {../local/Kin2002Marginalized.pdf},
file = {Kin2002Marginalized.pdf:local/Kin2002Marginalized.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://www.jsbi.org/journal/GIW02/GIW02F012.html}
}
@article{King1992Drug,
author = {R. D. King and S. Muggleton and R. A. Lewis and M. J. Sternberg},
title = {{D}rug design by machine learning: the use of inductive logic programming
to model the structure-activity relationships of trimethoprim analogues
binding to dihydrofolate reductase.},
journal = {Proc. Natl. Acad. Sci. USA},
year = {1992},
volume = {89},
pages = {11322--11326},
number = {23},
month = {Dec},
abstract = {The machine learning program GOLEM from the field of inductive logic
programming was applied to the drug design problem of modeling structure-activity
relationships. The training data for the program were 44 trimethoprim
analogues and their observed inhibition of Escherichia coli dihydrofolate
reductase. A further 11 compounds were used as unseen test data.
GOLEM obtained rules that were statistically more accurate on the
training data and also better on the test data than a Hansch linear
regression model. Importantly machine learning yields understandable
rules that characterized the chemistry of favored inhibitors in terms
of polarity, flexibility, and hydrogen-bonding character. These rules
agree with the stereochemistry of the interaction observed crystallographically.},
keywords = {Algorithms, Artificial Intelligence, Drug Design, Escherichia coli,
Folic Acid Antagonists, Molecular Structure, Mutagens, Nitroso Compounds,
Non-U.S. Gov't, Research Support, Structure-Activity Relationship,
Trimethoprim, 1454814},
owner = {mahe},
pmid = {1454814},
timestamp = {2006.09.06}
}
@article{King1996Structure-activity,
author = {King, R. D. and Muggleton, S. H. and Srinivasan, A. and Sternberg,
M. J.},
title = {{S}tructure-activity relationships derived by machine learning: the
use of atoms and their bond connectivities to predict mutagenicity
by inductive logic programming},
journal = {Proc. Natl. Acad. Sci. USA},
year = {1996},
volume = {93},
pages = {438--442},
number = {1},
month = {Jan},
abstract = {We present a general approach to forming structure-activity relationships
(SARs). This approach is based on representing chemical structure
by atoms and their bond connectivities in combination with the inductive
logic programming (ILP) algorithm PROGOL. Existing SAR methods describe
chemical structure by using attributes which are general properties
of an object. It is not possible to map chemical structure directly
to attribute-based descriptions, as such descriptions have no internal
organization. A more natural and general way to describe chemical
structure is to use a relational description, where the internal
construction of the description maps that of the object described.
Our atom and bond connectivities representation is a relational description.
ILP algorithms can form SARs with relational descriptions. We have
tested the relational approach by investigating the SARs of 230 aromatic
and heteroaromatic nitro compounds. These compounds had been split
previously into two subsets, 188 compounds that were amenable to
regression and 42 that were not. For the 188 compounds, a SAR was
found that was as accurate as the best statistical or neural network-generated
SARs. The PROGOL SAR has the advantages that it did not need the
use of any indicator variables handcrafted by an expert, and the
generated rules were easily comprehensible. For the 42 compounds,
PROGOL formed a SAR that was significantly (P < 0.025) more accurate
than linear regression, quadratic regression, and back-propagation.
This SAR is based on an automatically generated structural alert
for mutagenicity.},
owner = {mahe},
pmid = {8552655},
timestamp = {2006.09.06}
}
@article{Kirby1990Application,
author = {Kirby, M. and Sirovich, L.},
title = {Application of the {K}arhunen-{L}o{\`e}ve procedure for the characterization
of human faces},
journal = {I{EEE} {T}rans. {P}attern {A}nal. {M}ach. {I}ntell.},
year = {1990},
volume = {12},
pages = {103--108},
number = {1},
doi = {10.1109/34.41390},
pdf = {../local/Kirby1990Application.pdf},
file = {Kirby1990Application.pdf:local/Kirby1990Application.pdf:PDF},
subject = {ml},
url = {http://dx.doi.org/10.1109/34.41390}
}
@article{Kitano2004Cancer,
author = {Kitano, H.},
title = {Cancer as a robust system: implications for anticancer therapy},
journal = {Nat. {R}ev. {C}ancer},
year = {2004},
volume = {4},
pages = {227-235},
abstract = {Cancers are extremely complex, heterogeneous diseases. {M}any approaches
to anticancer treatment have had limited success ? cures are still
rare. {A} fundamental hurdle to cancer therapy is acquired tumour
'robustness'. {T}he goal of this article is to present a perspective
on cancer as a robust system to provide a framework from which the
complexity of tumours can be approached to yield novel therapies.},
doi = {10.1038/nrc1300},
pdf = {../local/Kitano2004Cancer.pdf},
file = {Kitano2004Cancer.pdf:local/Kitano2004Cancer.pdf:PDF},
url = {http://dx.doi.org/10.1038/nrc1300}
}
@article{Kitano2002Computational,
author = {Kitano, H.},
title = {Computational systems biology},
journal = {Nature},
year = {2002},
volume = {420},
pages = {206-210},
abstract = {To understand complex biological systems requires the integration
of experimental and computational research ? in other words a systems
biology approach. {C}omputational biology, through pragmatic modelling
and theoretical exploration, provides a powerful foundation from
which to address critical scientific questions head-on. {T}he reviews
in this {I}nsight cover many different aspects of this energetic
field, although all, in one way or another, illuminate the functioning
of modular circuits, including their robustness, design and manipulation.
{C}omputational systems biology addresses questions fundamental to
our understanding of life, yet progress here will lead to practical
innovations in medicine, drug discovery and engineering.},
doi = {10.1038/nature01254},
pdf = {../local/Kitano2002Computational.pdf},
file = {Kitano2002Computational.pdf:local/Kitano2002Computational.pdf:PDF},
url = {http://dx.doi.org/10.1038/nature01254}
}
@book{Kitano2001Foundations,
title = {Foundations of {S}ystems {B}iology},
publisher = {MIT Press},
year = {2001},
author = {Kitano, H.},
owner = {vert}
}
@article{Kitano2005NatBiotechnol,
author = {Kitano, H. and Funahashi, A. and Matsuoka, Y. and Oda, K.},
title = {Using process diagrams for the graphical representation of biological
networks},
journal = {Nat. {B}iotechnol.},
year = {2005},
volume = {8},
pages = {961-966},
abstract = {With the increased interest in understanding biological networks,
such as protein-protein interaction networks and gene regulatory
networks, methods for representing and communicating such networks
in both human- and machine-readable form have become increasingly
important. Although there has been significant progress in machine-readable
representation of networks, as exemplified by the Systems Biology
Mark-up Language (SBML) (http://www.sbml.org) issues in human-readable
representation have been largely ignored. This article discusses
human-readable diagrammatic representations and proposes a set of
notations that enhances the formality and richness of the information
represented. The process diagram is a fully state transition-based
diagram that can be translated into machine-readable forms such as
SBML in a straightforward way. It is supported by CellDesigner, a
diagrammatic network editing software (http://www.celldesigner.org/),
and has been used to represent a variety of networks of various sizes
(from only a few components to several hundred components).},
doi = {10.1038/nbt1111},
keywords = {csbcbook}
}
@article{Kitchen2004Docking,
author = {D. B. Kitchen and H. Decornez and J. R. Furr and J. Bajorath},
title = {{D}ocking and scoring in virtual screening for drug discovery: methods
and applications.},
journal = {Nat Rev Drug Discov},
year = {2004},
volume = {3},
pages = {935--949},
number = {11},
month = {Nov},
abstract = {Computational approaches that 'dock' small molecules into the structures
of macromolecular targets and 'score' their potential complementarity
to binding sites are widely used in hit identification and lead optimization.
Indeed, there are now a number of drugs whose development was heavily
influenced by or based on structure-based design and screening strategies,
such as HIV protease inhibitors. Nevertheless, there remain significant
challenges in the application of these approaches, in particular
in relation to current scoring schemes. Here, we review key concepts
and specific features of small-molecule-protein docking methods,
highlight selected applications and discuss recent advances that
aim to address the acknowledged limitations of established approaches.},
doi = {10.1038/nrd1549},
owner = {mahe},
pii = {nrd1549},
pmid = {15520816},
timestamp = {2006.08.16},
url = {http://dx.doi.org/10.1038/nrd1549}
}
@article{Klabunde2007Chemogenomic,
author = {Klabunde, T.},
title = {Chemogenomic approaches to drug discovery: similar receptors bind
similar ligands.},
journal = {Br. J. Pharmacol.},
year = {2007},
volume = {152},
pages = {5--7},
month = {May},
abstract = {Within recent years, a paradigm shift from traditional receptor-specific
studies to a cross-receptor view has taken place within pharmaceutical
research to increase the efficiency of modern drug discovery. Receptors
are no longer viewed as single entities but grouped into sets of
related proteins or receptor families that are explored in a systematic
manner. This interdisciplinary approach attempting to derive predictive
links between the chemical structures of bioactive molecules and
the receptors with which these molecules interact is referred to
as chemogenomics. Insights from chemogenomics are used for the rational
compilation of screening sets and for the rational design and synthesis
of directed chemical libraries to accelerate drug discovery.British
Journal of Pharmacology advance online publication, 29 May 2007;
doi:10.1038/sj.bjp.0707308.},
doi = {10.1038/sj.bjp.0707308},
keywords = {chemogenomics},
owner = {laurent},
pii = {0707308},
pmid = {17533415},
timestamp = {2007.07.30},
url = {http://dx.doi.org/10.1038/sj.bjp.0707308}
}
@incollection{Klabunde2006ChemogenomicsA,
author = {Klabunde, T.},
title = {Chemogenomics Approaches to Ligand Design},
booktitle = {Ligand Design for G Protein-coupled Receptors},
publisher = {Wiley-VCH},
year = {2006},
chapter = {7},
pages = {115-135},
address = {Great Britain}
}
@article{Klabunde2006Chemogenomics,
author = {T. Klabunde and R. J{\"a}ger},
title = {Chemogenomics approaches to G-protein coupled receptor lead finding.},
journal = {Ernst Schering Res Found Workshop},
year = {2006},
volume = {58},
pages = {31--46},
abstract = {G-protein coupled receptors (GPCRs) are promising targets for the
discovery of novel drugs. In order to identify novel chemical series,
high-throughput screening (HTS) is often complemented by rational
chemogenomics lead finding approaches. We have compiled a GPCR directed
screening set by ligand-based virtual screening of our corporate
compound database. This set of compounds is supplemented with novel
libraries synthesized around proprietary scaffolds. These target-directed
libraries are designed using the knowledge of privileged fragments
and pharmacophores to address specific GPCR subfamilies (e.g., purinergic
or chemokine-binding GPCRs). Experimental testing of the GPCR collection
has provided novel chemical series for several GPCR targets including
the adenosine A1, the P2Y12, and the chemokine CCR1 receptor. In
addition, GPCR sequence motifs linked to the recognition of GPCR
ligands (termed chemoprints) are identified using homology modeling,
molecular docking, and experimental profiling. These chemoprints
can support the design and synthesis of compound libraries tailor-made
for a novel GPCR target.},
keywords = {chemogenomics},
owner = {laurent},
pmid = {16708997},
timestamp = {2007.07.30}
}
@article{Klamt2004Minimal,
author = {Klamt, S. and Gilles, E. D.},
title = {Minimal cut sets in biochemical reaction networks.},
journal = {Bioinformatics},
year = {2004},
volume = {20},
pages = {226--234},
number = {2},
month = {Jan},
abstract = {Structural studies of metabolic networks yield deeper insight into
topology, functionality and capabilities of the metabolisms of different
organisms. Here, we address the analysis of potential failure modes
in metabolic networks whose occurrence will render the network structurally
incapable of performing certain functions. Such studies will help
to identify crucial parts in the network structure and to find suitable
targets for repressing undesired metabolic functions.We introduce
the concept of minimal cut sets for biochemical networks. A minimal
cut set (MCS) is a minimal (irreducible) set of reactions in the
network whose inactivation will definitely lead to a failure in certain
network functions. We present an algorithm which enables the computation
of the MCSs in a given network related to user-defined objective
reactions. This algorithm operates on elementary modes. A number
of potential applications are outlined, including network verifications,
phenotype predictions, assessing structural robustness and fragility,
metabolic flux analysis and target identification in drug discovery.
Applications are illustrated by the MCSs in the central metabolism
of Escherichia coli for growth on different substrates.Computation
and analysis of MCSs is an additional feature of the FluxAnalyzer
(freely available for academic users upon request, special contracts
for industrial companies; see web page below). Supplementary information:
http://www.mpi-magdeburg.mpg.de/projects/fluxanalyzer},
doi = {10.1093/bioinformatics/btg395},
institution = {Max Planck Institute for Dynamics of Complex Technical Systems, Sandtorstr.1,
D-39106 Magdeburg, Germany. klamt@mpi-magdeburg.mpg.de},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pmid = {14734314},
timestamp = {2011.11.30},
url = {http://dx.doi.org/10.1093/bioinformatics/btg395}
}
@article{Klebe2000Recent,
author = {G. Klebe},
title = {{R}ecent developments in structure-based drug design.},
journal = {J Mol Med},
year = {2000},
volume = {78},
pages = {269--281},
number = {5},
abstract = {Structure-based design has emerged as a new tool in medicinal chemistry.
A prerequisite for this new approach is an understanding of the principles
of molecular recognition in protein-ligand complexes. If the three-dimensional
structure of a given protein is known, this information can be directly
exploited for the retrieval and design of new ligands. Structure-based
ligand design is an iterative approach. First of all, it requires
the crystal structure or a model derived from the crystal structure
of a closely related homolog of the target protein, preferentially
complexed with a ligand. This complex unravels the binding mode and
conformation of a ligand under investigation and indicates the essential
aspects determining its binding affinity. It is then used to generate
new ideas about ways of improving an existing ligand or of developing
new alternative bonding skeletons. Computational methods supplemented
by molecular graphics are applied to assist this step of hypothesis
generation. The features of the protein binding pocket can be translated
into queries used for virtual computer screening of large compound
libraries or to design novel ligands de novo. These initial proposals
must be confirmed experimentally. Subsequently they are optimized
toward higher affinity and better selectivity. The latter aspect
is of utmost importance in defining and controlling the pharmacological
profile of a ligand. A prerequisite to tailoring selectivity by rational
design is a detailed understanding of molecular parameters determining
selectivity. Taking examples from current drug development programs
(HIV proteinase, t-RNA transglycosylase, thymidylate synthase, thrombin
and, related serine proteinases), we describe recent advances in
lead discovery via computer screening, iterative design, and understanding
of selectivity discrimination.},
keywords = {Animals, Chemistry, Computer Simulation, Cross-Over Studies, Crystallography,
Deglutition, Deglutition Disorders, Drug Design, Endoscopy, Enzyme
Inhibitors, Female, Fluoroscopy, Glossopharyngeal Nerve, HIV Protease
Inhibitors, Horse Diseases, Horses, Male, Models, Molecular, Nerve
Block, Non-U.S. Gov't, P.H.S., Pharmaceutical, Proteins, Quantitative
Structure-Activity Relationship, Random Allocation, Research Support,
Thrombin, Thymidylate Synthase, U.S. Gov't, X-Ray, 10954196},
owner = {mahe},
pmid = {10954196},
timestamp = {2006.09.05}
}
@article{Knight99Decoding,
author = {K. Knight},
title = {Decoding complexity in word-replacement translation models},
journal = {Computational Linguistics},
year = {1999},
volume = {25},
pages = {607--615}
}
@article{Knight2000Asymptotics,
author = {Knight, K. and Fu, W.},
title = {Asymptotics for lasso-type estimators},
journal = {Ann. Stat.},
year = {2000},
volume = {28},
pages = {1356--1378},
number = {5},
doi = {doi:10.1214/aos/1015957397},
keywords = {lasso},
owner = {jp},
timestamp = {2009.01.25},
url = {http://dx.doi.org/10.1214/aos/1015957397}
}
@misc{Knight-Yamada-02,
author = {Knight, K. and Yamada, K.},
title = {Integer Programming Decoder for Machine Translation},
howpublished = {Patent US 7,177,792},
year = {2007},
note = {Application filed in 2002}
}
@article{Knudson1971Mutation,
author = {Alfred G. Knudson},
title = {Mutation and Cancer: Statistical Study of Retinoblastoma},
journal = {Proceedings of the National Academy of Sciences},
year = {1971},
volume = {68},
pages = {820-823},
keywords = {csbcbook}
}
@article{Kobilka2007G,
author = {Kobilka, B. K.},
title = {G protein coupled receptor structure and activation.},
journal = {Biochim. Biophys. Acta},
year = {2007},
volume = {1768},
pages = {794--807},
number = {4},
month = {Apr},
abstract = {G protein coupled receptors (GPCRs) are remarkably versatile signaling
molecules. The members of this large family of membrane proteins
are activated by a spectrum of structurally diverse ligands, and
have been shown to modulate the activity of different signaling pathways
in a ligand specific manner. In this manuscript I will review what
is known about the structure and mechanism of activation of GPCRs
focusing primarily on two model systems, rhodopsin and the beta(2)
adrenoceptor.},
doi = {10.1016/j.bbamem.2006.10.021},
keywords = {chemogenomics},
owner = {laurent},
pii = {S0005-2736(06)00398-1},
pmid = {17188232},
timestamp = {2008.07.16},
url = {http://dx.doi.org/10.1016/j.bbamem.2006.10.021}
}
@inproceedings{Koehn-05,
author = {Koehn, P.},
title = {Europarl: A Parallel Corpus for Statistical Machine Translation},
booktitle = {{MT} Summit},
year = {2005}
}
@inproceedings{Koehn-et-al-03,
author = {Koehn, P. and Och, F. J. and Marcu, D.},
title = {Statistical phrase-based translation},
booktitle = {NAACL 2003},
year = {2003},
pages = {48--54},
address = {Morristown, NJ, USA},
publisher = {Association for Computational Linguistics},
doi = {http://dx.doi.org/10.3115/1073445.1073462},
location = {Edmonton, Canada}
}
@article{Koh2007interior,
author = {Koh, K. and Kim, S.J. and Boyd, S.},
title = {An interior-point method for large-scale l1-regularized logistic
regression},
journal = {Journal of Machine learning research},
year = {2007},
volume = {8},
pages = {1519--1555},
number = {8}
}
@article{Kohavi1997Wrappers,
author = {Kohavi, R. and John, G.},
title = {Wrappers for feature selection},
journal = {Artificial Intelligence},
year = {1997},
volume = {97},
pages = {273--324},
number = {1-2},
owner = {jp},
timestamp = {2011.01.11}
}
@article{Kohlmann2004Pediatric,
author = {Kohlmann, A. and Schoch, C. and Schnittger, S. and Dugas, M. and
Hiddemann, W. and Kern, W. and Haferlach, T.},
title = {Pediatric acute lymphoblastic leukemia ({ALL}) gene expression signatures
classify an independent cohort of adult {ALL} patients},
journal = {Leukemia},
year = {2004},
volume = {18},
pages = {63-71},
number = {1},
abstract = {Recent reports support a possible future application of gene expression
profiling for the diagnosis of leukemias. {H}owever, the robustness
of subtype-specific gene expression signatures has to be proven on
independent patient samples. {H}ere, we present gene expression data
of 34 adult acute lymphoblastic leukemia ({ALL}) patients ({A}ffymetrix
{U}133{A} microarrays). {S}upport {V}ector {M}achines ({SVM}s) were
applied to stratify our samples based on given gene lists reported
to predict {MLL}, {BCR}-{ABL}, and {T}-{ALL}, as well as {MLL} and
non-{MLL} gene rearrangement positive pediatric {ALL}. {I}n addition,
seven other {B}-precursor {ALL} cases not bearing t(9;22) or t(11q23)/{MLL}
chromosomal aberrations were analyzed. {U}sing top differentially
expressed genes, hierarchical cluster and principal component analyses
demonstrate that the genetically more heterogeneous {B}-precursor
{ALL} samples intercalate with {BCR}-{ABL}-positive cases, but were
clearly distinct from {T}-{ALL} and {MLL} profiles. {S}imilar expression
signatures were observed for both heterogeneous {B}-precursor {ALL}
and for {BCR}-{ABL}-positive cases. {A}s an unrelated laboratory,
we demonstrate that gene signatures defined for childhood {ALL} were
also capable of stratifying distinct subtypes in our cohort of adult
{ALL} patients. {A}s such, previously reported gene expression patterns
identified by microarray technology are validated and confirmed on
truly independent leukemia patient samples.},
doi = {10.1038/sj.leu.2403167},
pdf = {../local/Kohlmann2004Pediatric.pdf},
file = {Kohlmann2004Pediatric.pdf:local/Kohlmann2004Pediatric.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://dx.doi.org/10.1038/sj.leu.2403167}
}
@article{Koike2004Prediction,
author = {Koike, A. and Takagi, T.},
title = {Prediction of protein-protein interaction sites using support vector
machines},
journal = {Protein {E}ng. {D}es. {S}el.},
year = {2004},
volume = {17},
pages = {165-173},
number = {2},
month = {Feb},
abstract = {The identification of protein-protein interaction sites is essential
for the mutant design and prediction of protein-protein networks.
{T}he interaction sites of residue units were predicted using support
vector machines ({SVM}) and the profiles of sequentially/spatially
neighboring residues, plus additional information. {W}hen only sequence
information was used, prediction performance was highest using the
feature vectors, sequentially neighboring profiles and predicted
interaction site ratios, which were calculated by {SVM} regression
using amino acid compositions. {W}hen structural information was
also used, prediction performance was highest using the feature vectors,
spatially neighboring residue profiles, accessible surface areas,
and the with/without protein interaction sites ratios predicted by
{SVM} regression and amino acid compositions. {I}n the latter case,
the precision at recall = 50% was 54-56% for a homo-hetero mixed
test set and >20% higher than for random prediction. {A}pproximately
30% of the residues wrongly predicted as interaction sites were the
closest sequentially/spatially neighboring on the interaction site
residues. {T}he predicted residues covered 86-87% of the actual interfaces
(96-97% of interfaces with over 20 residues). {T}his prediction performance
appeared to be slightly higher than a previously reported study.
{C}omparing the prediction accuracy of each molecule, it seems to
be easier to predict interaction sites for stable complexes.},
doi = {10.1093/protein/gzh020},
pdf = {../local/Koike2004Prediction.pdf},
file = {Koike2004Prediction.pdf:local/Koike2004Prediction.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://dx.doi.org/10.1093/protein/gzh020}
}
@book{Koller2009Probabilistic,
title = {Probabilistic Graphical Models},
publisher = {MIT Press},
year = {2009},
author = {Koller, D. and Friedman, N.},
owner = {jp},
timestamp = {2010.10.13}
}
@unpublished{Koltchinskii2003Localized,
author = {Koltchinskii, V.},
title = {Localized {R}ademacher complexities},
note = {Manuscript},
month = {september},
year = {2003}
}
@article{Komura2005Multidimensional,
author = {Komura, D. and Nakamura, H. and Tsutsumi, S. and Aburatani, H. and
Ihara, S.},
title = {Multidimensional support vector machines for visualization of gene
expression data},
journal = {Bioinformatics},
year = {2005},
volume = {21},
pages = {439-444},
number = {4},
month = {Feb},
abstract = {Motivation: {S}ince {DNA} microarray experiments provide us with huge
amount of gene expression data, they should be analyzed with statistical
methods to extract the meanings of experimental results. {S}ome dimensionality
reduction methods such as {P}rincipal {C}omponent {A}nalysis ({PCA})
are used to roughly visualize the distribution of high dimensional
gene expression data. {H}owever, in the case of binary classification
of gene expression data, {PCA} does not utilize class information
when choosing axes. {T}hus clearly separable data in the original
space may not be so in the reduced space used in {PCA}.{R}esults:
{F}or visualization and class prediction of gene expression data,
we have developed a new {SVM}-based method called multidimensional
{SVM}s, that generate multiple orthogonal axes. {T}his method projects
high dimensional data into lower dimensional space to exhibit properties
of the data clearly and to visualize a distribution of the data roughly.
{F}urthermore, the multiple axes can be used for class prediction.
{T}he basic properties of conventional {SVM}s are retained in our
method: solutions of mathematical programming are sparse, and nonlinear
classification is implemented implicitly through the use of kernel
functions. {T}he application of our method to the experimentally
obtained gene expression datasets for patients' samples indicates
that our algorithm is efficient and useful for visualization and
class prediction.},
doi = {10.1093/bioinformatics/bti188},
pdf = {../local/Komura2005Multidimensional.pdf},
file = {Komura2005Multidimensional.pdf:local/Komura2005Multidimensional.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://bioinformatics.oupjournals.org/cgi/content/abstract/bti188v1}
}
@inproceedings{Kondor2008skew,
author = {Kondor, R. and Borgwardt, K. M.},
title = {The skew spectrum of graphs},
booktitle = {ICML '08: Proceedings of the 25th international conference on Machine
learning},
year = {2008},
pages = {496--503},
address = {New York, NY, USA},
publisher = {ACM},
doi = {http://doi.acm.org/10.1145/1390156.1390219},
pdf = {../local/Kondor2008skew.pdf},
file = {Kondor2008skew.pdf:Kondor2008skew.pdf:PDF},
isbn = {978-1-60558-205-4},
location = {Helsinki, Finland}
}
@inproceedings{Kondor2003Kernel,
author = {R. Kondor and T. Jebara},
title = {A kernel between sets of vectors},
booktitle = {ICML '03: Proceedings of the 20th international conference on Machine
learning},
year = {2003}
}
@inproceedings{Kondor2009graphlet,
author = {Kondor, R. and Shervashidze, N. and Borgwardt, K. M.},
title = {The graphlet spectrum},
booktitle = {ICML '09: Proceedings of the 26th Annual International Conference
on Machine Learning},
year = {2009},
pages = {529--536},
address = {New York, NY, USA},
publisher = {ACM},
doi = {http://doi.acm.org/10.1145/1553374.1553443},
isbn = {978-1-60558-516-1},
location = {Montreal, Quebec, Canada}
}
@incollection{Kondor2004Diffusion,
author = {Kondor, R. and Vert, J.-P.},
title = {Diffusion kernels},
booktitle = {Kernel {M}ethods in {C}omputational {B}iology},
publisher = {MIT Press},
year = {2004},
editor = {Sch{\"o}lkopf, B. and Tsuda, K. and Vert, J.P.},
pages = {171-192},
pdf = {../local/saigo.pdf:http\://cg.ensmp.fr/~vert/publi/04kmcbbook/saigo.pdf:PDF;saigo.pdf:http\},
file = {saigo.pdf:http\://cg.ensmp.fr/~vert/publi/04kmcbbook/saigo.pdf:PDF;saigo.pdf:http\://cg.ensmp.fr/~vert/publi/04kmcbbook/saigo.pdf:PDF},
keywords = {biosvm},
owner = {vert}
}
@inproceedings{Kondor2010Ranking,
author = {Kondor, R. I. and Barbosa, M. S.},
title = {Ranking with Kernels in Fourier space},
booktitle = {COLT 2010 - The 23rd Conference on Learning Theory, Haifa,
Israel, June 27-29, 2010},
year = {2010},
editor = {Kalai, A. T. and Mohri, M.},
pages = {451--463},
publisher = {Omnipress},
pdf = {../local/Kondor2010Ranking.pdf},
file = {Kondor2010Ranking.pdf:Kondor2010Ranking.pdf:PDF},
owner = {jp},
timestamp = {2011.04.08}
}
@inproceedings{Kondor2002Diffusion,
author = {R. I. Kondor and J. Lafferty},
title = {Diffusion kernels on graphs and other discrete input},
booktitle = {Proceedings of the Nineteenth International Conference on Machine
Learning},
year = {2002},
pages = {315--322},
address = {San Francisco, CA, USA},
publisher = {Morgan Kaufmann Publishers Inc.},
pdf = {../local/Kondor2002Diffusion.pdf},
file = {Kondor2002Diffusion.pdf:Kondor2002Diffusion.pdf:PDF},
keywords = {biosvm},
subject = {kernelnet}
}
@article{Kononen1998Tissue,
author = {J. Kononen and L. Bubendorf and A. Kallioniemi and M. Bärlund and
P. Schraml and S. Leighton and J. Torhorst and M. J. Mihatsch and
G. Sauter and O. P. Kallioniemi},
title = {Tissue microarrays for high-throughput molecular profiling of tumor
specimens.},
journal = {Nat Med},
year = {1998},
volume = {4},
pages = {844--847},
number = {7},
month = {Jul},
abstract = {Many genes and signalling pathways controlling cell proliferation,
death and differentiation, as well as genomic integrity, are involved
in cancer development. New techniques, such as serial analysis of
gene expression and cDNA microarrays, have enabled measurement of
the expression of thousands of genes in a single experiment, revealing
many new, potentially important cancer genes. These genome screening
tools can comprehensively survey one tumor at a time; however, analysis
of hundreds of specimens from patients in different stages of disease
is needed to establish the diagnostic, prognostic and therapeutic
importance of each of the emerging cancer gene candidates. Here we
have developed an array-based high-throughput technique that facilitates
gene expression and copy number surveys of very large numbers of
tumors. As many as 1000 cylindrical tissue biopsies from individual
tumors can be distributed in a single tumor tissue microarray. Sections
of the microarray provide targets for parallel in situ detection
of DNA, RNA and protein targets in each specimen on the array, and
consecutive sections allow the rapid analysis of hundreds of molecular
markers in the same set of specimens. Our detection of six gene amplifications
as well as p53 and estrogen receptor expression in breast cancer
demonstrates the power of this technique for defining new subgroups
of tumors.},
institution = {Laboratory of Cancer Genetics, National Human Genome Research Institute,
National Institutes of Health, Bethesda, MD 20892-4470, USA.},
keywords = {Animals; Breast Neoplasms, genetics/metabolism/pathology; Cyclin D1,
genetics/metabolism; Female; Genetic Techniques; Humans; Immunoenzyme
Techniques; In Situ Hybridization, Fluorescence; Mice; Oncogene Proteins
v-myb; Proto-Oncogene Proteins c-myc, genetics/metabolism; Rabbits;
Receptor, erbB-2, genetics/metabolism; Receptors, Estrogen, genetics/metabolism;
Retroviridae Proteins, Oncogenic, genetics/metabolism; Tumor Markers,
Biological, genetics/metabolism; Tumor Suppressor Protein p53, genetics/metabolism},
language = {eng},
medline-pst = {ppublish},
owner = {philippe},
pmid = {9662379},
timestamp = {2010.08.08}
}
@article{Korbel2009PEMer,
author = {Korbel, J. and Abyzov, A. and Mu, X. and Carriero, N. and Cayting,
P. and Zhang, Z. and Snyder, Z. and Gerstein, M.},
title = {{PEMer}: a computational framework with simulation-based error models
for inferring genomic structural variants from massive paired-end
sequencing data.},
journal = {Genome Biol.},
year = {2009},
volume = {10},
pages = {R23},
number = {2},
month = {Feb},
abstract = {ABSTRACT: Personal-genomics endeavors, such as the 1000 Genomes project,
are generating maps of genomic structural variants by analyzing ends
of massively sequenced genome fragments. To process these we developed
Paired-End Mapper (PEMer; http://sv.gersteinlab.org/pemer). This
comprises an analysis pipeline, compatible with several next-generation
sequencing platforms; simulation-based error models, yielding confidence-values
for each structural variant; and a back-end database. The simulations
demonstrated high structural variant reconstruction efficiency for
PEMer's coverage-adjusted multi-cutoff scoring-strategy and showed
its relative insensitivity to base-calling errors.},
doi = {10.1186/gb-2009-10-2-r23},
pdf = {../local/Korbel2009PEMer.pdf},
file = {Korbel2009PEMer.pdf:Korbel2009PEMer.pdf:PDF},
institution = {Gene Expression Unit, European Molecular Biology Laboratory (EMBL),
Meyerhofstr,, Heidelberg, 69117, Germany. korbel@embl.de.},
keywords = {ngs},
owner = {jp},
pii = {gb-2009-10-2-r23},
pmid = {19236709},
timestamp = {2009.10.09},
url = {http://dx.doi.org/10.1186/gb-2009-10-2-r23}
}
@article{Korbel2007Paired-end,
author = {Jan O Korbel and Alexander Eckehart Urban and Jason P Affourtit and
Brian Godwin and Fabian Grubert and Jan Fredrik Simons and Philip
M Kim and Dean Palejev and Nicholas J Carriero and Lei Du and Bruce
E Taillon and Zhoutao Chen and Andrea Tanzer and A. C Eugenia Saunders
and Jianxiang Chi and Fengtang Yang and Nigel P Carter and Matthew
E Hurles and Sherman M Weissman and Timothy T Harkins and Mark B
Gerstein and Michael Egholm and Michael Snyder},
title = {Paired-end mapping reveals extensive structural variation in the
human genome.},
journal = {Science},
year = {2007},
volume = {318},
pages = {420--426},
number = {5849},
month = {Oct},
abstract = {Structural variation of the genome involves kilobase- to megabase-sized
deletions, duplications, insertions, inversions, and complex combinations
of rearrangements. We introduce high-throughput and massive paired-end
mapping (PEM), a large-scale genome-sequencing method to identify
structural variants (SVs) approximately 3 kilobases (kb) or larger
that combines the rescue and capture of paired ends of 3-kb fragments,
massive 454 sequencing, and a computational approach to map DNA reads
onto a reference genome. PEM was used to map SVs in an African and
in a putatively European individual and identified shared and divergent
SVs relative to the reference genome. Overall, we fine-mapped more
than 1000 SVs and documented that the number of SVs among humans
is much larger than initially hypothesized; many of the SVs potentially
affect gene function. The breakpoint junction sequences of more than
200 SVs were determined with a novel pooling strategy and computational
analysis. Our analysis provided insights into the mechanisms of SV
formation in humans.},
doi = {10.1126/science.1149504},
pdf = {../local/Korbel2007Paired-end.pdf},
file = {Korbel2007Paired-end.pdf:Korbel2007Paired-end.pdf:PDF},
institution = {Molecular Biophysics and Biochemistry Department, Yale University,
New Haven, CT 06520, USA.},
keywords = {ngs},
owner = {jp},
pii = {1149504},
pmid = {17901297},
timestamp = {2009.10.09},
url = {http://dx.doi.org/10.1126/science.1149504}
}
@article{Korber2006Immunoinformatics,
author = {Bette Korber and Montiago LaBute and Karina Yusim},
title = {Immunoinformatics comes of age.},
journal = {PLoS Comput. Biol.},
year = {2006},
volume = {2},
pages = {e71},
number = {6},
month = {Jun},
abstract = {With the burgeoning immunological data in the scientific literature,
scientists must increasingly rely on Internet resources to inform
and enhance their work. Here we provide a brief overview of the adaptive
immune response and summaries of immunoinformatics resources, emphasizing
those with Web interfaces. These resources include searchable databases
of epitopes and immune-related molecules, and analysis tools for
T cell and B cell epitope prediction, vaccine design, and protein
structure comparisons. There is an agreeable synergy between the
growing collections in immune-related databases and the growing sophistication
of analysis software; the databases provide the foundation for developing
predictive computational tools, which in turn enable more rapid identification
of immune responses to populate the databases. Collectively, these
resources contribute to improved understanding of immune responses
and escape, and evolution of pathogens under immune pressure. The
public health implications are vast, including designing vaccines,
understanding autoimmune diseases, and defining the correlates of
immune protection.},
doi = {10.1371/journal.pcbi.0020071},
keywords = {Amino Acid Sequence; Animals; Computational Biology; Databases, Factual;
Epitopes, B-Lymphocyte; Epitopes, T-Lymphocyte; Humans; Immunity},
owner = {laurent},
pii = {06-PLCB-RV-0068},
pmid = {16846250},
timestamp = {2007.08.23},
url = {http://dx.doi.org/10.1371/journal.pcbi.0020071}
}
@article{Koren2007Autocorrelation,
author = {Amnon Koren and Itay Tirosh and Naama Barkai},
title = {Autocorrelation analysis reveals widespread spatial biases in microarray
experiments.},
journal = {BMC Genomics},
year = {2007},
volume = {8},
pages = {164},
abstract = {BACKGROUND: DNA microarrays provide the ability to interrogate multiple
genes in a single experiment and have revolutionized genomic research.
However, the microarray technology suffers from various forms of
biases and relatively low reproducibility. A particular source of
false data has been described, in which non-random placement of gene
probes on the microarray surface is associated with spurious correlations
between genes. RESULTS: In order to assess the prevalence of this
effect and better understand its origins, we applied an autocorrelation
analysis of the relationship between chromosomal position and expression
level to a database of over 2000 individual yeast microarray experiments.
We show that at least 60\% of these experiments exhibit spurious
chromosomal position-dependent gene correlations, which nonetheless
appear in a stochastic manner within each experimental dataset. Using
computer simulations, we show that large spatial biases caused in
the microarray hybridization step and independently of printing procedures
can exclusively account for the observed spurious correlations, in
contrast to previous suggestions. Our data suggest that such biases
may generate more than 15\% false data per experiment. Importantly,
spatial biases are expected to occur regardless of microarray design
and over a wide range of microarray platforms, organisms and experimental
procedures. CONCLUSIONS: Spatial biases comprise a major source of
noise in microarray studies; revision of routine experimental practices
and normalizations to account for these biases may significantly
and comprehensively improve the quality of new as well as existing
DNA microarray data.},
doi = {10.1186/1471-2164-8-164},
institution = {>},
keywords = {DNA Probes; Diagnostic Errors; Oligonucleotide Array Sequence Analysis,
methods/standards; Reproducibility of Results; Research, methods/standards;
Yeasts},
language = {eng},
medline-pst = {epublish},
owner = {philippe},
pii = {1471-2164-8-164},
pmid = {17565680},
timestamp = {2010.08.04},
url = {http://dx.doi.org/10.1186/1471-2164-8-164}
}
@article{Korn2007Cell-based,
author = {Korn, K. and Krausz, E.},
title = {Cell-based high-content screening of small-molecule libraries.},
journal = {Curr. Opin. Chem. Biol.},
year = {2007},
volume = {11},
pages = {503--510},
number = {5},
month = {Oct},
abstract = {Advanced microscopy and the corresponding image analysis have been
developed in recent years into a powerful tool for studying molecular
and morphological events in cells and tissues. Cell-based high-content
screening (HCS) is an upcoming methodology for the investigation
of cellular processes and their alteration by multiple chemical or
genetic perturbations. Multiparametric characterization of responses
to such changes can be analyzed using intact live cells as reporter.
These disturbances are screened for effects on a variety of molecular
and cellular targets, including subcellular localization and redistribution
of proteins. In contrast to biochemical screening, they detect the
responses within the context of the intercellular structural and
functional networks of normal and diseased cells, respectively. As
cell-based HCS of small-molecule libraries is applied to identify
and characterize new therapeutic lead compounds, large pharmaceutical
companies are major drivers of the technology and have already shown
image-based screens using more than 100,000 compounds.},
doi = {10.1016/j.cbpa.2007.08.030},
institution = {HT-Technology Development Studio (TDS), Max Planck Institute of Molecular
Cell Biology and Genetics (MPI-CBG), Pfotenhauerstrasse 108, D-01307
Dresden, Germany.},
owner = {jp},
pii = {S1367-5931(07)00114-7},
pmid = {17931958},
timestamp = {2008.12.09},
url = {http://dx.doi.org/10.1016/j.cbpa.2007.08.030}
}
@article{Kosorok2007Marginal,
author = {Kosorok, M. R. and Ma, S.},
title = {Marginal asymptotics for the "large p, small n" paradigm: With applications
to microarray data},
journal = {Ann. Stat.},
year = {2007},
volume = {35},
pages = {1456--1486},
number = {4},
abstract = {The “large p, small n” paradigm arises in microarray studies, image
analysis, high throughput molecular screening, astronomy, and in
many other high dimensional applications. False discovery rate (FDR)
methods are useful for resolving the accompanying multiple testing
problems. In cDNA microarray studies, for example, p-values may be
computed for each of p genes using data from n arrays, where typically
p is in the thousands and n is less than 30. For FDR methods to be
valid in identifying differentially expressed genes, the p-values
for the nondifferentially expressed genes must simultaneously have
uniform distributions marginally. While feasible for permutation
p-values, this uniformity is problematic for asymptotic based p-values
since the number of p-values involved goes to infinity and intuition
suggests that at least some of the p-values should behave erratically.
We examine this neglected issue when n is moderately large but p
is almost exponentially large relative to n. We show the somewhat
surprising result that, under very general dependence structures
and for both mean and median tests, the p-values are simultaneously
valid. A small simulation study and data analysis are used for illustration.},
doi = {10.1214/009053606000001433},
owner = {jp},
timestamp = {2010.01.10},
url = {http://dx.doi.org/10.1214/009053606000001433}
}
@article{Kote-Jarai2004Gene,
author = {Zsofia Kote-Jarai and Richard D Williams and Nicola Cattini and Maria
Copeland and Ian Giddings and Richard Wooster and Robert H tePoele
and Paul Workman and Barry Gusterson and John Peacock and Gerald
Gui and Colin Campbell and Ros Eeles},
title = {Gene expression profiling after radiation-induced {DNA} damage is
strongly predictive of {BRCA}1 mutation carrier status.},
journal = {Clin. {C}ancer {R}es.},
year = {2004},
volume = {10},
pages = {958-63},
number = {3},
month = {Feb},
abstract = {P{URPOSE}: {T}he impact of the presence of a germ-line {BRCA}1 mutation
on gene expression in normal breast fibroblasts after radiation-induced
{DNA} damage has been investigated. {EXPERIMENTAL} {DESIGN}: {H}igh-density
c{DNA} microarray technology was used to identify differential responses
to {DNA} damage in fibroblasts from nine heterozygous {BRCA}1 mutation
carriers compared with five control samples without personal or family
history of any cancer. {F}ibroblast cultures were irradiated, and
their expression profile was compared using intensity ratios of the
c{DNA} microarrays representing 5603 {IMAGE} clones. {RESULTS}: {C}lass
comparison and class prediction analysis has shown that {BRCA}1 mutation
carriers can be distinguished from controls with high probability
(approximately 85\%). {S}ignificance analysis of microarrays and
the support vector machine classifier identified gene sets that discriminate
the samples according to their mutation status. {T}hese include genes
already known to interact with {BRCA}1 such as {CDKN}1{B}, {ATR},
and {RAD}51. {CONCLUSIONS}: {T}he results of this initial study suggest
that normal cells from heterozygous {BRCA}1 mutation carriers display
a different gene expression profile from controls in response to
{DNA} damage. {A}daptations of this pilot result to other cell types
could result in the development of a functional assay for {BRCA}1
mutation status.},
pdf = {../local/Kote-Jarai2004Gene.pdf},
file = {Kote-Jarai2004Gene.pdf:local/Kote-Jarai2004Gene.pdf:PDF},
keywords = {biosvm , breastcancer},
url = {http://clincancerres.aacrjournals.org/cgi/content/abstract/10/3/958}
}
@article{Kou2002Karyotyping,
author = {Zhenzhen Kou and Liang Ji and Xuegong Zhang},
title = {Karyotyping of comparative genomic hybridization human metaphases
by using support vector machines.},
journal = {Cytometry},
year = {2002},
volume = {47},
pages = {17-23},
number = {1},
month = {Jan},
abstract = {B{ACKGROUND}: {C}omparative genomic hybridization ({CGH}) is a relatively
new molecular cytogenetic method for detecting chromosomal imbalance.
{K}aryotyping of human metaphases is an important step to assign
each chromosome to one of 23 or 24 classes (22 autosomes and two
sex chromosomes). {A}utomatic karyotyping in {CGH} analysis is needed.
{H}owever, conventional karyotyping approaches based on {DAPI} images
require complex image enhancement procedures. {METHODS}: {T}his paper
proposes a simple feature extraction method, one that generates density
profiles from original true color {CGH} images and uses normalized
profiles as feature vectors without quantization. {A} classifier
is developed by using support vector machine ({SVM}). {I}t has good
generalization ability and needs only limited training samples. {RESULTS}:
{E}xperiment results show that the feature extraction method of using
color information in {CGH} images can improve greatly the classification
success rate. {T}he {SVM} classifier is able to acquire knowledge
about human chromosomes from relatively few samples and has good
generalization ability. {A} success rate of moe than 90\% has been
achieved and the time for training and testing is very short. {CONCLUSIONS}:
{T}he feature extraction method proposed here and the {SVM}-based
classifier offer a promising computerized intelligent system for
automatic karyotyping of {CGH} human chromosomes.},
doi = {10.1002/cyto.10027},
pdf = {../local/Kou2002Karyotyping.pdf},
file = {Kou2002Karyotyping.pdf:local/Kou2002Karyotyping.pdf:PDF},
keywords = {cgh},
pii = {10.1002/cyto.10027},
url = {http://dx.doi.org/10.1002/cyto.10027}
}
@article{Kovatcheva2004Combinatorial,
author = {Assia Kovatcheva and Alexander Golbraikh and Scott Oloff and Yun-De
Xiao and Weifan Zheng and Peter Wolschann and Gerhard Buchbauer and
Alexander Tropsha},
title = {Combinatorial {QSAR} of ambergris fragrance compounds.},
journal = {J {C}hem {I}nf {C}omput {S}ci},
year = {2004},
volume = {44},
pages = {582-95},
number = {2},
abstract = {A combinatorial quantitative structure-activity relationships ({C}ombi-{QSAR})
approach has been developed and applied to a data set of 98 ambergris
fragrance compounds with complex stereochemistry. {T}he {C}ombi-{QSAR}
approach explores all possible combinations of different independent
descriptor collections and various individual correlation methods
to obtain statistically significant models with high internal (for
the training set) and external (for the test set) accuracy. {S}even
different descriptor collections were generated with commercially
available {MOE}, {C}o{MFA}, {C}o{MMA}, {D}ragon, {V}ol{S}urf, and
{M}olconn{Z} programs; we also included chirality topological descriptors
recently developed in our laboratory ({G}olbraikh, {A}.; {B}onchev,
{D}.; {T}ropsha, {A}. {J}. {C}hem. {I}nf. {C}omput. {S}ci. 2001,
41, 147-158). {C}o{MMA} descriptors were used in combination with
{MOE} descriptors. {M}olconn{Z} descriptors were used in combination
with chirality descriptors. {E}ach descriptor collection was combined
individually with four correlation methods, including k-nearest neighbors
(k{NN}) classification, {S}upport {V}ector {M}achines ({SVM}), decision
trees, and binary {QSAR}, giving rise to 28 different types of {QSAR}
models. {M}ultiple diverse and representative training and test sets
were generated by the divisions of the original data set in two.
{E}ach model with high values of leave-one-out cross-validated correct
classification rate for the training set was subjected to extensive
internal and external validation to avoid overfitting and achieve
reliable predictive power. {T}wo validation techniques were employed,
i.e., the randomization of the target property (in this case, odor
intensity) also known as the {Y}-randomization test and the assessment
of external prediction accuracy using test sets. {W}e demonstrate
that not every combination of the data modeling technique and the
descriptor collection yields a validated and predictive {QSAR} model.
k{NN} classification in combination with {C}o{MFA} descriptors was
found to be the best {QSAR} approach overall since predictive models
with correct classification rates for both training and test sets
of 0.7 and higher were obtained for all divisions of the ambergris
data set into the training and test sets. {M}any predictive {QSAR}
models were also found using a combination of k{NN} classification
method with other collections of descriptors. {T}he combinatorial
{QSAR} affords automation, computational efficiency, and higher probability
of identifying significant {QSAR} models for experimental data sets
than the traditional approaches that rely on a single {QSAR} method.},
doi = {10.1021/ci034203t},
pdf = {../local/Kovatcheva2004Combinatorial.pdf},
file = {Kovatcheva2004Combinatorial.pdf:local/Kovatcheva2004Combinatorial.pdf:PDF},
keywords = {Algorithms, Ambergris, Combinatorial Chemistry Techniques, Models,
Molecular, Molecular Conformation, Odors, P.H.S., Perfume, Predictive
Value of Tests, Quantitative Structure-Activity Relationship, Research
Support, U.S. Gov't, 15032539},
url = {http://dx.doi.org/10.1021/ci034203t}
}
@article{Koyutuerk2006Pairwise,
author = {Koyut{\"u}rk, M. and Kim, Y. and Topkara, U. and Subramaniam, S.
and Szpankowski, W. and Grama, A.},
title = {Pairwise alignment of protein interaction networks},
journal = {J. Comput. Biol.},
year = {2006},
volume = {13},
pages = {182--199},
number = {2},
month = {Mar},
abstract = {With an ever-increasing amount of available data on protein-protein
interaction (PPI) networks and research revealing that these networks
evolve at a modular level, discovery of conserved patterns in these
networks becomes an important problem. Although available data on
protein-protein interactions is currently limited, recently developed
algorithms have been shown to convey novel biological insights through
employment of elegant mathematical models. The main challenge in
aligning PPI networks is to define a graph theoretical measure of
similarity between graph structures that captures underlying biological
phenomena accurately. In this respect, modeling of conservation and
divergence of interactions, as well as the interpretation of resulting
alignments, are important design parameters. In this paper, we develop
a framework for comprehensive alignment of PPI networks, which is
inspired by duplication/divergence models that focus on understanding
the evolution of protein interactions. We propose a mathematical
model that extends the concepts of match, mismatch, and gap in sequence
alignment to that of match, mismatch, and duplication in network
alignment and evaluates similarity between graph structures through
a scoring function that accounts for evolutionary events. By relying
on evolutionary models, the proposed framework facilitates interpretation
of resulting alignments in terms of not only conservation but also
divergence of modularity in PPI networks. Furthermore, as in the
case of sequence alignment, our model allows flexibility in adjusting
parameters to quantify underlying evolutionary relationships. Based
on the proposed model, we formulate PPI network alignment as an optimization
problem and present fast algorithms to solve this problem. Detailed
experimental results from an implementation of the proposed framework
show that our algorithm is able to discover conserved interaction
patterns very effectively, in terms of both accuracies and computational
cost.},
doi = {10.1089/cmb.2006.13.182},
institution = {Department of Computer Sciences, Purdue University, West Lafayette,
IN 47907, USA. koyuturk@cs.purdue.edu},
owner = {jp},
pmid = {16597234},
timestamp = {2008.10.03},
url = {http://dx.doi.org/10.1089/cmb.2006.13.182}
}
@inproceedings{Kramer2001Feature,
author = {Kramer, S. and De Raedt, L.},
title = {Feature {C}onstruction with {V}ersion {S}paces for {B}iochemical
{A}pplications},
booktitle = {Proceedings of the {E}ighteenth {I}nternational {C}onference on {M}achine
{L}earning},
year = {2001},
editor = {Brodley, C.E. and Pohoreckyj Danyluk, A.},
pages = {258-265},
publisher = {Morgan Kaufmann},
owner = {mahe},
timestamp = {2006.08.09}
}
@article{Kramer2002Fragment,
author = {S. Kramer and E. Frank and C. Helma},
title = {Fragment generation and support vector machines for inducing {SAR}s.},
journal = {S{AR} {QSAR} {E}nviron {R}es},
year = {2002},
volume = {13},
pages = {509-23},
number = {5},
month = {Jul},
abstract = {We present a new approach to the induction of {SAR}s based on the
generation of structural fragments and support vector machines ({SVM}s).
{I}t is tailored for bio-chemical databases, where the examples are
two-dimensional descriptions of chemical compounds. {T}he fragment
generator finds all fragments (i.e. linearly connected atoms) that
satisfy user-specified constraints regarding their frequency and
generality. {I}n this paper, we are querying for fragments within
a minimum and a maximum frequency in the dataset. {A}fter fragment
generation, we propose to apply {SVM}s to the problem of inducing
{SAR}s from these fragments. {W}e conjecture that the {SVM}s are
particularly useful in this context, as they can deal with a large
number of features. {E}xperiments in the domains of carcinogenicity
and mutagenicity prediction show that the minimum and the maximum
frequency queries for fragments can be answered within a reasonable
time, and that the predictive accuracy obtained using these fragments
is satisfactory. {H}owever, further experiments will have to confirm
that this is a viable approach to inducing {SAR}s.},
doi = {10.1080/10629360290023340},
keywords = {biosvm},
url = {http://dx.doi.org/10.1080/10629360290023340}
}
@article{Kratochwil2005automated,
author = {Kratochwil, N. A. and Malherbe, P. and Lindemann, L. and Ebeling,
M. and Hoener, M. C. and M{\"u}hlemann, A. and Porter, R. H. P. and
Stahl, M. and Gerber, P. R.},
title = {An automated system for the analysis of {G} protein-coupled receptor
transmembrane binding pockets: alignment, receptor-based pharmacophores,
and their application.},
journal = {J. Chem. Inf. Model.},
year = {2005},
volume = {45},
pages = {1324--1336},
number = {5},
abstract = {G protein-coupled receptors (GPCRs) share a common architecture consisting
of seven transmembrane (TM) domains. Various lines of evidence suggest
that this fold provides a generic binding pocket within the TM region
for hosting agonists, antagonists, and allosteric modulators. Here,
a comprehensive and automated method allowing fast analysis and comparison
of these putative binding pockets across the entire GPCR family is
presented. The method relies on a robust alignment algorithm based
on conservation indices, focusing on pharmacophore-like relationships
between amino acids. Analysis of conservation patterns across the
GPCR family and alignment to the rhodopsin X-ray structure allows
the extraction of the amino acids lining the TM binding pocket in
a so-called ligand binding pocket vector (LPV). In a second step,
LPVs are translated to simple 3D receptor pharmacophore models, where
each amino acid is represented by a single spherical pharmacophore
feature and all atomic detail is omitted. Applications of the method
include the assessment of selectivity issues, support of mutagenesis
studies, and the derivation of rules for focused screening to identify
chemical starting points in early drug discovery projects. Because
of the coarseness of this 3D receptor pharmacophore model, however,
meaningful scoring and ranking procedures of large sets of molecules
are not justified. The LPV analysis of the trace amine-associated
receptor family and its experimental validation is discussed as an
example. The value of the 3D receptor model is demonstrated for a
class C GPCR family, the metabotropic glutamate receptors.},
doi = {10.1021/ci050221u},
pdf = {../local/Kratochwil2005automated.pdf},
file = {Kratochwil2005automated.pdf:Kratochwil2005automated.pdf:PDF},
keywords = {chemogenomics},
owner = {laurent},
pmid = {16180909},
timestamp = {2007.09.22},
url = {http://dx.doi.org/10.1021/ci050221u}
}
@article{Krichevskiy1998Laplace's,
author = {Krichevskiy, R. E.},
title = {Laplace's law of succession and universal encoding},
journal = {I{EEE} {T}rans. {I}nform. {T}heory},
year = {1998},
volume = {44},
pages = {296-303},
number = {1},
month = {Jan},
pdf = {../local/Krichevskiy1998Laplace's.pdf},
file = {Krichevskiy1998Laplace's.pdf:local/Krichevskiy1998Laplace's.pdf:PDF},
keywords = {information-theory source-coding},
owner = {vert}
}
@article{Krichevsky1981performance,
author = {Krichevsky, R. and Trofimov, V. },
title = {The performance of universal encoding},
journal = {I{EEE} {T}rans. {I}nform. {T}heory},
year = {1981},
volume = {27},
pages = {199--207},
number = {2},
month = {Mar},
abstract = {Universal coding theory is surveyed from the viewpoint of the interplay
between delay and redundancy. {T}he price for universality turns
out to be acceptably small.},
pdf = {../local/Krichevsky1981performance.pdf},
file = {Krichevsky1981performance.pdf:local/Krichevsky1981performance.pdf:PDF},
keywords = {information-theory source-coding},
owner = {vert}
}
@article{Krishnan2003comparative,
author = {Krishnan, V. G. and Westhead, D. R.},
title = {A comparative study of machine-learning methods to predict the effects
of single nucleotide polymorphisms on protein function},
journal = {Bioinformatics},
year = {2003},
volume = {19},
pages = {2199-2209},
number = {17},
abstract = {Motivation: {T}he large volume of single nucleotide polymorphism data
now available motivates the development of methods for distinguishing
neutral changes from those which have real biological effects. {H}ere,
two different machine-learning methods, decision trees and support
vector machines ({SVM}s), are applied for the first time to this
problem. {I}n common with most other methods, only non-synonymous
changes in protein coding regions of the genome are considered. {R}esults:
{I}n detailed cross-validation analysis, both learning methods are
shown to compete well with existing methods, and to out-perform them
in some key tests. {SVM}s show better generalization performance,
but decision trees have the advantage of generating interpretable
rules with robust estimates of prediction confidence. {I}t is shown
that the inclusion of protein structure information produces more
accurate methods, in agreement with other recent studies, and the
effect of using predicted rather than actual structure is evaluated.
{A}vailability: {S}oftware is available on request from the authors.},
pdf = {../local/Krishnan2003comparative.pdf},
file = {Krishnan2003comparative.pdf:local/Krishnan2003comparative.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://bioinformatics.oupjournals.org/cgi/content/abstract/19/17/2199}
}
@incollection{Krishnapuram2004Gene,
author = {Krishnapuram, B. and Carin, L. and Hartemink, A.},
title = {Gene expression analysis: joint feature selection and classifier
design},
booktitle = {Kernel {M}ethods in {C}omputational {B}iology},
publisher = {MIT Press},
year = {2004},
editor = {Schölkopf, B. and Tsuda, K. and Vert, J.P.},
pages = {299-317},
pdf = {../local/heterogeneous.pdf:http\://cg.ensmp.fr/~vert/publi/04kmcbbook/heterogeneous.pdf:PDF;heterogeneous.pdf:http\},
file = {heterogeneous.pdf:http\://cg.ensmp.fr/~vert/publi/04kmcbbook/heterogeneous.pdf:PDF;heterogeneous.pdf:http\://cg.ensmp.fr/~vert/publi/04kmcbbook/heterogeneous.pdf:PDF},
keywords = {biosvm},
owner = {vert}
}
@article{Krishnapuram2004Joint,
author = {Krishnapuram, B. and Carin, L. and Hartemink, A.},
title = {Joint {C}lassifier and {F}eature {O}ptimization for {C}omprehensive
{C}ancer {D}iagnosis {U}sing {G}ene {E}xpression {D}ata},
journal = {J. {C}omput. {B}iol.},
year = {2004},
volume = {11},
pages = {227-242},
number = {2-3},
abstract = {ecent research has demonstrated quite convincingly that accurate cancer
diagnosis can be achieved by constructing classifiers that are designed
to compare the gene expression profile of a tissue of unknown cancer
status to a database of stored expression profiles from tissues of
known cancer status. {T}his paper introduces the {JCFO}, a novel
algorithm that uses a sparse {B}ayesian approach to jointly identify
both the optimal nonlinear classifier for diagnosis and the optimal
set of genes on which to base that diagnosis. {W}e show that the
diagnostic classification accuracy of the proposed algorithm is superior
to a number of current state-of-the-art methods in a full leave-one-out
cross-validation study of five widely used benchmark datasets. {I}n
addition to its superior classification accuracy, the algorithm is
designed to automatically identify a small subset of genes (typically
around twenty in our experiments) that are capable of providing complete
discriminatory information for diagnosis. {F}ocusing attention on
a small subset of genes is useful not only because it produces a
classifier with good generalization capacity, but also because this
set of genes may provide insights into the mechanisms responsible
for the disease itself. {A} number of the genes identified by the
{JCFO} in our experiments are already in use as clinical markers
for cancer diagnosis; some of the remaining genes may be excellent
candidates for further clinical investigation. {I}f it is possible
to identify a small set of genes that is indeed capable of providing
complete discrimination, inexpensive diagnostic assays might be widely
deployable in clinical settings.},
doi = {10.1089/1066527041410463},
pdf = {../local/Krishnapuram2004Joint.pdf},
file = {Krishnapuram2004Joint.pdf:local/Krishnapuram2004Joint.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://dx.doi.org/10.1089/1066527041410463}
}
@article{Krishnapuram2004bayesian,
author = {Krishnapuram, B. and Hartemink, A. J. and Carin, L. and Figueiredo,
M. A. T.},
title = {A bayesian approach to joint feature selection and classifier design},
journal = {IEEE T. Pattern. Anal.},
year = {2004},
volume = {26},
pages = {1105-11},
number = {9},
month = {Sep},
abstract = {This paper adopts a {B}ayesian approach to simultaneously learn both
an optimal nonlinear classifier and a subset of predictor variables
(or features) that are most relevant to the classification task.
{T}he approach uses heavy-tailed priors to promote sparsity in the
utilization of both basis functions and features; these priors act
as regularizers for the likelihood function that rewards good classification
on the training data. {W}e derive an expectation-maximization ({EM})
algorithm to efficiently compute a maximum a posteriori ({MAP}) point
estimate of the various parameters. {T}he algorithm is an extension
of recent state-of-the-art sparse {B}ayesian classifiers, which in
turn can be seen as {B}ayesian counterparts of support vector machines.
{E}xperimental comparisons using kernel classifiers demonstrate both
parsimonious feature selection and excellent classification accuracy
on a range of synthetic and benchmark data sets.},
doi = {10.1109/TPAMI.2004.55},
pdf = {../local/Krishnapuram2004bayesian.pdf},
file = {Krishnapuram2004bayesian.pdf:local/Krishnapuram2004bayesian.pdf:PDF},
keywords = {biosvm},
url = {http://dx.doi.org/10.1109/TPAMI.2004.55}
}
@article{Kristiansen1996database,
author = {K. Kristiansen and S. G. Dahl and O. Edvardsen},
title = {A database of mutants and effects of site-directed mutagenesis experiments
on {G} protein-coupled receptors.},
journal = {Proteins},
year = {1996},
volume = {26},
pages = {81--94},
number = {1},
month = {Sep},
abstract = {A database system and computer programs for storage and retrieval
of information about guanine nucleotide-binding protein (G protein)
-coupled receptor mutants and associated biological effects have
been developed. Mutation data on the receptors were collected from
the literature and a database of mutants and effects of mutations
was developed. The G protein-coupled receptor, family A, point mutation
database (GRAP) provides detailed information on ligand-binding and
signal transduction properties of more than 2130 receptor mutants.
The amino acid sequences of receptors for which mutation experiments
have been reported were aligned, and from this alignment mutation
data may be retrieved. Alternatively, a search form allowing detailed
specification of which mutants to retrieve may be used, for example,
to search for specific amino acid substitutions, substitutions in
specific protein domains or reported biological effects. Furthermore,
ligand and bibliographic oriented queries may be performed. GRAP
is available on the Internet (URL: http://www-grap.fagmed.uit.no/GRAP/+
+homepage.html) using the World-Wide Web system.},
doi = {3.0.CO;2-J},
keywords = {Amino Acid Sequence; Computer Communication Networks; Computers; GTP-Binding
Proteins; Information Systems; Molecular Sequence Data; Mutagenesis,
Site-Directed; Mutation; Receptors, Cell Surface; Sequence Alignment},
owner = {laurent},
pii = {3.0.CO;2-J},
pmid = {8880932},
timestamp = {2008.01.16},
url = {http://dx.doi.org/3.0.CO;2-J}
}
@article{Kroemer2007Structure,
author = {Romano T Kroemer},
title = {Structure-based drug design: docking and scoring.},
journal = {Curr. Protein Pept. Sci.},
year = {2007},
volume = {8},
pages = {312--328},
number = {4},
month = {Aug},
abstract = {This review gives an introduction into ligand - receptor docking and
illustrates the basic underlying concepts. An overview of different
approaches and algorithms is provided. Although the application of
docking and scoring has led to some remarkable successes, there are
still some major challenges ahead, which are outlined here as well.
Approaches to address some of these challenges and the latest developments
in the area are presented. Some aspects of the assessment of docking
program performance are discussed. A number of successful applications
of structure-based virtual screening are described.},
institution = {ciences, Department of Chemistry, Nerviano Medical Sciences, Viale
Pasteur 10, 20014 Nerviano (MI), Italy. romano.kroemer@sanofi-aventis.com},
keywords = {Algorithms; Artificial Intelligence; Computational Biology; Computer
Simulation; Computer-Aided Design; Drug Design; Imaging, Three-Dimensional;
Ligands; Models, Molecular; Protein Binding; Protein Conformation;
Software; Structure-Activity Relationship},
owner = {bricehoffmann},
pmid = {17696866},
timestamp = {2009.02.13}
}
@article{Krogh1994Hidden,
author = {Krogh, A. and Brown, M. and Mian, I. and Sjolander, K. and Haussler,
D.},
title = {Hidden {M}arkov models in computational biology: {A}pplications to
protein modeling},
journal = {J. {M}ol. {B}iol.},
year = {1994},
volume = {235},
pages = {1501--1531}
}
@article{TransPath2006,
author = {Krull, M. and Pistor, S. and Voss, N. and Kel, A. and Reuter, I.
and Kronenberg, D. and Michael, H. and Schwarzer, K. and Potapov,
A. and Choi, C. and Kel-Margoulis, O. and Wingender, E.},
title = {T{RANSPATH}: an information resource for storing and visualizing
signaling pathways and their pathological aberrations},
journal = {Nucleic {A}cids {R}es},
year = {2006},
volume = {34},
pages = {D546-51},
number = {Database issue},
abstract = {T{RANSPATH} is a database about signal transduction events. {I}t provides
information about signaling molecules, their reactions and the pathways
these reactions constitute. {T}he representation of signaling molecules
is organized in a number of orthogonal hierarchies reflecting the
classification of the molecules, their species-specific or generic
features, and their post-translational modifications. {R}eactions
are similarly hierarchically organized in a three-layer architecture,
differentiating between reactions that are evidenced by individual
publications, generalizations of these reactions to construct species-independent
'reference pathways' and the 'semantic projections' of these pathways.
{A} number of search and browse options allow easy access to the
database contents, which can be visualized with the tool {P}athway{B}uildertrade
mark. {T}he module {P}atho{S}ign adds data about pathologically relevant
mutations in signaling components, including their genotypes and
phenotypes. {TRANSPATH} and {P}atho{S}ign can be used as encyclopaedia,
in the educational process, for vizualization and modeling of signal
transduction networks and for the analysis of gene expression data.
{TRANSPATH} {P}ublic 6.0 is freely accessible for users from non-profit
organizations under http://www.gene-regulation.com/pub/databases.html.}
}
@article{Kuang2005Profile-based,
author = {Kuang, R. and Ie, E. and Wang, K. and Wang, K. and Siddiqi, M. and
Freund, Y. and Leslie, C.},
title = {Profile-based string kernels for remote homology detection and motif
extraction.},
journal = {J. Bioinform. Comput. Biol.},
year = {2005},
volume = {3},
pages = {527--550},
number = {3},
month = {Jun},
abstract = {We introduce novel profile-based string kernels for use with support
vector machines (SVMs) for the problems of protein classification
and remote homology detection. These kernels use probabilistic profiles,
such as those produced by the PSI-BLAST algorithm, to define position-dependent
mutation neighborhoods along protein sequences for inexact matching
of k-length subsequences ("k-mers") in the data. By use of an efficient
data structure, the kernels are fast to compute once the profiles
have been obtained. For example, the time needed to run PSI-BLAST
in order to build the profiles is significantly longer than both
the kernel computation time and the SVM training time. We present
remote homology detection experiments based on the SCOP database
where we show that profile-based string kernels used with SVM classifiers
strongly outperform all recently presented supervised SVM methods.
We further examine how to incorporate predicted secondary structure
information into the profile kernel to obtain a small but significant
performance improvement. We also show how we can use the learned
SVM classifier to extract "discriminative sequence motifs"--short
regions of the original profile that contribute almost all the weight
of the SVM classification score--and show that these discriminative
motifs correspond to meaningful structural features in the protein
data. The use of PSI-BLAST profiles can be seen as a semi-supervised
learning technique, since PSI-BLAST leverages unlabeled data from
a large sequence database to build more informative profiles. Recently
presented "cluster kernels" give general semi-supervised methods
for improving SVM protein classification performance. We show that
our profile kernel results also outperform cluster kernels while
providing much better scalability to large datasets.},
keywords = {biosvm},
owner = {vert},
pii = {S021972000500120X},
pmid = {16108083},
timestamp = {2007.08.01}
}
@article{Kuang2004Profile-based,
author = {Kuang, R. and Ie, E. and Wang, K. and Wang, K. and Siddiqi, M. and
Freund, Y. and Leslie, C.},
title = {Profile-based string kernels for remote homology detection and motif
extraction.},
journal = {Proc IEEE Comput Syst Bioinform Conf},
year = {2004},
pages = {152--160},
abstract = {We introduce novel profile-based string kernels for use with support
vector machines (SVMs) for the problems of protein classification
and remote homology detection. These kernels use probabilistic profiles,
such as those produced by the PSI-BLAST algorithm, to define position-dependent
mutation neighborhoods along protein sequences for inexact matching
of k-length subsequences ("k-mers") in the data. By use of an efficient
data structure, the kernels are fast to compute once the profiles
have been obtained. For example, the time needed to run PSI-BLAST
in order to build the pro- files is significantly longer than both
the kernel computation time and the SVM training time. We present
remote homology detection experiments based on the SCOP database
where we show that profile-based string kernels used with SVM classifiers
strongly outperform all recently presented supervised SVM methods.
We also show how we can use the learned SVM classifier to extract
"discriminative sequence motifs" -- short regions of the original
profile that contribute almost all the weight of the SVM classification
score -- and show that these discriminative motifs correspond to
meaningful structural features in the protein data. The use of PSI-BLAST
profiles can be seen as a semi-supervised learning technique, since
PSI-BLAST leverages unlabeled data from a large sequence database
to build more informative profiles. Recently presented "cluster kernels"
give general semi-supervised methods for improving SVM protein classification
performance. We show that our profile kernel results are comparable
to cluster kernels while providing much better scalability to large
datasets.},
keywords = {biosvm},
owner = {vert},
pmid = {16448009},
timestamp = {2007.08.01}
}
@article{Kuang2004Protein,
author = {Kuang, R. and Leslie, C. S. and Yang, A.-S.},
title = {Protein backbone angle prediction with machine learning approaches},
journal = {Bioinformatics},
year = {2004},
volume = {20},
pages = {1612-1621},
number = {10},
abstract = {Motivation: {P}rotein backbone torsion angle prediction provides useful
local structural information that goes beyond conventional three-state
({alpha}, {beta} and coil) secondary structure predictions. {A}ccurate
prediction of protein backbone torsion angles will substantially
improve modeling procedures for local structures of protein sequence
segments, especially in modeling loop conformations that do not form
regular structures as in {alpha}-helices or {beta}-strands. {R}esults:
{W}e have devised two novel automated methods in protein backbone
conformational state prediction: one method is based on support vector
machines ({SVM}s); the other method combines a standard feed-forward
back-propagation artificial neural network ({NN}) with a local structure-based
sequence profile database ({LSBSP}1). {E}xtensive benchmark experiments
demonstrate that both methods have improved the prediction accuracy
rate over the previously published methods for conformation state
prediction when using an alphabet of three or four states. {A}vailability:
{LSBSP}1 and the {NN} algorithm have been implemented in {P}r{ISM}.1,
which is available from www.columbia.edu/~ay1/. {S}upplementary information:
{S}upplementary data for the {SVM} method can be downloaded from
the {W}ebsite www.cs.columbia.edu/compbio/backbone.},
pdf = {../local/Kuang2004Protein.pdf},
file = {Kuang2004Protein.pdf:local/Kuang2004Protein.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://bioinformatics.oupjournals.org/cgi/content/abstract/20/10/1612}
}
@inproceedings{Kubinyi2003Comparative,
author = {H. Kubinyi},
title = {Comparative {M}olecular {F}ield {A}nalysis},
booktitle = {Handbook of Chemoinformatics. From Data to Knowledge, Volume 4},
year = {2003},
editor = {J. Gasteiger},
pages = {1555-1574},
publisher = {Wiley-VCH, Weinheim},
keywords = {chemoinformatics},
owner = {mahe},
timestamp = {2006.08.31}
}
@article{Kubinyi2006Chemogenomics,
author = {H. Kubinyi},
title = {Chemogenomics in drug discovery.},
journal = {Ernst Schering Res Found Workshop},
year = {2006},
volume = {58},
pages = {1--19},
abstract = {Chemogenomics is a new strategy in drug discovery which, in principle,
searches for all molecules that are capable of interacting with any
biological target. Because of the almost infinite number of drug-like
organic molecules, this is an impossible task. Therefore chemogenomics
has been defined as the investigation of classes of compounds (libraries)
against families of functionally related proteins. In this definition,
chemogenomics deals with the systematic analysis of chemical-biological
interactions. Congeneric series of chemical analogs are probes to
investigate their action on specific target classes, e.g., GPCRs,
kinases, phosphodiesterases, ion channels, serine proteases, and
others. Whereas such a strategy developed in pharmaceutical industry
almost 20 years ago, it is now more systematically applied in the
search for target- and subtype-specific ligands. The term "privileged
structures" has been defined for scaffolds, such as the benzodiazepines,
which very often produce biologically active analogs in a target
family, in this case in the class of G-protein-coupled receptors.
The SOSA approach is a strategy to modify the selectivity of biologically
active compounds, generating new drug candidates from the side activities
of therapeutically used drugs.},
keywords = {Animals; Chemistry, Pharmaceutical; Combinatorial Chemistry Techniques;
Drug Design; Drug Industry; Genomics; Humans; Models, Chemical; Molecular
Structure; Mutation; Pharmacogenetics; Protein Binding},
owner = {laurent},
pmid = {16708995},
timestamp = {2007.09.22}
}
@article{Kuhn1955Hungarian,
author = {Kuhn, H. W.},
title = {The {H}ungarian method for the assignment problem},
journal = {Naval Research},
year = {1955},
volume = {2},
pages = {83-97},
publisher = {INFORMS}
}
@article{Kuhn2001Global,
author = {K. M. Kuhn and J. L. DeRisi and P. O. Brown and P. Sarnow},
title = {Global and specific translational regulation in the genomic response
of {S}accharomyces cerevisiae to a rapid transfer from a fermentable
to a nonfermentable carbon source},
journal = {Mol. {C}ell. {B}iol.},
year = {2001},
volume = {21},
pages = {916--927},
number = {3},
pdf = {../local/kuhn01.pdf},
file = {kuhn01.pdf:local/kuhn01.pdf:PDF},
subject = {microarray},
url = {http://mcb.asm.org/cgi/content/full/21/3/916?view=full&pmid=11154278}
}
@incollection{Kuich1986Semirings,
author = {Kuich, W. and Salomaa, A.},
title = {Semirings, {A}utomata, {L}anguages},
booktitle = {E{ATCS} {M}onographs on {C}omputer {S}cience},
publisher = {Springer-Verlag},
year = {1986},
volume = {5},
owner = {vert}
}
@inproceedings{Kuksa2008Scalable,
author = {Pavel P. Kuksa and Pai-Hsi Huang and Vladimir Pavlovic},
title = {Scalable Algorithms for String Kernels with Inexact Matching.},
booktitle = {NIPS},
year = {2008},
editor = {Daphne Koller and Dale Schuurmans and Yoshua Bengio and L{'e}on Bottou},
pages = {881-888},
publisher = {MIT Press},
date = {2009-04-15},
ee = {http://books.nips.cc/papers/files/nips21/NIPS2008_0373.pdf},
interhash = {e690e43f62810b0b94bf2db9f4993638},
intrahash = {107ac82150af9f121cee9968b1cf05e1},
url = {http://dblp.uni-trier.de/db/conf/nips/nips2008.html#KuksaHP08}
}
@article{Kumagai1980An,
author = {Kumagai, S.},
title = {An implicit function theorem: Comment},
journal = {{J}ournal of {O}ptimization {T}heory and {A}pplications},
year = {1980},
volume = {31},
pages = {285-288},
month = {Jun}
}
@article{Kumar2005BhairPred,
author = {Kumar, M. and Bhasin, M. and Natt, N. K. and Raghava, G. P. S.},
title = {Bhair{P}red: prediction of beta-hairpins in a protein from multiple
alignment information using {ANN} and {SVM} techniques.},
journal = {Nucleic {A}cids {R}es},
year = {2005},
volume = {33},
pages = {W154-9},
number = {Web Server issue},
month = {Jul},
abstract = {This paper describes a method for predicting a supersecondary structural
motif, beta-hairpins, in a protein sequence. {T}he method was trained
and tested on a set of 5102 hairpins and 5131 non-hairpins, obtained
from a non-redundant dataset of 2880 proteins using the {DSSP} and
{PROMOTIF} programs. {T}wo machine-learning techniques, an artificial
neural network ({ANN}) and a support vector machine ({SVM}), were
used to predict beta-hairpins. {A}n accuracy of 65.5\% was achieved
using {ANN} when an amino acid sequence was used as the input. {T}he
accuracy improved from 65.5 to 69.1\% when evolutionary information
({PSI}-{BLAST} profile), observed secondary structure and surface
accessibility were used as the inputs. {T}he accuracy of the method
further improved from 69.1 to 79.2\% when the {SVM} was used for
classification instead of the {ANN}. {T}he performances of the methods
developed were assessed in a test case, where predicted secondary
structure and surface accessibility were used instead of the observed
structure. {T}he highest accuracy achieved by the {SVM} based method
in the test case was 77.9\%. {A} maximum accuracy of 71.1\% with
{M}atthew's correlation coefficient of 0.41 in the test case was
obtained on a dataset previously used by {X}. {C}ruz, {E}. {G}. {H}utchinson,
{A}. {S}hephard and {J}. {M}. {T}hornton (2002) {P}roc. {N}atl {A}cad.
{S}ci. {USA}, 99, 11157-11162. {T}he performance of the method was
also evaluated on proteins used in the '6th community-wide experiment
on the critical assessment of techniques for protein structure prediction
({CASP}6)'. {B}ased on the algorithm described, a web server, {B}hair{P}red
(http://www.imtech.res.in/raghava/bhairpred/), has been developed,
which can be used to predict beta-hairpins in a protein using the
{SVM} approach.},
doi = {doi:10.1093/nar/gki588},
pdf = {../local/Kumar2005BhairPred.pdf},
file = {Kumar2005BhairPred.pdf:local/Kumar2005BhairPred.pdf:PDF},
keywords = {biosvm},
url = {http://dx.doi.org/doi:10.1093/nar/gki588}
}
@book{Kuncheva2004Combining,
title = {Combining Pattern Classifiers: Methods and Algorithms},
publisher = {Wiley-Interscience},
year = {2004},
author = {Kuncheva, Ludmila I.},
isbn = {0471210781}
}
@article{Kurata2006PlosCompBio,
author = {Hiroyuki Kurata and Hana El-Samad and Rei Iwasaki and Hisao Ohtake
and John C Doyle and Irina Grigorova and Carol A Gross and Mustafa
Khammash},
title = {Module-based analysis of robustness tradeoffs in the heat shock response
system.},
journal = {PLoS Comput Biol},
year = {2006},
volume = {2},
pages = {e59},
number = {7},
month = {Jul},
abstract = {Biological systems have evolved complex regulatory mechanisms, even
in situations where much simpler designs seem to be sufficient for
generating nominal functionality. Using module-based analysis coupled
with rigorous mathematical comparisons, we propose that in analogy
to control engineering architectures, the complexity of cellular
systems and the presence of hierarchical modular structures can be
attributed to the necessity of achieving robustness. We employ the
Escherichia coli heat shock response system, a strongly conserved
cellular mechanism, as an example to explore the design principles
of such modular architectures. In the heat shock response system,
the sigma-factor sigma32 is a central regulator that integrates multiple
feedforward and feedback modules. Each of these modules provides
a different type of robustness with its inherent tradeoffs in terms
of transient response and efficiency. We demonstrate how the overall
architecture of the system balances such tradeoffs. An extensive
mathematical exploration nevertheless points to the existence of
an array of alternative strategies for the existing heat shock response
that could exhibit similar behavior. We therefore deduce that the
evolutionary constraints facing the system might have steered its
architecture toward one of many robustly functional solutions.},
doi = {10.1371/journal.pcbi.0020059},
institution = {Department of Bioscience and Bioinformatics, Kyushu Institute of
Technology, Iizuka, Fukuoka, Japan. kurata@bio.kyutech.ac.jp},
keywords = {Computer Simulation; Escherichia coli Proteins, metabolism; Escherichia
coli, metabolism; Feedback, physiology; Gene Expression Regulation,
Bacterial, physiology; Heat-Shock Proteins, metabolism; Heat-Shock
Response, physiology; Models, Biological; Oxidative Stress, physiology;
Signal Transduction, physiology; Systems Biology, methods},
language = {eng},
medline-pst = {ppublish},
owner = {Andrei Zinovyev},
pii = {05-PLCB-RA-0264R4},
pmid = {16863396},
timestamp = {2011.04.08},
url = {http://dx.doi.org/10.1371/journal.pcbi.0020059}
}
@article{Kwon2000candidate,
author = {J. M. Kwon and A. M. Goate},
title = {The candidate gene approach.},
journal = {Alcohol Res Health},
year = {2000},
volume = {24},
pages = {164--168},
number = {3},
abstract = {Alcoholism has a significant genetic basis, and identifying genes
that confer a susceptibility to alcoholism will aid clinicians in
preventing and effectively treating the disease. One commonly used
technique to identify genetic risk factors for complex disorders
such as alcoholism is the candidate gene approach, which directly
tests the effects of genetic variants of a potentially contributing
gene in an association study. These studies, which may include members
of an affected family or unrelated cases and controls, can be performed
relatively quickly and inexpensively and may allow identification
of genes with small effects. However, the candidate gene approach
is limited by how much is known of the biology of the disease being
investigated. As researchers identify potential candidate genes using
animal studies or linking them to DNA regions implicated through
other analyses, the candidate gene approach will continue to be commonly
used.},
institution = {Washington University School of Medicine, St. Louis, Missouri, USA.},
keywords = {Alcoholism; Animals; Chromosome Mapping; Disease Models, Animal; Genetic
Predisposition to Disease; Genetic Variation; Humans; Mice; Mutation;
Pedigree; Polymorphism, Genetic; Quantitative Trait, Heritable},
owner = {fantine},
pmid = {11199286},
timestamp = {2010.10.20}
}
@article{Kohler2008Walking,
author = {K{\"o}hler, S. and Bauer, S. and Horn, D. and Robinson, P.N.},
title = {Walking the interactome for prioritization of candidate disease genes.},
journal = {Am. J. Hum. Genet.},
year = {2008},
volume = {82},
pages = {949--958},
number = {4},
month = {Apr},
abstract = {The identification of genes associated with hereditary disorders has
contributed to improving medical care and to a better understanding
of gene functions, interactions, and pathways. However, there are
well over 1500 Mendelian disorders whose molecular basis remains
unknown. At present, methods such as linkage analysis can identify
the chromosomal region in which unknown disease genes are located,
but the regions could contain up to hundreds of candidate genes.
In this work, we present a method for prioritization of candidate
genes by use of a global network distance measure, random walk analysis,
for definition of similarities in protein-protein interaction networks.
We tested our method on 110 disease-gene families with a total of
783 genes and achieved an area under the ROC curve of up to 98\%
on simulated linkage intervals of 100 genes surrounding the disease
gene, significantly outperforming previous methods based on local
distance measures. Our results not only provide an improved tool
for positional-cloning projects but also add weight to the assumption
that phenotypically similar diseases are associated with disturbances
of subnetworks within the larger protein interactome that extend
beyond the disease proteins themselves.},
doi = {10.1016/j.ajhg.2008.02.013},
institution = {Institute for Medical Genetics, Charité Universitätsmedizin Berlin,
Augustenburger Platz 1, 13353 Berlin, Germany.},
keywords = {Animals; Chromosome Mapping; Computational Biology; Databases, Genetic;
Genetic Diseases, Inborn; Genetic Predisposition to Disease; Humans;
Internet; Linkage (Genetics); Mice; Pedigree; Protein Interaction
Mapping; Software},
owner = {mordelet},
pii = {S0002-9297(08)00172-9},
pmid = {18371930},
timestamp = {2010.09.28},
url = {http://dx.doi.org/10.1016/j.ajhg.2008.02.013}
}
@article{LHeureux2004Locally,
author = {L'Heureux, P. J. and Carreau, J. and Bengio, Y. and Delalleau, O.
and Yue, S. Y.},
title = {Locally linear embedding for dimensionality reduction in {QSAR}.},
journal = {J. {C}omput. {A}ided {M}ol. {D}es.},
year = {2004},
volume = {18},
pages = {475-82},
number = {7-9},
abstract = {Current practice in {Q}uantitative {S}tructure {A}ctivity {R}elationship
({QSAR}) methods usually involves generating a great number of chemical
descriptors and then cutting them back with variable selection techniques.
{V}ariable selection is an effective method to reduce the dimensionality
but may discard some valuable information. {T}his paper introduces
{L}ocally {L}inear {E}mbedding ({LLE}), a local non-linear dimensionality
reduction technique, that can statistically discover a low-dimensional
representation of the chemical data. {LLE} is shown to create more
stable representations than other non-linear dimensionality reduction
algorithms, and to be capable of capturing non-linearity in chemical
data.},
doi = {10.1007/s10822-004-5319-9},
pdf = {../local/LHeureux2004Locally.pdf},
file = {LHeureux2004Locally.pdf:local/LHeureux2004Locally.pdf:PDF},
keywords = {dimred},
url = {http://dx.doi.org/10.1007/s10822-004-5319-9}
}
@article{LeCao2009Sparse,
author = {{L\^e Cao}, K.-A. and Martin, P. G. P. and Robert-Grani\'e, C. and
Besse, P.},
title = {Sparse canonical methods for biological data integration: application
to a cross-platform study.},
journal = {BMC Bioinformatics},
year = {2009},
volume = {10},
pages = {34},
abstract = {In the context of systems biology, few sparse approaches have been
proposed so far to integrate several data sets. It is however an
important and fundamental issue that will be widely encountered in
post genomic studies, when simultaneously analyzing transcriptomics,
proteomics and metabolomics data using different platforms, so as
to understand the mutual interactions between the different data
sets. In this high dimensional setting, variable selection is crucial
to give interpretable results. We focus on a sparse Partial Least
Squares approach (sPLS) to handle two-block data sets, where the
relationship between the two types of variables is known to be symmetric.
Sparse PLS has been developed either for a regression or a canonical
correlation framework and includes a built-in procedure to select
variables while integrating data. To illustrate the canonical mode
approach, we analyzed the NCI60 data sets, where two different platforms
(cDNA and Affymetrix chips) were used to study the transcriptome
of sixty cancer cell lines.We compare the results obtained with two
other sparse or related canonical correlation approaches: CCA with
Elastic Net penalization (CCA-EN) and Co-Inertia Analysis (CIA).
The latter does not include a built-in procedure for variable selection
and requires a two-step analysis. We stress the lack of statistical
criteria to evaluate canonical correlation methods, which makes biological
interpretation absolutely necessary to compare the different gene
selections. We also propose comprehensive graphical representations
of both samples and variables to facilitate the interpretation of
the results.sPLS and CCA-EN selected highly relevant genes and complementary
findings from the two data sets, which enabled a detailed understanding
of the molecular characteristics of several groups of cell lines.
These two approaches were found to bring similar results, although
they highlighted the same phenomenons with a different priority.
They outperformed CIA that tended to select redundant information.},
doi = {10.1186/1471-2105-10-34},
institution = {Station d'Amélioration Génétique des Animaux UR 631, Institut National
de Recherche Agronomique, F-31326 Castanet, France. k.lecao@imb.uq.edu.au},
keywords = {Computational Biology, methods; Genomics; Metabolomics; Proteomics;
Systems Biology, methods},
language = {eng},
medline-pst = {epublish},
owner = {jp},
pii = {1471-2105-10-34},
pmid = {19171069},
timestamp = {2012.02.29},
url = {http://dx.doi.org/10.1186/1471-2105-10-34}
}
@article{LaBaer2005Protein,
author = {Joshua LaBaer and Niroshan Ramachandran},
title = {Protein microarrays as tools for functional proteomics.},
journal = {Curr Opin Chem Biol},
year = {2005},
volume = {9},
pages = {14--19},
number = {1},
month = {Feb},
abstract = {Protein microarrays present an innovative and versatile approach to
study protein abundance and function at an unprecedented scale. Given
the chemical and structural complexity of the proteome, the development
of protein microarrays has been challenging. Despite these challenges
there has been a marked increase in the use of protein microarrays
to map interactions of proteins with various other molecules, and
to identify potential disease biomarkers, especially in the area
of cancer biology. In this review, we discuss some of the promising
advances made in the development and use of protein microarrays.},
doi = {10.1016/j.cbpa.2004.12.006},
institution = {Harvard Institute of Proteomics, Department of Biological Chemistry
and Molecular Pharmacology, Harvard Medical School, 320 Charles Street,
Cambridge, Massachusetts 02141, USA. joshua_labaer@hms.harvard.edu},
keywords = {Protein Array Analysis; Proteins; Proteomics; Surface Properties},
owner = {phupe},
pii = {S1367-5931(04)00165-6},
pmid = {15701447},
timestamp = {2010.08.12},
url = {http://dx.doi.org/10.1016/j.cbpa.2004.12.006}
}
@article{LaConte2005Support,
author = {Stephen LaConte and Stephen Strother and Vladimir Cherkassky and
Jon Anderson and Xiaoping Hu},
title = {Support vector machines for temporal classification of block design
f{MRI} data.},
journal = {Neuroimage},
year = {2005},
volume = {26},
pages = {317-29},
number = {2},
month = {Jun},
abstract = {This paper treats support vector machine ({SVM}) classification applied
to block design f{MRI}, extending our previous work with linear discriminant
analysis [{L}a{C}onte, {S}., {A}nderson, {J}., {M}uley, {S}., {A}she,
{J}., {F}rutiger, {S}., {R}ehm, {K}., {H}ansen, {L}.{K}., {Y}acoub,
{E}., {H}u, {X}., {R}ottenberg, {D}., {S}trother {S}., 2003a. {T}he
evaluation of preprocessing choices in single-subject {BOLD} f{MRI}
using {NPAIRS} performance metrics. {N}euro{I}mage 18, 10-27; {S}trother,
{S}.{C}., {A}nderson, {J}., {H}ansen, {L}.{K}., {K}jems, {U}., {K}ustra,
{R}., {S}iditis, {J}., {F}rutiger, {S}., {M}uley, {S}., {L}a{C}onte,
{S}., {R}ottenberg, {D}. 2002. {T}he quantitative evaluation of functional
neuroimaging experiments: the {NPAIRS} data analysis framework. {N}euro{I}mage
15, 747-771]. {W}e compare {SVM} to canonical variates analysis ({CVA})
by examining the relative sensitivity of each method to ten combinations
of preprocessing choices consisting of spatial smoothing, temporal
detrending, and motion correction. {I}mportant to the discussion
are the issues of classification performance, model interpretation,
and validation in the context of f{MRI}. {A}s the {SVM} has many
unique properties, we examine the interpretation of support vector
models with respect to neuroimaging data. {W}e propose four methods
for extracting activation maps from {SVM} models, and we examine
one of these in detail. {F}or both {CVA} and {SVM}, we have classified
individual time samples of whole brain data, with {TR}s of roughly
4 s, thirty slices, and nearly 30,000 brain voxels, with no averaging
of scans or prior feature selection.},
doi = {10.1016/j.neuroimage.2005.01.048},
pii = {S1053-8119(05)00089-3},
url = {http://dx.doi.org/10.1016/j.neuroimage.2005.01.048}
}
@unpublished{Lacoste-Julien2003introduction,
author = {Lacoste-Julien, S.},
title = {An introduction to {M}ax-{M}argin {M}arkov {N}etworks},
note = {UC Berkeley cs281a project report},
month = {December},
year = {2003},
pdf = {../local/Lacoste-Julien2003introduction.pdf},
file = {Lacoste-Julien2003introduction.pdf:local/Lacoste-Julien2003introduction.pdf:PDF},
keywords = {conditional-random-field},
owner = {vert}
}
@article{LaCount2005protein,
author = {Douglas J LaCount and Marissa Vignali and Rakesh Chettier and Amit
Phansalkar and Russell Bell and Jay R Hesselberth and Lori W Schoenfeld
and Irene Ota and Sudhir Sahasrabudhe and Cornelia Kurschner and
Stanley Fields and Robert E Hughes},
title = {{A} protein interaction network of the malaria parasite {P}lasmodium
falciparum.},
journal = {Nature},
year = {2005},
volume = {438},
pages = {103--107},
number = {7064},
month = {Nov},
abstract = {Plasmodium falciparum causes the most severe form of malaria and kills
up to 2.7 million people annually. Despite the global importance
of P. falciparum, the vast majority of its proteins have not been
characterized experimentally. Here we identify P. falciparum protein-protein
interactions using a high-throughput version of the yeast two-hybrid
assay that circumvents the difficulties in expressing P. falciparum
proteins in Saccharomyces cerevisiae. From more than 32,000 yeast
two-hybrid screens with P. falciparum protein fragments, we identified
2,846 unique interactions, most of which include at least one previously
uncharacterized protein. Informatic analyses of network connectivity,
coexpression of the genes encoding interacting fragments, and enrichment
of specific protein domains or Gene Ontology annotations were used
to identify groups of interacting proteins, including one implicated
in chromatin modification, transcription, messenger RNA stability
and ubiquitination, and another implicated in the invasion of host
cells. These data constitute the first extensive description of the
protein interaction network for this important human pathogen.},
doi = {10.1038/nature04104},
pdf = {../local/LaCount2005protein.pdf},
file = {LaCount2005protein.pdf:local/LaCount2005protein.pdf:PDF},
keywords = {plasmodium},
pii = {nature04104},
pmid = {16267556},
timestamp = {2006.04.13},
url = {http://dx.doi.org/10.1038/nature04104}
}
@inproceedings{Lafferty2001Conditional,
author = {Lafferty, J. and McCallum, A. and Pereira, F.},
title = {Conditional {R}andom {F}ields: {P}robabilistic {M}odels for {S}egmenting
and {L}abeling {S}equence {D}ata},
booktitle = {Proc. 18th {I}nternational {C}onf. on {M}achine {L}earning},
year = {2001},
pages = {282--289},
publisher = {Morgan Kaufmann, San Francisco, CA},
citeseerurl = {http://citeseer.ist.psu.edu/lafferty01conditional.html},
pdf = {../local/Lafferty2001Conditional.pdf},
file = {Lafferty2001Conditional.pdf:local/Lafferty2001Conditional.pdf:PDF},
keywords = {conditional-random-field},
url = {http://citeseer.ist.psu.edu/lafferty01conditional.html}
}
@article{Lage2007human,
author = {Lage, K. and Karlberg, E.O. and Størling, Z.M. and Olason, P.I. and
Pedersen, A.G. and Rigina, O. and Hinsby, A.M. and Tümer, Z. and
Pociot, F. and Tommerup, N. and Moreau, Y. and Brunak, S.},
title = {A human phenome-interactome network of protein complexes implicated
in genetic disorders},
journal = {Nat. Biotechnol.},
year = {2007},
volume = {25},
pages = {309--316},
number = {3},
month = {Mar},
abstract = {We performed a systematic, large-scale analysis of human protein complexes
comprising gene products implicated in many different categories
of human disease to create a phenome-interactome network. This was
done by integrating quality-controlled interactions of human proteins
with a validated, computationally derived phenotype similarity score,
permitting identification of previously unknown complexes likely
to be associated with disease. Using a phenomic ranking of protein
complexes linked to human disease, we developed a Bayesian predictor
that in 298 of 669 linkage intervals correctly ranks the known disease-causing
protein as the top candidate, and in 870 intervals with no identified
disease-causing gene, provides novel candidates implicated in disorders
such as retinitis pigmentosa, epithelial ovarian cancer, inflammatory
bowel disease, amyotrophic lateral sclerosis, Alzheimer disease,
type 2 diabetes and coronary heart disease. Our publicly available
draft of protein complexes associated with pathology comprises 506
complexes, which reveal functional relationships between disease-promoting
genes that will inform future experimentation.},
doi = {10.1038/nbt1295},
pdf = {../local/Lage2007human.pdf},
file = {Lage2007human.pdf:Lage2007human.pdf:PDF},
institution = {Center for Biological Sequence Analysis, BioCentrum-DTU, Technical
University of Denmark, Building 208, DK-2800 Lyngby, Denmark.},
owner = {mordelet},
pii = {nbt1295},
pmid = {17344885},
timestamp = {2010.09.28},
url = {http://dx.doi.org/10.1038/nbt1295}
}
@article{Lahav2004NatGenet,
author = {Lahav, G. and Rosenfeld, N. and Sigal, A. and Geva-Zatorsky, N. and
Levine, A. J. and Elowitz, M. B. and Alon, U.},
title = {Dynamics of the p53-Mdm2 feedback loop in individual cells},
journal = {Nat Genet},
year = {2004},
volume = {36},
pages = {147--50},
number = {2},
abstract = {The tumor suppressor p53, one of the most intensely investigated proteins,
is usually studied by experiments that are averaged over cell populations,
potentially masking the dynamic behavior in individual cells. We
present a system for following, in individual living cells, the dynamics
of p53 and its negative regulator Mdm2 (refs. 1,4-7): this system
uses functional p53-CFP and Mdm2-YFP fusion proteins and time-lapse
fluorescence microscopy. We found that p53 was expressed in a series
of discrete pulses after DNA damage. Genetically identical cells
had different numbers of pulses: zero, one, two or more. The mean
height and duration of each pulse were fixed and did not depend on
the amount of DNA damage. The mean number of pulses, however, increased
with DNA damage. This approach can be used to study other signaling
systems and suggests that the p53-Mdm2 feedback loop generates a
'digital' clock that releases well-timed quanta of p53 until damage
is repaired or the cell dies.},
keywords = {csbcbook}
}
@article{Lai2006comparison,
author = {Lai, C. and Reinders, M. J. T. and van't Veer, L. J. and Wessels,
L. F. A.},
title = {A comparison of univariate and multivariate gene selection techniques
for classification of cancer datasets},
journal = {BMC bioinformatics},
year = {2006},
volume = {7},
pages = {235},
number = {1},
doi = {10.1186/1471-2105-7-235},
pdf = {../local/Lai2006comparison.pdf},
file = {Lai2006comparison.pdf:Lai2006comparison.pdf:PDF},
issn = {1471-2105},
owner = {jp},
publisher = {BioMed Central Ltd},
timestamp = {2011.01.14},
url = {http://dx.doi.org/10.1186/1471-2105-7-235}
}
@article{Lai2000Kernel,
author = {P.L. Lai and C. Fyfe},
title = {Kernel and nonlinear canonical correlation analysis},
journal = {Int. {J}. {N}eural {S}yst.},
year = {2000},
volume = {10},
pages = {365--377},
number = {5},
pdf = {../local/lai00.pdf},
file = {lai00.pdf:local/lai00.pdf:PDF},
subject = {kernel},
url = {http://www.worldscinet.com/journals/ijns/10/sample/S012906570000034X.html}
}
@article{Laird2010Principles,
author = {Peter W Laird},
title = {Principles and challenges of genome-wide DNA methylation analysis.},
journal = {Nat Rev Genet},
year = {2010},
volume = {11},
pages = {191--203},
number = {3},
month = {Feb},
abstract = {Methylation of cytosine bases in DNA provides a layer of epigenetic
control in many eukaryotes that has important implications for normal
biology and disease. Therefore, profiling DNA methylation across
the genome is vital to understanding the influence of epigenetics.
There has been a revolution in DNA methylation analysis technology
over the past decade: analyses that previously were restricted to
specific loci can now be performed on a genome-scale and entire methylomes
can be characterized at single-base-pair resolution. However, there
is such a diversity of DNA methylation profiling techniques that
it can be challenging to select one. This Review discusses the different
approaches and their relative merits and introduces considerations
for data analysis.},
doi = {10.1038/nrg2732},
institution = {USC Epigenome Center, University of Southern California, Keck School
of Medicine, 1450 Biggy Street, Room G511B, Los Angeles, California90089-9601,
USA. plaird@usc.edu.},
language = {eng},
medline-pst = {aheadofprint},
owner = {philippe},
pii = {nrg2732},
pmid = {20125086},
timestamp = {2010.08.01},
url = {http://dx.doi.org/10.1038/nrg2732}
}
@article{Lal2004Support,
author = {Thomas Navin Lal and Michael Schröder and Thilo Hinterberger and
Jason Weston and Martin Bogdan and Niels Birbaumer and Bernhard Schölkopf},
title = {Support vector channel selection in {BCI}.},
journal = {I{EEE} {T}rans {B}iomed {E}ng},
year = {2004},
volume = {51},
pages = {1003-10},
number = {6},
month = {Jun},
abstract = {Designing a brain computer interface ({BCI}) system one can choose
from a variety of features that may be useful for classifying brain
activity during a mental task. {F}or the special case of classifying
electroencephalogram ({EEG}) signals we propose the usage of the
state of the art feature selection algorithms {R}ecursive {F}eature
{E}limination and {Z}ero-{N}orm {O}ptimization which are based on
the training of support vector machines ({SVM}). {T}hese algorithms
can provide more accurate solutions than standard filter methods
for feature selection. {W}e adapt the methods for the purpose of
selecting {EEG} channels. {F}or a motor imagery paradigm we show
that the number of used channels can be reduced significantly without
increasing the classification error. {T}he resulting best channels
agree well with the expected underlying cortical activity patterns
during the mental tasks. {F}urthermore we show how time dependent
task specific information can be visualized.},
keywords = {Algorithms, Animals, Antisense, Artificial Intelligence, Automated,
Autonomic Nervous System, Brain, Cell Line, Cerebral Cortex, Child,
Cluster Analysis, Cognition, Comparative Study, Computational Biology,
Computer Simulation, Computer-Assisted, DNA Fingerprinting, Databases,
Drug Evaluation, Electroencephalography, Emotions, Event-Related
Potentials, Evoked Potentials, Factual, Fluorescence, Fuzzy Logic,
Gene Silencing, Gene Targeting, Genetic, Hand, Hela Cells, Humans,
Imaging, Intracellular Space, Male, Microscopy, Models, Monitoring,
Motor, Neoplasms, Neural Networks (Computer), Non-U.S. Gov't, Oligonucleotides,
P.H.S., P300, Pattern Recognition, Peptides, Physiologic, Preclinical,
Predictive Value of Tests, Preschool, Prognosis, Protein Interaction
Mapping, Protein Structure, Proteins, Proteomics, Quantitative Structure-Activity
Relationship, Quaternary, RNA, RNA Interference, Recognition (Psychology),
Reproducibility of Results, Research Support, Sensitivity and Specificity,
Signal Processing, Small Interfering, Software, Thionucleotides,
Three-Dimensional, Tumor, U.S. Gov't, User-Computer Interface, Word
Processing, 15188871}
}
@article{Lanckriet2004Learning,
author = {Lanckriet, G.R.G. and Cristianini, N. and Bartlett, P. and El Ghaoui,
L. and Jordan, M.I.},
title = {Learning the kernel matrix with semidefinite programming},
journal = {J. Mach. Learn. Res.},
year = {2004},
volume = {5},
pages = {27-72},
pdf = {../local/Lanckriet2004Learning.pdf},
file = {Lanckriet2004Learning.pdf:Lanckriet2004Learning.pdf:PDF},
owner = {vert},
subject = {kernel},
url = {http://www.jmlr.org/papers/v5/lanckriet04a.html}
}
@incollection{Lanckriet2004Kernel-based,
author = {Lanckriet, G.R.G. and Cristianini, N. and Jordan, M.I. and Noble,
W.S.},
title = {Kernel-based integration of genomic data using semidefinite programming},
booktitle = {Kernel {M}ethods in {C}omputational {B}iology},
publisher = {MIT Press},
year = {2004},
editor = {Schölkopf, B. and Tsuda, K. and Vert, J.P.},
pages = {231-259},
keywords = {biosvm},
owner = {vert}
}
@inproceedings{Lanckriet2004Kernel-baseda,
author = {Lanckriet, G.R. and Deng, M. and Cristianini, N. and Jordan, M.I.
and Noble, W.S.},
title = {Kernel-based data fusion and its application to protein function
prediction in yeast.},
booktitle = {Proceedings of the {P}acific {S}ymposium on {B}iocomputing},
year = {2004},
pages = {300-311},
abstract = {Kernel methods provide a principled framework in which to represent
many types of data, including vectors, strings, trees and graphs.
{A}s such, these methods are useful for drawing inferences about
biological phenomena. {W}e describe a method for combining multiple
kernel representations in an optimal fashion, by formulating the
problem as a convex optimization problem that can be solved using
semidefinite programming techniques. {T}he method is applied to the
problem of predicting yeast protein functional classifications using
a support vector machine ({SVM}) trained on five types of data. {F}or
this problem, the new method performs better than a previously-described
{M}arkov random field method, and better than the {SVM} trained on
any single type of data.},
pdf = {../local/Lanckriet2004Kernel-baseda.pdf},
file = {Lanckriet2004Kernel-baseda.pdf:local/Lanckriet2004Kernel-baseda.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert}
}
@article{Lanckriet2004statistical,
author = {Lanckriet, G. R. G. and De Bie, T. and Cristianini, N. and Jordan,
M. I. and Noble, W. S.},
title = {A statistical framework for genomic data fusion},
journal = {Bioinformatics},
year = {2004},
volume = {20},
pages = {2626-2635},
number = {16},
abstract = {Motivation: {D}uring the past decade, the new focus on genomics has
highlighted a particular challenge: to integrate the different views
of the genome that are provided by various types of experimental
data. {R}esults: {T}his paper describes a computational framework
for integrating and drawing inferences from a collection of genome-wide
measurements. {E}ach dataset is represented via a kernel function,
which defines generalized similarity relationships between pairs
of entities, such as genes or proteins. {T}he kernel representation
is both flexible and efficient, and can be applied to many different
types of data. {F}urthermore, kernel functions derived from different
types of data can be combined in a straightforward fashion. {R}ecent
advances in the theory of kernel methods have provided efficient
algorithms to perform such combinations in a way that minimizes a
statistical loss function. {T}hese methods exploit semidefinite programming
techniques to reduce the problem of finding optimizing kernel combinations
to a convex optimization problem. {C}omputational experiments performed
using yeast genome-wide datasets, including amino acid sequences,
hydropathy profiles, gene expression data and known protein-protein
interactions, demonstrate the utility of this approach. {A} statistical
learning algorithm trained from all of these data to recognize particular
classes of proteins--membrane proteins and ribosomal proteins--performs
significantly better than the same algorithm trained on any single
type of data. {A}vailability: {S}upplementary data at http://noble.gs.washington.edu/proj/sdp-svm},
doi = {10.1093/bioinformatics/bth294},
pdf = {../local/Lanckriet2004statistical.pdf},
file = {Lanckriet2004statistical.pdf:local/Lanckriet2004statistical.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://bioinformatics.oupjournals.org/cgi/content/abstract/20/16/2626}
}
@techreport{Land1997Variable,
author = {Land, S. R. and Friedman, J. H.},
title = {Variable fusion: A new adaptive signal regression method},
institution = {Department of Statistics, Carnegie Mellon University Pittsburgh},
year = {1997},
number = {656},
keywords = {lasso, ordinal, regression}
}
@article{Lander1999Array,
author = {E. S. Lander},
title = {Array of hope.},
journal = {Nat Genet},
year = {1999},
volume = {21},
pages = {3--4},
number = {1 Suppl},
month = {Jan},
doi = {10.1038/4427},
institution = {Whitehead Institute for Biomedical Research and Department of Biology,
Institute of Technology, Cambridge 02142, USA. lander@genome.wi.mit.edu},
keywords = {Animals; DNA, analysis; Gene Expression; Genetic Variation; Genome;
Humans; Molecular Probe Techniques, trends; Oligonucleotide Array
Sequence Analysis, methods; RNA, Messenger, analysis; Saccharomyces
cerevisiae, genetics},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pmid = {9915492},
timestamp = {2012.02.29},
url = {http://dx.doi.org/10.1038/4427}
}
@article{Langford2001property,
author = {Langford, E. and Schwertman, N. and Owens, M.},
title = {Is the property of being positively correlated transitive?},
journal = {The American Statistician},
year = {2001},
volume = {55},
pages = {322--325},
number = {4},
publisher = {Taylor \& Francis}
}
@article{Langmead2009Ultrafast,
author = {Langmead, B. and Trapnell, C. and Pop, M. and Salzberg, S. L.},
title = {Ultrafast and memory-efficient alignment of short {DNA} sequences
to the human genome.},
journal = {Genome Biol},
year = {2009},
volume = {10},
pages = {R25},
number = {3},
__markedentry = {[jp:]},
abstract = {Bowtie is an ultrafast, memory-efficient alignment program for aligning
short DNA sequence reads to large genomes. For the human genome,
Burrows-Wheeler indexing allows Bowtie to align more than 25 million
reads per CPU hour with a memory footprint of approximately 1.3 gigabytes.
Bowtie extends previous Burrows-Wheeler techniques with a novel quality-aware
backtracking algorithm that permits mismatches. Multiple processor
cores can be used simultaneously to achieve even greater alignment
speeds. Bowtie is open source (http://bowtie.cbcb.umd.edu).},
doi = {10.1186/gb-2009-10-3-r25},
pdf = {../local/Langmead2009Ultrafast.pdf},
file = {Langmead2009Ultrafast.pdf:Langmead2009Ultrafast.pdf:PDF},
institution = {Center for Bioinformatics and Computational Biology, Institute for
Advanced Computer Studies, University of Maryland, College Park,
MD 20742, USA. langmead@cs.umd.edu},
keywords = {ngs},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {gb-2009-10-3-r25},
pmid = {19261174},
timestamp = {2013.03.29},
url = {http://dx.doi.org/10.1186/gb-2009-10-3-r25}
}
@article{Lao2004Morphological,
author = {Zhiqiang Lao and Dinggang Shen and Zhong Xue and Bilge Karacali and
Susan M Resnick and Christos Davatzikos},
title = {Morphological classification of brains via high-dimensional shape
transformations and machine learning methods.},
journal = {Neuroimage},
year = {2004},
volume = {21},
pages = {46-57},
number = {1},
month = {Jan},
abstract = {A high-dimensional shape transformation posed in a mass-preserving
framework is used as a morphological signature of a brain image.
{P}opulation differences with complex spatial patterns are then determined
by applying a nonlinear support vector machine ({SVM}) pattern classification
method to the morphological signatures. {S}ignificant reduction of
the dimensionality of the morphological signatures is achieved via
wavelet decomposition and feature reduction methods. {A}pplying the
method to {MR} images with simulated atrophy shows that the method
can correctly detect subtle and spatially complex atrophy, even when
the simulated atrophy represents only a 5\% variation from the original
image. {A}pplying this method to actual {MR} images shows that brains
can be correctly determined to be male or female with a successful
classification rate of 97\%, using the leave-one-out method. {T}his
proposed method also shows a high classification rate for old adults'
age classification, even under difficult test scenarios. {T}he main
characteristic of the proposed methodology is that, by applying multivariate
pattern classification methods, it can detect subtle and spatially
complex patterns of morphological group differences which are often
not detectable by voxel-based morphometric methods, because these
methods analyze morphological measurements voxel-by-voxel and do
not consider the entirety of the data simultaneously.},
pii = {S1053811903005731}
}
@article{Lapinsh2001Development,
author = {Lapinsh, M. and Prusis, P. and Gutcaits, A. and Lundstedt, T and
Wikberg, J. E. S.},
title = {Development of proteo-chemometrics: A novel technology of use for
analysis of drug-receptor interactions},
journal = {Biochem. Biophys. Acta},
year = {2001},
volume = {1525},
pages = {180--190},
owner = {vert},
timestamp = {2007.08.02}
}
@article{Lapinsh2005Improved,
author = {Lapinsh, M. and Prusis, P. and Uhl{\'e}n, S. and Wikberg, J. E. S.},
title = {Improved approach for proteochemometrics modeling: application to
organic compound--amine {G} protein-coupled receptor interactions.},
journal = {Bioinformatics},
year = {2005},
volume = {21},
pages = {4289--4296},
number = {23},
month = {Dec},
abstract = {MOTIVATION: Proteochemometrics is a novel technology for the analysis
of interactions of series of proteins with series of ligands. We
have here customized it for analysis of large datasets and evaluated
it for the modeling of the interaction of psychoactive organic amines
with all the five known families of amine G protein-coupled receptors
(GPCRs). RESULTS: The model exploited data for the binding of 22
compounds to 31 amine GPCRs, correlating chemical descriptions and
cross-descriptions of compounds and receptors to binding affinity
using a novel strategy. A highly valid model (q2 = 0.76) was obtained
which was further validated by external predictions using data for
10 other entirely independent compounds, yielding the high q2ext
= 0.67. Interpretation of the model reveals molecular interactions
that govern psychoactive organic amines overall affinity for amine
GPCRs, as well as their selectivity for particular amine GPCRs. The
new modeling procedure allows us to obtain fully interpretable proteochemometrics
models using essentially unlimited number of ligand and protein descriptors.},
doi = {10.1093/bioinformatics/bti703},
keywords = {chemogenomics},
owner = {laurent},
pii = {bti703},
pmid = {16204343},
timestamp = {2008.07.16},
url = {http://dx.doi.org/10.1093/bioinformatics/bti703}
}
@article{Larsen2005integrative,
author = {Mette Voldby Larsen and Claus Lundegaard and Kasper Lamberth and
S\o ren Buus and S\o ren Brunak and Ole Lund and Morten Nielsen},
title = {An integrative approach to {CTL} epitope prediction: a combined algorithm
integrating {MHC} class {I} binding, {TAP} transport efficiency,
and proteasomal cleavage predictions.},
journal = {Eur. J. Immunol.},
year = {2005},
volume = {35},
pages = {2295--2303},
number = {8},
month = {Aug},
abstract = {Reverse immunogenetic approaches attempt to optimize the selection
of candidate epitopes, and thus minimize the experimental effort
needed to identify new epitopes. When predicting cytotoxic T cell
epitopes, the main focus has been on the highly specific MHC class
I binding event. Methods have also been developed for predicting
the antigen-processing steps preceding MHC class I binding, including
proteasomal cleavage and transporter associated with antigen processing
(TAP) transport efficiency. Here, we use a dataset obtained from
the SYFPEITHI database to show that a method integrating predictions
of MHC class I binding affinity, TAP transport efficiency, and C-terminal
proteasomal cleavage outperforms any of the individual methods. Using
an independent evaluation dataset of HIV epitopes from the Los Alamos
database, the validity of the integrated method is confirmed. The
performance of the integrated method is found to be significantly
higher than that of the two publicly available prediction methods
BIMAS and SYFPEITHI. To identify 85\% of the epitopes in the HIV
dataset, 9\% and 10\% of all possible nonamers in the HIV proteins
must be tested when using the BIMAS and SYFPEITHI methods, respectively,
for the selection of candidate epitopes. This number is reduced to
7\% when using the integrated method. In practical terms, this means
that the experimental effort needed to identify an epitope in a hypothetical
protein with 85\% probability is reduced by 20-30\% when using the
integrated method.The method is available at http://www.cbs.dtu.dk/services/NetCTL.
Supplementary material is available at http://www.cbs.dtu.dk/suppl/immunology/CTL.php.},
doi = {10.1002/eji.200425811},
keywords = {Algorithms; Data Interpretation, Statistical; Epitopes, T-Lymphocyte;
Histocompatibility Antigens Class I; Humans; Hydrolysis; Predictive
Value of Tests; Proteasome Endopeptidase Complex; Protein Binding;
Research Support, N.I.H., Extramural; Research Support, Non-U.S.
Gov't; Research Support, U.S. Gov't, P.H.S.; T-Lymphocytes, Cytotoxic},
owner = {jacob},
pmid = {15997466},
timestamp = {2006.08.30},
url = {http://dx.doi.org/10.1002/eji.200425811}
}
@article{Lasonder2002Analysis,
author = {Edwin Lasonder and Yasushi Ishihama and Jens S Andersen and Adriaan
M W Vermunt and Arnab Pain and Robert W Sauerwein and Wijnand M C
Eling and Neil Hall and Andrew P Waters and Hendrik G Stunnenberg
and Matthias Mann},
title = {{A}nalysis of the {P}lasmodium falciparum proteome by high-accuracy
mass spectrometry.},
journal = {Nature},
year = {2002},
volume = {419},
pages = {537--542},
number = {6906},
month = {Oct},
abstract = {The annotated genomes of organisms define a 'blueprint' of their possible
gene products. Post-genome analyses attempt to confirm and modify
the annotation and impose a sense of the spatial, temporal and developmental
usage of genetic information by the organism. Here we describe a
large-scale, high-accuracy (average deviation less than 0.02 Da at
1,000 Da) mass spectrometric proteome analysis of selected stages
of the human malaria parasite Plasmodium falciparum. The analysis
revealed 1,289 proteins of which 714 proteins were identified in
asexual blood stages, 931 in gametocytes and 645 in gametes. The
last two groups provide insights into the biology of the sexual stages
of the parasite, and include conserved, stage-specific, secreted
and membrane-associated proteins. A subset of these proteins contain
domains that indicate a role in cell-cell interactions, and therefore
can be evaluated as potential components of a malaria vaccine formulation.
We also report a set of peptides with significant matches in the
parasite genome but not in the protein set predicted by computational
methods.},
doi = {10.1038/nature01111},
pdf = {../local/Lasonder2002Analysis.pdf},
file = {Lasonder2002Analysis.pdf:local/Lasonder2002Analysis.pdf:PDF},
keywords = {plasmodium},
pii = {nature01111},
pmid = {12368862},
timestamp = {2006.04.13},
url = {http://dx.doi.org/10.1038/nature01111}
}
@article{Lasso2005Vessel,
author = {András Lassó and Emanuele Trucco},
title = {Vessel enhancement in digital {X}-ray angiographic sequences by temporal
statistical learning.},
journal = {Comput {M}ed {I}maging {G}raph},
year = {2005},
volume = {29},
pages = {343-55},
number = {5},
month = {Jul},
doi = {10.1016/j.compmedimag.2005.02.002},
keywords = {Apoptosis, Gene Expression Profiling, Humans, Neoplasms, Non-U.S.
Gov't, Oligonucleotide Array Sequence Analysis, Polymerase Chain
Reaction, Proteins, Research Support, Subcellular Fractions, Unknown
Primary, 15893453},
pii = {S0895-6111(05)00032-7},
url = {http://dx.doi.org/10.1016/j.compmedimag.2005.02.002}
}
@article{Launay2008Homology,
author = {G. Launay and T. Simonson},
title = {Homology modelling of protein-protein complexes: a simple method
and its possibilities and limitations.},
journal = {BMC Bioinformatics},
year = {2008},
volume = {9},
pages = {427},
abstract = {BACKGROUND: Structure-based computational methods are needed to help
identify and characterize protein-protein complexes and their function.
For individual proteins, the most successful technique is homology
modelling. We investigate a simple extension of this technique to
protein-protein complexes. We consider a large set of complexes of
known structures, involving pairs of single-domain proteins. The
complexes are compared with each other to establish their sequence
and structural similarities and the relation between the two. Compared
to earlier studies, a simpler dataset, a simpler structural alignment
procedure, and an additional energy criterion are used. Next, we
compare the Xray structures to models obtained by threading the native
sequence onto other, homologous complexes. An elementary requirement
for a successful energy function is to rank the native structure
above any threaded structure. We use the DFIREbeta energy function,
whose quality and complexity are typical of the models used today.
Finally, we compare near-native models to distinctly non-native models.
RESULTS: If weakly stable complexes are excluded (defined by a binding
energy cutoff), as well as a few unusual complexes, a simple homology
principle holds: complexes that share more than 35\% sequence identity
share similar structures and interaction modes; this principle was
less clearcut in earlier studies. The energy function was then tested
for its ability to identify experimental structures among sets of
decoys, produced by a simple threading procedure. On average, the
experimental structure is ranked above 92\% of the alternate structures.
Thus, discrimination of the native structure is good but not perfect.
The discrimination of near-native structures is fair. Typically,
a single, alternate, non-native binding mode exists that has a native-like
energy. Some of the associated failures may correspond to genuine,
alternate binding modes and/or native complexes that are artefacts
of the crystal environment. In other cases, additional model filtering
with more sophisticated tools is needed. CONCLUSION: The results
suggest that the simple modelling procedure applied here could help
identify and characterize protein-protein complexes. The next step
is to apply it on a genomic scale.},
doi = {10.1186/1471-2105-9-427},
institution = {Laboratoire de Biochimie (UMR CNRS 7654), Department of Biology,
Ecole Polytechnique, 91128, Palaiseau, France. guillaume.launay@irisa.fr},
keywords = {Algorithms; Protein Binding; Protein Conformation; Protein Interaction
Domains and Motifs; Proteins, chemistry/metabolism; Structural Homology,
Protein},
owner = {bricehoffmann},
pii = {1471-2105-9-427},
pmid = {18844985},
timestamp = {2009.02.13},
url = {http://dx.doi.org/10.1186/1471-2105-9-427}
}
@article{Laurie2005Q-Site,
author = {Laurie,, A. T. R. and Jackson,, R. M.},
title = {Q-SiteFinder: an energy-based method for the prediction of protein--ligand
binding sites},
journal = {Bioinformatics},
year = {2005},
volume = {21},
pages = {1908--1916},
number = {9},
address = {Oxford, UK},
doi = {http://dx.doi.org/10.1093/bioinformatics/bti315},
issn = {1367-4803},
publisher = {Oxford University Press}
}
@book{Lauritzen1996Graphical,
title = {Graphical {M}odels},
publisher = {Oxford},
year = {1996},
author = {S. Lauritzen},
subject = {stat}
}
@article{Lavielle2005Using,
author = {Lavielle, M.},
title = {Using penalized contrasts for the change-point problem},
journal = {Signal Process.},
year = {2005},
volume = {85},
pages = {1501--1510},
number = {8},
doi = {10.1016/j.sigpro.2005.01.012},
pdf = {../local/Lavielle2005Using.pdf},
file = {Lavielle2005Using.pdf:Lavielle2005Using.pdf:PDF},
owner = {kb},
timestamp = {2011.04.19}
}
@inproceedings{Lavielle2005Adaptive,
author = {Lavielle, M. and Teyssi{\`e}re},
title = {Adaptive detection of multiple change-points in asset price volatility},
booktitle = {Long-Memory in Economics},
year = {2005},
editor = {Teyssi{\`e}re, G. and Kirman, A.},
pages = {129--156},
publisher = {Springer Verlag, Berlin},
owner = {jp},
timestamp = {2010.06.02}
}
@article{Lavielle2006Detection,
author = {Lavielle, M. and Teyssi{\`e}re, G.},
title = {Detection of multiple change-points in multivariate time series},
journal = {Lithuanian Mathematical Journal},
year = {2006},
volume = {46},
pages = {287--306},
number = {3},
doi = {10.1007/s10986-006-0028-9},
pdf = {../local/Lavielle2006Detection.pdf},
file = {Lavielle2006Detection.pdf:Lavielle2006Detection.pdf:PDF},
owner = {jp},
timestamp = {2010.06.02},
url = {http://dx.doi.org/10.1007/s10986-006-0028-9}
}
@article{Lavrik2007JBC,
author = {Lavrik, I. N. and Golks, A. and Riess, D. and Bentele, M. and Eils,
R. and Krammer, P. H.},
title = {Analysis of CD95 threshold signaling: triggering of CD95 (FAS/APO-1)
at low concentrations primarily results in survival signaling},
journal = {J Biol Chem},
year = {2007},
volume = {282},
pages = {13664-71},
number = {18},
abstract = {Recently we generated a mathematical model (Bentele, M., Lavrik, I.,
Ulrich, M., Stosser, S., Heermann, D. W., Kalthoff, H., Krammer,
P. H., and Eils, R. (2004) J. Cell Biol. 166, 839-851) of signaling
in CD95(Fas/APO-1)-mediated apoptosis. Mathematical modeling in combination
with experimental data provided new insights into CD95-mediated apoptosis
and allowed us to establish a threshold mechanism of life and death.
Here, we further assessed the predictability of the model experimentally
by a detailed analysis of the threshold behavior of CD95 signaling.
Using the model predictions for the mechanism of the threshold behavior
we found that the CD95 DISC (death-inducing signaling complex) is
formed at the cell membrane upon stimulation with low concentrations
of agonistic anti-APO-1 monoclonal antibodies; however, activation
of procaspase-8 at the DISC is blocked due to high cellular FLICE-inhibitory
protein recruitment into the DISC. Given that death signaling does
not occur upon CD95 stimulation at low (threshold) anti-APO-1 concentrations,
we also analyzed survival signaling, focusing on mitogen-activated
protein kinase activation. Interestingly, we found that mitogen-activated
protein kinase activation takes place under threshold conditions.
These findings show that triggering of CD95 can signal both life
or death, depending on the strength of the stimulus.},
keywords = {csbcbook}
}
@article{Lazar2012survey,
author = {Lazar, C. and Taminau, J. and Meganck, S. and Steenhoff, D. and Coletta,
A. and Molter, C. and de Schaetzen, V. and Duque, R. and Bersini,
H. and Now{\'e}, A.},
title = {A survey on filter techniques for feature selection in gene expression
microarray analysis},
journal = {IEEE/ACM Transactions on Computational Biology and Bioinformatics
(TCBB)},
year = {2012},
volume = {9},
pages = {1106--1119},
number = {4},
publisher = {IEEE Computer Society Press}
}
@article{Lazo2000Combinatorial,
author = {J. S. Lazo and P. Wipf},
title = {{C}ombinatorial chemistry and contemporary pharmacology.},
journal = {J. Pharmacol. Exp. Ther.},
year = {2000},
volume = {293},
pages = {705--709},
number = {3},
month = {Jun},
abstract = {Both solid- and liquid-phase combinatorial chemistry have emerged
as powerful tools for identifying pharmacologically active compounds
and optimizing the biological activity of a lead compound. Complementary
high-throughput in vitro assays are essential for compound evaluation.
Cell-based assays that use optical endpoints permit investigation
of a wide variety of functional properties of these compounds including
specific intracellular biochemical pathways, protein-protein interactions,
and the subcellular localization of targets. Integration of combinatorial
chemistry with contemporary pharmacology now represents an important
factor in drug discovery and development.},
keywords = {Alzheimer Disease, Animals, Antineoplastic Agents, Biological, Bleomycin,
Cell Cycle, Cell Cycle Proteins, Cell Death, Cell Line, Cell Nucleus,
Cell Shape, Cell Transformation, Combinatorial Chemistry Techniques,
Cultured, Drug Delivery Systems, Drug Design, Drug Evaluation, Enzyme
Inhibitors, Formazans, Gene Expression, Humans, Inhibitory Concentration
50, Kinetics, Magnetic Resonance Spectroscopy, Mass, Mitochondria,
Models, Molecular, Neoplasms, Neoplastic, Non-P.H.S., Non-U.S. Gov't,
P.H.S., Paclitaxel, Peptide Library, Pharmaceutical Preparations,
Pharmacology, Phosphoprotein Phosphatase, Preclinical, Protease Inhibitors,
Protein-Tyrosine-Phosphatase, Research Support, Sensitivity and Specificity,
Signal Transduction, Spectrum Analysis, Stereoisomerism, Structure-Activity
Relationship, Sulfonic Acids, Tetrazolium Salts, Thiazoles, Toxicity
Tests, Tumor, Tumor Cells, U.S. Gov't, cdc25 Phosphatase, 10869367},
owner = {mahe},
pmid = {10869367},
timestamp = {2006.08.22}
}
@article{Lenovere2001MELTING,
author = {Le Nov{\`e}re, M.},
title = {M{ELTING}, computing the melting temperature of nucleic acid duplex.},
journal = {Bioinformatics},
year = {2001},
volume = {17},
pages = {1226-7},
number = {12},
month = {Dec},
abstract = {M{ELTING} computes the enthalpy and entropy of an oligonucleotide
duplex helix-coil transition, and then its melting temperature. {T}he
program uses the method of nearest-neighbours. {T}he set of thermodynamic
parameters can be easily customized. {T}he program provides several
correction methods for the concentration of salt. {MELTING} is a
free program, available at no cost and open-source. {P}erl scripts
are provided to show how {MELTING} can be used to construct more
ambitious programs. {AVAILABILITY}: {MELTING} is available for several
platforms (http://www.pasteur.fr/recherche/unites/neubiomol/meltinghome.html)
and is accessible via a www server (http://bioweb.pasteur.fr/seqanal/interfaces/melting.html).
{CONTACT}: nl223@cus.cam.ac.uk}
}
@article{Lenovere2009The,
author = {Nicolas {Le Novère} and Michael Hucka and Huaiyu Mi and Stuart Moodie
and Falk Schreiber and Anatoly Sorokin and Emek Demir and Katja Wegner
and Mirit I Aladjem and Sarala M Wimalaratne and Frank T Bergman
and Ralph Gauges and Peter Ghazal and Hideya Kawaji and Lu Li and
Yukiko Matsuoka and Alice Villéger and Sarah E Boyd and Laurence
Calzone and Melanie Courtot and Ugur Dogrusoz and Tom C Freeman and
Akira Funahashi and Samik Ghosh and Akiya Jouraku and Sohyoung Kim
and Fedor Kolpakov and Augustin Luna and Sven Sahle and Esther Schmidt
and Steven Watterson and Guanming Wu and Igor Goryanin and Douglas
B Kell and Chris Sander and Herbert Sauro and Jacky L Snoep and Kurt
Kohn and Hiroaki Kitano},
title = {The Systems Biology Graphical Notation.},
journal = {Nat Biotechnol},
year = {2009},
volume = {27},
pages = {735--741},
number = {8},
month = {Aug},
abstract = {Circuit diagrams and Unified Modeling Language diagrams are just two
examples of standard visual languages that help accelerate work by
promoting regularity, removing ambiguity and enabling software tool
support for communication of complex information. Ironically, despite
having one of the highest ratios of graphical to textual information,
biology still lacks standard graphical notations. The recent deluge
of biological knowledge makes addressing this deficit a pressing
concern. Toward this goal, we present the Systems Biology Graphical
Notation (SBGN), a visual language developed by a community of biochemists,
modelers and computer scientists. SBGN consists of three complementary
languages: process diagram, entity relationship diagram and activity
flow diagram. Together they enable scientists to represent networks
of biochemical interactions in a standard, unambiguous way. We believe
that SBGN will foster efficient and accurate representation, visualization,
storage, exchange and reuse of information on all kinds of biological
knowledge, from gene regulation, to metabolism, to cellular signaling.},
doi = {10.1038/nbt.1558},
institution = {EMBL European Bioinformatics Institute, Hinxton, UK. lenov@ebi.ac.uk},
language = {eng},
medline-pst = {ppublish},
owner = {philippe},
pii = {nbt.1558},
pmid = {19668183},
timestamp = {2010.08.04},
url = {http://dx.doi.org/10.1038/nbt.1558}
}
@article{Le2008systematic,
author = {Le Roch, K. G. and Johnson, J. R. and Ahiboh, H. and Chung, D.-W.
D. and Prudhomme, J. and Plouffe, D. and Henson, K. and Zhou, Y.
and Witola, W. and Yates, J. R. and Ben Mamoun, C. and Winzeler,
E. A. and Vial, H.},
title = {A systematic approach to understand the mechanism of action of the
bisthiazolium compound {T4} on the human malaria parasite, {P}lasmodium
falciparum.},
journal = {BMC Genomics},
year = {2008},
volume = {9},
pages = {513},
abstract = {BACKGROUND: In recent years, a major increase in the occurrence of
drug resistant falciparum malaria has been reported. Choline analogs,
such as the bisthiazolium T4, represent a novel class of compounds
with strong potency against drug sensitive and resistant P. falciparum
clones. Although T4 and its analogs are presumed to target the parasite's
lipid metabolism, their exact mechanism of action remains unknown.
Here we have employed transcriptome and proteome profiling analyses
to characterize the global response of P. falciparum to T4 during
the intraerythrocytic cycle of this parasite. RESULTS: No significant
transcriptional changes were detected immediately after addition
of T4 despite the drug's effect on the parasite metabolism. Using
the Ontology-based Pattern Identification (OPI) algorithm with an
increased T4 incubation time, we demonstrated cell cycle arrest and
a general induction of genes involved in gametocytogenesis. Proteomic
analysis revealed a significant decrease in the level of the choline/ethanolamine-phosphotransferase
(PfCEPT), a key enzyme involved in the final step of synthesis of
phosphatidylcholine (PC). This effect was further supported by metabolic
studies, which showed a major alteration in the synthesis of PC from
choline and ethanolamine by the compound. CONCLUSION: Our studies
demonstrate that the bisthiazolium compound T4 inhibits the pathways
of synthesis of phosphatidylcholine from choline and ethanolamine
in P. falciparum, and provide evidence for post-transcriptional regulations
of parasite metabolism in response to external stimuli.},
doi = {10.1186/1471-2164-9-513},
institution = {Department of Cell Biology and Neuroscience, University of California,
Riverside, 900 University Avenue, Riverside, CA 92521, USA. karine.leroch@ucr.edu},
keywords = {lasso},
owner = {jp},
pii = {1471-2164-9-513},
pmid = {18973684},
timestamp = {2009.01.21},
url = {http://dx.doi.org/10.1186/1471-2164-9-513}
}
@article{LeRoch2003Discovery,
author = {Le Roch, K. G. and Zhou, Y. and Blair, P. L. and Grainger, M. and
Moch, J. K. and Haynes, J. D. and De la Vega, P. and Holder, A. A.
and Batalov, S. and Carucci, D. J. and Winzeler, E. A.},
title = {Discovery of Gene Function by Expression Profiling of the Malaria
Parasite Life Cycle},
journal = {Science},
year = {2003},
volume = {301},
pages = {1503-1508},
number = {5639},
abstract = {The completion of the genome sequence for {P}lasmodium falciparum,
the species responsible for most malaria human deaths, has the potential
to reveal hundreds of new drug targets and proteins involved in pathogenesis.
{H}owever, only approximately 35% of the genes code for proteins
with an identifiable function. {T}he absence of routine genetic tools
for studying {P}lasmodium parasites suggests that this number is
unlikely to change quickly if conventional serial methods are used
to characterize encoded proteins. {H}ere, we use a high-density oligonucleotide
array to generate expression profiles of human and mosquito stages
of the malaria parasite's life cycle. {G}enes with highly correlated
levels and temporal patterns of expression were often involved in
similar functions or cellular processes.},
doi = {10.1126/science.1087025},
pdf = {../local/LeRoch2003Discovery.pdf},
file = {LeRoch2003Discovery.pdf:LeRoch2003Discovery.pdf:PDF},
keywords = {microarray plasmodium},
owner = {vert},
url = {http://www.sciencemag.org/cgi/content/full/301/5639/1503}
}
@article{LeRoux2008Representational,
author = {{Le Roux}, Nicolas and Bengio, Yoshua},
title = {Representational power of restricted boltzmann machines and deep
belief networks.},
journal = {Neural Comput},
year = {2008},
volume = {20},
pages = {1631--1649},
number = {6},
month = {Jun},
abstract = {Deep belief networks (DBN) are generative neural network models with
many layers of hidden explanatory factors, recently introduced by
Hinton, Osindero, and Teh (2006) along with a greedy layer-wise unsupervised
learning algorithm. The building block of a DBN is a probabilistic
model called a restricted Boltzmann machine (RBM), used to represent
one layer of the model. Restricted Boltzmann machines are interesting
because inference is easy in them and because they have been successfully
used as building blocks for training deeper models. We first prove
that adding hidden units yields strictly improved modeling power,
while a second theorem shows that RBMs are universal approximators
of discrete distributions. We then study the question of whether
DBNs with more layers are strictly more powerful in terms of representational
power. This suggests a new and less greedy criterion for training
RBMs within DBNs.},
doi = {10.1162/neco.2008.04-07-510},
institution = {Département Informatique et Recherche Opérationnelle, Université
de Montréal, Montréal, Québec, H3C 3J7, Canada. lerouxni@iro.umontreal.ca},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pmid = {18254699},
timestamp = {2013.03.29},
url = {http://dx.doi.org/10.1162/neco.2008.04-07-510}
}
@book{Leach2003introduction,
title = {An introduction to chemoinformatics},
publisher = {Kluwer Academic Publishers},
year = {2003},
author = {Leach, A. R. and Gillet, V. J.}
}
@article{Leach2006Prediction,
author = {A. R. Leach and B. K. Shoichet and C. E. Peishoff},
title = {Prediction of protein-ligand interactions. Docking and scoring: successes
and gaps.},
journal = {J. Med. Chem.},
year = {2006},
volume = {49},
pages = {5851--5855},
number = {20},
month = {Oct},
doi = {10.1021/jm060999m},
institution = {GlaxoSmithKline Pharmaceuticals, 1250 South Collegeville Road, Collegeville,
Pennsylvania 19426, USA.},
keywords = {Binding Sites; Drug Design; Ligands; Models, Molecular; Protein Binding;
Proteins, chemistry; Quantitative Structure-Activity Relationship},
owner = {bricehoffmann},
pmid = {17004700},
timestamp = {2009.02.13},
url = {http://dx.doi.org/10.1021/jm060999m}
}
@article{Leclerc2008Survival,
author = {Leclerc, R.D.},
title = {Survival of the sparsest: robust gene networks are parsimonious.},
journal = {Mol Syst Biol},
year = {2008},
volume = {4},
pages = {213},
abstract = {Biological gene networks appear to be dynamically robust to mutation,
stochasticity, and changes in the environment and also appear to
be sparsely connected. Studies with computational models, however,
have suggested that denser gene networks evolve to be more dynamically
robust than sparser networks. We resolve this discrepancy by showing
that misassumptions about how to measure robustness in artificial
networks have inadvertently discounted the costs of network complexity.
We show that when the costs of complexity are taken into account,
that robustness implies a parsimonious network structure that is
sparsely connected and not unnecessarily complex; and that selection
will favor sparse networks when network topology is free to evolve.
Because a robust system of heredity is necessary for the adaptive
evolution of complex phenotypes, the maintenance of frugal network
complexity is likely a crucial design constraint that underlies biological
organization.},
doi = {10.1038/msb.2008.52},
institution = {Wagner Lab, Department of Ecology and Evolutionary Biology, Yale
University, New Haven, CT 06520, USA. robert.leclerc@yale.edu},
keywords = {Adaptation, Physiological; Computer Simulation; Evolution, Molecular;
Gene Regulatory Networks; Genotype; Heredity; Humans; Male; Models,
Genetic; Mutation; Reproducibility of Results; Selection, Genetic;
Stochastic Processes; Transcription, Genetic},
owner = {mordelet},
pii = {msb200852},
pmid = {18682703},
timestamp = {2010.10.18},
url = {http://dx.doi.org/10.1038/msb.2008.52}
}
@article{Lee2003Discovery,
author = {Dongkwon Lee and Sang Wook Choi and Myengsoo Kim and Jin Hyun Park
and Moonkyu Kim and Jungchul Kim and In-Beum Lee},
title = {Discovery of differentially expressed genes related to histological
subtype of hepatocellular carcinoma.},
journal = {Biotechnol {P}rog.},
year = {2003},
volume = {19},
pages = {1011-5},
number = {3},
abstract = {Hepatocellular carcinoma ({HCC}) is one of the most common human malignancies
in the world. {T}o identify the histological subtype-specific genes
of {HCC}, we analyzed the gene expression profile of 10 {HCC} patients
by means of c{DNA} microarray. {W}e proposed a systematic approach
for determining the discriminatory genes and revealing the biological
phenomena of {HCC} with c{DNA} microarray data. {F}irst, normalization
of c{DNA} microarray data was performed to reduce or minimize systematic
variations. {O}n the basis of the suitably normalized data, we identified
specific genes involved in histological subtype of {HCC}. {T}wo classification
methods, {F}isher's discriminant analysis ({FDA}) and support vector
machine ({SVM}), were used to evaluate the reliability of the selected
genes and discriminate the histological subtypes of {HCC}. {T}his
study may provide a clue for the needs of different chemotherapy
and the reason for heterogeneity of the clinical responses according
to histological subtypes.},
doi = {10.1021/bp025746a},
pdf = {../local/Lee2003Discovery.pdf},
file = {Lee2003Discovery.pdf:local/Lee2003Discovery.pdf:PDF},
keywords = {biosvm},
url = {http://dx.doi.org/10.1021/bp025746a}
}
@article{Lee1999Learning,
author = {Lee, D. D. and Seung, H. S.},
title = {Learning the parts of objects by non-negative matrix factorization.},
journal = {Nature},
year = {1999},
volume = {401},
pages = {788--791},
number = {6755},
month = {Oct},
abstract = {Is perception of the whole based on perception of its parts? There
is psychological and physiological evidence for parts-based representations
in the brain, and certain computational theories of object recognition
rely on such representations. But little is known about how brains
or computers might learn the parts of objects. Here we demonstrate
an algorithm for non-negative matrix factorization that is able to
learn parts of faces and semantic features of text. This is in contrast
to other methods, such as principal components analysis and vector
quantization, that learn holistic, not parts-based, representations.
Non-negative matrix factorization is distinguished from the other
methods by its use of non-negativity constraints. These constraints
lead to a parts-based representation because they allow only additive,
not subtractive, combinations. When non-negative matrix factorization
is implemented as a neural network, parts-based representations emerge
by virtue of two properties: the firing rates of neurons are never
negative and synaptic strengths do not change sign.},
doi = {10.1038/44565},
pdf = {../local/Lee1999Learning.pdf},
file = {Lee1999Learning.pdf:Lee1999Learning.pdf:PDF},
institution = {Bell Laboratories, Lucent Technologies, Murray Hill, New Jersey 07974,
USA.},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pmid = {10548103},
timestamp = {2011.12.30},
url = {http://dx.doi.org/10.1038/44565}
}
@article{Lee2008Inferring,
author = {Lee, E. and Chuang, H.-Y. and Kim, J. W. and Ideker, T. and Lee,
D.},
title = {Inferring pathway activity toward precise disease classification.},
journal = {PLoS Comput Biol},
year = {2008},
volume = {4},
pages = {e1000217},
number = {11},
month = {Nov},
abstract = {The advent of microarray technology has made it possible to classify
disease states based on gene expression profiles of patients. Typically,
marker genes are selected by measuring the power of their expression
profiles to discriminate among patients of different disease states.
However, expression-based classification can be challenging in complex
diseases due to factors such as cellular heterogeneity within a tissue
sample and genetic heterogeneity across patients. A promising technique
for coping with these challenges is to incorporate pathway information
into the disease classification procedure in order to classify disease
based on the activity of entire signaling pathways or protein complexes
rather than on the expression levels of individual genes or proteins.
We propose a new classification method based on pathway activities
inferred for each patient. For each pathway, an activity level is
summarized from the gene expression levels of its condition-responsive
genes (CORGs), defined as the subset of genes in the pathway whose
combined expression delivers optimal discriminative power for the
disease phenotype. We show that classifiers using pathway activity
achieve better performance than classifiers based on individual gene
expression, for both simple and complex case-control studies including
differentiation of perturbed from non-perturbed cells and subtyping
of several different kinds of cancer. Moreover, the new method outperforms
several previous approaches that use a static (i.e., non-conditional)
definition of pathways. Within a pathway, the identified CORGs may
facilitate the development of better diagnostic markers and the discovery
of core alterations in human disease.},
doi = {10.1371/journal.pcbi.1000217},
pdf = {../local/Lee2008Inferring.pdf},
file = {Lee2008Inferring.pdf:Lee2008Inferring.pdf:PDF},
institution = {Department of Bio and Brain Engineering, KAIST, Daejeon, South Korea.},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pmid = {18989396},
timestamp = {2011.08.07},
url = {http://dx.doi.org/10.1371/journal.pcbi.1000217}
}
@article{Lee2005improved,
author = {Jaewook Lee and Daewon Lee},
title = {An improved cluster labeling method for support vector clustering.},
journal = {I{EEE} {T}rans {P}attern {A}nal {M}ach {I}ntell},
year = {2005},
volume = {27},
pages = {461-4},
number = {3},
month = {Mar},
abstract = {The support vector clustering ({SVC}) algorithm is a recently emerged
unsupervised learning method inspired by support vector machines.
{O}ne key step involved in the {SVC} algorithm is the cluster assignment
of each data point. {A} new cluster labeling method for {SVC} is
developed based on some invariant topological properties of a trained
kernel radius function. {B}enchmark results show that the proposed
method outperforms previously reported labeling techniques.}
}
@article{Lee2005extensive,
author = {Lee, J. W. and Lee, J. B. and Park, M. and Song, S. H.},
title = {An extensive comparison of recent classification tools applied to
microarray datas},
journal = {Comput. Stat. Data An.},
year = {2005},
volume = {48},
pages = {869--885},
pdf = {../local/Lee2005extensive.pdf},
file = {Lee2005extensive.pdf:Lee2005extensive.pdf:PDF},
owner = {jp},
timestamp = {2012.03.04}
}
@article{Lee2004efficient,
author = {Martin M S Lee and S. Sathiya Keerthi and Chong Jin Ong and Dennis
DeCoste},
title = {An efficient method for computing leave-one-out error in support
vector machines with {G}aussian kernels.},
journal = {I{EEE} {T}rans {N}eural {N}etw},
year = {2004},
volume = {15},
pages = {750-7},
number = {3},
month = {May},
abstract = {In this paper, we give an efficient method for computing the leave-one-out
({LOO}) error for support vector machines ({SVM}s) with {G}aussian
kernels quite accurately. {I}t is particularly suitable for iterative
decomposition methods of solving {SVM}s. {T}he importance of various
steps of the method is illustrated in detail by showing the performance
on six benchmark datasets. {T}he new method often leads to speedups
of 10-50 times compared to standard {LOO} error computation. {I}t
has good promise for use in hyperparameter tuning and model comparison},
keywords = {Algorithms, Bayes Theorem, Computing Methodologies, Models, Neural
Networks (Computer), Non-U.S. Gov't, Normal Distribution, Research
Design, Research Support, Theoretical, 15384561}
}
@inproceedings{Lee1999oscillatory,
author = {R. S. T. Lee and J. N. K. Liu},
title = {An oscillatory elastic graph matching model for recognition of offline
handwritten Chinese characters},
booktitle = {KES},
year = {1999},
pages = {284-287},
bibsource = {DBLP, http://dblp.uni-trier.de},
ee = {http://dx.doi.org/10.1109/KES.1999.820179}
}
@article{Lee2003Application,
author = {Lee, S.-I. and Batzoglou, S.},
title = {Application of independent component analysis to microarrays.},
journal = {Genome Biol.},
year = {2003},
volume = {4},
pages = {R76},
number = {11},
doi = {10.1186/gb-2003-4-11-r76},
pdf = {../local/Lee2003Application.pdf},
file = {Lee2003Application.pdf:Lee2003Application.pdf:PDF},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {gb-2003-4-11-r76},
pmid = {14611662},
timestamp = {2011.12.30},
url = {http://dx.doi.org/10.1186/gb-2003-4-11-r76}
}
@inproceedings{Lee2003Learning,
author = {Lee, W. S. and Liu, B.},
title = {Learning with Positive and Unlabeled Examples Using Weighted Logistic
Regression},
booktitle = {Machine Learning, Proceedings of the Twentieth International Conference
(ICML 2003},
year = {2003},
editor = {Fawcett, T. and Mishra, N.},
pages = {448--455},
publisher = {AAAI Press},
owner = {jp},
timestamp = {2010.02.01}
}
@article{Lee2003Classification,
author = {Lee, Y. and Lee, C.-K.},
title = {Classification of multiple cancer types by multicategory support
vector machines using gene expression data},
journal = {Bioinformatics},
year = {2003},
volume = {19},
pages = {1132-1139},
number = {9},
abstract = {Motivation: {H}igh-density {DNA} microarray measures the activities
of several thousand genes simultaneously and the gene expression
profiles have been used for the cancer classification recently. {T}his
new approach promises to give better therapeutic measurements to
cancer patients by diagnosing cancer types with improved accuracy.
{T}he {S}upport {V}ector {M}achine ({SVM}) is one of the classification
methods successfully applied to the cancer diagnosis problems. {H}owever,
its optimal extension to more than two classes was not obvious, which
might impose limitations in its application to multiple tumor types.
{W}e briefly introduce the {M}ulticategory {SVM}, which is a recently
proposed extension of the binary {SVM}, and apply it to multiclass
cancer diagnosis problems {R}esults: {I}ts applicability is demonstrated
on the leukemia data ({G}olub et al., 1999) and the small round blue
cell tumors of childhood data ({K}han et al., 2001). {C}omparable
classification accuracy shown in the applications and its flexibility
render the {MSVM} a viable alternative to other classification methods
{S}upplementary {I}nformation: http://www.stat.ohio-state.edu/~yklee/msvm.html
{C}ontact: yklee@stat.ohio-state.edu},
pdf = {../local/Lee2003Classification.pdf},
file = {Lee2003Classification.pdf:local/Lee2003Classification.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://bioinformatics.oupjournals.org/cgi/content/abstract/19/9/1132}
}
@article{Lefkowitz2008crystal,
author = {Lefkowitz, R. J. and Sun, J.-P. and Shukla, A. K.},
title = {A crystal clear view of the beta2-adrenergic receptor},
journal = {Nat. Biotechnol.},
year = {2008},
volume = {26},
pages = {189--191},
number = {2},
month = {Feb},
doi = {10.1038/nbt0208-189},
keywords = {chemogenomics},
owner = {laurent},
pii = {nbt0208-189},
pmid = {18259173},
timestamp = {2008.07.16},
url = {http://dx.doi.org/10.1038/nbt0208-189}
}
@article{Legewie2006PLOSCompBiol,
author = {Legewie, S. and Blathgen, N. and Herzel, H.},
title = {Mathematical Modeling Identifies Inhibitors of Apoptosis as Mediators
of Positive Feedback and Bistability},
journal = {PLoS Comput Biol},
year = {2006},
volume = {2},
pages = {e120},
number = {9},
month = {09},
abstract = {The intrinsic, or mitochondrial, pathway of caspase activation is
essential for apoptosis induction by various stimuli including cytotoxic
stress. It depends on the cellular context, whether cytochrome c
released from mitochondria induces caspase activation gradually or
in an all-or-none fashion, and whether caspase activation irreversibly
commits cells to apoptosis. By analyzing a quantitative kinetic model,
we show that inhibition of caspase-3 (Casp3) and Casp9 by inhibitors
of apoptosis (IAPs) results in an implicit positive feedback, since
cleaved Casp3 augments its own activation by sequestering IAPs away
from Casp9. We demonstrate that this positive feedback brings about
bistability (i.e., all-or-none behaviour), and that it cooperates
with Casp3-mediated feedback cleavage of Casp9 to generate irreversibility
in caspase activation. Our calculations also unravel how cell-specific
protein expression brings about the observed qualitative differences
in caspase activation (gradual versus all-or-none and reversible
versus irreversible). Finally, known regulators of the pathway are
shown to efficiently shift the apoptotic threshold stimulus, suggesting
that the bistable caspase cascade computes multiple inputs into an
all-or-none caspase output. As cellular inhibitory proteins (e.g.,
IAPs) frequently inhibit consecutive intermediates in cellular signaling
cascades (e.g., Casp3 and Casp9), the feedback mechanism described
in this paper is likely to be a widespread principle on how cells
achieve ultrasensitivity, bistability, and irreversibility.},
doi = {10.1371/journal.pcbi.0020120},
keywords = {csbcbook},
publisher = {Public Library of Science},
url = {http://dx.plos.org/10.1371/journal.pcbi.0020120}
}
@article{Leighton1986Estimating,
author = { Leighton, F. and Rivest, R. },
title = {Estimating a probability using finite memory},
journal = {I{EEE} {T}rans. {I}nform. {T}heory},
year = {1986},
volume = {32},
pages = {733- 742},
number = {6},
month = {Nov},
abstract = { {L}et{{X}_{i}}_{i=1}^{infty}be a sequence of independent {B}ernoulli
random variables with probabilitypthat{X}_{i} = 1and probabilityq=1-pthat{X}_{i}
= 0for alli geq 1. {T}ime-invariant finite-memory (i.e., finite-state)
estimation procedures for the parameter p are considered which take{X}_{1},
cdotsas an input sequence. {I}n particular, an n-state deterministic
estimation procedure is described which can estimate p with mean-square
error{O}(log n/n)and ann-state probabilistic estimation procedure
which can estimatepwith mean-square error{O}(1/n). {I}t is proved
that the{O}(1/n)bound is optimal to within a constant factor. {I}n
addition, it is shown that linear estimation procedures are just
as powerful (up to the measure of mean-square error) as arbitrary
estimation procedures. {T}he proofs are based on an analog of the
well-known matrix tree theorem that is called the {M}arkov chain
tree theorem. },
pdf = {../local/Leighton1986Estimating.pdf},
file = {Leighton1986Estimating.pdf:local/Leighton1986Estimating.pdf:PDF},
owner = {vert}
}
@article{Lemarechal1997Practical,
author = {Claude Lemarechal and Claudia Sagastiz\'abal and Echal and Claudia
Sagastiz Abal and Pii S},
title = {Practical Aspects of the Moreau-Yosida Regularization: Theoretical
Preliminaries},
journal = {SIAM Journal on Optimization},
year = {1997},
volume = {7},
pages = {367--385}
}
@article{Lemmen2000Computational,
author = {C. Lemmen and T. Lengauer},
title = {Computational methods for the structural alignment of molecules.},
journal = {J. {C}omput. {A}ided. {M}ol. {D}es.},
year = {2000},
volume = {14},
pages = {215--232},
number = {3},
month = {Mar},
abstract = {In drug design, often enough, no structural information on a particular
receptor protein is available. {H}owever, frequently a considerable
number of different ligands is known together with their measured
binding affinities towards a receptor under consideration. {I}n such
a situation, a set of plausible relative superpositions of different
ligands, hopefully approximating their putative binding geometry,
is usually the method of choice for preparing data for the subsequent
application of 3{D} methods that analyze the similarity or diversity
of the ligands. {E}xamples are 3{D}-{QSAR} studies, pharmacophore
elucidation, and receptor modeling. {A}n aggravating fact is that
ligands are usually quite flexible and a rigorous analysis has to
incorporate molecular flexibility. {W}e review the past six years
of scientific publishing on molecular superposition. {O}ur focus
lies on automatic procedures to be performed on arbitrary molecular
structures. {M}ethodical aspects are our main concern here. {A}ccordingly,
plain application studies with few methodical elements are omitted
in this presentation. {W}hile this review cannot mention every contribution
to this actively developing field, we intend to provide pointers
to the recent literature providing important contributions to computational
methods for the structural alignment of molecules. {F}inally we provide
a perspective on how superposition methods can effectively be used
for the purpose of virtual database screening. {I}n our opinion it
is the ultimate goal to detect analogues in structure databases of
nontrivial size in order to narrow down the search space for subsequent
experiments.},
keywords = {chemoinformatics},
owner = {mahe},
pmid = {10756477},
timestamp = {2006.02.03}
}
@article{Leng2004note,
author = {Leng, C. and Lin, Y. and Wahba, G.},
title = {A note on the {L}asso and related procedures in model selection},
journal = {Statistica Sinica},
year = {2004},
volume = {16},
pages = {1273--1284},
number = {4},
pdf = {../local/Leng2004note.pdf},
file = {Leng2004note.pdf:Leng2004note.pdf:PDF}
}
@inproceedings{Leordeanu2005Spectral,
author = {M. Leordeanu and M. Hebert},
title = {A Spectral Technique for Correspondence Problems using Pairwise Constraints},
booktitle = {International Conference of Computer Vision (ICCV)},
year = {2005},
volume = {2},
pages = {1482 - 1489},
month = {October}
}
@inproceedings{Leordeanu2007Beyond,
author = {M. Leordeanu and M. Hebert and R. Sukthankar},
title = {Beyond Local Appearance: Category Recognition from Pairwise Interactions
of Simple Features},
booktitle = {Proceedings of CVPR},
year = {2007},
month = {June}
}
@inproceedings{Leslie2002spectrum,
author = {Leslie, C. and Eskin, E. and Noble, W.S.},
title = {The spectrum kernel: a string kernel for {SVM} protein classification},
booktitle = {Proceedings of the {P}acific {S}ymposium on {B}iocomputing 2002},
year = {2002},
editor = {Russ B. Altman and A. Keith Dunker and Lawrence Hunter and Kevin
Lauerdale and Teri E. Klein},
pages = {564--575},
address = {Singapore},
publisher = {World Scientific},
pdf = {../local/lesl02.pdf},
file = {lesl02.pdf:local/lesl02.pdf:PDF},
keywords = {biosvm},
subject = {biokernel}
}
@inproceedings{Leslie2003Mismatch,
author = {Leslie, C. and Eskin, E. and Weston, J. and Noble, W.S.},
title = {Mismatch {S}tring {K}ernels for {SVM} {P}rotein {C}lassification},
booktitle = {Advances in {N}eural {I}nformation {P}rocessing {S}ystems 15},
year = {2003},
editor = {Suzanna Becker and Sebastian Thrun and Klaus Obermayer},
publisher = {MIT Press},
pdf = {../local/lesl02b.pdf},
file = {lesl02b.pdf:local/lesl02b.pdf:PDF},
keywords = {biosvm},
subject = {biokernel},
url = {http://www.cs.columbia.edu/~cleslie/papers/mismatch-short.pdf}
}
@article{Leslie2004Fast,
author = {Leslie, C. and Kuang, R.},
title = {Fast string kernels using inexact matching for protein sequences},
journal = {J. Mach. Learn. Res.},
year = {2004},
volume = {5},
pages = {1435--1455},
owner = {vert},
timestamp = {2007.08.01}
}
@article{Leslie2004Mismatch,
author = {Leslie, C. S. and Eskin, E. and Cohen, A. and Weston, J. and Noble,
W. S.},
title = {Mismatch string kernels for discriminative protein classification},
journal = {Bioinformatics},
year = {2004},
volume = {20},
pages = {467-476},
number = {4},
abstract = {Motivation: {C}lassification of proteins sequences into functional
and structural families based on sequence homology is a central problem
in computational biology. {D}iscriminative supervised machine learning
approaches provide good performance, but simplicity and computational
efficiency of training and prediction are also important concerns.
{R}esults: {W}e introduce a class of string kernels, called mismatch
kernels, for use with support vector machines ({SVM}s) in a discriminative
approach to the problem of protein classification and remote homology
detection. {T}hese kernels measure sequence similarity based on shared
occurrences of fixed-length patterns in the data, allowing for mutations
between patterns. {T}hus, the kernels provide a biologically well-motivated
way to compare protein sequences without relying on family-based
generative models such as hidden {M}arkov models. {W}e compute the
kernels efficiently using a mismatch tree data structure, allowing
us to calculate the contributions of all patterns occurring in the
data in one pass while traversing the tree. {W}hen used with an {SVM},
the kernels enable fast prediction on test sequences. {W}e report
experiments on two benchmark {SCOP} datasets, where we show that
the mismatch kernel used with an {SVM} classifier performs competitively
with state-of-the-art methods for homology detection, particularly
when very few training examples are available. {E}xamination of the
highest-weighted patterns learned by the {SVM} classifier recovers
biologically important motifs in protein families and superfamilies.
{A}vailability: {SVM} software is publicly available at http://microarray.cpmc.columbia.edu/gist.
{M}ismatch kernel software is available upon request.},
pdf = {../local/Leslie2004Mismatch.pdf},
file = {Leslie2004Mismatch.pdf:local/Leslie2004Mismatch.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://bioinformatics.oupjournals.org/cgi/content/abstract/20/4/467}
}
@misc{Letouzey2000Learning,
author = {Letouzey, F. and Denis, F. and Gilleron, R.},
title = {Learning from Positive and Unlabeled Examples},
year = {2000},
booktitle = {Procs. of the 11th International Conference on Algorithmic Learning
Theory},
owner = {fantine},
pages = {71--85},
timestamp = {2009.06.22},
url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.9.1122}
}
@article{Lett2004Interaction,
author = {Lett, D. and Hsing, M. and Pio, F.},
title = {Interaction profile-based protein classification of death domain},
journal = {B{MC} {B}ioinformatics},
year = {2004},
volume = {5},
number = {75},
abstract = {Background {T}he increasing number of protein sequences and 3{D} structure
obtained from genomic initiatives is leading many of us to focus
on proteomics, and to dedicate our experimental and computational
efforts on the creation and analysis of information derived from
3{D} structure. {I}n particular, the high-throughput generation of
protein-protein interaction data from a few organisms makes such
an approach very important towards understanding the molecular recognition
that make-up the entire protein-protein interaction network. {S}ince
the generation of sequences, and experimental protein-protein interactions
increases faster than the 3{D} structure determination of protein
complexes, there is tremendous interest in developing in silico methods
that generate such structure for prediction and classification purposes.
{I}n this study we focused on classifying protein family members
based on their protein-protein interaction distinctiveness. {S}tructure-based
classification of protein-protein interfaces has been described initially
by {P}onstingl et al. [1] and more recently by {V}aldar et al. [2]
and {M}intseris et al. [3], from complex structures that have been
solved experimentally. {H}owever, little has been done on protein
classification based on the prediction of protein-protein complexes
obtained from homology modeling and docking simulation. {R}esults
{W}e have developed an in silico classification system entitled {HODOCO}
({H}omology modeling, {D}ocking and {C}lassification {O}racle), in
which protein {R}esidue {P}otential {I}nteraction {P}rofiles ({RPIPS})
are used to summarize protein-protein interaction characteristics.
{T}his system applied to a dataset of 64 proteins of the death domain
superfamily was used to classify each member into its proper subfamily.
{T}wo classification methods were attempted, heuristic and support
vector machine learning. {B}oth methods were tested with a 5-fold
cross-validation. {T}he heuristic approach yielded a 61% average
accuracy, while the machine learning approach yielded an 89% average
accuracy. {C}onclusion {W}e have confirmed the reliability and potential
value of classifying proteins via their predicted interactions. {O}ur
results are in the same range of accuracy as other studies that classify
protein-protein interactions from 3{D} complex structure obtained
experimentally. {W}hile our classification scheme does not take directly
into account sequence information our results are in agreement with
functional and sequence based classification of death domain family
members.},
doi = {10.1186/1471-2105-5-75},
pdf = {../local/Lett2004Interaction.pdf},
file = {Lett2004Interaction.pdf:local/Lett2004Interaction.pdf:PDF},
keywords = {biosvm},
owner = {vert},
url = {http://www.biomedcentral.com/1471-2105/5/75}
}
@article{Leung2001Representing,
author = {Leung, T. and Malik, J.},
title = {Representing and Recognizing the Visual Appearance of Materials using
Three-dimensional Textons},
journal = {Int. J. Comput. Vision},
year = {2001},
volume = {43},
pages = {29--44},
number = {1},
abstract = {We study the recognition of surfaces made from different materials
such as concrete, rug, marble, or leather on the basis of their textural
appearance. Such natural textures arise from spatial variation of
two surface attributes: (1) reflectance and (2) surface normal. In
this paper, we provide a unified model to address both these aspects
of natural texture. The main idea is to construct a vocabulary of
prototype tiny surface patches with associated local geometric and
photometric properties. We call these 3D textons. Examples might
be ridges, grooves, spots or stripes or combinations thereof. Associated
with each texton is an appearance vector, which characterizes the
local irradiance distribution, represented as a set of linear Gaussian
derivative filter outputs, under different lighting and viewing conditions.
Given a large collection of images of different materials, a clustering
approach is used to acquire a small (on the order of 100) 3D texton
vocabulary. Given a few (1 to 4) images of any material, it can be
characterized using these textons. We demonstrate the application
of this representation for recognition of the material viewed under
novel lighting and viewing conditions. We also illustrate how the
3D texton model can be used to predict the appearance of materials
under novel conditions.},
doi = {10.1023/A:1011126920638},
pdf = {../local/Leung2001Representing.pdf},
file = {Leung2001Representing.pdf:local/Leung2001Representing.pdf:PDF},
timestamp = {2008.07.29},
url = {http://dx.doi.org/10.1023/A:1011126920638}
}
@article{LevBarOr2000PNAS,
author = {Lev Bar-Or, R. and Maya, R. and Segel, L. A. and Alon, U. and Levine,
A. J. and Oren, M.},
title = {Generation of oscillations by the p53-Mdm2 feedback loop: a theoretical
and experimental study},
journal = {Proc Natl Acad Sci U S A},
year = {2000},
volume = {97},
pages = {11250--5},
number = {21},
abstract = {The intracellular activity of the p53 tumor suppressor protein is
regulated through a feedback loop involving its transcriptional target,
mdm2. We present a simple mathematical model suggesting that, under
certain circumstances, oscillations in p53 and Mdm2 protein levels
can emerge in response to a stress signal. A delay in p53-dependent
induction of Mdm2 is predicted to be required, albeit not sufficient,
for this oscillatory behavior. In line with the predictions of the
model, oscillations of both p53 and Mdm2 indeed occur on exposure
of various cell types to ionizing radiation. Such oscillations may
allow cells to repair their DNA without risking the irreversible
consequences of continuous excessive p53 activation.},
keywords = {csbcbook}
}
@inproceedings{Levin1995Stock,
author = {Levin, A. E.},
title = {Stock Selection via Nonlinear Multi-Factor Models},
booktitle = {Adv. Neural. Inform. Process Syst.},
year = {1995},
editor = {Touretzky, D. S. and Mozer, M. and Hasselmo, M. E.},
volume = {8},
pages = {966--972},
publisher = {MIT Press},
pdf = {../local/Levin1995Stock.pdf},
file = {Levin1995Stock.pdf:Levin1995Stock.pdf:PDF},
owner = {jp},
timestamp = {2011.04.08}
}
@article{Levine1979Review,
author = {H. A. Levine},
title = {Review of~: Solutions of ill posed problems},
journal = {Bull. Amer. Math. Soc.},
year = {1979},
volume = {1},
pages = {521-524}
}
@article{Levy2007Diploid,
author = {Samuel Levy and Granger Sutton and Pauline C Ng and Lars Feuk and
Aaron L Halpern and Brian P Walenz and Nelson Axelrod and Jiaqi Huang
and Ewen F Kirkness and Gennady Denisov and Yuan Lin and Jeffrey
R MacDonald and Andy Wing Chun Pang and Mary Shago and Timothy B
Stockwell and Alexia Tsiamouri and Vineet Bafna and Vikas Bansal
and Saul A Kravitz and Dana A Busam and Karen Y Beeson and Tina C
McIntosh and Karin A Remington and Josep F Abril and John Gill and
Jon Borman and Yu-Hui Rogers and Marvin E Frazier and Stephen W Scherer
and Robert L Strausberg and J. Craig Venter},
title = {The diploid genome sequence of an individual human.},
journal = {PLoS Biol},
year = {2007},
volume = {5},
pages = {e254},
number = {10},
month = {Sep},
abstract = {Presented here is a genome sequence of an individual human. It was
produced from approximately 32 million random DNA fragments, sequenced
by Sanger dideoxy technology and assembled into 4,528 scaffolds,
comprising 2,810 million bases (Mb) of contiguous sequence with approximately
7.5-fold coverage for any given region. We developed a modified version
of the Celera assembler to facilitate the identification and comparison
of alternate alleles within this individual diploid genome. Comparison
of this genome and the National Center for Biotechnology Information
human reference assembly revealed more than 4.1 million DNA variants,
encompassing 12.3 Mb. These variants (of which 1,288,319 were novel)
included 3,213,401 single nucleotide polymorphisms (SNPs), 53,823
block substitutions (2-206 bp), 292,102 heterozygous insertion/deletion
events (indels)(1-571 bp), 559,473 homozygous indels (1-82,711 bp),
90 inversions, as well as numerous segmental duplications and copy
number variation regions. Non-SNP DNA variation accounts for 22\%
of all events identified in the donor, however they involve 74\%
of all variant bases. This suggests an important role for non-SNP
genetic alterations in defining the diploid genome structure. Moreover,
44\% of genes were heterozygous for one or more variants. Using a
novel haplotype assembly strategy, we were able to span 1.5 Gb of
genome sequence in segments >200 kb, providing further precision
to the diploid nature of the genome. These data depict a definitive
molecular portrait of a diploid human genome that provides a starting
point for future genome comparisons and enables an era of individualized
genomic information.},
doi = {10.1371/journal.pbio.0050254},
institution = {J. Craig Venter Institute, Rockville, Maryland, USA. slevy@jcvi.org},
keywords = {Base Sequence; Chromosome Mapping, instrumentation/methods; Chromosomes,
Human; Chromosomes, Human, Y, genetics; Diploidy; Gene Dosage; Genome,
Human; Genotype; Haplotypes; Human Genome Project; Humans; INDEL
Mutation; In Situ Hybridization, Fluorescence; Male; Microarray Analysis;
Middle Aged; Molecular Sequence Data; Pedigree; Phenotype; Polymorphism,
Single Nucleotide; Reproducibility of Results; Sequence Analysis,
DNA, instrumentation/methods},
language = {eng},
medline-pst = {ppublish},
owner = {philippe},
pii = {07-PLBI-RA-1258},
pmid = {17803354},
timestamp = {2010.07.28},
url = {http://dx.doi.org/10.1371/journal.pbio.0050254}
}
@article{Lewis2005Conserved,
author = {Benjamin P Lewis and Christopher B Burge and David P Bartel},
title = {Conserved seed pairing, often flanked by adenosines, indicates that
thousands of human genes are microRNA targets.},
journal = {Cell},
year = {2005},
volume = {120},
pages = {15--20},
number = {1},
month = {Jan},
abstract = {We predict regulatory targets of vertebrate microRNAs (miRNAs) by
identifying mRNAs with conserved complementarity to the seed (nucleotides
2-7) of the miRNA. An overrepresentation of conserved adenosines
flanking the seed complementary sites in mRNAs indicates that primary
sequence determinants can supplement base pairing to specify miRNA
target recognition. In a four-genome analysis of 3' UTRs, approximately
13,000 regulatory relationships were detected above the estimate
of false-positive predictions, thereby implicating as miRNA targets
more than 5300 human genes, which represented 30\% of our gene set.
Targeting was also detected in open reading frames. In sum, well
over one third of human genes appear to be conserved miRNA targets.},
doi = {10.1016/j.cell.2004.12.035},
pdf = {../local/Lewis2005Conserved.pdf},
file = {Lewis2005Conserved.pdf:Lewis2005Conserved.pdf:PDF},
keywords = {sirna},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {S0092867404012607},
pmid = {15652477},
timestamp = {2009.10.28},
url = {http://dx.doi.org/10.1016/j.cell.2004.12.035}
}
@article{Lewis2003Prediction,
author = {Benjamin P Lewis and I-hung Shih and Matthew W Jones-Rhoades and
David P Bartel and Christopher B Burge},
title = {Prediction of mammalian microRNA targets.},
journal = {Cell},
year = {2003},
volume = {115},
pages = {787--798},
number = {7},
month = {Dec},
abstract = {MicroRNAs (miRNAs) can play important gene regulatory roles in nematodes,
insects, and plants by basepairing to mRNAs to specify posttranscriptional
repression of these messages. However, the mRNAs regulated by vertebrate
miRNAs are all unknown. Here we predict more than 400 regulatory
target genes for the conserved vertebrate miRNAs by identifying mRNAs
with conserved pairing to the 5' region of the miRNA and evaluating
the number and quality of these complementary sites. Rigorous tests
using shuffled miRNA controls supported a majority of these predictions,
with the fraction of false positives estimated at 31\% for targets
identified in human, mouse, and rat and 22\% for targets identified
in pufferfish as well as mammals. Eleven predicted targets (out of
15 tested) were supported experimentally using a HeLa cell reporter
system. The predicted regulatory targets of mammalian miRNAs were
enriched for genes involved in transcriptional regulation but also
encompassed an unexpectedly broad range of other functions.},
institution = {Department of Biology, Massachusetts Institute of Technology, Cambridge,
MA 02139, USA.},
keywords = {sirna},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {S0092867403010183},
pmid = {14697198},
timestamp = {2009.10.28}
}
@article{Li2009BriefBioinform,
author = {Li, C. and Courtot, M. and Le Novere, N. and Laibe, C.},
title = {BioModels.net Web Services, a free and integrated toolkit for computational
modelling software},
journal = {Brief Bioinform},
year = {2009},
abstract = {Exchanging and sharing scientific results are essential for researchers
in the field of computational modelling. BioModels.net defines agreed-upon
standards for model curation. A fundamental one, MIRIAM (Minimum
Information Requested in the Annotation of Models), standardises
the annotation and curation process of quantitative models in biology.
To support this standard, MIRIAM Resources maintains a set of standard
data types for annotating models, and provides services for manipulating
these annotations. Furthermore, BioModels.net creates controlled
vocabularies, such as SBO (Systems Biology Ontology) which strictly
indexes, defines and links terms used in Systems Biology. Finally,
BioModels Database provides a free, centralised, publicly accessible
database for storing, searching and retrieving curated and annotated
computational models. Each resource provides a web interface to submit,
search, retrieve and display its data. In addition, the BioModels.net
team provides a set of Web Services which allows the community to
programmatically access the resources. A user is then able to perform
remote queries, such as retrieving a model and resolving all its
MIRIAM Annotations, as well as getting the details about the associated
SBO terms. These web services use established standards. Communications
rely on SOAP (Simple Object Access Protocol) messages and the available
queries are described in a WSDL (Web Services Description Language)
file. Several libraries are provided in order to simplify the development
of client software. BioModels.net Web Services make one step further
for the researchers to simulate and understand the entirety of a
biological system, by allowing them to retrieve biological models
in their own tool, combine queries in workflows and efficiently analyse
models.},
keywords = {csbcbook}
}
@article{Li2007JBiosci,
author = {Li, C. and Ge, Q. W. and Nakata, M. and Matsuno, H. and Miyano, S.},
title = {Modelling and simulation of signal transductions in an apoptosis
pathway by using timed Petri nets},
journal = {J Biosci},
year = {2007},
volume = {32},
pages = {113--27},
number = {1},
abstract = {This paper first presents basic Petri net components representing
molecular interactions and mechanisms of signalling pathways, and
introduces a method to construct a Petri net model of a signalling
pathway with these components. Then a simulation method of determining
the delay time of transitions, by using timed Petri nets - i.e. the
time taken in fi ring of each transition - is proposed based on some
simple principles that the number of tokens flowed into a place is
equivalent to the number of tokens fl owed out. Finally, the availability
of proposed method is confirmed by observing signalling transductions
in biological pathways through simulation experiments of the apoptosis
signalling pathways as an example.},
keywords = {csbcbook}
}
@article{Li2008Mapping,
author = {Li, H. and Ruan, J. and Durbin, R.},
title = {Mapping short {DNA} sequencing reads and calling variants using mapping
quality scores.},
journal = {Genome Res.},
year = {2008},
volume = {18},
pages = {1851--1858},
number = {11},
month = {Nov},
abstract = {New sequencing technologies promise a new era in the use of DNA sequence.
However, some of these technologies produce very short reads, typically
of a few tens of base pairs, and to use these reads effectively requires
new algorithms and software. In particular, there is a major issue
in efficiently aligning short reads to a reference genome and handling
ambiguity or lack of accuracy in this alignment. Here we introduce
the concept of mapping quality, a measure of the confidence that
a read actually comes from the position it is aligned to by the mapping
algorithm. We describe the software MAQ that can build assemblies
by mapping shotgun short reads to a reference genome, using quality
scores to derive genotype calls of the consensus sequence of a diploid
genome, e.g., from a human sample. MAQ makes full use of mate-pair
information and estimates the error probability of each read alignment.
Error probabilities are also derived for the final genotype calls,
using a Bayesian statistical model that incorporates the mapping
qualities, error probabilities from the raw sequence quality scores,
sampling of the two haplotypes, and an empirical model for correlated
errors at a site. Both read mapping and genotype calling are evaluated
on simulated data and real data. MAQ is accurate, efficient, versatile,
and user-friendly. It is freely available at http://maq.sourceforge.net.},
doi = {10.1101/gr.078212.108},
pdf = {../local/Li2008Mapping.pdf},
file = {Li2008Mapping.pdf:Li2008Mapping.pdf:PDF},
institution = {The Wellcome Trust Sanger Institute, Hinxton CB10 1SA, United Kingdom.},
keywords = {ngs},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {gr.078212.108},
pmid = {18714091},
timestamp = {2011.10.28},
url = {http://dx.doi.org/10.1101/gr.078212.108}
}
@article{Li2005Prediction,
author = {H. Li and C. Ung and C. Yap and Y. Xue and Z. Li and Z. Cao and Y.
Chen},
title = {Prediction of genotoxicity of chemical compounds by statistical learning
methods.},
journal = {Chem. {R}es. {T}oxicol.},
year = {2005},
volume = {18},
pages = {1071-1080},
number = {6},
month = {Jun},
abstract = {Various toxicological profiles, such as genotoxic potential, need
to be studied in drug discovery processes and submitted to the drug
regulatory authorities for drug safety evaluation. {A}s part of the
effort for developing low cost and efficient adverse drug reaction
testing tools, several statistical learning methods have been used
for developing genotoxicity prediction systems with an accuracy of
up to 73.8\% for genotoxic ({GT}+) and 92.8\% for nongenotoxic ({GT}-)
agents. {T}hese systems have been developed and tested by using less
than 400 known {GT}+ and {GT}- agents, which is significantly less
in number and diversity than the 860 {GT}+ and {GT}- agents known
at present. {T}here is a need to examine if a similar level of accuracy
can be achieved for the more diverse set of molecules and to evaluate
other statistical learning methods not yet applied to genotoxicity
prediction. {T}his work is intended for testing several statistical
learning methods by using 860 {GT}+ and {GT}- agents, which include
support vector machines ({SVM}), probabilistic neural network ({PNN}),
k-nearest neighbor (k-{NN}), and {C}4.5 decision tree ({DT}). {A}
feature selection method, recursive feature elimination, is used
for selecting molecular descriptors relevant to genotoxicity study.
{T}he overall accuracies of {SVM}, k-{NN}, and {PNN} are comparable
to and those of {DT} lower than the results from earlier studies,
with {SVM} giving the highest accuracies of 77.8\% for {GT}+ and
92.7\% for {GT}- agents. {O}ur study suggests that statistical learning
methods, particularly {SVM}, k-{NN}, and {PNN}, are useful for facilitating
the prediction of genotoxic potential of a diverse set of molecules.},
doi = {10.1021/tx049652h},
pdf = {../local/Li2005Prediction.pdf},
file = {Li2005Prediction.pdf:local/Li2005Prediction.pdf:PDF},
keywords = {biosvm chemoinformatics},
url = {http://dx.doi.org/10.1021/tx049652h}
}
@article{Li2002Involvement,
author = {Jiwen Li and Qiushi Lin and Ho-Geun Yoon and Zhi-Qing Huang and Brian
D Strahl and C. David Allis and Jiemin Wong},
title = {Involvement of histone methylation and phosphorylation in regulation
of transcription by thyroid hormone receptor.},
journal = {Mol Cell Biol},
year = {2002},
volume = {22},
pages = {5688--5697},
number = {16},
month = {Aug},
abstract = {Previous studies have established an important role of histone acetylation
in transcriptional control by nuclear hormone receptors. With chromatin
immunoprecipitation assays, we have now investigated whether histone
methylation and phosphorylation are also involved in transcriptional
regulation by thyroid hormone receptor (TR). We found that repression
by unliganded TR is associated with a substantial increase in methylation
of H3 lysine 9 (H3-K9) and a decrease in methylation of H3 lysine
4 (H3-K4), methylation of H3 arginine 17 (H3-R17), and a dual modification
of phosphorylation of H3 serine 10 and acetylation of lysine 14 (pS10/acK14).
On the other hand, transcriptional activation by liganded TR is coupled
with a substantial decrease in both H3-K4 and H3-K9 methylation and
a robust increase in H3-R17 methylation and the dual modification
of pS10/acK14. Trichostatin A treatment results in not only histone
hyperacetylation but also an increase in methylation of H3-K4, increase
in dual modification of pS10/acK14, and reduction in methylation
of H3-K9, revealing an extensive interplay between histone acetylation,
methylation, and phosphorylation. In an effort to understand the
underlying mechanism for an increase in H3-K9 methylation during
repression by unliganded TR, we demonstrated that TR interacts in
vitro with an H3-K9-specific histone methyltransferase (HMT), SUV39H1.
Functional analysis indicates that SUV39H1 can facilitate repression
by unliganded TR and in so doing requires its HMT activity. Together,
our data uncover a novel role of H3-K9 methylation in repression
by unliganded TR and provide strong evidence for the involvement
of multiple distinct histone covalent modifications (acetylation,
methylation, and phosphorylation) in transcriptional control by nuclear
hormone receptors.},
institution = {Department of Molecular and Cellular Biology, Baylor College of Medicine,
Houston, Texas 77030, USA.},
keywords = {Animals; Cell Fractionation; Gene Expression Regulation, drug effects;
Genes, Reporter; Histone-Lysine N-Methyltransferase; Histones, chemistry/genetics/metabolism;
Humans; Hydroxamic Acids, pharmacology; Methylation; Methyltransferases,
metabolism; Oocytes, physiology; Phosphorylation; Protein Methyltransferases;
Protein Synthesis Inhibitors, pharmacology; Receptors, Thyroid Hormone,
metabolism; Transcription, Genetic; Xenopus laevis, physiology},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pmid = {12138181},
timestamp = {2010.11.23}
}
@article{Li2003Simple,
author = {Jinyan Li and Huiqing Liu and James R Downing and Allen Eng-Juh Yeoh
and Limsoon Wong},
title = {Simple rules underlying gene expression profiles of more than six
subtypes of acute lymphoblastic leukemia ({ALL}) patients.},
journal = {Bioinformatics},
year = {2003},
volume = {19},
pages = {71-8},
number = {1},
month = {Jan},
abstract = {M{OTIVATIONS} {AND} {RESULTS}: {F}or classifying gene expression profiles
or other types of medical data, simple rules are preferable to non-linear
distance or kernel functions. {T}his is because rules may help us
understand more about the application in addition to performing an
accurate classification. {I}n this paper, we discover novel rules
that describe the gene expression profiles of more than six subtypes
of acute lymphoblastic leukemia ({ALL}) patients. {W}e also introduce
a new classifier, named {PCL}, to make effective use of the rules.
{PCL} is accurate and can handle multiple parallel classifications.
{W}e evaluate this method by classifying 327 heterogeneous {ALL}
samples. {O}ur test error rate is competitive to that of support
vector machines, and it is 71\% better than {C}4.5, 50\% better than
{N}aive {B}ayes, and 43\% better than k-nearest neighbour. {E}xperimental
results on another independent data sets are also presented to show
the strength of our method. {AVAILABILITY}: {U}nder http://sdmc.lit.org.sg/{GED}atasets/,
click on {S}upplementary {I}nformation.},
keywords = {Acute, Algorithms, Automated, Base Pair Mismatch, Base Pairing, Base
Sequence, Biological, Biosensing Techniques, Cluster Analysis, Comparative
Study, Computer-Assisted, DNA, Gene Expression Profiling, Gene Expression
Regulation, Genes, Genetic, Genetic Markers, Hemolysins, Humans,
Leukemia, Lymphocytic, Markov Chains, Messenger, Models, Molecular
Probe Techniques, Molecular Sequence Data, Nanotechnology, Neoplasm,
Neoplastic, Neural Networks (Computer), Non-U.S. Gov't, Nucleic Acid
Conformation, Oligonucleotide Array Sequence Analysis, Pattern Recognition,
Quality Control, RNA, Research Support, Signal Processing, Statistical,
Stomach Neoplasms, Tumor Markers, 12499295}
}
@article{Li2011Sparse,
author = {Li, J. J. and Jiang, C.-R. and Brown, J. B. and Huang, H. and Bickel,
P. J.},
title = {Sparse linear modeling of next-generation {mRNA} sequencing ({RNA-Seq})
data for isoform discovery and abundance estimation},
journal = {Proc. Natl. Acad. Sci. USA},
year = {2011},
volume = {108},
pages = {19867--19872},
number = {50},
month = dec,
abstract = {{Since the inception of next-generation mRNA sequencing (RNA-Seq)
technology, various attempts have been made to utilize RNA-Seq data
in assembling full-length mRNA isoforms de novo and estimating abundance
of isoforms. However, for genes with more than a few exons, the problem
tends to be challenging and often involves identifiability issues
in statistical modeling. We have developed a statistical method called
â sparse linear modeling of RNA-Seq data for isoform discovery
and abundance estimationâ (SLIDE) that takes exon boundaries and
RNA-Seq data as input to discern the set of mRNA isoforms that are
most likely to present in an RNA-Seq sample. SLIDE is based on a
linear model with a design matrix that models the sampling probability
of RNA-Seq reads from different mRNA isoforms. To tackle the model
unidentifiability issue, SLIDE uses a modified Lasso procedure for
parameter estimation. Compared with deterministic isoform assembly
algorithms (e.g., Cufflinks), SLIDE considers the stochastic aspects
of RNA-Seq reads in exons from different isoforms and thus has increased
power in detecting more novel isoforms. Another advantage of SLIDE
is its flexibility of incorporating other transcriptomic data such
as RACE, CAGE, and EST into its model to further increase isoform
discovery accuracy. SLIDE can also work downstream of other RNA-Seq
assembly algorithms to integrate newly discovered genes and exons.
Besides isoform discovery, SLIDE sequentially uses the same linear
model to estimate the abundance of discovered isoforms. Simulation
and real data studies show that SLIDE performs as well as or better
than major competitors in both isoform discovery and abundance estimation.
The SLIDE software package is available at https://sites.google.com/site/jingyijli/SLIDE.zip.}},
citeulike-article-id = {10102447},
citeulike-linkout-0 = {http://dx.doi.org/10.1073/pnas.1113972108},
citeulike-linkout-1 = {http://www.pnas.org/content/early/2011/11/23/1113972108.abstract},
citeulike-linkout-2 = {http://www.pnas.org/content/early/2011/11/23/1113972108.full.pdf},
citeulike-linkout-3 = {http://www.pnas.org/cgi/content/abstract/108/50/19867},
citeulike-linkout-4 = {http://view.ncbi.nlm.nih.gov/pubmed/22135461},
citeulike-linkout-5 = {http://www.hubmed.org/display.cgi?uids=22135461},
day = {13},
doi = {10.1073/pnas.1113972108},
pdf = {../local/Li2011Sparse.pdf},
file = {Li2011Sparse.pdf:Li2011Sparse.pdf:PDF},
issn = {1091-6490},
keywords = {ngs, rnaseq},
pmid = {22135461},
posted-at = {2011-12-16 22:07:32},
priority = {2},
publisher = {National Academy of Sciences},
url = {http://dx.doi.org/10.1073/pnas.1113972108}
}
@article{Li2005robust,
author = {Li, L. and Jiang, W. and Li, X. and Moser, K.L. and Guo, Z. and Du,
L. and Wang, Q. and Topol, E.J. and Wang, Q. and Rao, S.},
title = {A robust hybrid between genetic algorithm and support vector machine
for extracting an optimal feature gene subset},
journal = {Genomics},
year = {2005},
volume = {85},
pages = {16-23},
number = {1},
abstract = {Development of a robust and efficient approach for extracting useful
information from microarray data continues to be a significant and
challenging task. {M}icroarray data are characterized by a high dimension,
high signal-to-noise ratio, and high correlations between genes,
but with a relatively small sample size. {C}urrent methods for dimensional
reduction can further be improved for the scenario of the presence
of a single (or a few) high influential gene(s) in which its effect
in the feature subset would prohibit inclusion of other important
genes. {W}e have formalized a robust gene selection approach based
on a hybrid between genetic algorithm and support vector machine.
{T}he major goal of this hybridization was to exploit fully their
respective merits (e.g., robustness to the size of solution space
and capability of handling a very large dimension of feature genes)
for identification of key feature genes (or molecular signatures)
for a complex biological phenotype. {W}e have applied the approach
to the microarray data of diffuse large {B} cell lymphoma to demonstrate
its behaviors and properties for mining the high-dimension data of
genome-wide gene expression profiles. {T}he resulting classifier(s)
(the optimal gene subset(s)) has achieved the highest accuracy (99%)
for prediction of independent microarray samples in comparisons with
marginal filters and a hybrid between genetic algorithm and {K} nearest
neighbors.},
doi = {10.1016/j.ygeno.2004.09.007},
pdf = {../local/Li2005robust.pdf},
file = {Li2005robust.pdf:local/Li2005robust.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://dx.doi.org/10.1016/j.ygeno.2004.09.007}
}
@article{Li2004Data,
author = {Li, L. and Tang, H. and Wu, Z. and Gong, J. and Gruidl, M. and Zou,
J. and Tockman, M. and Clark, R.A.},
title = {Data mining techniques for cancer detection using serum proteomic
profiling.},
journal = {Artif. {I}ntell. {M}ed.},
year = {2004},
volume = {32},
pages = {71-83},
number = {2},
abstract = {O{BJECTIVE}: {P}athological changes in an organ or tissue may be reflected
in proteomic patterns in serum. {I}t is possible that unique serum
proteomic patterns could be used to discriminate cancer samples from
non-cancer ones. {D}ue to the complexity of proteomic profiling,
a higher order analysis such as data mining is needed to uncover
the differences in complex proteomic patterns. {T}he objectives of
this paper are (1) to briefly review the application of data mining
techniques in proteomics for cancer detection/diagnosis; (2) to explore
a novel analytic method with different feature selection methods;
(3) to compare the results obtained on different datasets and that
reported by {P}etricoin et al. in terms of detection performance
and selected proteomic patterns. {METHODS} {AND} {MATERIAL}: {T}hree
serum {SELDI} {MS} data sets were used in this research to identify
serum proteomic patterns that distinguish the serum of ovarian cancer
cases from non-cancer controls. {A} support vector machine-based
method is applied in this study, in which statistical testing and
genetic algorithm-based methods are used for feature selection respectively.
{L}eave-one-out cross validation with receiver operating characteristic
({ROC}) curve is used for evaluation and comparison of cancer detection
performance. {RESULTS} {AND} {CONCLUSIONS}: {T}he results showed
that (1) data mining techniques can be successfully applied to ovarian
cancer detection with a reasonably high performance; (2) the classification
using features selected by the genetic algorithm consistently outperformed
those selected by statistical testing in terms of accuracy and robustness;
(3) the discriminatory features (proteomic patterns) can be very
different from one selection method to another. {I}n other words,
the pattern selection and its classification efficiency are highly
classifier dependent. {T}herefore, when using data mining techniques,
the discrimination of cancer from normal does not depend solely upon
the identity and origination of cancer-related proteins.},
doi = {10.1016/j.artmed.2004.03.006},
pdf = {../local/Li2004Data.pdf},
file = {Li2004Data.pdf:local/Li2004Data.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://dx.doi.org/10.1016/j.artmed.2004.03.006}
}
@article{Li2009SNP,
author = {Li, R. and Li, Y. and Fang, X. and Yang, H. and Wang, J. and Kristiansen,
K. and Wang, J.},
title = {{SNP} detection for massively parallel whole-genome resequencing.},
journal = {Genome Res.},
year = {2009},
volume = {19},
pages = {1124--1132},
number = {6},
month = {Jun},
abstract = {Next-generation massively parallel sequencing technologies provide
ultrahigh throughput at two orders of magnitude lower unit cost than
capillary Sanger sequencing technology. One of the key applications
of next-generation sequencing is studying genetic variation between
individuals using whole-genome or target region resequencing. Here,
we have developed a consensus-calling and SNP-detection method for
sequencing-by-synthesis Illumina Genome Analyzer technology. We designed
this method by carefully considering the data quality, alignment,
and experimental errors common to this technology. All of this information
was integrated into a single quality score for each base under Bayesian
theory to measure the accuracy of consensus calling. We tested this
methodology using a large-scale human resequencing data set of 36x
coverage and assembled a high-quality nonrepetitive consensus sequence
for 92.25\% of the diploid autosomes and 88.07\% of the haploid X
chromosome. Comparison of the consensus sequence with Illumina human
1M BeadChip genotyped alleles from the same DNA sample showed that
98.6\% of the 37,933 genotyped alleles on the X chromosome and 98\%
of 999,981 genotyped alleles on autosomes were covered at 99.97\%
and 99.84\% consistency, respectively. At a low sequencing depth,
we used prior probability of dbSNP alleles and were able to improve
coverage of the dbSNP sites significantly as compared to that obtained
using a nonimputation model. Our analyses demonstrate that our method
has a very low false call rate at any sequencing depth and excellent
genome coverage at a high sequencing depth.},
doi = {10.1101/gr.088013.108},
pdf = {../local/Li2009SNP.pdf},
file = {Li2009SNP.pdf:Li2009SNP.pdf:PDF},
institution = {Beijing Genomics Institute at Shenzhen, Shenzhen 518000, China},
keywords = {ngs},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {gr.088013.108},
pmid = {19420381},
timestamp = {2011.10.28},
url = {http://dx.doi.org/10.1101/gr.088013.108}
}
@article{Li2004Fusing,
author = {Shutao Li and James Tin-Yau Kwok and Ivor Wai-Hung Tsang and Yaonan
Wang},
title = {Fusing images with different focuses using support vector machines.},
journal = {I{EEE} {T}rans {N}eural {N}etw},
year = {2004},
volume = {15},
pages = {1555-61},
number = {6},
month = {Nov},
abstract = {Many vision-related processing tasks, such as edge detection, image
segmentation and stereo matching, can be performed more easily when
all objects in the scene are in good focus. {H}owever, in practice,
this may not be always feasible as optical lenses, especially those
with long focal lengths, only have a limited depth of field. {O}ne
common approach to recover an everywhere-in-focus image is to use
wavelet-based image fusion. {F}irst, several source images with different
focuses of the same scene are taken and processed with the discrete
wavelet transform ({DWT}). {A}mong these wavelet decompositions,
the wavelet coefficient with the largest magnitude is selected at
each pixel location. {F}inally, the fused image can be recovered
by performing the inverse {DWT}. {I}n this paper, we improve this
fusion procedure by applying the discrete wavelet frame transform
({DWFT}) and the support vector machines ({SVM}). {U}nlike {DWT},
{DWFT} yields a translation-invariant signal representation. {U}sing
features extracted from the {DWFT} coefficients, a {SVM} is trained
to select the source image that has the best focus at each pixel
location, and the corresponding {DWFT} coefficients are then incorporated
into the composite wavelet representation. {E}xperimental results
show that the proposed method outperforms the traditional approach
both visually and quantitatively.},
keywords = {Algorithms, Amino Acid, Amino Acids, Artificial Intelligence, Ascomycota,
Automated, Base Sequence, Chromosome Mapping, Codon, Colonic Neoplasms,
Comparative Study, Computer Simulation, Computer-Assisted, Computing
Methodologies, Crystallography, DNA, DNA Primers, Databases, Diagnostic
Imaging, Enzymes, Fixation, Gene Expression Profiling, Genetic, Hordeum,
Host-Parasite Relations, Humans, Image Enhancement, Image Interpretation,
Informatics, Information Storage and Retrieval, Kinetics, Magnetic
Resonance Spectroscopy, Models, Nanotechnology, Neural Networks (Computer),
Non-P.H.S., Non-U.S. Gov't, Ocular, Oligonucleotide Array Sequence
Analysis, P.H.S., Pattern Recognition, Plant, Plants, Predictive
Value of Tests, Protein, Protein Conformation, Research Support,
Sample Size, Selection (Genetics), Sequence Alignment, Sequence Analysis,
Sequence Homology, Signal Processing, Skin, Software, Statistical,
Subtraction Technique, Theoretical, Thermodynamics, U.S. Gov't, Viral
Proteins, X-Ray, 15565781}
}
@article{Li2004comparative,
author = {Li, T. and Zhang, C. and Ogihara, M.},
title = {A comparative study of feature selection and multiclass classification
methods for tissue classification based on gene expression},
journal = {Bioinformatics},
year = {2004},
volume = {20},
pages = {2429-2437},
number = {15},
abstract = {Summary: {T}his paper studies the problem of building multiclass classifiers
for tissue classification based on gene expression. {T}he recent
development of microarray technologies has enabled biologists to
quantify gene expression of tens of thousands of genes in a single
experiment. {B}iologists have begun collecting gene expression for
a large number of samples. {O}ne of the urgent issues in the use
of microarray data is to develop methods for characterizing samples
based on their gene expression. {T}he most basic step in the research
direction is binary sample classification, which has been studied
extensively over the past few years. {T}his paper investigates the
next step--multiclass classification of samples based on gene expression.
{T}he characteristics of expression data (e.g. large number of genes
with small sample size) makes the classification problem more challenging.
{T}he process of building multiclass classifiers is divided into
two components: (i) selection of the features (i.e. genes) to be
used for training and testing and (ii) selection of the classification
method. {T}his paper compares various feature selection methods as
well as various state-of-the-art classification methods on various
multiclass gene expression datasets. {O}ur study indicates that multiclass
classification problem is much more difficult than the binary one
for the gene expression datasets. {T}he difficulty lies in the fact
that the data are of high dimensionality and that the sample size
is small. {T}he classification accuracy appears to degrade very rapidly
as the number of classes increases. {I}n particular, the accuracy
was very low regardless of the choices of the methods for large-class
datasets (e.g. {NCI}60 and {GCM}). {W}hile increasing the number
of samples is a plausible solution to the problem of accuracy degradation,
it is important to develop algorithms that are able to analyze effectively
multiple-class expression data for these special datasets.},
pdf = {../local/Li2004comparative.pdf},
file = {Li2004comparative.pdf:local/Li2004comparative.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://bioinformatics.oupjournals.org/cgi/content/abstract/20/15/2429}
}
@article{Li2011IsoLasso,
author = {Li, W. and Feng, J. and Jiang, T.},
title = {IsoLasso: a {LASSO} regression approach to {RNA-Seq} based transcriptome
assembly.},
journal = {J Comput Biol},
year = {2011},
volume = {18},
pages = {1693--1707},
number = {11},
month = {Nov},
__markedentry = {[jp:6]},
abstract = {The new second generation sequencing technology revolutionizes many
biology-related research fields and poses various computational biology
challenges. One of them is transcriptome assembly based on RNA-Seq
data, which aims at reconstructing all full-length mRNA transcripts
simultaneously from millions of short reads. In this article, we
consider three objectives in transcriptome assembly: the maximization
of prediction accuracy, minimization of interpretation, and maximization
of completeness. The first objective, the maximization of prediction
accuracy, requires that the estimated expression levels based on
assembled transcripts should be as close as possible to the observed
ones for every expressed region of the genome. The minimization of
interpretation follows the parsimony principle to seek as few transcripts
in the prediction as possible. The third objective, the maximization
of completeness, requires that the maximum number of mapped reads
(or ?expressed segments? in gene models) be explained by (i.e., contained
in) the predicted transcripts in the solution. Based on the above
three objectives, we present IsoLasso, a new RNA-Seq based transcriptome
assembly tool. IsoLasso is based on the well-known LASSO algorithm,
a multivariate regression method designated to seek a balance between
the maximization of prediction accuracy and the minimization of interpretation.
By including some additional constraints in the quadratic program
involved in LASSO, IsoLasso is able to make the set of assembled
transcripts as complete as possible. Experiments on simulated and
real RNA-Seq datasets show that IsoLasso achieves, simultaneously,
higher sensitivity and precision than the state-of-art transcript
assembly tools.},
doi = {10.1089/cmb.2011.0171},
pdf = {../local/Li2011IsoLasso.pdf},
file = {Li2011IsoLasso.pdf:Li2011IsoLasso.pdf:PDF},
institution = {Department of Computer Science and Engineering, University of California,
Riverside, Riverside, CA 92507, USA. liw@cs.ucr.edu},
keywords = {ngs, rnaseq},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pmid = {21951053},
timestamp = {2013.03.29},
url = {http://dx.doi.org/10.1089/cmb.2011.0171}
}
@inproceedings{Li2003Learning,
author = {Li, X. and Liu, B.},
title = {Learning to classify texts using positive and unlabeled data},
booktitle = {IJCAI'03: Proceedings of the 18th international joint conference
on Artificial intelligence},
year = {2003},
editor = {Gottlob, G. and Walsh, T.},
pages = {587--592},
address = {San Francisco, CA, USA},
publisher = {Morgan Kaufmann Publishers Inc.},
abstract = {In traditional text classification, a classifier is built using labeled
training documents of every class. This paper studies a different
problem. Given a set P of documents of a particular class (called
positive class) and a set U of unlabeled documents that contains
documents from class P and also other types of documents (called
negative class documents), we want to build a classifier to classify
the documents in U into documents from P and documents not from P.
The key feature of this problem is that there is no labeled negative
document, which makes traditional text classification techniques
inapplicable. In this paper, we propose an effective technique to
solve the problem. It combines the Rocchio method and the SVM technique
for classifier building. Experimental results show that the new method
outperforms existing methods significantly.},
owner = {jp},
timestamp = {2010.01.31}
}
@article{Liang2001Detection,
author = {H. Liang and Z. Lin},
title = {Detection of delayed gastric emptying from electrogastrograms with
support vector machine.},
journal = {I{EEE} {T}rans {B}iomed {E}ng},
year = {2001},
volume = {48},
pages = {601-4},
number = {5},
month = {May},
abstract = {A recent study reported a conventional neural network ({NN}) approach
for the noninvasive diagnosis of delayed gastric emptying from the
cutaneous electrogastrograms. {U}sing support vector machine, we
show that this relatively new technique can be used for detection
of delayed gastric emptying and is in fact able to outdo the conventional
{NN}.},
keywords = {Algorithms, Amino Acid Sequence, Artificial Intelligence, Biological,
Cell Compartmentation, Comparative Study, Computer Simulation, Computer-Assisted,
Decision Trees, Diagnosis, Discriminant Analysis, Electrophysiology,
Gastric Emptying, Humans, Logistic Models, Melanoma, Models, Neural
Networks (Computer), Nevus, Non-U.S. Gov't, Organelles, P.H.S., Pigmented,
Predictive Value of Tests, Proteins, Reproducibility of Results,
Research Support, Skin Diseases, Skin Neoplasms, Skin Pigmentation,
Stomach Diseases, U.S. Gov't, 11341535}
}
@article{Liang2008Gene,
author = {Liang, K-C. and Wang, X.},
title = {Gene regulatory network reconstruction using conditional mutual information.},
journal = {EURASIP J Bioinform Syst Biol},
year = {2008},
pages = {253894},
abstract = {The inference of gene regulatory network from expression data is an
important area of research that provides insight to the inner workings
of a biological system. The relevance-network-based approaches provide
a simple and easily-scalable solution to the understanding of interaction
between genes. Up until now, most works based on relevance network
focus on the discovery of direct regulation using correlation coefficient
or mutual information. However, some of the more complicated interactions
such as interactive regulation and co-regulation are not easily detected.
In this work, we propose a relevance network model for gene regulatory
network inference which employs both mutual information and conditional
mutual information to determine the interactions between genes. For
this purpose, we propose a conditional mutual information estimator
based on adaptive partitioning which allows us to condition on both
discrete and continuous random variables. We provide experimental
results that demonstrate that the proposed regulatory network inference
algorithm can provide better performance when the target network
contains coregulated and interactively regulated genes.},
doi = {10.1155/2008/253894},
institution = {Department of Electrical Engineering, Columbia University, New York,
NY 10027, USA.},
owner = {fantine},
pmid = {18584050},
timestamp = {2010.10.21},
url = {http://dx.doi.org/10.1155/2008/253894}
}
@article{Liang1998Reveal,
author = {Liang, S. and Fuhrman, S. and Somogyi, R.},
title = {{REVEAL}, a general reverse engineering algorithm for inference of
genetic network architectures},
journal = {Pac. Symp. Biocomput.},
year = {1998},
volume = {3},
pages = {18--29},
abstract = {Given the immanent gene expression mapping covering whole genomes
during development, health and disease, we seek computational methods
to maximize functional inference from such large data sets. Is it
possible, in principle, to completely infer a complex regulatory
network architecture from input/output patterns of its variables?
We investigated this possibility using binary models of genetic networks.
Trajectories, or state transition tables of Boolean nets, resemble
time series of gene expression. By systematically analyzing the mutual
information between input states and output states, one is able to
infer the sets of input elements controlling each element or gene
in the network. This process is unequivocal and exact for complete
state transition tables. We implemented this REVerse Engineering
ALgorithm (REVEAL) in a C program, and found the problem to be tractable
within the conditions tested so far. For n = 50 (elements) and k
= 3 (inputs per element), the analysis of incomplete state transition
tables (100 state transition pairs out of a possible 10(15)) reliably
produced the original rule and wiring sets. While this study is limited
to synchronous Boolean networks, the algorithm is generalizable to
include multi-state models, essentially allowing direct application
to realistic biological data sets. The ability to adequately solve
the inverse problem may enable in-depth analysis of complex dynamic
systems in biology and other fields.},
pdf = {../local/Liang1998Reveal.pdf},
file = {Liang1998Reveal.pdf:Liang1998Reveal.pdf:PDF},
institution = {SETI Institute, NASA Ames Research Center, Moffett Field, CA 94035,
USA. sliang@mail.arc.nasa.gov},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pmid = {9697168},
timestamp = {2012.04.03}
}
@article{Liao2003Network,
author = {Liao, J. C. and Boscolo, R. and Yang, Y.-L. and Tran, L. M. and Sabatti,
C. and Roychowdhury, V. P.},
title = {Network component analysis: Reconstruction of regulatory signals
in biological systems},
journal = {Proc. Natl. Acad. Sci. USA},
year = {2003},
volume = {100},
pages = {15522--15527},
number = {26},
doi = {10.1073/pnas.2136632100},
pdf = {../local/Liao2003Network.pdf},
file = {Liao2003Network.pdf:Liao2003Network.pdf:PDF},
owner = {jp},
timestamp = {2011.07.23},
url = {http://dx.doi.org/10.1073/pnas.2136632100}
}
@article{Liao2003Combining,
author = {Liao, L. and Noble, W.S.},
title = {Combining {P}airwise {S}equence {S}imilarity and {S}upport {V}ector
{M}achines for {D}etecting {R}emote {P}rotein {E}volutionary and
{S}tructural {R}elationships},
journal = {J. {C}omput. {B}iol.},
year = {2003},
volume = {10},
pages = {857-868},
number = {6},
abstract = {One key element in understanding the molecular machinery of the cell
is to understand the structure and function of each protein encoded
in the genome. {A} very successful means of inferring the structure
or function of a previously unannotated protein is via sequence similarity
with one or more proteins whose structure or function is already
known. {T}oward this end, we propose a means of representing proteins
using pairwise sequence similarity scores. {T}his representation,
combined with a discriminative classification algorithm known as
the support vector machine ({SVM}), provides a powerful means of
detecting subtle structural and evolutionary relationships among
proteins. {T}he algorithm, called {SVM}-pairwise, when tested on
its ability to recognize previously unseen families from the {SCOP}
database, yields significantly better performance than {SVM}-{F}isher,
profile {HMM}s, and {PSI}-{BLAST}.},
pdf = {../local/Liao2003Combining.pdf},
file = {Liao2003Combining.pdf:local/Liao2003Combining.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://www.liebertonline.com/doi/abs/10.1089/106652703322756113}
}
@inproceedings{Liao2002Combining,
author = {Liao, L. and Noble, W. S.},
title = {Combining pairwise sequence similarity and support vector machines
for remote protein homology detection},
booktitle = {Proceedings of the {S}ixth {I}nternational {C}onference on {C}omputational
{M}olecular {B}iology},
year = {2002},
pdf = {../local/liao02.pdf},
file = {liao02.pdf:local/liao02.pdf:PDF},
keywords = {biosvm},
subject = {biokernelcasp},
url = {http://www.cs.columbia.edu/~bgrundy/papers/fps-svm.html}
}
@article{Liaw2002Classification,
author = {Liaw, A. and Wiener, M.},
title = {{Classification and Regression by randomForest}},
journal = {R News},
year = {2002},
volume = {2},
pages = {18--22},
number = {3},
owner = {jp},
timestamp = {2012.07.31}
}
@article{Liben-Nowell2007link-prediction,
author = {Liben-Nowell, D. and Kleinberg, J.},
title = {The link-prediction problem for social networks},
journal = {J. Am. Soc. Inf. Sci. Technol.},
year = {2007},
volume = {58},
pages = {1019--1031},
number = {7},
month = {May},
doi = {10.1002/asi.v58:7},
owner = {jp},
timestamp = {2011.09.20},
url = {http://dx.doi.org/10.1002/asi.v58:7}
}
@article{Liberles2002use,
author = {Liberles, D. A. and Thor{\'e}n, A. and von Heijne, G. and Elofsson,
A.},
title = {The use of phylogenetic profiles for gene predictions},
journal = {Curr. {G}enom.},
year = {2002},
note = {To appear},
pdf = {../local/libe02.pdf},
file = {libe02.pdf:local/libe02.pdf:PDF},
subject = {bio},
url = {http://www.sbc.su.se/~arne/papers/phylo.pdf}
}
@article{Lieberman-Aiden2009Comprehensive,
author = {Erez Lieberman-Aiden and Nynke L van Berkum and Louise Williams and
Maxim Imakaev and Tobias Ragoczy and Agnes Telling and Ido Amit and
Bryan R Lajoie and Peter J Sabo and Michael O Dorschner and Richard
Sandstrom and Bradley Bernstein and M. A. Bender and Mark Groudine
and Andreas Gnirke and John Stamatoyannopoulos and Leonid A Mirny
and Eric S Lander and Job Dekker},
title = {Comprehensive mapping of long-range interactions reveals folding
principles of the human genome.},
journal = {Science},
year = {2009},
volume = {326},
pages = {289--293},
number = {5950},
month = {Oct},
abstract = {We describe Hi-C, a method that probes the three-dimensional architecture
of whole genomes by coupling proximity-based ligation with massively
parallel sequencing. We constructed spatial proximity maps of the
human genome with Hi-C at a resolution of 1 megabase. These maps
confirm the presence of chromosome territories and the spatial proximity
of small, gene-rich chromosomes. We identified an additional level
of genome organization that is characterized by the spatial segregation
of open and closed chromatin to form two genome-wide compartments.
At the megabase scale, the chromatin conformation is consistent with
a fractal globule, a knot-free, polymer conformation that enables
maximally dense packing while preserving the ability to easily fold
and unfold any genomic locus. The fractal globule is distinct from
the more commonly used globular equilibrium model. Our results demonstrate
the power of Hi-C to map the dynamic conformations of whole genomes.},
doi = {10.1126/science.1181369},
pdf = {../local/Lieberman-Aiden2009Comprehensive.pdf},
file = {Lieberman-Aiden2009Comprehensive.pdf:Lieberman-Aiden2009Comprehensive.pdf:PDF},
institution = {Broad Institute of Harvard and Massachusetts Institute of Technology
(MIT), MA 02139, USA.},
keywords = {hic, ngs},
owner = {phupe},
pii = {326/5950/289},
pmid = {19815776},
timestamp = {2010.08.26},
url = {http://dx.doi.org/10.1126/science.1181369}
}
@article{Liebermeister2002Linear,
author = {Liebermeister, W.},
title = {Linear modes of gene expression determined by independent component
analysis.},
journal = {Bioinformatics},
year = {2002},
volume = {18},
pages = {51--60},
number = {1},
month = {Jan},
abstract = {MOTIVATION: The expression of genes is controlled by specific combinations
of cellular variables. We applied Independent Component Analysis
(ICA) to gene expression data, deriving a linear model based on hidden
variables, which we term 'expression modes'. The expression of each
gene is a linear function of the expression modes, where, according
to the ICA model, the linear influences of different modes show a
minimal statistical dependence, and their distributions deviate sharply
from the normal distribution. RESULTS: Studying cell cycle-related
gene expression in yeast, we found that the dominant expression modes
could be related to distinct biological functions, such as phases
of the cell cycle or the mating response. Analysis of human lymphocytes
revealed modes that were related to characteristic differences between
cell types. With both data sets, the linear influences of the dominant
modes showed distributions with large tails, indicating the existence
of specifically up- and downregulated target genes. The expression
modes and their influences can be used to visualize the samples and
genes in low-dimensional spaces. A projection to expression modes
helps to highlight particular biological functions, to reduce noise,
and to compress the data in a biologically sensible way.},
doi = {10.1093/bioinformatics/18.1.51},
pdf = {../local/Liebermeister2002Linear.pdf},
file = {Liebermeister2002Linear.pdf:Liebermeister2002Linear.pdf:PDF},
institution = {Theoretische Biophysik, Institut für Biologie, Humboldt-Universität
zu Berlin, Invalidenstrasse 42, 10115 Berlin, Germany. wolfram.liebermeister@rz-hu-berlin.de},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pmid = {11836211},
timestamp = {2011.12.30},
url = {http://dx.doi.org/10.1093/bioinformatics/18.1.51}
}
@article{Lievens2009Mammalian,
author = {Sam Lievens and Irma Lemmens and Jan Tavernier},
title = {Mammalian two-hybrids come of age.},
journal = {Trends Biochem Sci},
year = {2009},
volume = {34},
pages = {579--588},
number = {11},
month = {Nov},
abstract = {A diverse series of mammalian two-hybrid technologies for the detection
of protein-protein interactions have emerged in the past few years,
complementing the established yeast two-hybrid approach. Given the
mammalian background in which they operate, these assays open new
avenues to study the dynamics of mammalian protein interaction networks,
i.e. the temporal, spatial and functional modulation of protein-protein
associations. In addition, novel assay formats are available that
enable high-throughput mammalian two-hybrid applications, facilitating
their use in large-scale interactome mapping projects. Finally, as
they can be applied in drug discovery and development programs, these
techniques also offer exciting new opportunities for biomedical research.},
doi = {10.1016/j.tibs.2009.06.009},
institution = {Department of Medical Protein Research, VIB, A. Baertsoenkaai 3,
9000 Ghent, Belgium},
keywords = {Animals; Genes, Reporter; Humans; Models, Biological; Protein Binding;
Protein Interaction Mapping; Proteins; Recombinant Fusion Proteins;
Transfection; Two-Hybrid System Techniques},
owner = {phupe},
pii = {S0968-0004(09)00158-3},
pmid = {19786350},
timestamp = {2010.08.31},
url = {http://dx.doi.org/10.1016/j.tibs.2009.06.009}
}
@article{Lim2005Microarray,
author = {Lee P Lim and Nelson C Lau and Philip Garrett-Engele and Andrew Grimson
and Janell M Schelter and John Castle and David P Bartel and Peter
S Linsley and Jason M Johnson},
title = {Microarray analysis shows that some microRNAs downregulate large
numbers of target mRNAs.},
journal = {Nature},
year = {2005},
volume = {433},
pages = {769--773},
number = {7027},
month = {Feb},
abstract = {MicroRNAs (miRNAs) are a class of noncoding RNAs that post-transcriptionally
regulate gene expression in plants and animals. To investigate the
influence of miRNAs on transcript levels, we transfected miRNAs into
human cells and used microarrays to examine changes in the messenger
RNA profile. Here we show that delivering miR-124 causes the expression
profile to shift towards that of brain, the organ in which miR-124
is preferentially expressed, whereas delivering miR-1 shifts the
profile towards that of muscle, where miR-1 is preferentially expressed.
In each case, about 100 messages were downregulated after 12 h. The
3' untranslated regions of these messages had a significant propensity
to pair to the 5' region of the miRNA, as expected if many of these
messages are the direct targets of the miRNAs. Our results suggest
that metazoan miRNAs can reduce the levels of many of their target
transcripts, not just the amount of protein deriving from these transcripts.
Moreover, miR-1 and miR-124, and presumably other tissue-specific
miRNAs, seem to downregulate a far greater number of targets than
previously appreciated, thereby helping to define tissue-specific
gene expression in humans.},
doi = {10.1038/nature03315},
institution = {Rosetta Inpharmatics, Merck and Co, 401 Terry Avenue N, Seattle,
Washington 98109, USA. lee_lim@merck.com},
keywords = {sirna},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {nature03315},
pmid = {15685193},
timestamp = {2009.10.28},
url = {http://dx.doi.org/10.1038/nature03315}
}
@article{Lima-Mendez2009powerful,
author = {Lima-Mendez, G. and van Helden, J.},
title = {The powerful law of the power law and other myths in network biology.},
journal = {Mol Biosyst},
year = {2009},
volume = {5},
pages = {1482--1493},
number = {12},
month = {Dec},
abstract = {For almost 10 years, topological analysis of different large-scale
biological networks (metabolic reactions, protein interactions, transcriptional
regulation) has been highlighting some recurrent properties: power
law distribution of degree, scale-freeness, small world, which have
been proposed to confer functional advantages such as robustness
to environmental changes and tolerance to random mutations. Stochastic
generative models inspired different scenarios to explain the growth
of interaction networks during evolution. The power law and the associated
properties appeared so ubiquitous in complex networks that they were
qualified as "universal laws". However, these properties are no longer
observed when the data are subjected to statistical tests: in most
cases, the data do not fit the expected theoretical models, and the
cases of good fitting merely result from sampling artefacts or improper
data representation. The field of network biology seems to be founded
on a series of myths, i.e. widely believed but false ideas. The weaknesses
of these foundations should however not be considered as a failure
for the entire domain. Network analysis provides a powerful frame
for understanding the function and evolution of biological processes,
provided it is brought to an appropriate level of description, by
focussing on smaller functional modules and establishing the link
between their topological properties and their dynamical behaviour.},
doi = {10.1039/b908681a},
institution = {Bioinformatique des Génomes et des Réseaux-BiGRe, Université Libre
de Bruxelles, Campus Plaine, CP 263, Boulevard du Triomphe, B-1050
Bruxelles, Belgium. gipsi@bigre.ulb.ac.be},
keywords = {Computational Biology, methods; Gene Regulatory Networks; Metabolic
Networks and Pathways; Models, Biological; Semantics; Signal Transduction},
language = {eng},
medline-pst = {ppublish},
owner = {Andrei Zinovyev},
pmid = {20023717},
timestamp = {2011.04.07},
url = {http://dx.doi.org/10.1039/b908681a}
}
@article{Lin2002Conserved,
author = {Lin, K. and Kuang, Y. and Joseph, J. S. and Kolatkar, P. R.},
title = {Conserved codon composition of ribosomal protein coding genes in
{E}scherichia coli, {M}ycobacterium tuberculosis and {S}accharomyces
cerevisiae: lessons from supervised machine learning in functional
genomics},
journal = {Nucl. {A}cids {R}es.},
year = {2002},
volume = {30},
pages = {2599-2607},
number = {11},
abstract = {Genomics projects have resulted in a flood of sequence data. {F}unctional
annotation currently relies almost exclusively on inter-species sequence
comparison and is restricted in cases of limited data from related
species and widely divergent sequences with no known homologs. {H}ere,
we demonstrate that codon composition, a fusion of codon usage bias
and amino acid composition signals, can accurately discriminate,
in the absence of sequence homology information, cytoplasmic ribosomal
protein genes from all other genes of known function in {S}accharomyces
cerevisiae, {E}scherichia coli and {M}ycobacterium tuberculosis using
an implementation of support vector machines, {SVM}light. {A}nalysis
of these codon composition signals is instructive in determining
features that confer individuality to ribosomal protein genes. {E}ach
of the sets of positively charged, negatively charged and small hydrophobic
residues, as well as codon bias, contribute to their distinctive
codon composition profile. {T}he representation of all these signals
is sensitively detected, combined and augmented by the {SVM}s to
perform an accurate classification. {O}f special mention is an obvious
outlier, yeast gene {RPL}22{B}, highly homologous to {RPL}22{A} but
employing very different codon usage, perhaps indicating a non-ribosomal
function. {F}inally, we propose that codon composition be used in
combination with other attributes in gene/protein classification
by supervised machine learning algorithms.},
pdf = {../local/Lin2002Conserved.pdf},
file = {Lin2002Conserved.pdf:local/Lin2002Conserved.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://nar.oupjournals.org/cgi/content/abstract/30/11/2599}
}
@article{Lin73Effective,
author = {S. Lin and B. W. Kernighan},
title = {An Effective Heuristic Algorithm for the Traveling-Salesman Problem},
journal = {Operations Res.},
year = {1973},
volume = {21},
pages = {498--516}
}
@article{Lin2004Orphan,
author = {Lin, S. H. S. and Civelli, O.},
title = {Orphan {G} protein-coupled receptors: targets for new therapeutic
interventions.},
journal = {Ann. Med.},
year = {2004},
volume = {36},
pages = {204--214},
number = {3},
abstract = {With the completion of the human genome, many genes will be uncovered
with unknown functions. The 'orphan' G protein coupled receptors
(GPCRs) are examples of genes without known functions. These are
genes that exhibit the seven helical conformation hallmark of the
GPCRs but that are called 'orphans' because they are activated by
none of the primary messengers known to activate GPCRs in vivo. They
are the targets of undiscovered transmitters and this lack of knowledge
precludes understanding their function. Yet, because they belong
to the supergene family that has the widest regulatory role in the
organism, the orphan GPCRs have generated much excitement in academia
and industry. They hold much hope for revealing new intercellular
interactions that will open new areas of basic research which ultimately
will lead to new therapeutic applications. However, the first step
in understanding the function of orphan GPCRs is to 'deorphanize'
them, to identify their natural transmitters. Here we review the
search for the natural primary messengers of orphan GPCRs and focus
on two recently deorphanized GPCR systems, the melanin-concentrating
hormone (MCH) and prolactin-releasing peptide (PrRP) systems, to
illustrate the strategies applied to solve their function and to
exemplify the therapeutic potentials that such systems hold.},
doi = {10.1080/07853890310024668},
keywords = {chemogenomics},
owner = {laurent},
pmid = {15181976},
timestamp = {2008.01.16},
url = {http://dx.doi.org/10.1080/07853890310024668}
}
@article{Lin2004Adaptive,
author = {Tzu-Chao Lin and Pao-Ta Yu},
title = {Adaptive two-pass median filter based on support vector machines
for image restoration.},
journal = {Neural {C}omput},
year = {2004},
volume = {16},
pages = {332-53},
number = {2},
month = {Feb},
abstract = {In this letter, a novel adaptive filter, the adaptive two-pass median
({ATM}) filter based on support vector machines ({SVM}s), is proposed
to preserve more image details while effectively suppressing impulse
noise for image restoration. {T}he proposed filter is composed of
a noise decision maker and two-pass median filters. {O}ur new approach
basically uses an {SVM} impulse detector to judge whether the input
pixel is noise. {I}f a pixel is detected as a corrupted pixel, the
noise-free reduction median filter will be triggered to replace it.
{O}therwise, it remains unchanged. {T}hen, to improve the quality
of the restored image, a decision impulse filter is put to work in
the second-pass filtering procedure. {A}s for the noise suppressing
both fixed-valued and random-valued impulses without degrading the
quality of the fine details, the results of our extensive experiments
demonstrate that the proposed filter outperforms earlier median-based
filters in the literature. {O}ur new filter also provides excellent
robustness at various percentages of impulse noise.},
doi = {10.1162/089976604322742056},
keywords = {Adaptation, Algorithms, Ambergris, Animals, Artifacts, Artificial
Intelligence, Automated, Cadmium, Candida, Candida albicans, Capillary,
Cluster Analysis, Combinatorial Chemistry Techniques, Computer-Assisted,
Electrophoresis, Eye Enucleation, Humans, Image Processing, Magnetic
Resonance Spectroscopy, Melanoma, Models, Molecular, Molecular Conformation,
Neural Networks (Computer), Non-U.S. Gov't, Nonlinear Dynamics, Odors,
P.H.S., Pattern Recognition, Perfume, Physiological, Predictive Value
of Tests, Prognosis, Prospective Studies, Quantitative Structure-Activity
Relationship, Rats, Research Support, Signal Processing, U.S. Gov't,
Uveal Neoplasms, Visual, 15006099},
url = {http://dx.doi.org/10.1162/089976604322742056}
}
@article{Lin2004Classification,
author = {WuMei Lin and Xin Yuan and Powing Yuen and William I Wei and Jonathan
Sham and PengCheng Shi and Jianan Qu},
title = {Classification of in vivo autofluorescence spectra using support
vector machines.},
journal = {J {B}iomed {O}pt},
year = {2004},
volume = {9},
pages = {180-6},
number = {1},
abstract = {An algorithm based on support vector machines ({SVM}), the most recent
advance in pattern recognition, is presented for use in classifying
light-induced autofluorescence collected from cancerous and normal
tissues. {T}he in vivo autofluorescence spectra used for development
and evaluation of {SVM} diagnostic algorithms were measured from
85 nasopharyngeal carcinoma ({NPC}) lesions and 131 normal tissue
sites from 59 subjects during routine nasal endoscopy. {L}eave-one-out
cross-validation was used to evaluate the performance of the algorithms.
{A}n overall diagnostic accuracy of 96\%, a sensitivity of 94\%,
and a specificity of 97\% for discriminating nasopharyngeal carcinomas
from normal tissues were achieved using a linear {SVM} algorithm.
{A} diagnostic accuracy of 98\%, a sensitivity of 95\%, and a specificity
of 99\% for detecting {NPC} were achieved with a nonlinear {SVM}
algorithm. {I}n a comparison with previously developed algorithms
using the same dataset and the principal component analysis ({PCA})
technique, the {SVM} algorithms produced better diagnostic accuracy
in all instances. {I}n addition, we investigated a method combining
{PCA} and {SVM} techniques for reducing the complexity of the {SVM}
algorithms.},
doi = {10.1117/1.1628244},
pdf = {../local/Lin2004Classification.pdf},
file = {Lin2004Classification.pdf:local/Lin2004Classification.pdf:PDF},
url = {http://dx.doi.org/10.1117/1.1628244}
}
@article{Lin2002Support,
author = {Lin, Y.},
title = {Support vector machines and the {B}ayes rule in classification},
journal = {Data {M}ining and {K}nowledge {D}iscovery},
year = {2002},
volume = {6},
pages = {259-275},
number = {3},
doi = {10.1023/A:1015469627679},
pdf = {../local/Lin2002Support.pdf},
file = {Lin2002Support.pdf:local/Lin2002Support.pdf:PDF},
owner = {jeanphilippevert},
url = {http://dx.doi.org/10.1023/A:1015469627679}
}
@article{Lind2003Support,
author = {P. Lind and T. Maltseva},
title = {Support vector machines for the estimation of aqueous solubility.},
journal = {J {C}hem {I}nf {C}omput {S}ci},
year = {2003},
volume = {43},
pages = {1855-9},
number = {6},
abstract = {Support {V}ector {M}achines ({SVM}s) are used to estimate aqueous
solubility of organic compounds. {A} {SVM} equipped with a {T}animoto
similarity kernel estimates solubility with accuracy comparable to
results from other reported methods where the same data sets have
been studied. {C}omplete cross-validation on a diverse data set resulted
in a root-mean-squared error = 0.62 and {R}(2) = 0.88. {T}he data
input to the machine is in the form of molecular fingerprints. {N}o
physical parameters are explicitly involved in calculations.},
doi = {10.1021/ci034107s},
pdf = {../local/Lind2003Support.pdf},
file = {Lind2003Support.pdf:local/Lind2003Support.pdf:PDF},
keywords = {biosvm chemoinformatics},
url = {http://dx.doi.org/10.1021/ci034107s}
}
@article{Linghu2009Genome-wide,
author = {Linghu, B. and Snitkin, E.S. and Hu, Z. and Xia, Y. and Delisi, C.},
title = {Genome-wide prioritization of disease genes and identification of
disease-disease associations from an integrated human functional
linkage network.},
journal = {Genome Biol.},
year = {2009},
volume = {10},
pages = {R91},
number = {9},
abstract = {We integrate 16 genomic features to construct an evidence-weighted
functional-linkage network comprising 21,657 human genes. The functional-linkage
network is used to prioritize candidate genes for 110 diseases, and
to reliably disclose hidden associations between disease pairs having
dissimilar phenotypes, such as hypercholesterolemia and Alzheimer's
disease. Many of these disease-disease associations are supported
by epidemiology, but with no previous genetic basis. Such associations
can drive novel hypotheses on molecular mechanisms of diseases and
therapies.},
doi = {10.1186/gb-2009-10-9-r91},
institution = {Bioinformatics Program, Boston University, 24 Cummington Street,
Boston, MA 02215, USA. blinghu@bu.edu},
owner = {mordelet},
pii = {gb-2009-10-9-r91},
pmid = {19728866},
timestamp = {2010.09.28},
url = {http://dx.doi.org/10.1186/gb-2009-10-9-r91}
}
@article{Lipinski2001Experimental,
author = {Lipinski, C. A. and Lombardo, F. and Dominy, B. W. and Feeney, P.
J.},
title = {Experimental and computational approaches to estimate solubility
and permeability in drug discovery and development settings},
journal = {Adv. {D}rug. {D}eliv. {R}ev},
year = {2001},
volume = {46},
pages = {3--26},
number = {1-3},
month = {Mar},
abstract = {Experimental and computational approaches to estimate solubility and
permeability in discovery and development settings are described.
{I}n the discovery setting 'the rule of 5' predicts that poor absorption
or permeation is more likely when there are more than 5 {H}-bond
donors, 10 {H}-bond acceptors, the molecular weight ({MWT}) is greater
than 500 and the calculated {L}og {P} ({CL}og{P}) is greater than
5 (or {M}log{P} > 4.15). {C}omputational methodology for the rule-based
{M}origuchi {L}og {P} ({ML}og{P}) calculation is described. {T}urbidimetric
solubility measurement is described and applied to known drugs. {H}igh
throughput screening ({HTS}) leads tend to have higher {MWT} and
{L}og {P} and lower turbidimetric solubility than leads in the pre-{HTS}
era. {I}n the development setting, solubility calculations focus
on exact value prediction and are difficult because of polymorphism.
{R}ecent work on linear free energy relationships and {L}og {P} approaches
are critically reviewed. {U}seful predictions are possible in closely
related analog series when coupled with experimental thermodynamic
solubility measurements.},
keywords = {chemoinformatics},
owner = {mahe},
pii = {S0169409X00001290},
pmid = {11259830},
timestamp = {2006.02.03}
}
@article{Listgarten2004Predictive,
author = {Listgarten, J. and Damaraju, S. and Poulin, B. and Cook, L. and Dufour,
J. and Driga, A. and Mackey, J. and Wishart, D. and Greiner, R. and
Zanke, B.},
title = {Predictive {M}odels for {B}reast {C}ancer {S}usceptibility from {M}ultiple
{S}ingle {N}ucleotide {P}olymorphisms},
journal = {Clin. {C}ancer {R}es.},
year = {2004},
volume = {10},
pages = {2725-2737},
number = {8},
abstract = {Hereditary predisposition and causative environmental exposures have
long been recognized in human malignancies. {I}n most instances,
cancer cases occur sporadically, suggesting that environmental influences
are critical in determining cancer risk. {T}o test the influence
of genetic polymorphisms on breast cancer risk, we have measured
98 single nucleotide polymorphisms ({SNP}s) distributed over 45 genes
of potential relevance to breast cancer etiology in 174 patients
and have compared these with matched normal controls. {U}sing machine
learning techniques such as support vector machines ({SVM}s), decision
trees, and naive {B}ayes, we identified a subset of three {SNP}s
as key discriminators between breast cancer and controls. {T}he {SVM}s
performed maximally among predictive models, achieving 69% predictive
power in distinguishing between the two groups, compared with a 50%
baseline predictive power obtained from the data after repeated random
permutation of class labels (individuals with cancer or controls).
{H}owever, the simpler naive {B}ayes model as well as the decision
tree model performed quite similarly to the {SVM}. {T}he three {SNP}
sites most useful in this model were (a) the +4536{T}/{C} site of
the aldosterone synthase gene {CYP}11{B}2 at amino acid residue 386
{V}al/{A}la ({T}/{C}) (rs4541); (b) the +4328{C}/{G} site of the
aryl hydrocarbon hydroxylase {CYP}1{B}1 at amino acid residue 293
{L}eu/{V}al ({C}/{G}) (rs5292); and (c) the +4449{C}/{T} site of
the transcription factor {BCL}6 at amino acid 387 {A}sp/{A}sp (rs1056932).
{N}o single {SNP} site on its own could achieve more than 60% in
predictive accuracy. {W}e have shown that multiple {SNP} sites from
different genes over distant parts of the genome are better at identifying
breast cancer patients than any one {SNP} alone. {A}s high-throughput
technology for {SNP}s improves and as more {SNP}s are identified,
it is likely that much higher predictive accuracy will be achieved
and a useful clinical tool developed.},
eprint = {http://clincancerres.aacrjournals.org/cgi/reprint/10/8/2725.pdf},
pdf = {../local/Listgarten2004Predictive.pdf},
file = {Listgarten2004Predictive.pdf:local/Listgarten2004Predictive.pdf:PDF},
keywords = {biosvm, breastcancer},
owner = {jeanphilippevert},
url = {http://clincancerres.aacrjournals.org/cgi/content/abstract/10/8/2725}
}
@inproceedings{Liu2003Building,
author = {Liu, B. and Dai, Y. and Li, X. and Lee, W. S. and Yu, P. S.},
title = {Building Text Classifiers Using Positive and Unlabeled Examples},
booktitle = {Proceedings of the Third IEEE International Conference on Data Mining},
year = {2003},
editor = {Wu, X. and Tuzhilin, A. and Shavlik, J.},
series = {ICDM '03},
pages = {179--186},
address = {Washington, DC, USA},
publisher = {IEEE Computer Society},
acmid = {952139},
pdf = {../local/Liu2003Building.pdf},
file = {Liu2003Building.pdf:Liu2003Building.pdf:PDF},
isbn = {0-7695-1978-4},
keywords = {PUlearning},
owner = {fantine},
review = {Two-step method
1) identifying a set of reliable negative documents
2) building a set of classifiers by iteratively applying a classification
algorithm and selecting a good classifier from the set
Contributions
-Proposal for step 1+2 : Naive Bayes+SVM
-Evaluation of all possible combinations of step 1 and 2techniques
accross the existing two-steps methods --> Benchmark system (LPU)
-Proposal of a biased formulation of SVMs to solve the problem
Biased SVMs
Label known positive examples (P) +1 and unlabeled examples (U) -1.
Discriminate between P and U, allowing misclassification for unlabeled
examples.
In a noiseless version, positive misclassifications are not allowed.
To account for noise, errors on P might allowed but to a smaller
extent than errors on U. Namely, positive errors are weighted by
C+ whereas negative errors are weighted by C-, and C+ >> C-. These
weights should be learned thanks to a separate validation set.
Empirical evaluation
2 datasets : Reuters (10 categories) and Usenet articles (20 newsgroups).
They combine every step 1 technique with every step 2 technique and
report macroaveraged F scores (like our global average performance
on TFs).
The data set is divided into a validation set (30%) and a training
set. The validation set is used with the criterion described above
to select parameters C+ and C-. Then F score is computed on the training
set.
Simulation of different level of knowledge by including in the training
set a proportion gamma of positives in the unlabeled set (gamma =
from 10% to 90%).
For gamma = 0.3 and 0.7, biased SVM outperforms PEBL, S-EM, Roc-SVM
and NB.},
timestamp = {2009.07.01},
url = {http://dl.acm.org/citation.cfm?id=951949.952139}
}
@inproceedings{Liu2002Partially,
author = {Liu, B. and Lee, W. S. and Yu, P. S. and Li, X.},
title = {Partially Supervised Classification of Text Documents},
booktitle = {ICML '02: Proceedings of the Nineteenth International Conference
on Machine Learning},
year = {2002},
editor = {Sammut, C. and Hoffmann, A. G.},
pages = {387--394},
address = {San Francisco, CA, USA},
publisher = {Morgan Kaufmann Publishers Inc.},
pdf = {../local/Liu2002Partially.pdf},
file = {Liu2002Partially.pdf:Liu2002Partially.pdf:PDF},
isbn = {1-55860-873-7},
keywords = {PUlearning},
owner = {fantine},
timestamp = {2009.07.02},
url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.11.9010}
}
@article{Liu2004Gabor-based,
author = {Chengjun Liu},
title = {Gabor-based kernel {PCA} with fractional power polynomial models
for face recognition.},
journal = {I{EEE} {T}rans {P}attern {A}nal {M}ach {I}ntell},
year = {2004},
volume = {26},
pages = {572-81},
number = {5},
month = {May},
abstract = {This paper presents a novel {G}abor-based kernel {P}rincipal {C}omponent
{A}nalysis ({PCA}) method by integrating the {G}abor wavelet representation
of face images and the kernel {PCA} method for face recognition.
{G}abor wavelets first derive desirable facial features characterized
by spatial frequency, spatial locality, and orientation selectivity
to cope with the variations due to illumination and facial expression
changes. {T}he kernel {PCA} method is then extended to include fractional
power polynomial models for enhanced face recognition performance.
{A} fractional power polynomial, however, does not necessarily define
a kernel function, as it might not define a positive semidefinite
{G}ram matrix. {N}ote that the sigmoid kernels, one of the three
classes of widely used kernel functions (polynomial kernels, {G}aussian
kernels, and sigmoid kernels), do not actually define a positive
semidefinite {G}ram matrix either. {N}evertheless, the sigmoid kernels
have been successfully used in practice, such as in building support
vector machines. {I}n order to derive real kernel {PCA} features,
we apply only those kernel {PCA} eigenvectors that are associated
with positive eigenvalues. {T}he feasibility of the {G}abor-based
kernel {PCA} method with fractional power polynomial models has been
successfully tested on both frontal and pose-angled face recognition,
using two data sets from the {FERET} database and the {CMU} {PIE}
database, respectively. {T}he {FERET} data set contains 600 frontal
face images of 200 subjects, while the {PIE} data set consists of
680 images across five poses (left and right profiles, left and right
half profiles, and frontal view) with two different facial expressions
(neutral and smiling) of 68 subjects. {T}he effectiveness of the
{G}abor-based kernel {PCA} method with fractional power polynomial
models is shown in terms of both absolute performance indices and
comparative performance against the {PCA} method, the kernel {PCA}
method with polynomial kernels, the kernel {PCA} method with fractional
power polynomial models, the {G}abor wavelet-based {PCA} method,
and the {G}abor wavelet-based kernel {PCA} method with polynomial
kernels.}
}
@article{Liu2004Using,
author = {Huiqing Liu and Hao Han and Jinyan Li and Limsoon Wong},
title = {Using amino acid patterns to accurately predict translation initiation
sites.},
journal = {In {S}ilico {B}iol.},
year = {2004},
volume = {4},
pages = {255-69},
number = {3},
abstract = {The translation initiation site ({TIS}) prediction problem is about
how to correctly identify {TIS} in m{RNA}, c{DNA}, or other types
of genomic sequences. {H}igh prediction accuracy can be helpful in
a better understanding of protein coding from nucleotide sequences.
{T}his is an important step in genomic analysis to determine protein
coding from nucleotide sequences. {I}n this paper, we present an
in silico method to predict translation initiation sites in vertebrate
c{DNA} or m{RNA} sequences. {T}his method consists of three sequential
steps as follows. {I}n the first step, candidate features are generated
using k-gram amino acid patterns. {I}n the second step, a small number
of top-ranked features are selected by an entropy-based algorithm.
{I}n the third step, a classification model is built to recognize
true {TIS}s by applying support vector machines or ensembles of decision
trees to the selected features. {W}e have tested our method on several
independent data sets, including two public ones and our own extracted
sequences. {T}he experimental results achieved are better than those
reported previously using the same data sets. {O}ur high accuracy
not only demonstrates the feasibility of our method, but also indicates
that there might be "amino acid" patterns around {TIS} in c{DNA}
and m{RNA} sequences.},
keywords = {biosvm},
pii = {2004040022},
url = {http://www.bioinfo.de/isb/2004/04/0022/}
}
@article{Liu2003in-silico,
author = {Huiqing Liu and Hao Han and Jinyan Li and Limsoon Wong},
title = {An in-silico method for prediction of polyadenylation signals in
human sequences.},
journal = {Genome {I}nform {S}er {W}orkshop {G}enome {I}nform},
year = {2003},
volume = {14},
pages = {84-93},
abstract = {This paper presents a machine learning method to predict polyadenylation
signals ({PAS}es) in human {DNA} and m{RNA} sequences by analysing
features around them. {T}his method consists of three sequential
steps of feature manipulation: generation, selection and integration
of features. {I}n the first step, new features are generated using
k-gram nucleotide acid or amino acid patterns. {I}n the second step,
a number of important features are selected by an entropy-based algorithm.
{I}n the third step, support vector machines are employed to recognize
true {PAS}es from a large number of candidates. {O}ur study shows
that true {PAS}es in {DNA} and m{RNA} sequences can be characterized
by different features, and also shows that both upstream and downstream
sequence elements are important for recognizing {PAS}es from {DNA}
sequences. {W}e tested our method on several public data sets as
well as our own extracted data sets. {I}n most cases, we achieved
better validation results than those reported previously on the same
data sets. {T}he important motifs observed are highly consistent
with those reported in literature.},
keywords = {biosvm}
}
@incollection{Liu2009Nonparametric,
author = {Han Liu and John Lafferty and Larry Wasserman},
title = {Nonparametric regression and classification with joint sparsity constraints},
booktitle = {Advances in Neural Information Processing Systems 21},
publisher = {MIT Press},
year = {2009},
editor = {D. Koller and D. Schuurmans and Y. Bengio and L. Bottou},
pages = {969--976}
}
@inproceedings{Liu2002Comparative,
author = {Liu, H. and Li, J. and Wong, L.},
title = {A {C}omparative {S}tudy on {F}eature {S}election and {C}lassification
{M}ethods {U}sing {G}ene {E}xpression {P}rofiles and {P}roteomic
{P}atterns},
booktitle = {Genome {I}nformatics 2002},
year = {2002},
editor = {R. Lathrop and K. Nakai and S. Miyano and T. Takagi and M. Kanehisa},
volume = {12},
address = {Tokyo},
publisher = {Universal Academy Press},
abstract = {Feature selection plays an important role in classification. {W}e
present a comparative study on six feature selection heuristics by
applying them to two sets of data. {T}he first set of data are gene
expression profiles from {A}cute {L}ymphoblastic {L}eukemia ({ALL})
patients. {T}he second set of data are proteomic patterns from ovarian
cancer patients. {B}ased on features chosen by these methods, error
rates of several classification algorithms were obtained for analysis.
{O}ur results demonstrate the importance of feature selection in
accurately classifying new samples.},
pdf = {../local/Liu2002Comparative.pdf},
file = {Liu2002Comparative.pdf:local/Liu2002Comparative.pdf:PDF},
owner = {jeanphilippevert},
url = {http://www.jsbi.org/journal/GIW02/GIW02F006.html}
}
@article{Liu2005Use,
author = {Huiqing Liu and Jinyan Li and Limsoon Wong},
title = {Use of extreme patient samples for outcome prediction from gene expression
data.},
journal = {Bioinformatics},
year = {2005},
month = {Jun},
abstract = {M{OTIVATION}: {P}atient outcome prediction using microarray technologies
is an important application in bioinformatics. {B}ased on patients'
genotypic microarray data, predictions are made to estimate patients'
survival time and their risk of tumor metastasis or recurrence. {S}o,
accurate prediction can potentially help to provide better treatment
for patients. {RESULTS}: {W}e present a new computational method
for patient outcome prediction. {I}n the training phase of this method,
we make use of two types of extreme patient samples: short-term survivors
who got an unfavorable outcome within a short period and long-term
survivors who were maintaining a favorable outcome after a long follow-up
time. {T}hese extreme training samples yield a clear platform for
us to identify relevant genes whose expression is closely related
to the outcome. {T}he selected extreme samples and the relevant genes
are then integrated by a support vector machine to build a prediction
model, by which each validation sample is assigned a risk score that
falls into one of special pre-defined risk groups. {W}e apply this
method to several public data sets. {I}n most cases, patients in
high and low risk groups stratified by our method have clearly distinguishable
outcome status as seen in their {K}aplan-{M}eier curves. {W}e also
show that the idea of selecting only extreme patient samples for
training is effective for improving the prediction accuracy when
different gene selection methods are used. {SUPPLEMENTARY} {INFORMATION}:
http://research.i2r.a-star.edu.sg/huiqing/supplementaldata/survival/survival.html.},
doi = {10.1093/bioinformatics/bti544},
pdf = {../local/Liu2005Use.pdf},
file = {Liu2005Use.pdf:local/Liu2005Use.pdf:PDF},
keywords = {biosvm},
pii = {bti544},
url = {http://dx.doi.org/10.1093/bioinformatics/bti544}
}
@book{Liu2007Computational,
title = {Computational methods of feature selection},
publisher = {Chapman \& Hall/CRC},
year = {2007},
author = {Liu, H. and Motoda, H.}
}
@article{Liu2004Quantitative,
author = {H. X. Liu and C. X. Xue and R. S. Zhang and X. J. Yao and M. C. Liu
and Z. D. Hu and B. T. Fan},
title = {Quantitative prediction of logk of peptides in high-performance liquid
chromatography based on molecular descriptors by using the heuristic
method and support vector machine.},
journal = {J {C}hem {I}nf {C}omput {S}ci},
year = {2004},
volume = {44},
pages = {1979-86},
number = {6},
abstract = {A new method support vector machine ({SVM}) and the heuristic method
({HM}) were used to develop the nonlinear and linear models between
the capacity factor (logk) and seven molecular descriptors of 75
peptides for the first time. {T}he molecular descriptors representing
the structural features of the compounds only included the constitutional
and topological descriptors, which can be obtained easily without
optimizing the structure of the molecule. {T}he seven molecular descriptors
selected by the heuristic method in {CODESSA} were used as inputs
for {SVM}. {T}he results obtained by {SVM} were compared with those
obtained by the heuristic method. {T}he prediction result of the
{SVM} model is better than that of heuristic method. {F}or the test
set, a predictive correlation coefficient {R} = 0.9801 and root-mean-square
error of 0.1523 were obtained. {T}he prediction results are in very
good agreement with the experimental values. {B}ut the linear model
of the heuristic method is easier to understand and ready to use
for a chemist. {T}his paper provided a new and effective method for
predicting the chromatography retention of peptides and some insight
into the structural features which are related to the capacity factor
of peptides.},
doi = {10.1021/ci049891a},
pdf = {../local/Liu2004Quantitative.pdf},
file = {Liu2004Quantitative.pdf:local/Liu2004Quantitative.pdf:PDF},
keywords = {biosvm},
url = {http://dx.doi.org/10.1021/ci049891a}
}
@article{Liu2003Diagnosing,
author = {H. X. Liu and R. S. Zhang and F. Luan and X. J. Yao and M. C. Liu
and Z. D. Hu and B. T. Fan},
title = {Diagnosing breast cancer based on support vector machines},
journal = {J. Chem. Inf. Comput. Sci.},
year = {2003},
volume = {43},
pages = {900-7},
number = {3},
abstract = {The {S}upport {V}ector {M}achine ({SVM}) classification algorithm,
recently developed from the machine learning community, was used
to diagnose breast cancer. {A}t the same time, the {SVM} was compared
to several machine learning techniques currently used in this field.
{T}he classification task involves predicting the state of diseases,
using data obtained from the {UCI} machine learning repository. {SVM}
outperformed k-means cluster and two artificial neural networks on
the whole. {I}t can be concluded that nine samples could be mislabeled
from the comparison of several machine learning techniques.},
doi = {10.1021/ci0256438},
pdf = {../local/Liu2003Diagnosing.pdf},
file = {Liu2003Diagnosing.pdf:local/Liu2003Diagnosing.pdf:PDF},
keywords = {breastcancer},
url = {http://dx.doi.org/10.1021/ci0256438}
}
@article{Liu2004Prediction,
author = {H. X. Liu and R. S. Zhang and X. J. Yao and M. C. Liu and Z. D. Hu
and B. T. Fan},
title = {Prediction of the isoelectric point of an amino acid based on {GA}-{PLS}
and {SVM}s.},
journal = {J {C}hem {I}nf {C}omput {S}ci},
year = {2004},
volume = {44},
pages = {161-7},
number = {1},
abstract = {The support vector machine ({SVM}), as a novel type of a learning
machine, for the first time, was used to develop a {QSPR} model that
relates the structures of 35 amino acids to their isoelectric point.
{M}olecular descriptors calculated from the structure alone were
used to represent molecular structures. {T}he seven descriptors selected
using {GA}-{PLS}, which is a sophisticated hybrid approach that combines
{GA} as a powerful optimization method with {PLS} as a robust statistical
method for variable selection, were used as inputs of {RBFNN}s and
{SVM} to predict the isoelectric point of an amino acid. {T}he optimal
{QSPR} model developed was based on support vector machines, which
showed the following results: the root-mean-square error of 0.2383
and the prediction correlation coefficient {R}=0.9702 were obtained
for the whole data set. {S}atisfactory results indicated that the
{GA}-{PLS} approach is a very effective method for variable selection,
and the support vector machine is a very promising tool for the nonlinear
approximation.},
doi = {10.1021/ci034173u},
pdf = {../local/Liu2004Prediction.pdf},
file = {Liu2004Prediction.pdf:local/Liu2004Prediction.pdf:PDF},
keywords = {biosvm},
url = {http://dx.doi.org/10.1021/ci034173u}
}
@article{Liu2004QSAR,
author = {H. X. Liu and R. S. Zhang and X. J. Yao and M. C. Liu and Z. D. Hu
and B. T. Fan},
title = {Q{SAR} and classification models of a novel series of {COX}-2 selective
inhibitors: 1,5-diarylimidazoles based on support vector machines.},
journal = {J {C}omput {A}ided {M}ol {D}es},
year = {2004},
volume = {18},
pages = {389-99},
number = {6},
month = {Jun},
abstract = {The support vector machine, which is a novel algorithm from the machine
learning community, was used to develop quantitation and classification
models which can be used as a potential screening mechanism for a
novel series of {COX}-2 selective inhibitors. {E}ach compound was
represented by calculated structural descriptors that encode constitutional,
topological, geometrical, electrostatic, and quantum-chemical features.
{T}he heuristic method was then used to search the descriptor space
and select the descriptors responsible for activity. {Q}uantitative
modelling results in a nonlinear, seven-descriptor model based on
{SVM}s with root mean-square errors of 0.107 and 0.136 for training
and prediction sets, respectively. {T}he best classification results
are found using {SVM}s: the accuracy for training and test sets is
91.2\% and 88.2\%, respectively. {T}his paper proposes a new and
effective method for drug design and screening.},
keywords = {biosvm chemoinformatics}
}
@article{Liu2003QSAR,
author = {H. X. Liu and R. S. Zhang and X. J. Yao and M. C. Liu and Z. D. Hu
and B. T. Fan},
title = {Q{SAR} study of ethyl 2-[(3-methyl-2,5-dioxo(3-pyrrolinyl))amino]-4-(trifluoromethyl)
pyrimidine-5-carboxylate: an inhibitor of {AP}-1 and {NF}-kappa {B}
mediated gene expression based on support vector machines.},
journal = {J {C}hem {I}nf {C}omput {S}ci},
year = {2003},
volume = {43},
pages = {1288-96},
number = {4},
abstract = {The support vector machine, as a novel type of learning machine, for
the first time, was used to develop a {QSAR} model of 57 analogues
of ethyl 2-[(3-methyl-2,5-dioxo(3-pyrrolinyl))amino]-4-(trifluoromethyl)pyrimidine-5-carboxylate
({EPC}), an inhibitor of {AP}-1 and {NF}-kappa {B} mediated gene
expression, based on calculated quantum chemical parameters. {T}he
quantum chemical parameters involved in the model are {K}ier and
{H}all index (order3) ({KHI}3), {I}nformation content (order 0) ({IC}0),
{YZ} {S}hadow ({YZS}) and {M}ax partial charge for an {N} atom ({M}ax{PCN}),
{M}in partial charge for an {N} atom ({M}in{PCN}). {T}he mean relative
error of the training set, the validation set, and the testing set
is 1.35\%, 1.52\%, and 2.23\%, respectively, and the maximum relative
error is less than 5.00\%.},
doi = {10.1021/ci0340355},
pdf = {../local/Liu2003QSAR.pdf},
file = {Liu2003QSAR.pdf:local/Liu2003QSAR.pdf:PDF},
keywords = {biosvm},
url = {http://dx.doi.org/10.1021/ci0340355}
}
@article{Liu2005[Establishment,
author = {Jian Liu and Shu Zheng and Jie-kai Yu and Xue-bin Yu and Wei-guo
Liu and Jian-min Zhang and Xun Hu},
title = {[{E}stablishment of diagnostic model of cerebrospinal protein fingerprint
pattern for glioma and its clinical application.]},
journal = {Zhejiang {D}a {X}ue {X}ue {B}ao {Y}i {X}ue {B}an},
year = {2005},
volume = {34},
pages = {141-7},
number = {2},
month = {Mar},
abstract = {O{BJECTIVE}: {T}o establish the diagnostic model of cerebrospinal
protein profile for gliomas by surface-enhanced laser desorption/ionization
time-of-flight mass spectrometry ({SELDI}-{TOF} {MS}) and bioinformatics.
{METHODS}: {S}eventy-five samples of cerebrospinal fluid from patients
with gliomas, benign brain tumors and mild brain traumas were collected.
{A} total of 50 samples from gliomas and non-brain-tumors were divided
into training sets (33 cases including 17 gliomas and 16 non-brain-tumors)
and testing sets (17 cases including 5 gliomas and 12 non-brain-tumors).
{T}he cerebrospinal proteins bound to {H}4 chip were detected by
{SELDI}-{TOF} {MS}, the profiles of cerebrospinal protein were gained
and then analyzed with artificial neural network algorithm ({ANN});
and the diagnostic model of cerebrospinal protein profiles for differentiating
gliomas from non-brain-tumors was established. {F}orty-seven of cerebrospinal
samples of gliomas and benign brain tumors were divided into training
sets (31 cases including 13 gliomas and 18 benign brain tumors) and
testing sets (16 cases including 9 gliomas and 7 benign brain tumors),
the diagnostic model of cerebrospinal protein profiles for differentiating
gliomas from benign brain tumors was established based on the same
method. {T}he support vector machine ({SVM}) algorithm was also used
for evaluation, both results were very similar, but the result derived
from {ANN} was more stable than that from {SVM}. {RESULT}: {T}he
diagnostic model of cerebrospinal protein profiles for differentiating
gliomas from non-brain-tumors was established and was challenged
with the test set randomly, the sensitivity and specificity were
100\% and 91.7\%, respectively. {T}he cerebrospinal protein profiling
model for differentiating gliomas from benign brain tumors was also
developed and was challenged with the test set randomly, the sensitivity
and specificity were 88.9\%, and 100\%, respectively. {CONCLUSION}:
{T}he technology of {SELDI}-{TOF} {MS} which combined with analysis
tools of bioinformatics is a novel effective method for screening
and identifying tumor biomarkers of gliomas and it may provide a
new approach for the clinical diagnosis of glioma.},
keywords = {Algorithms, Animals, Artificial Intelligence, Computer-Assisted, Diagnosis,
Electrodes, Electroencephalography, Feedback, Implanted, Male, Motor
Cortex, Movement, Non-P.H.S., Non-U.S. Gov't, Rats, Research Support,
Sprague-Dawley, Therapy, U.S. Gov't, User-Computer Interface, 15812888}
}
@article{Liu2005Multiclass,
author = {Jane Jijun Liu and Gene Cutler and Wuxiong Li and Zheng Pan and Sihua
Peng and Tim Hoey and Liangbiao Chen and Xuefeng Bruce Ling},
title = {Multiclass cancer classification and biomarker discovery using {GA}-based
algorithms.},
journal = {Bioinformatics},
year = {2005},
volume = {21},
pages = {2691-7},
number = {11},
month = {Jun},
abstract = {M{OTIVATION}: {T}he development of microarray-based high-throughput
gene profiling has led to the hope that this technology could provide
an efficient and accurate means of diagnosing and classifying tumors,
as well as predicting prognoses and effective treatments. {H}owever,
the large amount of data generated by microarrays requires effective
reduction of discriminant gene features into reliable sets of tumor
biomarkers for such multiclass tumor discrimination. {T}he availability
of reliable sets of biomarkers, especially serum biomarkers, should
have a major impact on our understanding and treatment of cancer.
{RESULTS}: {W}e have combined genetic algorithm ({GA}) and all paired
({AP}) support vector machine ({SVM}) methods for multiclass cancer
categorization. {P}redictive features can be automatically determined
through iterative {GA}/{SVM}, leading to very compact sets of non-redundant
cancer-relevant genes with the best classification performance reported
to date. {I}nterestingly, these different classifier sets harbor
only modest overlapping gene features but have similar levels of
accuracy in leave-one-out cross-validations ({LOOCV}). {F}urther
characterization of these optimal tumor discriminant features, including
the use of nearest shrunken centroids ({NSC}), analysis of annotations
and literature text mining, reveals previously unappreciated tumor
subclasses and a series of genes that could be used as cancer biomarkers.
{W}ith this approach, we believe that microarray-based multiclass
molecular analysis can be an effective tool for cancer biomarker
discovery and subsequent molecular cancer diagnosis.},
doi = {10.1093/bioinformatics/bti419},
pdf = {../local/Liu2005Multiclass.pdf},
file = {Liu2005Multiclass.pdf:local/Liu2005Multiclass.pdf:PDF},
keywords = {biosvm},
pii = {bti419},
url = {http://dx.doi.org/10.1093/bioinformatics/bti419}
}
@article{Liu2007Network-based,
author = {Liu, M. and Liberzon, A. and Kong, A. W. and Lai, W. R. and Park,
P. J. and Kohane, I. S. and Kasif, S.},
title = {Network-based analysis of affected biological processes in type 2
diabetes models.},
journal = {PLoS Genet.},
year = {2007},
volume = {3},
pages = {e96},
number = {6},
month = {Jun},
abstract = {Type 2 diabetes mellitus is a complex disorder associated with multiple
genetic, epigenetic, developmental, and environmental factors. Animal
models of type 2 diabetes differ based on diet, drug treatment, and
gene knockouts, and yet all display the clinical hallmarks of hyperglycemia
and insulin resistance in peripheral tissue. The recent advances
in gene-expression microarray technologies present an unprecedented
opportunity to study type 2 diabetes mellitus at a genome-wide scale
and across different models. To date, a key challenge has been to
identify the biological processes or signaling pathways that play
significant roles in the disorder. Here, using a network-based analysis
methodology, we identified two sets of genes, associated with insulin
signaling and a network of nuclear receptors, which are recurrent
in a statistically significant number of diabetes and insulin resistance
models and transcriptionally altered across diverse tissue types.
We additionally identified a network of protein-protein interactions
between members from the two gene sets that may facilitate signaling
between them. Taken together, the results illustrate the benefits
of integrating high-throughput microarray studies, together with
protein-protein interaction networks, in elucidating the underlying
biological processes associated with a complex disorder.},
doi = {10.1371/journal.pgen.0030096},
pdf = {../local/Liu2007Network-based.pdf},
file = {Liu2007Network-based.pdf:Liu2007Network-based.pdf:PDF},
institution = {Department of Biomedical Engineering, Boston University, Boston,
Massachusetts, United States of America. manwayl@bu.edu},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {06-PLGE-RA-0555},
pmid = {17571924},
timestamp = {2011.10.08},
url = {http://dx.doi.org/10.1371/journal.pgen.0030096}
}
@article{Liu2004Comments,
author = {Xiaomei Liu and Lawrence O Hall and Kevin W Bowyer},
title = {Comments on "a parallel mixture of {SVM}s for very large scale problems".},
journal = {Neural {C}omput},
year = {2004},
volume = {16},
pages = {1345-51},
number = {7},
month = {Jul},
abstract = {Collobert, {B}engio, and {B}engio (2002) recently introduced a novel
approach to using a neural network to provide a class prediction
from an ensemble of support vector machines ({SVM}s). {T}his approach
has the advantage that the required computation scales well to very
large data sets. {E}xperiments on the {F}orest {C}over data set show
that this parallel mixture is more accurate than a single {SVM},
with 90.72\% accuracy reported on an independent test set. {A}lthough
this accuracy is impressive, their article does not consider alternative
types of classifiers. {W}e show that a simple ensemble of decision
trees results in a higher accuracy, 94.75\%, and is computationally
efficient. {T}his result is somewhat surprising and illustrates the
general value of experimental comparisons using different types of
classifiers.},
doi = {10.1162/089976604323057416},
url = {http://dx.doi.org/10.1162/089976604323057416}
}
@article{Liu2004Active,
author = {Liu, Y.},
title = {Active learning with support vector machine applied to gene expression
data for cancer classification},
journal = {J. {C}hem. {I}nf. {C}omput. {S}ci.},
year = {2004},
volume = {44},
pages = {1936-1941},
number = {6},
abstract = {There is growing interest in the application of machine learning techniques
in bioinformatics. {T}he supervised machine learning approach has
been widely applied to bioinformatics and gained a lot of success
in this research area. {W}ith this learning approach researchers
first develop a large training set, which is a time-consuming and
costly process. {M}oreover, the proportion of the positive examples
and negative examples in the training set may not represent the real-world
data distribution, which causes concept drift. {A}ctive learning
avoids these problems. {U}nlike most conventional learning methods
where the training set used to derive the model remains static, the
classifier can actively choose the training data and the size of
training set increases. {W}e introduced an algorithm for performing
active learning with support vector machine and applied the algorithm
to gene expression profiles of colon cancer, lung cancer, and prostate
cancer samples. {W}e compared the classification performance of active
learning with that of passive learning. {T}he results showed that
employing the active learning method can achieve high accuracy and
significantly reduce the need for labeled training instances. {F}or
lung cancer classification, to achieve 96% of the total positives,
only 31 labeled examples were needed in active learning whereas in
passive learning 174 labeled examples were required. {T}hat meant
over 82% reduction was realized by active learning. {I}n active learning
the areas under the receiver operating characteristic ({ROC}) curves
were over 0.81, while in passive learning the areas under the {ROC}
curves were below 0.50.},
doi = {10.1021/ci049810a},
pdf = {../local/Liu2004Active.pdf},
file = {Liu2004Active.pdf:local/Liu2004Active.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://dx.doi.org/10.1021/ci049810a}
}
@article{Liu2004comparative,
author = {Y. Liu},
title = {A comparative study on feature selection methods for drug discovery.},
journal = {J {C}hem {I}nf {C}omput {S}ci},
year = {2004},
volume = {44},
pages = {1823-8},
number = {5},
abstract = {Feature selection is frequently used as a preprocessing step to machine
learning. {T}he removal of irrelevant and redundant information often
improves the performance of learning algorithms. {T}his paper is
a comparative study of feature selection in drug discovery. {T}he
focus is on aggressive dimensionality reduction. {F}ive methods were
evaluated, including information gain, mutual information, a chi2-test,
odds ratio, and {GSS} coefficient. {T}wo well-known classification
algorithms, {N}aïve {B}ayesian and {S}upport {V}ector {M}achine
({SVM}), were used to classify the chemical compounds. {T}he results
showed that {N}aïve {B}ayesian benefited significantly from the
feature selection, while {SVM} performed better when all features
were used. {I}n this experiment, information gain and chi2-test were
most effective feature selection methods. {U}sing information gain
with a {N}aïve {B}ayesian classifier, removal of up to 96\% of the
features yielded an improved classification accuracy measured by
sensitivity. {W}hen information gain was used to select the features,
{SVM} was much less sensitive to the reduction of feature space.
{T}he feature set size was reduced by 99\%, while losing only a few
percent in terms of sensitivity (from 58.7\% to 52.5\%) and specificity
(from 98.4\% to 97.2\%). {I}n contrast to information gain and chi2-test,
mutual information had relatively poor performance due to its bias
toward favoring rare features and its sensitivity to probability
estimation errors.},
doi = {10.1021/ci049875d},
pdf = {../local/Liu2004comparative.pdf},
file = {Liu2004comparative.pdf:local/Liu2004comparative.pdf:PDF},
keywords = {biosvm},
url = {http://dx.doi.org/10.1021/ci049875d}
}
@article{Liu2005Gene,
author = {Zhenqiu Liu and Dechang Chen and Halima Bensmail},
title = {Gene expression data classification with kernel principal component
analysis.},
journal = {J {B}iomed {B}iotechnol},
year = {2005},
volume = {2005},
pages = {155-9},
number = {2},
abstract = {One important feature of the gene expression data is that the number
of genes ${M}$ far exceeds the number of samples ${N}$ . {S}tandard
statistical methods do not work well when ${N} < {M}$ . {D}evelopment
of new methodologies or modification of existing methodologies is
needed for the analysis of the microarray data. {I}n this paper,
we propose a novel analysis procedure for classifying the gene expression
data. {T}his procedure involves dimension reduction using kernel
principal component analysis ({KPCA}) and classification with logistic
regression (discrimination). {KPCA} is a generalization and nonlinear
version of principal component analysis. {T}he proposed algorithm
was applied to five different gene expression datasets involving
human tumor samples. {C}omparison with other popular classification
methods such as support vector machines and neural networks shows
that our algorithm is very promising in classifying gene expression
data.},
doi = {10.1155/JBB.2005.155},
pdf = {../local/Liu2005Gene.pdf},
file = {Liu2005Gene.pdf:local/Liu2005Gene.pdf:PDF},
keywords = {biosvm},
pii = {S1110724304406032_THIS_PII_IS_INCORRECT_},
url = {http://dx.doi.org/10.1155/JBB.2005.155}
}
@article{Llinas2006Comparative,
author = {Llin{\'a}s, M. and Bozdech, Z. and Wong, E. D. and Adai, A. T. and
DeRisi, J. L.},
title = {{C}omparative whole genome transcriptome analysis of three {P}lasmodium
falciparum strains.},
journal = {Nucleic Acids Res.},
year = {2006},
volume = {34},
pages = {1166--1173},
number = {4},
abstract = {Gene expression patterns have been demonstrated to be highly variable
between similar cell types, for example lab strains and wild strains
of Saccharomyces cerevisiae cultured under identical growth conditions
exhibit a wide range of expression differences. We have used a genome-wide
approach to characterize transcriptional differences between strains
of Plasmodium falciparum by characterizing the transcriptome of the
48 h intraerythrocytic developmental cycle (IDC) for two strains,
3D7 and Dd2 and compared these results to our prior work using the
HB3 strain. These three strains originate from geographically diverse
locations and possess distinct drug sensitivity phenotypes. Our goal
was to identify transcriptional differences related to phenotypic
properties of these strains including immune evasion and drug sensitivity.
We find that the highly streamlined transcriptome is remarkably well
conserved among all three strains, and differences in gene expression
occur mainly in genes coding for surface antigens involved in parasite-host
interactions. Our analysis also detects several transcripts that
are unique to individual strains as well as identifying large chromosomal
deletions and highly polymorphic regions across strains. The majority
of these genes are uncharacterized and have no homology to other
species. These tractable transcriptional differences provide important
phenotypes for these otherwise highly related strains of Plasmodium.},
doi = {10.1093/nar/gkj517},
keywords = {plasmodium},
pii = {34/4/1166},
pmid = {16493140},
timestamp = {2007.10.04},
url = {http://dx.doi.org/10.1093/nar/gkj517}
}
@article{Lo2005Effect,
author = {Siaw Ling Lo and Cong Zhong Cai and Yu Zong Chen and Maxey C M Chung},
title = {Effect of training datasets on support vector machine prediction
of protein-protein interactions.},
journal = {Proteomics},
year = {2005},
volume = {5},
pages = {876-84},
number = {4},
month = {Mar},
abstract = {Knowledge of protein-protein interaction is useful for elucidating
protein function via the concept of 'guilt-by-association'. {A} statistical
learning method, {S}upport {V}ector {M}achine ({SVM}), has recently
been explored for the prediction of protein-protein interactions
using artificial shuffled sequences as hypothetical noninteracting
proteins and it has shown promising results ({B}ock, {J}. {R}., {G}ough,
{D}. {A}., {B}ioinformatics 2001, 17, 455-460). {I}t remains unclear
however, how the prediction accuracy is affected if real protein
sequences are used to represent noninteracting proteins. {I}n this
work, this effect is assessed by comparison of the results derived
from the use of real protein sequences with that derived from the
use of shuffled sequences. {T}he real protein sequences of hypothetical
noninteracting proteins are generated from an exclusion analysis
in combination with subcellular localization information of interacting
proteins found in the {D}atabase of {I}nteracting {P}roteins. {P}rediction
accuracy using real protein sequences is 76.9\% compared to 94.1\%
using artificial shuffled sequences. {T}he discrepancy likely arises
from the expected higher level of difficulty for separating two sets
of real protein sequences than that for separating a set of real
protein sequences from a set of artificial sequences. {T}he use of
real protein sequences for training a {SVM} classification system
is expected to give better prediction results in practical cases.
{T}his is tested by using both {SVM} systems for predicting putative
protein partners of a set of thioredoxin related proteins. {T}he
prediction results are consistent with observations, suggesting that
real sequence is more practically useful in development of {SVM}
classification system for facilitating protein-protein interaction
prediction.},
doi = {10.1002/pmic.200401118},
pdf = {../local/Lo2005Effect.pdf},
file = {Lo2005Effect.pdf:local/Lo2005Effect.pdf:PDF},
keywords = {biosvm},
url = {http://dx.doi.org/10.1002/pmic.200401118}
}
@article{Lobo1998Applications,
author = {Miguel Sousa Lobo and Lobo I and Lieyen Vandenberghe and Herv Lebret
and Stephen Boyd},
title = {Applications of Second-order Cone Programming},
journal = {Linear Algebra and its Applications},
year = {1998},
volume = {284},
pages = {193--228},
month = {November}
}
@article{Lockhart2000Genomics,
author = {Lockhart, D.J. and Winzeler, E.A. and others},
title = {Genomics, gene expression and DNA arrays},
journal = {NATURE-LONDON-},
year = {2000},
pages = {827--836},
publisher = {MACMILLAN MAGAZINES LTD}
}
@article{Lodhi2002Text,
author = {Lodhi, H. and Saunders, C. and Shawe-Taylor, J. and Cristianini,
N. and Watkins, C.je n'ai pas vraiment d'éléments de réponse.},
title = {Text classification using string kernels},
journal = {J. {M}ach. {L}earn. {R}es.},
year = {2002},
volume = {2},
pages = {419--444},
pdf = {../local/lodh02.pdf},
file = {lodh02.pdf:local/lodh02.pdf:PDF},
keywords = {biosvm},
subject = {kernel},
url = {http://www.ai.mit.edu/projects/jmlr/papers/volume2/lodhi02a/abstract.html}
}
@inproceedings{Lodhi2000Text,
author = {Lodhi, H. and Shawe-Taylor, J. and Cristianini, N. and Watkins, C.
J. C. H.},
title = {Text {C}lassification using {S}tring {K}ernels},
booktitle = {Adv. {N}eural {I}nform. {P}rocess. {S}yst.},
year = {2000},
pages = {563-569},
pdf = {../local/lodh00.pdf},
file = {lodh00.pdf:local/lodh00.pdf:PDF},
keywords = {biosvm},
subject = {kernel},
url = {http://www.neurocolt.com/tech_reps/2000/00079.ps.gz}
}
@article{Lodish2000Molecular,
author = {Lodish, H. and Berk, A. and Zipursky, S.L. and Matsudaira, P. and
Baltimore, D. and Darnell, J.},
title = {Molecular cell biology},
journal = {New York},
year = {2000},
publisher = {Wiley Online Library}
}
@techreport{Logan2001Study,
author = {Logan, B. and Moreno, P. and Suzek, B. and Weng, Z. and Kasif, S.},
title = {A {S}tudy of {R}emote {H}omology {D}etection},
institution = {Compaq Cambridge Research laboratory},
year = {2001},
number = {CRL 2001/05},
month = {June},
abstract = {Functional annotation of newly sequenced genomes is an important challenge
for computational biology systems. {W}hile much progress has been
made towards scalingup experimental methods for functional assignment
to putative genes, most current genomic annotation systems rely on
computational solutions for homology modeling via sequence or structural
similarity. {W}e present a new method for remote homology detection
that relies on combining probabilistic modeling and supervised learning
in high-dimensional features spaces. {O}ur system uses a transformation
that converts protein domains to fixed-dimension representative feature
vectors, where each feature records the sensitivity of each protein
domain to a previously learned set of ?protein motifs? or ?blocks?.
{S}ubsequently, the system utilizes {S}upport {V}ector {M}achine
({SVM}) classifiers to learn the boundaries between structural protein
classes. {O}ur experiments suggest that this technique performs well
relative to several other remote homology methods for the majority
of protein domains in {SCOP} 1.37 {PDB}90.},
pdf = {../local/Logan2001Study.pdf},
file = {Logan2001Study.pdf:local/Logan2001Study.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert}
}
@article{Loi2007Definition,
author = {Loi, Sherene and Haibe-Kains, Benjamin and Desmedt, Christine and
Lallemand, Françoise and Tutt, Andrew M. and Gillet, Cheryl and Ellis,
Paul and Harris, Adrian and Bergh, Jonas and Foekens, John A. and
Klijn, Jan G M. and Larsimont, Denis and Buyse, Marc and Bontempi,
Gianluca and Delorenzi, Mauro and Piccart, Martine J. and Sotiriou,
Christos},
title = {Definition of clinically distinct molecular subtypes in estrogen
receptor-positive breast carcinomas through genomic grade.},
journal = {J Clin Oncol},
year = {2007},
volume = {25},
pages = {1239--1246},
number = {10},
month = {Apr},
abstract = {A number of microarray studies have reported distinct molecular profiles
of breast cancers (BC), such as basal-like, ErbB2-like, and two to
three luminal-like subtypes. These were associated with different
clinical outcomes. However, although the basal and the ErbB2 subtypes
are repeatedly recognized, identification of estrogen receptor (ER)
-positive subtypes has been inconsistent. Therefore, refinement of
their molecular definition is needed.We have previously reported
a gene expression grade index (GGI), which defines histologic grade
based on gene expression profiles. Using this algorithm, we assigned
ER-positive BC to either high-or low-genomic grade subgroups and
compared these with previously reported ER-positive molecular classifications.
As further validation, we classified 666 ER-positive samples into
subtypes and assessed their clinical outcome.Two ER-positive molecular
subgroups (high and low genomic grade) could be defined using the
GGI. Despite tracking a single biologic pathway, these were highly
comparable to the previously described luminal A and B classification
and significantly correlated to the risk groups produced using the
21-gene recurrence score. The two subtypes were associated with statistically
distinct clinical outcome in both systemically untreated and tamoxifen-treated
populations.The use of genomic grade can identify two clinically
distinct ER-positive molecular subtypes in a simple and highly reproducible
manner across multiple data sets. This study emphasizes the important
role of proliferation-related genes in predicting prognosis in ER-positive
BC.},
doi = {10.1200/JCO.2006.07.1522},
institution = {Jules Bordet Institute; Machine Learning Group, Université Libre
de Bruxelles, Brussels, Belgium.},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {25/10/1239},
pmid = {17401012},
timestamp = {2012.03.05},
url = {http://dx.doi.org/10.1200/JCO.2006.07.1522}
}
@unpublished{Loosli2006SimpleSVM,
author = {Loosli, G.},
title = {SimpleSVM Toolbox},
note = {Available at http://asi.insa-rouen.fr/~gloosli/simpleSVM.html},
year = {2006},
timestamp = {2008.02.10}
}
@article{Lopez08Statistical,
author = {Lopez,, A.},
title = {Statistical machine translation},
journal = {ACM Comput. Surv.},
year = {2008},
volume = {40},
pages = {1--49},
number = {3},
address = {New York, NY, USA},
doi = {http://doi.acm.org/10.1145/1380584.1380586},
issn = {0360-0300},
publisher = {ACM}
}
@article{Lounici2008Sup-norm,
author = {Lounici, K.},
title = {Sup-norm convergence rate and sign concentration property of Lasso
and Dantzig estimators},
journal = {Electron. J. Statist.},
year = {2008},
volume = {2},
pages = {90--102},
doi = {10.1214/08-EJS177},
pdf = {../local/Lounici2008Sup-norm.pdf},
file = {Lounici2008Sup-norm.pdf:Lounici2008Sup-norm.pdf:PDF},
keywords = {lasso},
owner = {jp},
timestamp = {2009.05.02},
url = {http://dx.doi.org/10.1214/08-EJS177}
}
@inproceedings{Lounici2009Taking,
author = {Karim Lounici and Massimiliano Pontil and Alexandre B. Tsybakov and
Sara van de Geer},
title = {Taking Advantage of Sparsity in Multi-Task Learning},
booktitle = {Proceedings of COLT},
year = {2009}
}
@article{Lowery2008MicroRNAs,
author = {Lowery, A. J. and Miller, N. and McNeill, R. E. and Kerin, M. J.},
title = {{MicroRNAs} as prognostic indicators and therapeutic targets: potential
effect on breast cancer management.},
journal = {Clin. Cancer Res.},
year = {2008},
volume = {14},
pages = {360--365},
number = {2},
month = {Jan},
abstract = {The discovery of microRNAs (miRNA) as novel modulators of gene expression
has resulted in a rapidly expanding repertoire of molecules in this
family, as reflected in the concomitant expansion of scientific literature.
MiRNAs are a category of naturally occurring RNA molecules that play
important regulatory roles in plants and animals by targeting mRNAs
for cleavage or translational repression. Characteristically, miRNAs
are noncoding, single-stranded short (18-22 nucleotides) RNAs, features
which possibly explain why they had not been intensively investigated
until recently. Accumulating experimental evidence indicates that
miRNAs play a pivotal role in many cellular functions via the regulation
of gene expression. Furthermore, their dysregulation and/or mutation
has been shown in carcinogenesis. We provide a brief review of miRNA
biogenesis and discuss the technical challenges of modifying experimental
techniques to facilitate the identification and characterization
of these small RNAs. MiRNA function and their involvement in malignancy,
particularly their putative role as oncogenes or tumor suppressors
is also discussed, with a specific emphasis on breast cancer. Finally,
we comment on the potential role of miRNAs in breast cancer management,
particularly in improving current prognostic tools and achieving
the goal of individualized cancer treatment.},
doi = {10.1158/1078-0432.CCR-07-0992},
pdf = {../local/Lowery2008MicroRNAs.pdf},
file = {Lowery2008MicroRNAs.pdf:Lowery2008MicroRNAs.pdf:PDF},
institution = {Department of Surgery, National University of Ireland, Galway, Ireland.},
keywords = {csbcbook, csbcbook-ch3},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {14/2/360},
pmid = {18223209},
timestamp = {2011.11.30},
url = {http://dx.doi.org/10.1158/1078-0432.CCR-07-0992}
}
@article{Lu2003Preoperative,
author = {Lu, C. and Van Gestel, T. and Suykens, J.A. and Van Huffel, S. and
Vergote, I. and Timmerman, D.},
title = {Preoperative prediction of malignancy of ovarian tumors using least
squares support vector machines.},
journal = {Artif. {I}ntell. {M}ed.},
year = {2003},
volume = {28},
pages = {281-306},
number = {3},
abstract = {In this work, we develop and evaluate several least squares support
vector machine ({LS}-{SVM}) classifiers within the {B}ayesian evidence
framework, in order to preoperatively predict malignancy of ovarian
tumors. {T}he analysis includes exploratory data analysis, optimal
input variable selection, parameter estimation, and performance evaluation
via receiver operating characteristic ({ROC}) curve analysis. {LS}-{SVM}
models with linear and radial basis function ({RBF}) kernels, and
logistic regression models have been built on 265 training data,
and tested on 160 newly collected patient data. {T}he {LS}-{SVM}
model with nonlinear {RBF} kernel achieves the best performance,
on the test set with the area under the {ROC} curve ({AUC}), sensitivity
and specificity equal to 0.92, 81.5% and 84.0%, respectively. {T}he
best averaged performance over 30 runs of randomized cross-validation
is also obtained by an {LS}-{SVM} {RBF} model, with {AUC}, sensitivity
and specificity equal to 0.94, 90.0% and 80.6%, respectively. {T}hese
results show that the {LS}-{SVM} models have the potential to obtain
a reliable preoperative distinction between benign and malignant
ovarian tumors, and to assist the clinicians for making a correct
diagnosis.},
doi = {10.1016/S0933-3657(03)00051-4},
pdf = {../local/Lu2003Preoperative.pdf},
file = {Lu2003Preoperative.pdf:local/Lu2003Preoperative.pdf:PDF},
owner = {jeanphilippevert},
url = {http://dx.doi.org/10.1016/S0933-3657(03)00051-4}
}
@article{Lu2005MicroRNA,
author = {Lu, J. and Getz, G. and Miska, E. A. and Alvarez-Saavedra, E. and
Lamb, J. and Peck, D. and Sweet-Cordero, A. and Ebert, D. L. and
Mak, R. H. and Ferrando, A. A. and Downing, J. R. and Jacks, T. and
Horvitz, H. R. and Golub, T. R.},
title = {MicroRNA expression profiles classify human cancers.},
journal = {Nature},
year = {2005},
volume = {435},
pages = {834--838},
number = {7043},
month = {Jun},
abstract = {Recent work has revealed the existence of a class of small non-coding
RNA species, known as microRNAs (miRNAs), which have critical functions
across various biological processes. Here we use a new, bead-based
flow cytometric miRNA expression profiling method to present a systematic
expression analysis of 217 mammalian miRNAs from 334 samples, including
multiple human cancers. The miRNA profiles are surprisingly informative,
reflecting the developmental lineage and differentiation state of
the tumours. We observe a general downregulation of miRNAs in tumours
compared with normal tissues. Furthermore, we were able to successfully
classify poorly differentiated tumours using miRNA expression profiles,
whereas messenger RNA profiles were highly inaccurate when applied
to the same samples. These findings highlight the potential of miRNA
profiling in cancer diagnosis.},
doi = {10.1038/nature03702},
pdf = {../local/Lu2005MicroRNA.pdf},
file = {Lu2005MicroRNA.pdf:Lu2005MicroRNA.pdf:PDF},
institution = {Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02141,
USA.},
keywords = {csbcbook, csbcbook-ch3},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {nature03702},
pmid = {15944708},
timestamp = {2011.11.30},
url = {http://dx.doi.org/10.1038/nature03702}
}
@article{Lu2005Potential,
author = {Wei-Zhen Lu and Wen-Jian Wang},
title = {Potential assessment of the "support vector machine" method in forecasting
ambient air pollutant trends.},
journal = {Chemosphere},
year = {2005},
volume = {59},
pages = {693-701},
number = {5},
month = {Apr},
abstract = {Monitoring and forecasting of air quality parameters are popular and
important topics of atmospheric and environmental research today
due to the health impact caused by exposing to air pollutants existing
in urban air. {T}he accurate models for air pollutant prediction
are needed because such models would allow forecasting and diagnosing
potential compliance or non-compliance in both short- and long-term
aspects. {A}rtificial neural networks ({ANN}) are regarded as reliable
and cost-effective method to achieve such tasks and have produced
some promising results to date. {A}lthough {ANN} has addressed more
attentions to environmental researchers, its inherent drawbacks,
e.g., local minima, over-fitting training, poor generalization performance,
determination of the appropriate network architecture, etc., impede
the practical application of {ANN}. {S}upport vector machine ({SVM}),
a novel type of learning machine based on statistical learning theory,
can be used for regression and time series prediction and have been
reported to perform well by some promising results. {T}he work presented
in this paper aims to examine the feasibility of applying {SVM} to
predict air pollutant levels in advancing time series based on the
monitored air pollutant database in {H}ong {K}ong downtown area.
{A}t the same time, the functional characteristics of {SVM} are investigated
in the study. {T}he experimental comparisons between the {SVM} model
and the classical radial basis function ({RBF}) network demonstrate
that the {SVM} is superior to the conventional {RBF} network in predicting
air quality parameters with different time series and of better generalization
performance than the {RBF} model.},
doi = {10.1016/j.chemosphere.2004.10.032},
pdf = {../local/Lu2005Potential.pdf},
file = {Lu2005Potential.pdf:local/Lu2005Potential.pdf:PDF},
pii = {S0045-6535(04)00988-9},
url = {http://dx.doi.org/10.1016/j.chemosphere.2004.10.032}
}
@article{Lu2003Expression,
author = {Lu, Y.J. and Williamson, D. and Wang, R. and Summersgill, B. and
Rodriguez, S. and Rogers, S. and Pritchard-Jones, K. and Campbell,
C. and Shipley, J.},
title = {Expression profiling targeting chromosomes for tumor classification
and prediction of clinical behavior.},
journal = {Genes {C}hromosomes {C}ancer},
year = {2003},
volume = {38},
pages = {207-214},
number = {3},
abstract = {Tumors are associated with altered or deregulated gene products that
affect critical cellular functions. {H}ere we assess the use of a
global expression profiling technique that identifies chromosome
regions corresponding to differential gene expression, termed comparative
expressed sequence hybridization ({CESH}). {CESH} analysis was performed
on a total of 104 tumors with a diagnosis of rhabdomyosarcoma, leiomyosarcoma,
prostate cancer, and favorable-histology {W}ilms tumors. {T}hrough
the use of the chromosome regions identified as variables, support
vector machine analysis was applied to assess classification potential,
and feature selection (recursive feature elimination) was used to
identify the best discriminatory regions. {W}e demonstrate that the
{CESH} profiles have characteristic patterns in tumor groups and
were also able to distinguish subgroups of rhabdomyosarcoma. {T}he
overall {CESH} profiles in favorable-histology {W}ilms tumors were
found to correlate with subsequent clinical behavior. {C}lassification
by use of {CESH} profiles was shown to be similar in performance
to previous microarray expression studies and highlighted regions
for further investigation. {W}e conclude that analysis of chromosomal
expression profiles can group, subgroup, and even predict clinical
behavior of tumors to a level of performance similar to that of microarray
analysis. {CESH} is independent of selecting sequences for interrogation
and is a simple, rapid, and widely accessible approach to identify
clinically useful differential expression.},
doi = {10.1002/gcc.10276},
pdf = {../local/Lu2003Expression.pdf},
file = {Lu2003Expression.pdf:local/Lu2003Expression.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert}
}
@article{Luan2005Classification,
author = {Feng Luan and Ruisheng Zhang and Chunyan Zhao and Xiaojun Yao and
Mancang Liu and Zhide Hu and Botao Fan},
title = {Classification of the carcinogenicity of {N}-nitroso compounds based
on support vector machines and linear discriminant analysis.},
journal = {Chem {R}es {T}oxicol},
year = {2005},
volume = {18},
pages = {198-203},
number = {2},
month = {Feb},
abstract = {The support vector machine ({SVM}), as a novel type of learning machine,
was used to develop a classification model of carcinogenic properties
of 148 {N}-nitroso compounds. {T}he seven descriptors calculated
solely from the molecular structures of compounds selected by forward
stepwise linear discriminant analysis ({LDA}) were used as inputs
of the {SVM} model. {T}he obtained results confirmed the discriminative
capacity of the calculated descriptors. {T}he result of {SVM} (total
accuracy of 95.2\%) is better than that of {LDA} (total accuracy
of 89.8\%).},
doi = {10.1021/tx049782q},
pdf = {../local/Luan2005Classification.pdf},
file = {Luan2005Classification.pdf:local/Luan2005Classification.pdf:PDF},
keywords = {biosvm},
url = {http://dx.doi.org/10.1021/tx049782q}
}
@unpublished{Lugosi2003Concentration-of-measure,
author = {G. Lugosi},
title = {Concentration-of-measure inequalities},
note = {Lecture notes},
month = {January},
year = {2003}
}
@unpublished{Lugosi1998On,
author = {Lugosi, G.},
title = {On concentration-of-measure inequalities},
note = {Seminar notes},
year = {1998},
pdf = {../local/lugo98.pdf},
file = {lugo98.pdf:local/lugo98.pdf:PDF},
subject = {stat},
url = {http://www.econ.upf.es/~lugosi/concmeas.ps}
}
@article{Lugosi1999Adaptive,
author = {Lugosi, G. and Nobel, A.},
title = {Adaptive {M}odel {S}election {U}sing {E}mpirical {C}omplexities},
journal = {Ann. {S}tat.},
year = {1999},
volume = {27},
pages = {1830--1864},
number = {6},
month = dec,
pdf = {../local/lugo99.pdf},
file = {lugo99.pdf:local/lugo99.pdf:PDF},
subject = {stat},
url = {http://www.econ.upf.es/~lugosi/amsec.ps}
}
@article{Lugosi2004On,
author = {Lugosi, G. and Vayatis, N.},
title = {On the {B}ayes-risk consistency of regularized boosting methods},
journal = {Ann. {S}tat.},
year = {2004},
volume = {32},
pages = {30-55},
doi = {10.1214/aos/1079120129},
pdf = {../local/Lugosi2004On.pdf},
file = {Lugosi2004On.pdf:local/Lugosi2004On.pdf:PDF},
owner = {jeanphilippevert},
url = {http://dx.doi.org/10.1214/aos/1079120129}
}
@article{Lugosi1996Concept,
author = {Lugosi, G. and Zeger, K. },
title = {Concept learning using complexity regularization},
journal = {I{EEE} {T}rans. {I}nform. {T}heory},
year = {1996},
volume = {42},
pages = {48-54},
number = {1},
month = {Jan},
abstract = {In pattern recognition or, as it has also been called, concept learning,
the value of a { 0,1}-valued random variable {Y} is to be predicted
based upon observing an {R}d-valued random variable {X}. {W}e apply
the method of complexity regularization to learn concepts from large
concept classes. {T}he method is shown to automatically find a good
balance between the approximation error and the estimation error.
{I}n particular, the error probability of the obtained classifier
is shown to decrease as {O}(?(logn/n)) to the achievable optimum,
for large nonparametric classes of distributions, as the sample size
n grows. {W}e also show that if the {B}ayes error probability is
zero and the {B}ayes rule is in a known family of decision rules,
the error probability is {O}(logn/n) for many large families, possibly
with infinite {VC} dimension },
pdf = {../local/Lugosi1996Concept.pdf},
file = {Lugosi1996Concept.pdf:local/Lugosi1996Concept.pdf:PDF},
keywords = {information-theory},
owner = {vert}
}
@article{Lugosi1995Nonparametric,
author = {Lugosi, G. and Zeger, K. },
title = {Nonparametric estimation via empirical risk minimization},
journal = {I{EEE} {T}rans. {I}nform. {T}heory},
year = {1995},
volume = {41},
pages = {677-687},
number = {3},
month = {May},
abstract = {A general notion of universal consistency of nonparametric estimators
is introduced that applies to regression estimation, conditional
median estimation, curve fitting, pattern recognition, and learning
concepts. {G}eneral methods for proving consistency of estimators
based on minimizing the empirical error are shown. {I}n particular,
distribution-free almost sure consistency of neural network estimates
and generalized linear estimators is established },
pdf = {../local/Lugosi1995Nonparametric.pdf},
file = {Lugosi1995Nonparametric.pdf:local/Lugosi1995Nonparametric.pdf:PDF},
keywords = {information-theory},
owner = {vert}
}
@article{Lukas2004Brain,
author = {L. Lukas and A. Devos and J. A K Suykens and L. Vanhamme and F. A.
Howe and C. Majós and A. Moreno-Torres and M. Van der Graaf and
A. R. Tate and C. Arús and S. Van Huffel},
title = {Brain tumor classification based on long echo proton {MRS} signals.},
journal = {Artif. {I}ntell. {M}ed.},
year = {2004},
volume = {31},
pages = {73-89},
number = {1},
month = {May},
abstract = {There has been a growing research interest in brain tumor classification
based on proton magnetic resonance spectroscopy (1{H} {MRS}) signals.
{F}our research centers within the {EU} funded {INTERPRET} project
have acquired a significant number of long echo 1{H} {MRS} signals
for brain tumor classification. {I}n this paper, we present an objective
comparison of several classification techniques applied to the discrimination
of four types of brain tumors: meningiomas, glioblastomas, astrocytomas
grade {II} and metastases. {L}inear and non-linear classifiers are
compared: linear discriminant analysis ({LDA}), support vector machines
({SVM}) and least squares {SVM} ({LS}-{SVM}) with a linear kernel
as linear techniques and {LS}-{SVM} with a radial basis function
({RBF}) kernel as a non-linear technique. {K}ernel-based methods
can perform well in processing high dimensional data. {T}his motivates
the inclusion of {SVM} and {LS}-{SVM} in this study. {T}he analysis
includes optimal input variable selection, (hyper-) parameter estimation,
followed by performance evaluation. {T}he classification performance
is evaluated over 200 stratified random samplings of the dataset
into training and test sets. {R}eceiver operating characteristic
({ROC}) curve analysis measures the performance of binary classification,
while for multiclass classification, we consider the accuracy as
performance measure. {B}ased on the complete magnitude spectra, automated
binary classifiers are able to reach an area under the {ROC} curve
({AUC}) of more than 0.9 except for the hard case glioblastomas versus
metastases. {A}lthough, based on the available long echo 1{H} {MRS}
data, we did not find any statistically significant difference between
the performances of {LDA} and the kernel-based methods, the latter
have the strength that no dimensionality reduction is required to
obtain such a high performance.},
doi = {10.1016/j.artmed.2004.01.001},
pdf = {../local/Lukas2004Brain.pdf},
file = {Lukas2004Brain.pdf:local/Lukas2004Brain.pdf:PDF},
pii = {S0933365704000120},
url = {http://dx.doi.org/10.1016/j.artmed.2004.01.001}
}
@article{Lunn1929Isomerism,
author = {A. C. Lunn and J. K. Senior},
title = {Isomerism and configuration},
journal = {J. Phys. Chem.},
year = {1929},
volume = {33},
pages = {1027--1079}
}
@inproceedings{Luo2000Alignement,
author = {Luo, B. and Hancock, E. R.},
title = {Alignment and Correspondence Using Singular Value Decomposition},
booktitle = {Proceedings of the Joint IAPR International Workshops on Advances
in Pattern Recognition},
year = {2000},
pages = {226--235},
address = {London, UK},
publisher = {Springer-Verlag},
isbn = {3-540-67946-4}
}
@article{Luo2004gene-silencing,
author = {Luo, K. Q. and Chang, D. C.},
title = {The gene-silencing efficiency of si{RNA} is strongly dependent on
the local structure of m{RNA} at the targeted region.},
journal = {Biochem. {B}iophys. {R}es. {C}ommun.},
year = {2004},
volume = {318},
pages = {303-10},
number = {1},
month = {May},
abstract = {The gene-silencing effect of short interfering {RNA} (si{RNA}) is
known to vary strongly with the targeted position of the m{RNA}.
{A} number of hypotheses have been suggested to explain this phenomenon.
{W}e would like to test if this positional effect is mainly due to
the secondary structure of the m{RNA} at the target site. {W}e proposed
that this structural factor can be characterized by a single parameter
called "the hydrogen bond ({H}-b) index," which represents the average
number of hydrogen bonds formed between nucleotides in the target
region and the rest of the m{RNA}. {T}his index can be determined
using a computational approach. {W}e tested the correlation between
the {H}-b index and the gene-silencing effects on three genes ({B}cl-2,
h{TF}, and cyclin {B}1) using a variety of si{RNA}s. {W}e found that
the gene-silencing effect is inversely dependent on the {H}-b index,
indicating that the local m{RNA} structure at the targeted site is
the main cause of the positional effect. {B}ased on this finding,
we suggest that the {H}-b index can be a useful guideline for future
si{RNA} design.},
doi = {10.1016/j.bbrc.2004.04.027},
keywords = {Animals, Apoptosis, Base Composition, Base Pairing, Base Sequence,
Binding Sites, Cell Cycle, Cell Proliferation, Comparative Study,
Cultured, Cyclin B, Cyclin D1, DNA-Binding Proteins, Down-Regulation,
Extramural, Fluorescence, Gene Silencing, Gene Targeting, Genetic
Vectors, Green Fluorescent Proteins, Hela Cells, Humans, Hydrogen
Bonding, Luminescent Proteins, Male, Messenger, Mice, Microscopy,
Models, Molecular, Molecular Sequence Data, N.I.H., Non-U.S. Gov't,
Nucleic Acid Conformation, Nude, P.H.S., Prostatic Neoplasms, Proto-Oncogene
Proteins c-bcl-2, Proto-Oncogene Proteins c-myc, RNA, Regression
Analysis, Research Support, STAT3 Transcription Factor, Small Interfering,
Thromboplastin, Trans-Activators, Tumor Cells, U.S. Gov't, 15110788},
pii = {S0006291X04007284},
url = {http://dx.doi.org/10.1016/j.bbrc.2004.04.027}
}
@article{Luo2004Recognizing,
author = {Tong Luo and Kurt Kramer and Dmitry B Goldgof and Lawrence O Hall
and Scott Samson and Andrew Remsen and Thomas Hopkins},
title = {Recognizing plankton images from the shadow image particle profiling
evaluation recorder.},
journal = {I{EEE} {T}rans {S}yst {M}an {C}ybern {B} {C}ybern},
year = {2004},
volume = {34},
pages = {1753-62},
number = {4},
month = {Aug},
abstract = {We present a system to recognize underwater plankton images from the
shadow image particle profiling evaluation recorder ({SIPPER}). {T}he
challenge of the {SIPPER} image set is that many images do not have
clear contours. {T}o address that, shape features that do not heavily
depend on contour information were developed. {A} soft margin support
vector machine ({SVM}) was used as the classifier. {W}e developed
a way to assign probability after multiclass {SVM} classification.
{O}ur approach achieved approximately 90\% accuracy on a collection
of plankton images. {O}n another larger image set containing manually
unidentifiable particles, it also provided 75.6\% overall accuracy.
{T}he proposed approach was statistically significantly more accurate
on the two data sets than a {C}4.5 decision tree and a cascade correlation
neural network. {T}he single {SVM} significantly outperformed ensembles
of decision trees created by bagging and random forests on the smaller
data set and was slightly better on the other data set. {T}he 15-feature
subset produced by our feature selection approach provided slightly
better accuracy than using all 29 features. {O}ur probability model
gave us a reasonable rejection curve on the larger data set.}
}
@article{Luo1997Mammalian,
author = {Y. Luo and A. Batalao and H. Zhou and L. Zhu},
title = {Mammalian two-hybrid system: a complementary approach to the yeast
two-hybrid system.},
journal = {Biotechniques},
year = {1997},
volume = {22},
pages = {350--352},
number = {2},
month = {Feb},
abstract = {Here we demonstrate the use of a mammalian two-hybrid system to study
protein-protein interactions. Like the yeast two-hybrid system, this
is a genetic, in vivo assay based on the reconstitution of the function
of a transcriptional activator. In this system, one protein of interest
is expressed as a fusion to the Gal4 DNA-binding domain and another
protein is expressed as a fusion to the activation domain of the
VP16 protein of the herpes simplex virus. The vectors that express
these fusion proteins are cotransfected with a reporter chloramphenicol
acetyltransferase (CAT) vector into a mammalian cell line. The reporter
plasmid contains a cat gene under the control of five consensus Gal4
binding sites. If the two fusion proteins interact, there will be
a significant increase in expression of the cat reporter gene. Previously,
it was reported that mouse p53 antitumor protein and simian virus
40 large T antigen interact in a yeast two-hybrid system. Using a
mammalian two-hybrid system, we were able to independently confirm
this interaction. The mammalian two-hybrid system can be used as
a complementary approach to verify protein-protein interactions detected
by a yeast two-hybrid system screening. In addition, the mammalian
two-hybrid system has two main advantages: (i) Assay results can
be obtained within 48 h of transfection, and (ii) protein interactions
in mammalian cells may better mimic actual in vivo interactions.},
institution = {CLONTECH Laboratories, Palo Alto, CA, USA. yluo@clontech.com},
keywords = {Antigens, Polyomavirus Transforming; Binding Sites; Chloramphenicol
O-Acetyltransferase; DNA; DNA-Binding Proteins; Fungal Proteins;
Genes, Reporter; Genetic Vectors; Hela Cells; Herpes Simplex Virus
Protein Vmw65; Humans; Promoter Regions, Genetic; Recombinant Fusion
Proteins; Saccharomyces cerevisiae Proteins; Simian virus 40; Transcription
Factors; Transfection; Tumor Suppressor Protein p53},
owner = {phupe},
pmid = {9043710},
timestamp = {2010.08.31}
}
@article{VonLuxburg2007A,
author = {von Luxburg, Ulrike},
title = {A tutorial on spectral clustering},
journal = {Statistics and Computing},
year = {2007},
volume = {17},
pages = {395--416},
number = {4},
month = {December},
abstract = {Abstract\ \ In recent years, spectral clustering has become
one of the most popular modern clustering algorithms. It is simple
to implement, can be solved efficiently by standard linear algebra
software, and very often outperforms traditional clustering algorithms
such as the k-means algorithm. On the first glance spectral clustering
appears slightly mysterious, and it is not obvious to see why it
works at all and what it really does. The goal of this tutorial is
to give some intuition on those questions. We describe different
graph Laplacians and their basic properties, present the most common
spectral clustering algorithms, and derive those algorithms from
scratch by several different approaches. Advantages and disadvantages
of the different spectral clustering algorithms are discussed.},
doi = {10.1007/s11222-007-9033-z},
url = {http://dx.doi.org/10.1007/s11222-007-9033-z}
}
@article{Lytle2007Target,
author = {J. Robin Lytle and Therese A Yario and Joan A Steitz},
title = {Target mRNAs are repressed as efficiently by microRNA-binding sites
in the 5' UTR as in the 3' UTR.},
journal = {Proc Natl Acad Sci U S A},
year = {2007},
volume = {104},
pages = {9667--9672},
number = {23},
month = {Jun},
abstract = {In animals, microRNAs (miRNAs) bind to the 3' UTRs of their target
mRNAs and interfere with translation, although the exact mechanism
of inhibition of protein synthesis remains unclear. Functional miRNA-binding
sites in the coding regions or 5' UTRs of endogenous mRNAs have not
been identified. We studied the effect of introducing miRNA target
sites into the 5' UTR of luciferase reporter mRNAs containing internal
ribosome entry sites (IRESs), so that potential steric hindrance
by a microribonucleoprotein complex would not interfere with the
initiation of translation. In human HeLa cells, which express endogenous
let-7a miRNA, the translational efficiency of these IRES-containing
reporters with 5' let-7 complementary sites from the Caenorhabditis
elegans lin-41 3' UTR was repressed. Similarly, the IRES-containing
reporters were translationally repressed when human Ago2 was tethered
to either the 5' or 3' UTR. Interestingly, the method of DNA transfection
affected our ability to observe miRNA-mediated repression. Our results
suggest that association with any position on a target mRNA is mechanistically
sufficient for a microribonucleoprotein to exert repression of translation
at some step downstream of initiation.},
doi = {10.1073/pnas.0703820104},
institution = {Department of Molecular Biophysics and Biochemistry, Howard Hughes
Medical Institute, Yale University School of Medicine, New Haven,
CT 06536, USA.},
keywords = {sirna},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {0703820104},
pmid = {17535905},
timestamp = {2009.10.28},
url = {http://dx.doi.org/10.1073/pnas.0703820104}
}
@article{Levy-Leduc2009Detection,
author = {L{\'e}vy-Leduc, C. and Roueff, F.},
title = {Detection and localization of change-points in high-dimensional network
traffic data},
journal = {Ann. Appl. Stat.},
year = {2009},
volume = {3},
pages = {637--662},
number = {2},
doi = {10.1214/08-AOAS232},
pdf = {../local/Levy-Leduc2009Detection.pdf},
file = {Levy-Leduc2009Detection.pdf:Levy-Leduc2009Detection.pdf:PDF},
owner = {jp},
timestamp = {2011.04.13},
url = {http://dx.doi.org/10.1214/08-AOAS232}
}
@article{Lopez-Bigas2004Genome-wide,
author = {L{\'o}pez-Bigas, N. and Ouzounis, C. A.},
title = {Genome-wide identification of genes likely to be involved in human
genetic disease},
journal = {Nucleic Acids Res.},
year = {2004},
volume = {32},
pages = {3108--3114},
number = {10},
abstract = {Sequence analysis of the group of proteins known to be associated
with hereditary diseases allows the detection of key distinctive
features shared within this group. The disease proteins are characterized
by greater length of their amino acid sequence, a broader phylogenetic
extent, and specific conservation and paralogy profiles compared
with all human proteins. This unique property pattern provides insights
into the global nature of hereditary diseases and moreover can be
used to predict novel disease genes. We have developed a computational
method that allows the detection of genes likely to be involved in
hereditary disease in the human genome. The probability score assignments
for the human genome are accessible at http://maine.ebi. ac.uk:8000/services/dgp.},
doi = {10.1093/nar/gkh605},
pdf = {../local/Lopez-Bigas2004Genome-wide.pdf},
file = {Lopez-Bigas2004Genome-wide.pdf:Lopez-Bigas2004Genome-wide.pdf:PDF},
institution = {Cambridge Outstation, Cambridge CB10 1SD, UK.},
owner = {jp},
pii = {32/10/3108},
pmid = {15181176},
timestamp = {2009.03.18},
url = {http://dx.doi.org/10.1093/nar/gkh605}
}
@article{Loenning2007Breast,
author = {L{\o}nning, P. E.},
title = {Breast cancer prognostication and prediction: are we making progress?},
journal = {Ann. Oncol.},
year = {2007},
volume = {18 Suppl 8},
pages = {viii3--viii7},
month = {Sep},
abstract = {Currently, much effort is being invested in the identification of
new, accurate prognostic and predictive factors in breast cancer.
Prognostic factors assess the patient's risk of relapse based on
indicators such as intrinsic tumor biology and disease stage at diagnosis,
and are traditionally used to identify patients who can be spared
unnecessary adjuvant therapy based only on the risk of relapse. Lymph
node status and tumor size are accepted as well-defined prognostic
factors in breast cancer. Predictive factors, in contrast, determine
the responsiveness of a particular tumor to a specific treatment.
Despite recent advances in the understanding of breast cancer biology
and changing practices in disease management, with the exception
of hormone receptor status, which predicts responsiveness to endocrine
treatment, no predictive factor for response to systemic therapy
in breast cancer is widely accepted. While gene expression studies
have provided important new information with regard to tumor biology
and prognostication, attempts to identify predictive factors have
not been successful so far. This article will focus on recent advances
in prognostication and prediction, with emphasis on findings from
gene expression profiling studies.},
doi = {10.1093/annonc/mdm260},
pdf = {../local/Loenning2007Breast.pdf},
file = {Loenning2007Breast.pdf:Loenning2007Breast.pdf:PDF},
institution = {Institute of Medicine, University of Bergen, Department of Oncology,
Haukeland University Hospital, Bergen, Norway. per.lonning@helse-bergen.no},
keywords = {csbcbook, csbcbook-ch3},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {18/suppl_8/viii3},
pmid = {17890212},
timestamp = {2011.04.07},
url = {http://dx.doi.org/10.1093/annonc/mdm260}
}
@article{Ma2001Hormone-dependent,
author = {H. Ma and C. T. Baumann and H. Li and B. D. Strahl and R. Rice and
M. A. Jelinek and D. W. Aswad and C. D. Allis and G. L. Hager and
M. R. Stallcup},
title = {Hormone-dependent, CARM1-directed, arginine-specific methylation
of histone H3 on a steroid-regulated promoter.},
journal = {Curr Biol},
year = {2001},
volume = {11},
pages = {1981--1985},
number = {24},
month = {Dec},
abstract = {Activation of gene transcription involves chromatin remodeling by
coactivator proteins that are recruited by DNA-bound transcription
factors. Local modification of chromatin structure at specific gene
promoters by ATP-dependent processes and by posttranslational modifications
of histone N-terminal tails provides access to RNA polymerase II
and its accompanying transcription initiation complex. While the
roles of lysine acetylation, serine phosphorylation, and lysine methylation
of histones in chromatin remodeling are beginning to emerge, low
levels of arginine methylation of histones have only recently been
documented, and its physiological role is unknown. The coactivator
CARM1 methylates histone H3 at Arg17 and Arg26 in vitro and cooperates
synergistically with p160-type coactivators (e.g., GRIP1, SRC-1,
ACTR) and coactivators with histone acetyltransferase activity (e.g.,
p300, CBP) to enhance gene activation by steroid and nuclear hormone
receptors (NR) in transient transfection assays. In the current study,
CARM1 cooperated with GRIP1 to enhance steroid hormone-dependent
activation of stably integrated mouse mammary tumor virus (MMTV)
promoters, and this coactivator function required the methyltransferase
activity of CARM1. Chromatin immunoprecipitation assays and immunofluorescence
studies indicated that CARM1 and the CARM1-methylated form of histone
H3 specifically associated with a large tandem array of MMTV promoters
in a hormone-dependent manner. Thus, arginine-specific histone methylation
by CARM1 is an important part of the transcriptional activation process.},
institution = {Department of Pathology, HMR 301, University of Southern California,
2011 Zonal Avenue, Los Angeles, CA 90089, USA.},
keywords = {Acetylation; Arginine, metabolism; Fluorescent Antibody Technique;
Histones, chemistry/metabolism; Hormones, physiology; Lysine, metabolism;
Mammary Tumor Virus, Mouse, genetics; Methylation; Phosphorylation;
Precipitin Tests; Promoter Regions, Genetic; Protein-Arginine N-Methyltransferases,
physiology; Serine, metabolism; Steroids, physiology},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {S0960-9822(01)00600-5},
pmid = {11747826},
timestamp = {2010.11.23}
}
@article{Ma2004Structural,
author = {Ma, J.B. and Ye, K. and Patel, D.J.},
title = {{S}tructural basis for overhang-specific small interfering {RNA}
recognition by the {PAZ} domain.},
journal = {Nature},
year = {2004},
volume = {429},
pages = {318--322},
number = {6989},
month = {May},
abstract = {Short RNAs mediate gene silencing, a process associated with virus
resistance, developmental control and heterochromatin formation in
eukaryotes. RNA silencing is initiated through Dicer-mediated processing
of double-stranded RNA into small interfering RNA (siRNA). The siRNA
guide strand associates with the Argonaute protein in silencing effector
complexes, recognizes complementary sequences and targets them for
silencing. The PAZ domain is an RNA-binding module found in Argonaute
and some Dicer proteins and its structure has been determined in
the free state. Here, we report the 2.6 A crystal structure of the
PAZ domain from human Argonaute eIF2c1 bound to both ends of a 9-mer
siRNA-like duplex. In a sequence-independent manner, PAZ anchors
the 2-nucleotide 3' overhang of the siRNA-like duplex within a highly
conserved binding pocket, and secures the duplex by binding the 7-nucleotide
phosphodiester backbone of the overhang-containing strand and capping
the 5'-terminal residue of the complementary strand. On the basis
of the structure and on binding assays, we propose that PAZ might
serve as an siRNA-end-binding module for siRNA transfer in the RNA
silencing pathway, and as an anchoring site for the 3' end of guide
RNA within silencing effector complexes.},
doi = {10.1038/nature02519},
keywords = {sirna},
pii = {nature02519},
pmid = {15152257},
timestamp = {2006.07.08},
url = {http://dx.doi.org/10.1038/nature02519}
}
@article{Ma2005Structural,
author = {Ma, J-B. and Yuan, Y.-R. and Meister, G.. and Pei, Y. and Tuschl,
T. and Patel D.J.},
title = {Structural basis for 5'-end-specific recognition of guide {RNA} by
the {A}. fulgidus {PIWI} protein},
journal = {Nature},
year = {2005},
volume = {434},
pages = {666-670},
keywords = {sirna},
owner = {vert},
timestamp = {2006.04.27}
}
@article{Ma2005PNAS,
author = {Ma, L. and Wagner, J. and Rice, J. J. and Hu, W. and Levine, A. J.
and Stolovitzky, G. A.},
title = {A plausible model for the digital response of p53 to DNA damage},
journal = {Proc Natl Acad Sci U S A},
year = {2005},
volume = {102},
pages = {14266--71},
number = {40},
abstract = {Recent observations show that the single-cell response of p53 to ionizing
radiation (IR) is "digital" in that it is the number of oscillations
rather than the amplitude of p53 that shows dependence on the radiation
dose. We present a model of this phenomenon. In our model, double-strand
break (DSB) sites induced by IR interact with a limiting pool of
DNA repair proteins, forming DSB-protein complexes at DNA damage
foci. The persisting complexes are sensed by ataxia telangiectasia
mutated (ATM), a protein kinase that activates p53 once it is phosphorylated
by DNA damage. The ATM-sensing module switches on or off the downstream
p53 oscillator, consisting of a feedback loop formed by p53 and its
negative regulator, Mdm2. In agreement with experiments, our simulations
show that by assuming stochasticity in the initial number of DSBs
and the DNA repair process, p53 and Mdm2 exhibit a coordinated oscillatory
dynamics upon IR stimulation in single cells, with a stochastic number
of oscillations whose mean increases with IR dose. The damped oscillations
previously observed in cell populations can be explained as the aggregate
behavior of single cells.},
keywords = {csbcbook}
}
@article{Ma2008Penalized,
author = {Ma, S. and Huang, J.},
title = {Penalized feature selection and classification in bioinformatics},
journal = {Briefings in bioinformatics},
year = {2008},
volume = {9},
pages = {392--403},
number = {5},
publisher = {Oxford Univ Press}
}
@article{Ma2006MSB,
author = {Wenzhe Ma and Luhua Lai and Qi Ouyang and Chao Tang},
title = {Robustness and modular design of the Drosophila segment polarity
network.},
journal = {Mol Syst Biol},
year = {2006},
volume = {2},
pages = {70},
abstract = {Biomolecular networks have to perform their functions robustly. A
robust function may have preferences in the topological structures
of the underlying network. We carried out an exhaustive computational
analysis on network topologies in relation to a patterning function
in Drosophila embryogenesis. We found that whereas the vast majority
of topologies can either not perform the required function or only
do so very fragilely, a small fraction of topologies emerges as particularly
robust for the function. The topology adopted by Drosophila, that
of the segment polarity network, is a top ranking one among all topologies
with no direct autoregulation. Furthermore, we found that all robust
topologies are modular-each being a combination of three kinds of
modules. These modules can be traced back to three subfunctions of
the patterning function, and their combinations provide a combinatorial
variability for the robust topologies. Our results suggest that the
requirement of functional robustness drastically reduces the choices
of viable topology to a limited set of modular combinations among
which nature optimizes its choice under evolutionary and other biological
constraints.},
doi = {10.1038/msb4100111},
institution = {Center for Theoretical Biology, Peking University, Beijing, China.},
keywords = {Animals; Biological Evolution; Body Patterning; Computer Simulation;
Drosophila Proteins, physiology; Drosophila melanogaster, anatomy
/&/ histology/physiology; Feedback, Physiological; Gene Expression
Regulation, Developmental; Genes, Insect; Models, Biological; Signal
Transduction; Systems Biology, methods; Transcription Factors},
language = {eng},
medline-pst = {ppublish},
owner = {Andrei Zinovyev},
pii = {msb4100111},
pmid = {17170765},
timestamp = {2011.04.08},
url = {http://dx.doi.org/10.1038/msb4100111}
}
@article{Ma2003GadE,
author = {Ma, Z. and Gong, S. and Richard, H. and Tucker, D. L. and Conway,
T. and Foster, J. W.},
title = {{G}ad{E} ({Y}hi{E}) activates glutamate decarboxylase-dependent acid
resistance in {E}scherichia coli {K}-12.},
journal = {Mol. Microbiol.},
year = {2003},
volume = {49},
pages = {1309--1320},
number = {5},
month = {Sep},
abstract = {Commensal and pathogenic strains of Escherichia coli possess three
inducible acid resistance systems that collaboratively protect cells
against acid stress to pH 2 or below. The most effective system requires
glutamate in the acid challenge media and relies on two glutamate
decarboxylases (GadA and B) combined with a putative glutamate:gamma-aminobutyric
acid antiporter (GadC). A complex network of regulators mediates
induction of this system in response to various media, pH and growth
phase signals. We report that the LuxR-like regulator GadE (formerly
YhiE) is required for expression of gadA and gadBC regardless of
media or growth conditions. This protein binds directly to the 20
bp GAD box sequence found in the control regions of both loci. Two
previously identified AraC-like regulators, GadX and GadW, are only
needed for gadA/BC expression under some circumstances. Overexpression
of GadX or GadW will not overcome a need for GadE. However, overexpression
of GadE can supplant a requirement for GadX and W. Data provided
also indicate that GadX and GadE can simultaneously bind the area
around the GAD box region and probably form a complex. The gadA,
gadBC and gadE genes are all induced by low pH in exponential phase
cells grown in minimal glucose media. The acid induction of gadA/BC
results primarily from the acid induction of gadE. Constitutive expression
of GadE removes most pH control over the glutamate decarboxylase
and antiporter genes. The small amount of remaining pH control is
governed by GadX and W. The finding that gadE mutations also diminish
the effectiveness of the other two acid resistance systems suggests
that GadE influences the expression of additional acid resistance
components. The number of regulatory proteins (five), sigma factors
(two) and regulatory feedback loops focused on gadA/BC expression
make this one of the most intensively regulated systems in E. coli.},
pii = {3633},
pmid = {12940989},
timestamp = {2008.02.12}
}
@article{Maby2004Analysis,
author = {E. Maby and R. Le Bouquin Jeannès and C. Liégeois-Chauvel and B.
Gourevitch and G. Faucon},
title = {Analysis of auditory evoked potential parameters in the presence
of radiofrequency fields using a support vector machines method.},
journal = {Med {B}iol {E}ng {C}omput},
year = {2004},
volume = {42},
pages = {562-8},
number = {4},
month = {Jul},
abstract = {The paper presents a study of global system for mobile ({GSM}) phone
radiofrequency effects on human cerebral activity. {T}he work was
based on the study of auditory evoked potentials ({AEP}s) recorded
from healthy humans and epileptic patients. {T}he protocol allowed
the comparison of {AEP}s recorded with or without exposure to electrical
fields. {T}en variables measured from {AEP}s were employed in the
design of a supervised support vector machines classifier. {T}he
classification performance measured the classifier's ability to discriminate
features performed with or without radiofrequency exposure. {M}ost
significant features were chosen by a backward sequential selection
that ranked the variables according to their pertinence for the discrimination.
{F}inally, the most discriminating features were analysed statistically
by a {W}ilcoxon signed rank test. {F}or both populations, the {N}100
amplitudes were reduced under the influence of {GSM} radiofrequency
(mean attenuation of -0.36 micro{V} for healthy subjects and -0.60
micro{V} for epileptic patients). {H}ealthy subjects showed a {N}100
latency decrease (-5.23 ms in mean), which could be consistent with
mild, localised heating. {T}he auditory cortical activity in humans
was modified by {GSM} phone radiofrequencies, but an effect on brain
functionality has not been proven.}
}
@article{MacBeath2002Protein,
author = {Gavin MacBeath},
title = {Protein microarrays and proteomics.},
journal = {Nat Genet},
year = {2002},
volume = {32 Suppl},
pages = {526--532},
month = {Dec},
abstract = {The system-wide study of proteins presents an exciting challenge in
this information-rich age of whole-genome biology. Although traditional
investigations have yielded abundant information about individual
proteins, they have been less successful at providing us with an
integrated understanding of biological systems. The promise of proteomics
is that, by studying many components simultaneously, we will learn
how proteins interact with each other, as well as with non-proteinaceous
molecules, to control complex processes in cells, tissues and even
whole organisms. Here, I discuss the role of microarray technology
in this burgeoning area.},
doi = {10.1038/ng1037},
institution = {Department of Chemistry and Chemical Biology, and Bauer Center for
Genomics Research, Harvard University, 12 Oxford Street, Cambridge,
Massachusetts 02138, USA. macbeath@chemistry.harvard.edu},
keywords = {Forecasting; Humans; Immunoassay, methods; Protein Array Analysis,
methods; Proteomics, methods},
language = {eng},
medline-pst = {ppublish},
owner = {philippe},
pii = {ng1037},
pmid = {12454649},
timestamp = {2010.07.28},
url = {http://dx.doi.org/10.1038/ng1037}
}
@article{Machado2005Detection,
author = {Roberto F Machado and Daniel Laskowski and Olivia Deffenderfer and
Timothy Burch and Shuo Zheng and Peter J Mazzone and Tarek Mekhail
and Constance Jennings and James K Stoller and Jacqueline Pyle and
Jennifer Duncan and Raed A Dweik and Serpil C Erzurum},
title = {Detection of lung cancer by sensor array analyses of exhaled breath.},
journal = {Am {J} {R}espir {C}rit {C}are {M}ed},
year = {2005},
volume = {171},
pages = {1286-91},
number = {11},
month = {Jun},
abstract = {R{ATIONALE}: {E}lectronic noses are successfully used in commercial
applications, including detection and analysis of volatile organic
compounds in the food industry. {OBJECTIVES}: {W}e hypothesized that
the electronic nose could identify and discriminate between lung
diseases, especially bronchogenic carcinoma. {METHODS}: {I}n a discovery
and training phase, exhaled breath of 14 individuals with bronchogenic
carcinoma and 45 healthy control subjects or control subjects without
cancer was analyzed. {P}rincipal components and canonic discriminant
analysis of the sensor data was used to determine whether exhaled
gases could discriminate between cancer and noncancer. {D}iscrimination
between classes was performed using {M}ahalanobis distance. {S}upport
vector machine analysis was used to create and apply a cancer prediction
model prospectively in a separate group of 76 individuals, 14 with
and 62 without cancer. {MAIN} {RESULTS}: {P}rincipal components and
canonic discriminant analysis demonstrated discrimination between
samples from patients with lung cancer and those from other groups.
{I}n the validation study, the electronic nose had 71.4\% sensitivity
and 91.9\% specificity for detecting lung cancer; positive and negative
predictive values were 66.6 and 93.4\%, respectively. {I}n this population
with a lung cancer prevalence of 18\%, positive and negative predictive
values were 66.6 and 94.5\%, respectively. {CONCLUSION}: {T}he exhaled
breath of patients with lung cancer has distinct characteristics
that can be identified with an electronic nose. {T}he results provide
feasibility to the concept of using the electronic nose for managing
and detecting lung cancer.},
doi = {10.1164/rccm.200409-1184OC},
pdf = {../local/Machado2005Detection.pdf},
file = {Machado2005Detection.pdf:local/Machado2005Detection.pdf:PDF},
pii = {200409-1184OC},
url = {http://dx.doi.org/10.1164/rccm.200409-1184OC}
}
@inproceedings{MacQueen1967Some,
author = {MacQueen, J.B.},
title = {Some Methods for classification and Analysis of Multivariate Observations},
booktitle = {{P}roceedings of 5th {B}erkeley {S}ymposium on {M}athematical {S}tatistics
and {P}robability},
year = {1967},
pages = {281-297},
publisher = {{U}niversity of {C}alifornia {P}ress},
owner = {kb},
timestamp = {2011.05.05}
}
@article{Madeira2004Biclustering,
author = {Madeira, S. C. and Oliveira, A. L.},
title = {Biclustering algorithms for biological data analysis: a survey.},
journal = {IEEE/ACM Trans Comput Biol Bioinform},
year = {2004},
volume = {1},
pages = {24--45},
number = {1},
abstract = {A large number of clustering approaches have been proposed for the
analysis of gene expression data obtained from microarray experiments.
However, the results from the application of standard clustering
methods to genes are limited. This limitation is imposed by the existence
of a number of experimental conditions where the activity of genes
is uncorrelated. A similar limitation exists when clustering of conditions
is performed. For this reason, a number of algorithms that perform
simultaneous clustering on the row and column dimensions of the data
matrix has been proposed. The goal is to find submatrices, that is,
subgroups of genes and subgroups of conditions, where the genes exhibit
highly correlated activities for every condition. In this paper,
we refer to this class of algorithms as biclustering. Biclustering
is also referred in the literature as coclustering and direct clustering,
among others names, and has also been used in fields such as information
retrieval and data mining. In this comprehensive survey, we analyze
a large number of existing approaches to biclustering, and classify
them in accordance with the type of biclusters they can find, the
patterns of biclusters that are discovered, the methods used to perform
the search, the approaches used to evaluate the solution, and the
target applications.},
doi = {10.1109/TCBB.2004.2},
institution = {University of Beira Interior, Rua Marquês D'Avila e Bolama, Covilhã,
Portugal. smadeira@di.ubi.pt},
keywords = {Algorithms; Cluster Analysis; Computational Biology, methods; Gene
Expression Profiling, statistics /&/ numerical data; Gene Expression,
genetics; Humans; Models, Statistical; Oligonucleotide Array Sequence
Analysis, methods; Saccharomyces cerevisiae, genetics},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pmid = {17048406},
timestamp = {2012.02.27},
url = {http://dx.doi.org/10.1109/TCBB.2004.2}
}
@article{Maglogiannis2004Characterization,
author = {Maglogiannis, I. G. and Zafiropoulos, E. P.},
title = {Characterization of digital medical images utilizing support vector
machines},
journal = {B{MC} {M}ed. {I}nformat. {D}ecis. {M}aking},
year = {2004},
volume = {4},
number = {4},
abstract = {Background {I}n this paper we discuss an efficient methodology for
the image analysis and characterization of digital images containing
skin lesions using {S}upport {V}ector {M}achines and present the
results of a preliminary study. {M}ethods {T}he methodology is based
on the support vector machines algorithm for data classification
and it has been applied to the problem of the recognition of malignant
melanoma versus dysplastic naevus. {B}order and colour based features
were extracted from digital images of skin lesions acquired under
reproducible conditions, using basic image processing techniques.
{T}wo alternative classification methods, the statistical discriminant
analysis and the application of neural networks were also applied
to the same problem and the results are compared. {R}esults {T}he
{SVM} ({S}upport {V}ector {M}achines) algorithm performed quite well
achieving 94.1% correct classification, which is better than the
performance of the other two classification methodologies. {T}he
method of discriminant analysis classified correctly 88% of cases
(71% of {M}alignant {M}elanoma and 100% of {D}ysplastic {N}aevi),
while the neural networks performed approximately the same. {C}onclusion
{T}he use of a computer-based system, like the one described in this
paper, is intended to avoid human subjectivity and to perform specific
tasks according to a number of criteria. {H}owever the presence of
an expert dermatologist is considered necessary for the overall visual
assessment of the skin lesion and the final diagnosis.},
doi = {10.1186/1472-6947-4-4},
pdf = {../local/Maglogiannis2004Characterization.pdf},
file = {Maglogiannis2004Characterization.pdf:local/Maglogiannis2004Characterization.pdf:PDF},
owner = {vert}
}
@techreport{Mahe2006pharmacophorea,
author = {P. Mah\'{e} and L. Ralaivola and V. Stoven and J.-P. Vert},
title = {The pharmacophore kernel for virtual screening with support vector
machines},
institution = {Ecole des {M}ines de {P}aris},
year = {2006},
number = {Technical Report HAL:ccsd-00020066},
month = {march},
keywords = {chemoinformatics kernel-theory},
owner = {mahe},
timestamp = {2006.07.31},
url = {http://hal.ccsd.cnrs.fr/ccsd-00020066}
}
@article{Mahe2009Graph,
author = {Mah\'{e}, P. and Vert, J. P.},
title = {Graph kernels based on tree patterns for molecules},
journal = {Mach. Learn.},
year = {2009},
volume = {75},
pages = {3--35},
number = {1},
doi = {10.1007/s10994-008-5086-2},
pdf = {../local/Mahe2009Graph.pdf},
file = {Mahe2009Graph.pdf:Mahe2009Graph.pdf:PDF},
owner = {jp},
timestamp = {2009.03.10},
url = {http://dx.doi.org/10.1007/s10994-008-5086-2}
}
@article{Mahe2006Pharmacophore,
author = {P. Mah{\'e} and L. Ralaivola and V. Stoven and J.-P. Vert},
title = {The Pharmacophore Kernel for Virtual Screening with Support Vector
Machines},
journal = {J. Chem. Inf. Model.},
year = {2006},
volume = {46},
pages = {2003-2014},
number = {5},
abstract = {We introduce a family of positive definite kernels specifically optimized
for the manipulation of 3D structures of molecules with kernel methods.
The kernels are based on the comparison of the three-point pharmacophores
present in the 3D structures of molecules, a set of molecular features
known to be particularly relevant for virtual screening applications.
We present a computationally demanding exact implementation of these
kernels, as well as fast approximations related to the classical
fingerprint-based approaches. Experimental results suggest that this
new approach is competitive with state-of-the-art algorithms based
on the 2D structure of molecules for the detection of inhibitors
of several drug targets.},
doi = {10.1021/ci060138m},
pdf = {../local/Mahe2006Pharmacophore.pdf},
file = {Mahe2006Pharmacophore.pdf:Mahe2006Pharmacophore.pdf:PDF},
keywords = {chemoinformatics},
owner = {mahe},
timestamp = {2006.09.13},
url = {http://dx.doi.org/10.1021/ci060138m}
}
@inproceedings{Mahe2004Extensions,
author = {Mah{\'e}, P. and Ueda, N. and Akutsu, T. and Perret, J.-L. and Vert,
J.-P.},
title = {Extensions of marginalized graph kernels},
booktitle = {Proceedings of the {T}wenty-{F}irst {I}nternational {C}onference
on {M}achine {L}earning ({ICML} 2004)},
year = {2004},
editor = {Greiner, R. and Schuurmans, D.},
pages = {552-559},
publisher = {ACM Press},
abstract = {Positive definite kernels between labeled graphs have recently been
proposed.{T}hey enable the application of kernel methods, such as
support vectormachines, to the analysis and classification of graphs,
for example, chemicalcompounds. {T}hese graph kernels are obtained
by marginalizing a kernel betweenpaths with respect to a random walk
model on the graph vertices along theedges. {W}e propose two extensions
of these graph kernels, with the double goal toreduce their computation
time and increase their relevance as measure ofsimilarity between
graphs. {F}irst, we propose to modify the label of eachvertex by
automatically adding information about its environment with the useof
the {M}organ algorithm. {S}econd, we suggest a modification of the
random walkmodel to prevent the walk from coming back to a vertex
that was just visited.{T}hese extensions are then tested on benchmark
experiments of chemicalcompounds classification, with promising results.},
pdf = {../local/icmlMod.pdf:http\://cg.ensmp.fr/~vert/publi/04icml/icmlMod.pdf:PDF;icmlMod.pdf:http\},
file = {icmlMod.pdf:http\://cg.ensmp.fr/~vert/publi/04icml/icmlMod.pdf:PDF;icmlMod.pdf:http\://cg.ensmp.fr/~vert/publi/04icml/icmlMod.pdf:PDF},
keywords = {biosvm chemoinformatics},
owner = {vert}
}
@article{Mahe2005Graph,
author = {Mah{\'e}, P. and Ueda, N. and Akutsu, T. and Perret, J.-L. and Vert,
J.-P.},
title = {Graph kernels for molecular structure-activity relationship analysis
with support vector machines},
journal = {J. Chem. Inf. Model.},
year = {2005},
volume = {45},
pages = {939-51},
number = {4},
abstract = {The support vector machine algorithm together with graph kernel functions
has recently been introduced to model structure-activity relationships
({SAR}) of molecules from their 2{D} structure, without the need
for explicit molecular descriptor computation. {W}e propose two extensions
to this approach with the double goal to reduce the computational
burden associated with the model and to enhance its predictive accuracy:
description of the molecules by a {M}organ index process and definition
of a second-order {M}arkov model for random walks on 2{D} structures.
{E}xperiments on two mutagenicity data sets validate the proposed
extensions, making this approach a possible complementary alternative
to other modeling strategies.},
doi = {10.1021/ci050039t},
pdf = {../local/Mahe2005Graph.pdf},
file = {Mahe2005Graph.pdf:local/Mahe2005Graph.pdf:PDF},
keywords = {biosvm chemoinformatics},
url = {http://dx.doi.org/10.1021/ci050039t}
}
@techreport{Mahe2006Graph,
author = {P. Mah{\'e} and J.-P. Vert},
title = {Graph kernels based on tree patterns for molecules},
institution = {HAL},
year = {2006},
number = {ccsd-00095488},
month = {September},
keywords = {chemoinformatics kernel-theory},
location = {Mines ParisTech},
owner = {mahe},
timestamp = {2006.10.10},
url = {https://hal.ccsd.cnrs.fr/ccsd-00095488}
}
@phdthesis{Mairal2010Sparse,
author = {Mairal, J.},
title = {Sparse coding for machine learning, image processing and computer
vision},
school = {{\'E}cole normale sup{\'e}rieure de Cachan-ENS Cachan},
year = {2010}
}
@article{Mairal2010Online,
author = {Mairal, J. and Bach, F. and Ponce, J. and Sapiro, G.},
title = {Online Learning for Matrix Factorization and Sparse Coding},
journal = {J. Mach. Learn. Res.},
year = {2010},
volume = {11},
pages = {19--60},
abstract = {Sparse coding—that is, modelling data vectors as sparse linear combinations
of basis elements—is widely used in machine learning, neuroscience,
signal processing, and statistics. This paper fo- cuses on the large-scale
matrix factorization problem that consists of learning the basis
set in order to adapt it to specific data. Variations of this problem
include dictionary learning in signal pro- cessing, non-negative
matrix factorization and sparse principal component analysis. In
this paper, we propose to address these tasks with a new online optimization
algorithm, based on stochastic approximations, which scales up gracefully
to large data sets with millions of training samples, and extends
naturally to various matrix factorization formulations, making it
suitable for a wide range of learning problems. A proof of convergence
is presented, along with experiments with natural images and genomic
data demonstrating that it leads to state-of-the-art performance
in terms of speed and optimization for both small and large data
sets.},
pdf = {../local/Mairal2010Online.pdf},
file = {Mairal2010Online.pdf:Mairal2010Online.pdf:PDF},
owner = {jp},
timestamp = {2010.04.13},
url = {http://jmlr.csail.mit.edu/papers/v11/mairal10a.html}
}
@unpublished{Mairal2012Path,
author = {Mairal, J. and Yu, .B},
title = {Path Coding Penalties for Directed Acyclic Graphs},
year = {2012},
owner = {jp},
timestamp = {2012.03.06}
}
@article{Majoros2005Efficient,
author = {Majoros, W. H. and Pertea, L. and Salzberg, S. L.},
title = {Efficient implementation of a generalized pair hidden {M}arkov model
for comparative gene finding.},
journal = {Bioinformatics},
year = {2005},
volume = {21},
pages = {1782--1788},
number = {9},
month = {May},
abstract = {M{OTIVATION}: {T}he increased availability of genome sequences of
closely related organisms has generated much interest in utilizing
homology to improve the accuracy of gene prediction programs. {G}eneralized
pair hidden {M}arkov models ({GPHMM}s) have been proposed as one
means to address this need. {H}owever, all {GPHMM} implementations
currently available are either closed-source or the details of their
operation are not fully described in the literature, leaving a significant
hurdle for others wishing to advance the state of the art in {GPHMM}
design. {RESULTS}: {W}e have developed an open-source {GPHMM} gene
finder, {TWAIN}, which performs very well on two related {A}spergillus
species, {A}.fumigatus and {A}.nidulans, finding 89\% of the exons
and predicting 74\% of the gene models exactly correctly in a test
set of 147 conserved gene pairs. {W}e describe the implementation
of this {GPHMM} and we explicitly address the assumptions and limitations
of the system. {W}e suggest possible ways of relaxing those assumptions
to improve the utility of the system without sacrificing efficiency
beyond what is practical. {AVAILABILITY}: {A}vailable at http://www.tigr.org/software/pirate/twain/twain.html
under the open-source {A}rtistic {L}icense.},
doi = {10.1093/bioinformatics/bti297},
pdf = {../local/Majoros2005Efficient.pdf},
file = {Majoros2005Efficient.pdf:local/Majoros2005Efficient.pdf:PDF},
keywords = {biogm},
owner = {vert},
pii = {bti297},
pmid = {15691859},
timestamp = {2006.01.18},
url = {http://dx.doi.org/10.1093/bioinformatics/bti297}
}
@article{Majumder2005Relevance,
author = {Shovan K Majumder and Nirmalya Ghosh and Pradeep K Gupta},
title = {Relevance vector machine for optical diagnosis of cancer.},
journal = {Lasers {S}urg {M}ed},
year = {2005},
volume = {36},
pages = {323-33},
number = {4},
month = {Apr},
abstract = {B{ACKGROUND} {AND} {OBJECTIVES}: {A} probability-based, robust diagnostic
algorithm is an essential requirement for successful clinical use
of optical spectroscopy for cancer diagnosis. {T}his study reports
the use of the theory of relevance vector machine ({RVM}), a recent
{B}ayesian machine-learning framework of statistical pattern recognition,
for development of a fully probabilistic algorithm for autofluorescence
diagnosis of early stage cancer of human oral cavity. {I}t also presents
a comparative evaluation of the diagnostic efficacy of the {RVM}
algorithm with that based on support vector machine ({SVM}) that
has recently received considerable attention for this purpose. {STUDY}
{DESIGN}/{MATERIALS} {AND} {METHODS}: {T}he diagnostic algorithms
were developed using in vivo autofluorescence spectral data acquired
from human oral cavity with a {N}(2) laser-based portable fluorimeter.
{T}he spectral data of both patients as well as normal volunteers,
enrolled at {O}ut {P}atient department of the {G}ovt. {C}ancer {H}ospital,
{I}ndore for screening of oral cavity, were used for this purpose.
{T}he patients selected had no prior confirmed malignancy and were
diagnosed of squamous cell carcinoma ({SCC}), {G}rade-{I} on the
basis of histopathology of biopsy taken from abnormal site subsequent
to acquisition of spectra. {A}utofluorescence spectra were recorded
from a total of 171 tissue sites from 16 patients and 154 healthy
squamous tissue sites from 13 normal volunteers. {O}f 171 tissues
sites from patients, 83 were {SCC} and the rest were contralateral
uninvolved squamous tissue. {E}ach site was treated separately and
classified via the diagnostic algorithm developed. {I}nstead of the
spectral data from uninvolved sites of patients, the data from normal
volunteers were used as the normal database for the development of
diagnostic algorithms. {RESULTS}: {T}he diagnostic algorithms based
on {RVM} were found to provide classification performance comparable
to the state-of-the-art {SVM}s, while at the same time explicitly
predicting the probability of class membership. {T}he sensitivity
and specificity towards cancer were up to 88\% and 95\% for the training
set data based on leave- one-out cross validation and up to 91\%
and 96\% for the validation set data. {W}hen implemented on the spectral
data of the uninvolved oral cavity sites from the patients, it yielded
a specificity of up to 91\%. {CONCLUSIONS}: {T}he {B}ayesian framework
of {RVM} formulation makes it possible to predict the posterior probability
of class membership in discriminating early {SCC} from the normal
squamous tissue sites of the oral cavity in contrast to dichotomous
classification provided by the non-{B}ayesian {SVM}. {S}uch classification
is very helpful in handling asymmetric misclassification costs like
assigning different weights for having a false negative result for
identifying cancer compared to false positive. {T}he results further
demonstrate that for comparable diagnostic performances, the {RVM}-based
algorithms use significantly fewer kernel functions and do not need
to estimate any hoc parameters associated with the learning or the
optimization technique to be used. {T}his implies a considerable
saving in memory and computation in a practical implementation.},
doi = {10.1002/lsm.20160},
pdf = {../local/Majumder2005Relevance.pdf},
file = {Majumder2005Relevance.pdf:local/Majumder2005Relevance.pdf:PDF},
keywords = {, , 15825208},
url = {http://dx.doi.org/10.1002/lsm.20160}
}
@article{Majumder2005Support,
author = {S. K. Majumder and N. Ghosh and P. K. Gupta},
title = {Support vector machine for optical diagnosis of cancer.},
journal = {J {B}iomed {O}pt},
year = {2005},
volume = {10},
pages = {024034},
number = {2},
abstract = {We report the application of a support vector machine ({SVM}) for
the development of diagnostic algorithms for optical diagnosis of
cancer. {B}oth linear and nonlinear {SVM}s have been investigated
for this purpose. {W}e develop a methodology that makes use of {SVM}
for both feature extraction and classification jointly by integrating
the newly developed recursive feature elimination ({RFE}) in the
framework of {SVM}. {T}his leads to significantly improved classification
results compared to those obtained when an independent feature extractor
such as principal component analysis ({PCA}) is used. {T}he integrated
{SVM}-{RFE} approach is also found to outperform the classification
results yielded by traditional {F}isher's linear discriminant ({FLD})-based
algorithms. {A}ll the algorithms are developed using spectral data
acquired in a clinical in vivo laser-induced fluorescence ({LIF})
spectroscopic study conducted on patients being screened for cancer
of the oral cavity and normal volunteers. {T}he best sensitivity
and specificity values provided by the nonlinear {SVM}-{RFE} algorithm
over the data sets investigated are 95 and 96\% toward cancer for
the training set data based on leave-one-out cross validation and
93 and 97\% toward cancer for the independent validation set data.
{W}hen tested on the spectral data of the uninvolved oral cavity
sites from the patients it yielded a specificity of 85\%.},
doi = {10.1117/1.1897396},
pdf = {../local/Majumder2005Support.pdf},
file = {Majumder2005Support.pdf:local/Majumder2005Support.pdf:PDF},
url = {http://dx.doi.org/10.1117/1.1897396}
}
@article{Mallat1989theory,
author = {Mallat, S. G.},
title = {A theory for multiresolution signal decomposition: the wavelet representation},
journal = {IEEE T. Pattern. Anal.},
year = {1989},
volume = {2},
pages = {674--693},
pdf = {../local/Mallat1989theory.pdf},
file = {Mallat1989theory.pdf:Mallat1989theory.pdf:PDF},
owner = {jp},
timestamp = {2012.12.13}
}
@article{Mallat1993Matching,
author = {Mallat, S. G. and Zhang, Zhifeng},
title = {Matching pursuits with time-frequency dictionaries},
journal = {Signal Processing, IEEE Transactions on},
year = {1993},
volume = {41},
pages = {3397--3415},
number = {12},
abstract = {The authors introduce an algorithm, called matching pursuit, that
decomposes any signal into a linear expansion of waveforms that are
selected from a redundant dictionary of functions. These waveforms
are chosen in order to best match the signal structures. Matching
pursuits are general procedures to compute adaptive signal representations.
With a dictionary of Gabor functions a matching pursuit defines an
adaptive time-frequency transform. They derive a signal energy distribution
in the time-frequency plane, which does not include interference
terms, unlike Wigner and Cohen class distributions. A matching pursuit
isolates the signal structures that are coherent with respect to
a given dictionary. An application to pattern extraction from noisy
signals is described. They compare a matching pursuit decomposition
with a signal expansion over an optimized wavepacket orthonormal
basis, selected with the algorithm of Coifman and Wickerhauser see
(IEEE Trans. Informat. Theory, vol. 38, Mar. 1992)},
doi = {10.1109/78.258082},
keywords = {pursuit},
url = {http://dx.doi.org/10.1109/78.258082}
}
@article{Mallows1973Some,
author = {Mallows, C. L.},
title = {Some comments on $C_p$},
journal = {Technometrics},
year = {1973},
volume = {15},
pages = {661--675},
keywords = {criteria, model, selection},
url = {http://www.math.tau.ac.il/~yekutiel/MA%20seminar/Malows%202000.pdf}
}
@inproceedings{Malyutov2010Recovery,
author = {Malyutov, M. },
title = {Recovery of sparse active inputs in general systems: A review},
booktitle = {Proc. IEEE Region 8 Int Computational Technologies in Electrical
and Electronics Engineering (SIBIRCON) Conf},
year = {2010},
pages = {15--22},
doi = {10.1109/SIBIRCON.2010.5555301},
owner = {jp},
timestamp = {2011.09.19}
}
@article{Mamanova2010Target-enrichment,
author = {Lira Mamanova and Alison J Coffey and Carol E Scott and Iwanka Kozarewa
and Emily H Turner and Akash Kumar and Eleanor Howard and Jay Shendure
and Daniel J Turner},
title = {Target-enrichment strategies for next-generation sequencing.},
journal = {Nat Methods},
year = {2010},
volume = {7},
pages = {111--118},
number = {2},
month = {Feb},
abstract = {We have not yet reached a point at which routine sequencing of large
numbers of whole eukaryotic genomes is feasible, and so it is often
necessary to select genomic regions of interest and to enrich these
regions before sequencing. There are several enrichment approaches,
each with unique advantages and disadvantages. Here we describe our
experiences with the leading target-enrichment technologies, the
optimizations that we have performed and typical results that can
be obtained using each. We also provide detailed protocols for each
technology so that end users can find the best compromise between
sensitivity, specificity and uniformity for their particular project.},
doi = {10.1038/nmeth.1419},
institution = {The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus,
Hinxton, Cambridge, UK.},
keywords = {Chromosome Mapping; Forecasting; Gene Targeting; In Situ Hybridization;
Molecular Probe Techniques; Polymerase Chain Reaction; Sequence Analysis,
DNA},
owner = {phupe},
pii = {nmeth.1419},
pmid = {20111037},
timestamp = {2010.08.30},
url = {http://dx.doi.org/10.1038/nmeth.1419}
}
@article{Mamitsuka1998Predicting,
author = {Mamitsuka, H.},
title = {{P}redicting peptides that bind to {MHC} molecules using supervised
learning of hidden {M}arkov models.},
journal = {Proteins},
year = {1998},
volume = {33},
pages = {460--474},
number = {4},
month = {Dec},
abstract = {The binding of a major histocompatibility complex (MHC) molecule to
a peptide originating in an antigen is essential to recognizing antigens
in immune systems, and it has proved to be important to use computers
to predict the peptides that will bind to an MHC molecule. The purpose
of this paper is twofold: First, we propose to apply supervised learning
of hidden Markov models (HMMs) to this problem, which can surpass
existing methods for the problem of predicting MHC-binding peptides.
Second, we generate peptides that have high probabilities to bind
to a certain MHC molecule, based on our proposed method using peptides
binding to MHC molecules as a set of training data. From our experiments,
in a type of cross-validation test, the discrimination accuracy of
our supervised learning method is usually approximately 2-15\% better
than those of other methods, including backpropagation neural networks,
which have been regarded as the most effective approach to this problem.
Furthermore, using an HMM trained for HLA-A2, we present new peptide
sequences that are provided with high binding probabilities by the
HMM and that are thus expected to bind to HLA-A2 proteins. Peptide
sequences not shown in this paper but with rather high binding probabilities
can be obtained from the author.},
keywords = {immunoinformatics},
pii = {3.0.CO;2-M},
pmid = {9849933},
timestamp = {2007.01.25}
}
@article{Mammen1999Smooth,
author = {Mammen, E. and Tsybakov, A.},
title = {Smooth discrimination analysis},
journal = {Ann. {S}tat.},
year = {1999},
volume = {27},
pages = {1808-1829},
number = {6},
doi = {10.1214/aos/1017939240},
pdf = {../local/Mammen1999Smooth.pdf},
file = {Mammen1999Smooth.pdf:local/Mammen1999Smooth.pdf:PDF},
url = {http://dx.doi.org/10.1214/aos/1017939240}
}
@article{Man2004Evaluating,
author = {Man, M.Z. and Dyson, G. and Johnson, K. and Liao, B.},
title = {Evaluating methods for classifying expression data.},
journal = {J. {B}iopharm. {S}tat.},
year = {2004},
volume = {14},
pages = {1065-1084},
number = {4},
abstract = {An attractive application of expression technologies is to predict
drug efficacy or safety using expression data of biomarkers. {T}o
evaluate the performance of various classification methods for building
predictive models, we applied these methods on six expression datasets.
{T}hese datasets were from studies using microarray technologies
and had either two or more classes. {F}rom each of the original datasets,
two subsets were generated to simulate two scenarios in biomarker
applications. {F}irst, a 50-gene subset was used to simulate a candidate
gene approach when it might not be practical to measure a large number
of genes/biomarkers. {N}ext, a 2000-gene subset was used to simulate
a whole genome approach. {W}e evaluated the relative performance
of several classification methods by using leave-one-out cross-validation
and bootstrap cross-validation. {A}lthough all methods perform well
in both subsets for a relative easy dataset with two classes, differences
in performance do exist among methods for other datasets. {O}verall,
partial least squares discriminant analysis ({PLS}-{DA}) and support
vector machines ({SVM}) outperform all other methods. {W}e suggest
a practical approach to take advantage of multiple methods in biomarker
applications.},
doi = {10.1081/BIP-200035491},
pdf = {../local/Man2004Evaluating.pdf},
file = {Man2004Evaluating.pdf:local/Man2004Evaluating.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert}
}
@article{Manallack1999Neural,
author = {D.T. Manallack and D.J. Livingstone},
title = {Neural networks in drug-discovery: have they lived up with their
promise?},
journal = {Eur. J. Med. Chem.},
year = {1999},
volume = {34},
pages = {195-208},
owner = {mahe},
timestamp = {2006.09.06}
}
@article{Manevitz2001One-Class,
author = {Manevitz, L. M. and Yousef, M},
title = {One-Class {SVM}s for Document Classification},
journal = {J. Mach. Learn. Res.},
year = {2001},
volume = {2},
pages = {139--154},
pdf = {../local/Manevitz2001One-Class.pdf},
file = {Manevitz2001One-Class.pdf:Manevitz2001One-Class.pdf:PDF},
owner = {fantine},
timestamp = {2009.06.09},
url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.6.6083}
}
@article{Manly2001impact,
author = {C. Manly and S. Louise-May and J. Hammer},
title = {The impact of informatics and computational chemistry on synthesis
and screening.},
journal = {Drug {D}iscov. {T}oday},
year = {2001},
volume = {6},
pages = {1101--1110},
number = {21},
month = {Nov},
abstract = {High-throughput synthesis and screening technologies have enhanced
the impact of computational chemistry on the drug discovery process.
{F}rom the design of targeted, drug-like libraries to 'virtual' optimization
of potency, selectivity and {ADME}/{T}ox properties, computational
chemists are able to efficiently manage costly resources and dramatically
shorten drug discovery cycle times. {T}his review will describe some
of the successful strategies and applications of state-of-the-art
algorithms to enhance drug discovery, as well as key points in the
drug discovery process where computational methods can have, and
have had, greatest impact.},
keywords = {chemoinformatics},
owner = {mahe},
pii = {S1359644601019900},
pmid = {11677167},
timestamp = {2006.02.03}
}
@article{Mann1947test,
author = {Mann, H.B. and Whitney, D.R.},
title = {On a test of whether one of two random variables is stochastically
larger than the other},
journal = {The annals of mathematical statistics},
year = {1947},
volume = {18},
pages = {50--60},
number = {1},
publisher = {Institute of Mathematical Statistics}
}
@article{Mao2004Feature,
author = {K. Z. Mao},
title = {Feature subset selection for support vector machines through discriminative
function pruning analysis.},
journal = {I{EEE} {T}rans {S}yst {M}an {C}ybern {B} {C}ybern},
year = {2004},
volume = {34},
pages = {60-7},
number = {1},
month = {Feb},
abstract = {In many pattern classification applications, data are represented
by high dimensional feature vectors, which induce high computational
cost and reduce classification speed in the context of support vector
machines ({SVM}s). {T}o reduce the dimensionality of pattern representation,
we develop a discriminative function pruning analysis ({DFPA}) feature
subset selection method in the present study. {T}he basic idea of
the {DFPA} method is to learn the {SVM} discriminative function from
training data using all input variables available first, and then
to select feature subset through pruning analysis. {I}n the present
study, the pruning is implement using a forward selection procedure
combined with a linear least square estimation algorithm, taking
advantage of linear-in-the-parameter structure of the {SVM} discriminative
function. {T}he strength of the {DFPA} method is that it combines
good characters of both filter and wrapper methods. {F}irstly, it
retains the simplicity of the filter method avoiding training of
a large number of {SVM} classifier. {S}econdly, it inherits the good
performance of the wrapper method by taking the {SVM} classification
algorithm into account.}
}
@article{Mao2005Multiclass,
author = {Yong Mao and Xiaobo Zhou and Daoying Pi and Youxian Sun and Stephen
T C Wong},
title = {Multiclass cancer classification by using fuzzy support vector machine
and binary decision tree with gene selection.},
journal = {J {B}iomed {B}iotechnol},
year = {2005},
volume = {2005},
pages = {160-71},
number = {2},
abstract = {We investigate the problems of multiclass cancer classification with
gene selection from gene expression data. {T}wo different constructed
multiclass classifiers with gene selection are proposed, which are
fuzzy support vector machine ({FSVM}) with gene selection and binary
classification tree based on {SVM} with gene selection. {U}sing {F}
test and recursive feature elimination based on {SVM} as gene selection
methods, binary classification tree based on {SVM} with {F} test,
binary classification tree based on {SVM} with recursive feature
elimination based on {SVM}, and {FSVM} with recursive feature elimination
based on {SVM} are tested in our experiments. {T}o accelerate computation,
preselecting the strongest genes is also used. {T}he proposed techniques
are applied to analyze breast cancer data, small round blue-cell
tumors, and acute leukemia data. {C}ompared to existing multiclass
cancer classifiers and binary classification tree based on {SVM}
with {F} test or binary classification tree based on {SVM} with recursive
feature elimination based on {SVM} mentioned in this paper, {FSVM}
based on recursive feature elimination based on {SVM} can find most
important genes that affect certain types of cancer with high recognition
accuracy.},
doi = {10.1155/JBB.2005.160},
pdf = {../local/Mao2005Multiclass.pdf},
file = {Mao2005Multiclass.pdf:local/Mao2005Multiclass.pdf:PDF},
keywords = {biosvm},
pii = {S1110724304406044_THIS_PII_IS_INCORRECT_},
url = {http://dx.doi.org/10.1155/JBB.2005.160}
}
@article{Marbach2012Wisdom,
author = {Marbach, D. and Costello, J.C. and K\"{u}ffner, R. and Vega, N. and
Prill, R.J. and Camacho, D.M. and Allison, K.R. and the DREAM5 Consortium
and Kellis, M. and Collins, J.J. and Stolovitzky, G.},
title = {Wisdom of crowds for robust gene network inference},
journal = {Nat. Methods},
year = {2012},
volume = {9},
pages = {796--804},
number = {8},
doi = {10.1038/nmeth.2016},
pdf = {../local/Marbach2012Wisdom.pdf},
file = {Marbach2012Wisdom.pdf:Marbach2012Wisdom.pdf:PDF},
owner = {anne-clairehaury},
timestamp = {2012.03.23},
url = {http://dx.doi.org/10.1038/nmeth.2016}
}
@article{Marbach2009Replaying,
author = {Marbach, D. and Mattiussi, C. and Floreano, D.},
title = {Replaying the evolutionary tape: biomimetic reverse engineering of
gene networks.},
journal = {Ann N Y Acad Sci},
year = {2009},
volume = {1158},
pages = {234--245},
month = {Mar},
abstract = {In this paper, we suggest a new approach for reverse engineering gene
regulatory networks, which consists of using a reconstruction process
that is similar to the evolutionary process that created these networks.
The aim is to integrate prior knowledge into the reverse-engineering
procedure, thus biasing the search toward biologically plausible
solutions. To this end, we propose an evolutionary method that abstracts
and mimics the natural evolution of gene regulatory networks. Our
method can be used with a wide range of nonlinear dynamical models.
This allows us to explore novel model types such as the log-sigmoid
model introduced here. We apply the biomimetic method to a gold-standard
dataset from an in vivo gene network. The obtained results won a
reverse engineering competition of the second DREAM conference (Dialogue
on Reverse Engineering Assessments and Methods 2007, New York, NY).},
doi = {10.1111/j.1749-6632.2008.03944.x},
institution = {Laboratory of Intelligent Systems, Ecole Polytechnique Fédérale de
Lausanne, Lausanne, Switzerland.},
keywords = {Algorithms; Biomimetics; Computational Biology; Databases, Genetic;
Evolution; Gene Regulatory Networks; Models, Biological; Nonlinear
Dynamics},
owner = {fantine},
pii = {NYAS03944},
pmid = {19348645},
timestamp = {2010.10.19},
url = {http://dx.doi.org/10.1111/j.1749-6632.2008.03944.x}
}
@article{Marbach2010Revealing,
author = {Marbach, D. and Prill, R. J. and Schaffter, T. and Mattiussi, C.
and Floreano, D. and Stolovitzky, G.},
title = {Revealing strengths and weaknesses of methods for gene network inference},
journal = {Proc. {N}atl. {A}cad. {S}ci. {USA}},
year = {2010},
volume = {107},
pages = {6286-6291},
number = {14},
abstract = {Numerous methods have been developed for inferring gene regulatory
networks from expression data, however, both their absolute and comparative
performance remain poorly understood. In this paper, we introduce
a framework for critical performance assessment of methods for gene
network inference. We present an in silico benchmark suite that we
provided as a blinded, community-wide challenge within the context
of the DREAM (Dialogue on Reverse Engineering Assessment and Methods)
project. We assess the performance of 29 gene-network-inference methods,
which have been applied independently by participating teams. Performance
profiling reveals that current inference methods are affected, to
various degrees, by different types of systematic prediction errors.
In particular, all but the best-performing method failed to accurately
infer multiple regulatory inputs (combinatorial regulation) of genes.
The results of this community-wide experiment show that reliable
network inference from gene expression data remains an unsolved problem,
and they indicate potential ways of network reconstruction improvements.},
doi = {10.1073/pnas.0913357107},
eprint = {http://www.pnas.org/content/107/14/6286.full.pdf+html},
url = {http://www.pnas.org/content/107/14/6286.abstract}
}
@article{Marbach2009Generating,
author = {Marbach, D. and Schaffter, T. and Mattiussi, C. and Floreano, D.},
title = {Generating realistic in silico gene networks for performance assessment
of reverse engineering methods},
journal = {J. Comput. Biol.},
year = {2009},
volume = {16},
pages = {229--239},
number = {2},
doi = {10.1089/cmb.2008.09TT},
publisher = {Mary Ann Liebert, Inc. 2 Madison Avenue Larchmont, NY 10538 USA},
url = {http://online.liebertpub.com/doi/abs/10.1089/cmb.2008.09TT}
}
@article{Marchal2003Bioinformatics,
author = {Marchal, I. and Golfier, G. and Dugas, O. and Majed, M.},
title = {Bioinformatics in glycobiology.},
journal = {Biochimie},
year = {2003},
volume = {85},
pages = {75-81},
number = {1-2},
abstract = {In comparison with genes and proteins, attention paid to oligosaccharides
that modify proteins is still marginal. {A}ccordingly, bioinformatics
is so far poorly involved in glycobiology. {S}ome initiatives have
been taken, however, to collect in databases all glycobiology-relevant
information or to design specific data mining algorithms to infer
predictions or identify oligosaccharide structures. {I}n this review,
we make a non-exhaustive survey of the available glycobiology-related
bioinformatic resources, focussing mainly on those resources that
are available through the {W}orld {W}ide {W}eb. {S}ome well-curated
databases are identified, but the development of specialised algorithms
appears to be limited.},
keywords = {glycans},
pii = {S0300908403000683}
}
@article{Marcotte1999Detecting,
author = {Marcotte, E.M. and Pellegrini, M. and Ng, H.-L. and Rice, D.W. and
Yeates, T.O. and Eisenberg, D.},
title = {Detecting {P}rotein {F}unction and {P}rotein-{P}rotein {I}nteractions
from {G}enome {S}equences},
journal = {Science},
year = {1999},
volume = {285},
pages = {751--753},
pdf = {../local/marc99b.pdf},
file = {marc99b.pdf:local/marc99b.pdf:PDF},
subject = {bio},
url = {http://www.sciencemag.org/cgi/reprint/285/5428/751.pdf}
}
@article{Marcotte1999combined,
author = {Marcotte, E. M. and Pellegrini, M. and Thompson, M. J. and Yeates,
T. O. and Eisenberg, D.},
title = {A combined algorithm for genome-wide prediction of protein function},
journal = {Nature},
year = {1999},
volume = {402},
pages = {83--86},
month = {November},
pdf = {../local/marc99.pdf},
file = {marc99.pdf:local/marc99.pdf:PDF},
subject = {bio},
url = {http://www.nature.com/cgi-taf/DynaPage.taf?file=/nature/journal/v402/n6757/full/402083a0_fs.html&content_filetype=PDF}
}
@article{Mardis2008Impact,
author = {Elaine R. Mardis},
title = {The impact of next-generation sequencing technology on genetics},
journal = {Trends Genet.},
year = {2008},
volume = {24},
pages = {133-141}
}
@article{Mardis2008Next,
author = {Elaine R Mardis},
title = {Next-generation DNA sequencing methods.},
journal = {Annu. Rev. Genomics Hum. Genet.},
year = {2008},
volume = {9},
pages = {387--402},
abstract = {Recent scientific discoveries that resulted from the application of
next-generation DNA sequencing technologies highlight the striking
impact of these massively parallel platforms on genetics. These new
methods have expanded previously focused readouts from a variety
of DNA preparation protocols to a genome-wide scale and have fine-tuned
their resolution to single base precision. The sequencing of RNA
also has transitioned and now includes full-length cDNA analyses,
serial analysis of gene expression (SAGE)-based methods, and noncoding
RNA discovery. Next-generation sequencing has also enabled novel
applications such as the sequencing of ancient DNA samples, and has
substantially widened the scope of metagenomic analysis of environmentally
derived samples. Taken together, an astounding potential exists for
these technologies to bring enormous change in genetic and biological
research and to enhance our fundamental biological knowledge.},
doi = {10.1146/annurev.genom.9.081307.164359},
institution = {Department of Genetics and Molecular Microbiology and Genome Sequencing
Center, Washington University School of Medicine, St. Louis MO 63108,
USA. emardis@wustl.edu},
keywords = {Chromatin Immunoprecipitation; Fossils; Gene Expression Profiling;
Genome, Human; Genomics; Humans; RNA, Untranslated; Sequence Analysis,
DNA},
owner = {ljacob},
pmid = {18576944},
timestamp = {2009.09.14},
url = {http://dx.doi.org/10.1146/annurev.genom.9.081307.164359}
}
@article{Margolin2006ARACNE,
author = {Margolin, A. A. and Nemenman, I. and Basso, K. and Wiggins, C. and
Stolovitzky, G. and Dalla Favera, R. and Califano, A.},
title = {{ARACNE}: an algorithm for the reconstruction of gene regulatory
networks in a mammalian cellular contexts},
journal = {BMC Bioinformatics},
year = {2006},
volume = {7 Suppl 1},
pages = {S7},
abstract = {BACKGROUND: Elucidating gene regulatory networks is crucial for understanding
normal cell physiology and complex pathologic phenotypes. Existing
computational methods for the genome-wide "reverse engineering" of
such networks have been successful only for lower eukaryotes with
simple genomes. Here we present ARACNE, a novel algorithm, using
microarray expression profiles, specifically designed to scale up
to the complexity of regulatory networks in mammalian cells, yet
general enough to address a wider range of network deconvolution
problems. This method uses an information theoretic approach to eliminate
the majority of indirect interactions inferred by co-expression methods.
RESULTS: We prove that ARACNE reconstructs the network exactly (asymptotically)
if the effect of loops in the network topology is negligible, and
we show that the algorithm works well in practice, even in the presence
of numerous loops and complex topologies. We assess ARACNE's ability
to reconstruct transcriptional regulatory networks using both a realistic
synthetic dataset and a microarray dataset from human B cells. On
synthetic datasets ARACNE achieves very low error rates and outperforms
established methods, such as Relevance Networks and Bayesian Networks.
Application to the deconvolution of genetic networks in human B cells
demonstrates ARACNE's ability to infer validated transcriptional
targets of the cMYC proto-oncogene. We also study the effects of
misestimation of mutual information on network reconstruction, and
show that algorithms based on mutual information ranking are more
resilient to estimation errors. CONCLUSION: ARACNE shows promise
in identifying direct transcriptional interactions in mammalian cellular
networks, a problem that has challenged existing reverse engineering
algorithms. This approach should enhance our ability to use microarray
data to elucidate functional mechanisms that underlie cellular processes
and to identify molecular targets of pharmacological compounds in
mammalian cellular networks.},
doi = {10.1186/1471-2105-7-S1-S7},
pdf = {../local/Margolin2006ARACNE.pdf},
file = {Margolin2006ARACNE.pdf:Margolin2006ARACNE.pdf:PDF},
pii = {1471-2105-7-S1-S7},
pmid = {16723010},
timestamp = {2008.02.04},
url = {http://dx.doi.org/10.1186/1471-2105-7-S1-S7}
}
@article{Markowetz2010How,
author = {Florian Markowetz},
title = {How to understand the cell by breaking it: network analysis of gene
perturbation screens.},
journal = {PLoS Comput Biol},
year = {2010},
volume = {6},
pages = {e1000655},
number = {2},
doi = {10.1371/journal.pcbi.1000655},
institution = {Cancer Research UK Cambridge Research Institute, Cambridge, United
Kingdom.},
keywords = {Animals; Cell Physiological Processes; Cluster Analysis; Gene Regulatory
Networks; Genomics; Humans; Models, Genetic; Models, Statistical;
Phenotype; Signal Transduction; Systems Biology},
owner = {phupe},
pmid = {20195495},
timestamp = {2010.08.30},
url = {http://dx.doi.org/10.1371/journal.pcbi.1000655}
}
@article{Markowetz2003Support,
author = {F. Markowetz and L. Edler and M. Vingron},
title = {Support {V}ector {M}achines for {P}rotein {F}old {C}lass {P}rediction},
journal = {Biometrical {J}ournal},
year = {2003},
volume = {45},
pages = {377-389},
number = {3},
abstract = {Knowledge of the three-dimensional structure of a protein is essential
for describing and understanding its function. {T}oday, a large number
of known protein sequences faces a small number of identified structures.
{T}hus, the need arises to predict structure from sequence without
using time-consuming experimental identification. {I}n this paper
the performance of {S}upport {V}ector {M}achines ({SVM}s) is compared
to {N}eural {N}etworks and to standard statistical classification
methods as {D}iscriminant {A}nalysis and {N}earest {N}eighbor {C}lassification.
{W}e show that {SVM}s can beat the competing methods on a dataset
of 268 protein sequences to be classified into a set of 42 fold classes.
{W}e discuss misclassification with respect to biological function
and similarity. {I}n a second step we examine the performance of
{SVM}s if the embedding is varied from frequencies of single amino
acids to frequencies of tripletts of amino acids. {T}his work shows
that {SVM} provide a promising alternative to standard statistical
classification and prediction methods in functional genomics.},
doi = {10.1002/bimj.200390019},
pdf = {../local/Markowetz2003Support.pdf},
file = {Markowetz2003Support.pdf:local/Markowetz2003Support.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://www3.interscience.wiley.com/cgi-bin/abstract/104525729/START}
}
@article{Markowetz2007Inferring,
author = {Markowetz, F. and Spang, R.},
title = {Inferring cellular networks - a review},
journal = {B{MC} {B}ioinformatics},
year = {2007},
volume = {8},
pages = {S5},
number = {Suppl 6},
abstract = {In this review we give an overview of computational and statistical
methods to reconstruct cellular networks. Although this area of research
is vast and fast developing, we show that most currently used methods
can be organized by a few key concepts. The first part of the review
deals with conditional independence models including Gaussian graphical
models and Bayesian networks. The second part discusses probabilistic
and graph-based methods for data from experimental interventions
and perturbations.},
doi = {10.1186/1471-2105-8-S6-S5},
issn = {1471-2105},
pubmedid = {17903286},
url = {http://www.biomedcentral.com/1471-2105/8/S6/S5}
}
@article{Markowitz1952Portfolio,
author = {H. Markowitz},
title = {Portfolio Selection},
journal = {The {J}ournal of {F}inance},
year = {1952},
volume = {7},
pages = {77--91},
number = {1},
month = {March}
}
@article{Marsland2002self-organising,
author = {Stephen Marsland and Jonathan Shapiro and Ulrich Nehmzow},
title = {A self-organising network that grows when required.},
journal = {Neural {N}etw},
year = {2002},
volume = {15},
pages = {1041-58},
number = {8-9},
abstract = {The ability to grow extra nodes is a potentially useful facility for
a self-organising neural network. {A} network that can add nodes
into its map space can approximate the input space more accurately,
and often more parsimoniously, than a network with predefined structure
and size, such as the {S}elf-{O}rganising {M}ap. {I}n addition, a
growing network can deal with dynamic input distributions. {M}ost
of the growing networks that have been proposed in the literature
add new nodes to support the node that has accumulated the highest
error during previous iterations or to support topological structures.
{T}his usually means that new nodes are added only when the number
of iterations is an integer multiple of some pre-defined constant,
{A}. {T}his paper suggests a way in which the learning algorithm
can add nodes whenever the network in its current state does not
sufficiently match the input. {I}n this way the network grows very
quickly when new data is presented, but stops growing once the network
has matched the data. {T}his is particularly important when we consider
dynamic data sets, where the distribution of inputs can change to
a new regime after some time. {W}e also demonstrate the preservation
of neighbourhood relations in the data by the network. {T}he new
network is compared to an existing growing network, the {G}rowing
{N}eural {G}as ({GNG}), on a artificial dataset, showing how the
network deals with a change in input distribution after some time.
{F}inally, the new network is applied to several novelty detection
tasks and is compared with both the {GNG} and an unsupervised form
of the {R}educed {C}oulomb {E}nergy network on a robotic inspection
task and with a {S}upport {V}ector {M}achine on two benchmark novelty
detection tasks.},
keywords = {Acute, Algorithms, Animals, Anion Exchange Resins, Artificial Intelligence,
Automated, Base Pair Mismatch, Base Pairing, Base Sequence, Biological,
Biosensing Techniques, Carcinoma, Chemical, Chromatography, Citric
Acid Cycle, Classification, Cluster Analysis, Comparative Study,
Computational Biology, Computer-Assisted, Cystadenoma, DNA, Databases,
Decision Making, Diagnosis, Differential, Drug, Drug Design, Electrostatics,
Eukaryotic Cells, Factual, Feasibility Studies, Female, Gene Expression,
Gene Expression Profiling, Gene Expression Regulation, Genes, Genetic,
Genetic Heterogeneity, Genetic Markers, Hemolysins, Humans, Internet,
Ion Exchange, Leukemia, Ligands, Likelihood Functions, Logistic Models,
Lung Neoplasms, Lymphocytic, Lymphoma, Markov Chains, Mathematics,
Messenger, Models, Molecular, Molecular Probe Techniques, Molecular
Sequence Data, Nanotechnology, Neoplasm, Neoplasms, Neoplastic, Neural
Networks (Computer), Non-P.H.S., Non-Small-Cell Lung, Non-U.S. Gov't,
Nucleic Acid Conformation, Nucleic Acid Hybridization, Observer Variation,
Oligonucleotide Array Sequence Analysis, Ovarian Neoplasms, P.H.S.,
Pattern Recognition, Probability, Probability Learning, Protein Binding,
Protein Conformation, Proteins, Quality Control, Quantum Theory,
RNA, RNA Splicing, Receptors, Reference Values, Regression Analysis,
Reproducibility of Results, Research Support, Robotics, Saccharomyces
cerevisiae Proteins, Sensitivity and Specificity, Sequence Analysis,
Signal Processing, Software, Statistical, Stomach Neoplasms, Structural,
Structure-Activity Relationship, Thermodynamics, Transcription, Tumor
Markers, U.S. Gov't, 12416693}
}
@article{Martin2005Predicting,
author = {Martin, S. and Roe, D. and Faulon, J.-L.},
title = {Predicting protein-protein interactions using signature products},
journal = {Bioinformatics},
year = {2005},
volume = {21},
pages = {218-226},
number = {2},
month = {Jan},
abstract = {Motivation: {P}roteome-wide prediction of protein-protein interaction
is a difficult and important problem in biology. {A}lthough there
have been recent advances in both experimental and computational
methods for predicting protein-protein interactions, we are only
beginning to see a confluence of these techniques. {I}n this paper,
we describe a very general, high-throughput method for predicting
protein-protein interactions. {O}ur method combines a sequence-based
description of proteins with experimental information that can be
gathered from any type of protein-protein interaction screen. {T}he
method uses a novel description of interacting proteins by extending
the signature descriptor, which has demonstrated success in predicting
peptide/protein binding interactions for individual proteins. {T}his
descriptor is extended to protein pairs by taking signature products.
{T}he signature product is implemented within a support vector machine
classifier as a kernel function. {R}esults: {W}e have applied our
method to publicly available yeast, {H}elicobacter pylori, human
and mouse datasets. {W}e used the yeast and {H}.pylori datasets to
verify the predictive ability of our method, achieving from 70 to
80% accuracy rates using 10-fold cross-validation. {W}e used the
human and mouse datasets to demonstrate that our method is capable
of cross-species prediction. {F}inally, we reused the yeast dataset
to explore the ability of our algorithm to predict domains. {C}ontact:
smartin@sandia.gov.},
doi = {10.1093/bioinformatics/bth483},
pdf = {../local/Martin2005Predicting.pdf},
file = {Martin2005Predicting.pdf:local/Martin2005Predicting.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://bioinformatics.oupjournals.org/cgi/content/abstract/21/2/218}
}
@article{Martin2004Classification,
author = {T. C. Martin and J. Moecks and A. Belooussov and S. Cawthraw and
B. Dolenko and M. Eiden and J. Von Frese and W. Kohler and J. Schmitt
and R. Somorjai and T. Udelhoven and S. Verzakov and W. Petrich},
title = {Classification of signatures of {B}ovine {S}pongiform {E}ncephalopathy
in serum using infrared spectroscopy.},
journal = {Analyst},
year = {2004},
volume = {129},
pages = {897-901},
number = {10},
month = {Oct},
abstract = {Signatures of {B}ovine {S}pongiform {E}ncephalopathy ({BSE}) have
been identified in serum by means of "{D}iagnostic {P}attern {R}ecognition
({DPR})". {F}or {DPR}-analysis, mid-infrared spectroscopy of dried
films of 641 serum samples was performed using disposable silicon
sample carriers and a semi-automated {DPR} research system operating
at room temperature. {T}he combination of four mathematical classification
approaches (principal component analysis plus linear discriminant
analysis, robust linear discriminant analysis, artificial neural
network, support vector machine) allowed for a reliable assignment
of spectra to the class "{BSE}-positive" or "{BSE}-negative". {A}n
independent, blinded validation study was carried out on a second
{DPR} research system at the {V}eterinary {L}aboratory {A}gency,
{W}eybridge, {UK}. {O}ut of 84 serum samples originating from terminally-ill,
{BSE}-positive cattle, 78 were classified correctly. {S}imilarly,
73 out of 76 {BSE}-negative samples were correctly identified by
{DPR} such that, numerically, an accuracy of 94.4 \% can be calculated.
{A}t a confidence level of 0.95 (alpha = 0.05) these results correspond
to a sensitivity > 85\% and a specificity > 90\%. {I}dentical class
assignment by all four classifiers occurred in 75\% of the cases
while ambiguous results were obtained in only 8 of the 160 cases.
{W}ith an area under the {ROC} (receiver operating charateristics)
curve of 0.991, {DPR} may potentially supply a valuable surrogate
marker for {BSE} even in cases in which a deliberate bias towards
improved sensitivity or specificity is desired. {T}o the best of
our knowledge, {DPR} is the first and--up to now--only method which
has demonstrated its capability of detecting {BSE}-related signatures
in serum.},
doi = {10.1039/b408950m},
pdf = {../local/Martin2004Classification.pdf},
file = {Martin2004Classification.pdf:local/Martin2004Classification.pdf:PDF},
url = {http://dx.doi.org/10.1039/b408950m}
}
@article{Martin2005bioavailability,
author = {Martin, Y. C.},
title = {A bioavailability score},
journal = {J. Med. Chem.},
year = {2005},
volume = {48},
pages = {3164--3170},
number = {9},
month = {May},
abstract = {Responding to a demonstrated need for scientists to forecast the permeability
and bioavailability (F) properties of compounds before their purchase,
synthesis, or advanced testing, we have developed a score that assigns
the probability that a compound will have F > 10\% in the rat. Neither
the rule-of-five, log P, log D, nor the combination of the number
of rotatable bonds and polar surface area successfully categorized
compounds. Instead, different properties govern the bioavailability
of compounds depending on their predominant charge at biological
pH. The fraction of anions with >10\% F falls from 85\% if the polar
surface area (PSA) is < or = 75 A(2), to 56\% if 75 < PSA < 150 A(2),
to 11\% if PSA is > or = 150 A(2). On the other hand, whereas 55\%
of the neutral, zwitterionic, or cationic compounds that pass the
rule-of-five have >10\% F, only 17\% of those that fail have > 10\%
F. This same categorization distinguishes compounds that are poorly
permeable from those that are permeable in Caco-2 cells. Further
validation is provided with human bioavailability values from the
literature.},
doi = {10.1021/jm0492002},
keywords = {chemogenomics},
owner = {laurent},
pmid = {15857122},
timestamp = {2008.07.16},
url = {http://dx.doi.org/10.1021/jm0492002}
}
@article{Martin1974Discriminant,
author = {Y. C. Martin and J. B. Holland and C. H. Jarboe and N. Plotnikoff},
title = {{D}iscriminant analysis of the relationship between physical properties
and the inhibition of monoamine oxidase by aminotetralins and aminoindans.},
journal = {J. Med. Chem.},
year = {1974},
volume = {17},
pages = {409--413},
number = {4},
month = {Apr},
keywords = {Animals, Brain, Dihydroxyphenylalanine, Dose-Response Relationship,
Drug, Drug Synergism, Indenes, Mathematics, Mice, Monoamine Oxidase
Inhibitors, Naphthalenes, Oxidation-Reduction, Oxotremorine, Reserpine,
Structure-Activity Relationship, Tryptamines, 4830537},
owner = {mahe},
pmid = {4830537},
timestamp = {2006.09.06}
}
@article{Martoglio2002decomposition,
author = {Ann-Marie Martoglio and James W Miskin and Stephen K Smith and David
J C MacKay},
title = {A decomposition model to track gene expression signatures: preview
on observer-independent classification of ovarian cancer.},
journal = {Bioinformatics},
year = {2002},
volume = {18},
pages = {1617-24},
number = {12},
month = {Dec},
abstract = {M{OTIVATION}: {A} number of algorithms and analytical models have
been employed to reduce the multidimensional complexity of {DNA}
array data and attempt to extract some meaningful interpretation
of the results. {T}hese include clustering, principal components
analysis, self-organizing maps, and support vector machine analysis.
{E}ach method assumes an implicit model for the data, many of which
separate genes into distinct clusters defined by similar expression
profiles in the samples tested. {A} point of concern is that many
genes may be involved in a number of distinct behaviours, and should
therefore be modelled to fit into as many separate clusters as detected
in the multidimensional gene expression space. {T}he analysis of
gene expression data using a decomposition model that is independent
of the observer involved would be highly beneficial to improve standard
and reproducible classification of clinical and research samples.
{RESULTS}: {W}e present a variational independent component analysis
({ICA}) method for reducing high dimensional {DNA} array data to
a smaller set of latent variables, each associated with a gene signature.
{W}e present the results of applying the method to data from an ovarian
cancer study, revealing a number of tissue type-specific and tissue
type-independent gene signatures present in varying amounts among
the samples surveyed. {T}he observer independent results of such
molecular analysis of biological samples could help identify patients
who would benefit from different treatment strategies. {W}e further
explore the application of the model to similar high-throughput studies.},
keywords = {Acute, Algorithms, Automated, Base Pair Mismatch, Base Pairing, Base
Sequence, Biological, Biosensing Techniques, Cluster Analysis, Comparative
Study, Computer-Assisted, Cystadenoma, DNA, Female, Gene Expression,
Gene Expression Profiling, Gene Expression Regulation, Genes, Genetic,
Genetic Markers, Hemolysins, Humans, Leukemia, Lymphocytic, Markov
Chains, Messenger, Models, Molecular Probe Techniques, Molecular
Sequence Data, Nanotechnology, Neoplasm, Neoplastic, Neural Networks
(Computer), Non-U.S. Gov't, Nucleic Acid Conformation, Observer Variation,
Oligonucleotide Array Sequence Analysis, Ovarian Neoplasms, Pattern
Recognition, Quality Control, RNA, Reference Values, Reproducibility
of Results, Research Support, Sensitivity and Specificity, Signal
Processing, Statistical, Stomach Neoplasms, Transcription, Tumor
Markers, 12490446}
}
@article{Maslov2002Specificity,
author = {Maslov, S. and Sneppen, K.},
title = {Specificity and stability in topology of protein networks},
journal = {Science},
year = {2002},
volume = {296},
pages = {910--913},
pdf = {../local/masl02.pdf},
file = {masl02.pdf:local/masl02.pdf:PDF},
subject = {bionet},
url = {http://www.sciencemag.org/cgi/reprint/296/5569/910.pdf}
}
@article{Mason1999New,
author = {J. S. Mason and I. Morize and P. R. Menard and D. L. Cheney and C.
Hulme and R. F. Labaudiniere},
title = {{N}ew 4-point pharmacophore method for molecular similarity and diversity
applications: overview of the method and applications, including
a novel approach to the design of combinatorial libraries containing
privileged substructures.},
journal = {J. Med. Chem.},
year = {1999},
volume = {42},
pages = {3251--3264},
number = {17},
month = {Aug},
abstract = {A new 4-point pharmacophore method for molecular similarity and diversity
that rapidly calculates all potential pharmacophores/pharmacophoric
shapes for a molecule or a protein site is described. The method,
an extension to the ChemDiverse/Chem-X software (Oxford Molecular,
Oxford, England), has also been customized to enable a new internally
referenced measure of pharmacophore diversity. The "privileged" substructure
concept for the design of high-affinity ligands is presented, and
an example of this new method is described for the design of combinatorial
libraries for 7-transmembrane G-protein-coupled receptor targets,
where "privileged" substructures are used as special features to
internally reference the pharmacophoric shapes. Up to 7 features
and 15 distance ranges are considered, giving up to 350 million potential
4-point 3D pharmacophores/molecule. The resultant pharmacophore "key"
("fingerprint") serves as a powerful measure for diversity or similarity,
calculable for both a ligand and a protein site, and provides a consistent
frame of reference for comparing molecules, sets of molecules, and
protein sites. Explicit "on-the-fly" conformational sampling is performed
for a molecule to enable the calculation of all geometries accessible
for all combinations of four features (i.e., 4-point pharmacophores)
at any desired sampling resolution. For a protein site, complementary
site points to groups displayed in the site are generated and all
combinations of four site points are considered. In this paper we
report (i) the details of our customized implementation of the method
and its modification to systematically measure 4-point pharmacophores
relative to a "special" substructure of interest present in the molecules
under study; (ii) comparisons of 3- and 4-point pharmacophore methods,
highlighting the much increased resolution of the 4-point method;
(iii) applications of the 4-point potential pharmacophore descriptors
as a new measure of molecular similarity and diversity and for the
design of focused/biased combinatorial libraries.},
doi = {10.1021/jm9806998},
owner = {mahe},
pii = {jm9806998},
pmid = {10464012},
timestamp = {2006.08.22},
url = {http://dx.doi.org/10.1021/jm9806998}
}
@article{Massart2000Some,
author = {Massart, P.},
title = {Some applications of concentration inequalities to statistics},
journal = {Ann. {F}ac. {S}c. {T}oulouse},
year = {2000},
volume = {IX},
pages = {245-303},
number = {2}
}
@incollection{Matache2002Hilbert,
author = {Matache, M. T. and Matache, V.},
title = {Hilbert spaces induced by {T}oeplitz covariance kernels},
booktitle = {Lecture {N}otes in {C}ontrol and {I}nformation {S}ciences},
publisher = {Springer},
year = {2002},
volume = {280},
pages = {319-334},
month = {Jan}
}
@article{Mateos2002Systematic,
author = {Alvaro Mateos and JoaquÃn Dopazo and Ronald Jansen and Yuhai Tu
and Mark Gerstein and Gustavo Stolovitzky},
title = {Systematic learning of gene functional classes from {DNA} array expression
data by using multilayer perceptrons.},
journal = {Genome {R}es.},
year = {2002},
volume = {12},
pages = {1703-15},
number = {11},
month = {Nov},
abstract = {Recent advances in microarray technology have opened new ways for
functional annotation of previously uncharacterised genes on a genomic
scale. {T}his has been demonstrated by unsupervised clustering of
co-expressed genes and, more importantly, by supervised learning
algorithms. {U}sing prior knowledge, these algorithms can assign
functional annotations based on more complex expression signatures
found in existing functional classes. {P}reviously, support vector
machines ({SVM}s) and other machine-learning methods have been applied
to a limited number of functional classes for this purpose. {H}ere
we present, for the first time, the comprehensive application of
supervised neural networks ({SNN}s) for functional annotation. {O}ur
study is novel in that we report systematic results for ~100 classes
in the {M}unich {I}nformation {C}enter for {P}rotein {S}equences
({MIPS}) functional catalog. {W}e found that only ~10\% of these
are learnable (based on the rate of false negatives). {A} closer
analysis reveals that false positives (and negatives) in a machine-learning
context are not necessarily "false" in a biological sense. {W}e show
that the high degree of interconnections among functional classes
confounds the signatures that ought to be learned for a unique class.
{W}e term this the "{B}orges effect" and introduce two new numerical
indices for its quantification. {O}ur analysis indicates that classification
systems with a lower {B}orges effect are better suitable for machine
learning. {F}urthermore, we introduce a learning procedure for combining
false positives with the original class. {W}e show that in a few
iterations this process converges to a gene set that is learnable
with considerably low rates of false positives and negatives and
contains genes that are biologically related to the original class,
allowing for a coarse reconstruction of the interactions between
associated biological pathways. {W}e exemplify this methodology using
the well-studied tricarboxylic acid cycle.},
doi = {10.1101/gr.192502},
pdf = {../local/Mateos2002Systematic.pdf},
file = {Mateos2002Systematic.pdf:local/Mateos2002Systematic.pdf:PDF},
keywords = {Acute, Algorithms, Animals, Anion Exchange Resins, Artificial Intelligence,
Automated, Base Pair Mismatch, Base Pairing, Base Sequence, Biological,
Biosensing Techniques, Carcinoma, Chemical, Chromatography, Citric
Acid Cycle, Classification, Cluster Analysis, Comparative Study,
Computational Biology, Computer-Assisted, Cystadenoma, DNA, Databases,
Decision Making, Diagnosis, Differential, Drug, Drug Design, Electrostatics,
Eukaryotic Cells, Factual, Feasibility Studies, Female, Gene Expression,
Gene Expression Profiling, Gene Expression Regulation, Genes, Genetic,
Genetic Heterogeneity, Genetic Markers, Hemolysins, Humans, Internet,
Ion Exchange, Leukemia, Ligands, Likelihood Functions, Logistic Models,
Lung Neoplasms, Lymphocytic, Lymphoma, Markov Chains, Mathematics,
Messenger, Models, Molecular, Molecular Probe Techniques, Molecular
Sequence Data, Nanotechnology, Neoplasm, Neoplasms, Neoplastic, Neural
Networks (Computer), Non-P.H.S., Non-Small-Cell Lung, Non-U.S. Gov't,
Nucleic Acid Conformation, Nucleic Acid Hybridization, Observer Variation,
Oligonucleotide Array Sequence Analysis, Ovarian Neoplasms, P.H.S.,
Pattern Recognition, Probability, Protein Binding, Protein Conformation,
Proteins, Quality Control, Quantum Theory, RNA, RNA Splicing, Receptors,
Reference Values, Regression Analysis, Reproducibility of Results,
Research Support, Saccharomyces cerevisiae Proteins, Sensitivity
and Specificity, Sequence Analysis, Signal Processing, Software,
Statistical, Stomach Neoplasms, Structural, Structure-Activity Relationship,
Thermodynamics, Transcription, Tumor Markers, U.S. Gov't, 12421757},
url = {http://dx.doi.org/10.1101/gr.192502}
}
@article{Mathews1999Expanded,
author = {Mathews, D. H. and Sabina, J. and Zuker, M. and Turner, D. H.},
title = {Expanded sequence dependence of thermodynamic parameters improves
prediction of {RNA} secondary structure.},
journal = {J. {M}ol. {B}iol.},
year = {1999},
volume = {288},
pages = {911-40},
number = {5},
month = {May},
abstract = {An improved dynamic programming algorithm is reported for {RNA} secondary
structure prediction by free energy minimization. {T}hermodynamic
parameters for the stabilities of secondary structure motifs are
revised to include expanded sequence dependence as revealed by recent
experiments. {A}dditional algorithmic improvements include reduced
search time and storage for multibranch loop free energies and improved
imposition of folding constraints. {A}n extended database of 151,503
nt in 955 structures? determined by comparative sequence analysis
was assembled to allow optimization of parameters not based on experiments
and to test the accuracy of the algorithm. {O}n average, the predicted
lowest free energy structure contains 73 \% of known base-pairs when
domains of fewer than 700 nt are folded; this compares with 64 \%
accuracy for previous versions of the algorithm and parameters. {F}or
a given sequence, a set of 750 generated structures contains one
structure that, on average, has 86 \% of known base-pairs. {E}xperimental
constraints, derived from enzymatic and flavin mononucleotide cleavage,
improve the accuracy of structure predictions.},
doi = {10.1006/jmbi.1999.2700},
keywords = {sirna},
pii = {S0022-2836(99)92700-6},
url = {http://dx.doi.org/10.1006/jmbi.1999.2700}
}
@article{Mathews1999Expandeda,
author = {D. H. Mathews and J. Sabina and M. Zuker and D. H. Turner},
title = {{E}xpanded sequence dependence of thermodynamic parameters improves
prediction of {RNA} secondary structure.},
journal = {J. Mol. Biol.},
year = {1999},
volume = {288},
pages = {911--940},
number = {5},
month = {May},
abstract = {An improved dynamic programming algorithm is reported for RNA secondary
structure prediction by free energy minimization. Thermodynamic parameters
for the stabilities of secondary structure motifs are revised to
include expanded sequence dependence as revealed by recent experiments.
Additional algorithmic improvements include reduced search time and
storage for multibranch loop free energies and improved imposition
of folding constraints. An extended database of 151,503 nt in 955
structures? determined by comparative sequence analysis was assembled
to allow optimization of parameters not based on experiments and
to test the accuracy of the algorithm. On average, the predicted
lowest free energy structure contains 73 \% of known base-pairs when
domains of fewer than 700 nt are folded; this compares with 64 \%
accuracy for previous versions of the algorithm and parameters. For
a given sequence, a set of 750 generated structures contains one
structure that, on average, has 86 \% of known base-pairs. Experimental
constraints, derived from enzymatic and flavin mononucleotide cleavage,
improve the accuracy of structure predictions.},
doi = {10.1006/jmbi.1999.2700},
keywords = {16S, 23S, 5S, Affinity, Algorithms, Aluminum Silicates, Amino Acid,
Amino Acid Sequence, Amyloidosis, Archaeal, Bacillus, Bacterial,
Bacterial Proteins, Bacteriophage T4, Base Sequence, Chloroplast,
Chromatography, Circular Dichroism, Comparative Study, Computational
Biology, Databases, Electrophoresis, Entropy, Enzyme Stability, Escherichia
coli, Factual, Fibroblast Growth Factor 2, Flavin Mononucleotide,
Fluorescence, Genetic, Guanidine, Humans, Huntington Disease, Kinetics,
Light, Models, Molecular Sequence Data, Non-P.H.S., Non-U.S. Gov't,
Nucleic Acid Conformation, P.H.S., Peptides, Phylogeny, Polyacrylamide
Gel, Predictive Value of Tests, Protein Binding, Protein Denaturation,
Protein Folding, Protein Structure, RNA, Radiation, Recombinant Proteins,
Research Support, Ribosomal, Scattering, Secondary, Sequence Homology,
Solutions, Spectrometry, Statistical, Temperature, Thermodynamics,
Time Factors, Trinucleotide Repeat Expansion, U.S. Gov't, alpha-Amylase,
10329189},
owner = {vert},
pii = {S0022-2836(99)92700-6},
pmid = {10329189},
timestamp = {2006.04.27},
url = {http://dx.doi.org/10.1006/jmbi.1999.2700}
}
@article{Matsuda2005novel,
author = {Matsuda, A. and Vert, J.-P. and Saigo, H. and Ueda, N. and Toh, H.
and Akutsu, T.},
title = {A novel representation of protein sequences for prediction of subcellular
location using support vector machines},
journal = {Protein {S}ci.},
year = {2005},
volume = {14},
pages = {2804-2813},
number = {11},
abstract = {As the number of complete genomes rapidly increases, accurate methods
to automatically predict the subcellular location of proteins are
increasingly useful to help their functional annotation. {I}n order
to improve the predictive accuracy of the many prediction methods
developed to date, a novel representation of protein sequences is
proposed. {T}his representation involves local compositions of amino
acids and twin amino acids, and local frequencies of distance between
successive (basic, hydrophobic, and other) amino acids. {F}or calculating
the local features, each sequence is split into three parts: {N}-terminal,
middle, and {C}-terminal. {T}he {N}-terminal part is further divided
into four regions to consider ambiguity in the length and position
of signal sequences. {W}e tested this representation with support
vector machines on two data sets extracted from the {SWISS}-{PROT}
database. {T}hrough fivefold cross-validation tests, overall accuracies
of more than 87% and 91% were obtained for eukaryotic and prokaryotic
proteins, respectively. {I}t is concluded that considering the respective
features in the {N}-terminal, middle, and {C}-terminal parts is helpful
to predict the subcellular location.},
doi = {10.1110/ps.051597405},
keywords = {biosvm},
url = {http://dx.doi.org/10.1110/ps.051597405}
}
@article{Matter1999Comparing,
author = {H. Matter and T. P\"{o}tter},
title = {Comparing 3{D} pharmacophore triplets and 2{D} fingerprints for selecting
diverse compound subsets},
journal = {J. Chem. Inf. Comput. Sci.},
year = {1999},
volume = {39},
pages = {1211-1225},
number = {6},
abstract = {The performance of two important 2D and 3D molecular descriptors for
rational design to maximize the structural diversity of databases
is investigated in this publication. Those methods are based either
on a 2D description using a binary fingerprint, which accounts for
the absence or presence of molecular fragments, or a 3D description
based on the geometry of pharmacophoric features encoded in a fingerprint
(pharmacophoric definition triplets, PDTs). Both descriptors in combination
with maximum dissimilarity selections, complete linkage hierarchical
cluster analysis, or sequential dissimilarity selections were compared
to random subsets as reference. This comparison is based on their
ability to cover representative biological classes from parent databases
(coverage analysis) and the degree of separation between active and
inactive compounds for a biological target from hierarchical clustering
(cluster separation analysis). While the similarity coefficients
(Tanimoto, cosine) show only a minor influence, the number of conformations
to generate the 3D PDT fingerprint lead to remarkably different results.
PDT fingerprints derived from a lower number of conformers perform
significantly better, but they are not comparable to a 2D fingerprint-based
design. When 2D and 3D descriptors are combined with weighting factors
> 0.5 for 2D fingerprints, a significant improvement of coverage
and cluster separation results is observed for a small number of
PDT conformers and medium sized subsets. Some combined descriptors
outperform 2D fingerprints, but not for all subset populations. Applying
sequential dissimilarity selection to PDT descriptors reveals that
its performance is dependent on the initial ordering of compounds,
while presorting according to 2D fingerprint diversity does not improve
results. Finally the relationship between biological activity and
similarity was investigated, showing that PDTs quantify smaller structural
differences due to the large number of bits in the fingerprint.},
doi = {10.1021/ci980185h},
pdf = {../local/Matter1999Comparing.pdf},
file = {Matter1999Comparing.pdf:Matter1999Comparing.pdf:PDF},
keywords = {chemoinformatics},
owner = {mahe},
timestamp = {2006.02.03},
url = {http://dx.doi.org}
}
@article{Mattfeldt2003Classification,
author = {Mattfeldt, T. and Gottfried, H.W. and Wolter, H. and Schmidt, V.
and Kestler, H.A. and Mayer, J.},
title = {Classification of prostatic carcinoma with artificial neural networks
using comparative genomic hybridization and quantitative stereological
data},
journal = {Pathol. {R}es. {P}ract.},
year = {2003},
volume = {199},
pages = {773-784},
number = {12},
abstract = {Staging of prostate cancer is a mainstay of treatment decisions and
prognostication. {I}n the present study, 50 p{T}2{N}0 and 28 p{T}3{N}0
prostatic adenocarcinomas were characterized by {G}leason grading,
comparative genomic hybridization ({CGH}), and histological texture
analysis based on principles of stereology and stochastic geometry.
{T}he cases were classified by learning vector quantization and support
vector machines. {T}he quality of classification was tested by cross-validation.
{C}orrect prediction of stage from primary tumor data was possible
with an accuracy of 74-80% from different data sets. {T}he accuracy
of prediction was similar when the {G}leason score was used as input
variable, when stereological data were used, or when a combination
of {CGH} data and stereological data was used. {T}he results of classification
by learning vector quantization were slightly better than those by
support vector machines. {A} method is briefly sketched by which
training of neural networks can be adapted to unequal sample sizes
per class. {P}rogression from p{T}2 to p{T}3 prostate cancer is correlated
with complex changes of the epithelial cells in terms of volume fraction,
of surface area, and of second-order stereological properties. {G}enetically,
this progression is accompanied by a significant global increase
in losses and gains of {DNA}, and specifically by increased numerical
aberrations on chromosome arms 1q, 7p, and 8p.},
doi = {10.1078/0344-0338-00496},
keywords = {biosvm, cgh},
owner = {jeanphilippevert},
url = {http://dx.doi.org/10.1078/0344-0338-00496}
}
@article{Mattfeldt2004Prediction,
author = {T. Mattfeldt and H. A. Kestler and H. P. Sinn},
title = {Prediction of the axillary lymph node status in mammary cancer on
the basis of clinicopathological data and flow cytometry.},
journal = {Med {B}iol {E}ng {C}omput},
year = {2004},
volume = {42},
pages = {733-9},
number = {6},
month = {Nov},
abstract = {Axillary lymph node status is a major prognostic factor in mammary
carcinoma. {I}t is clinically desirable to predict the axillary lymph
node status from data from the mammary cancer specimen. {I}n the
study, the axillary lymph node status, routine histological parameters
and flow-cytometric data were retrospectively obtained from 1139
specimens of invasive mammary cancer. {T}he ten variables: age, tumour
type, tumour grade, tumour size, skin infiltration, lymphangiosis
carcinomatosa, p{T}4 category, percentage of tumour cells in {G}2/{M}-
and {S}-phases of the cell cycle, and ploidy index were considered
as predictor variables, and the single variable lymph node metastasis
p{N} (0 for p{N}0, or 1 for p{N}1 or p{N}2) was used as an output
variable. {A} stepwise logistic regression analysis, with the axillary
lymph node as a dependent variable, was used for feature selection.
{O}nly lymphangiosis carcinomatosa and tumour size proved to be significant
as independent predictor variables; the other variables were non-contributory.
{T}hree paradigms with supervised learning rules (multilayer perceptron,
learning vector quantisation and support vector machines) were used
for the purpose of prediction. {I}f any of these paradigms was used
with the information from all ten input variables, 73\% of cases
could be correctly predicted, with specificity ranging from 82 to
84\% and sensitivity ranging from 60 to 63\%. {I}f only the two significant
input variables were used, lymphangiosis carcinomatosa and tumour
diameter, the prediction accuracy was no worse. {N}early identical
results were obtained by two different techniques of cross-validation
(leave-one-out against ten-fold cross validation). {I}t was concluded
that: artificial neural networks can be used for risk stratification
on the basis of routine data in individual cases of mammary cancer;
and lymphangiosis carcinomatosa and tumour size are independent predictors
of axillary lymph node metastasis in mammary cancer.},
keywords = {breastcancer}
}
@article{Mattfeldt2004Classification,
author = {Torsten Mattfeldt and Danilo Trijic and Hans-Werner Gottfried and
Hans A Kestler},
title = {Classification of incidental carcinoma of the prostate using learning
vector quantization and support vector machines.},
journal = {Cell {O}ncol},
year = {2004},
volume = {26},
pages = {45-55},
number = {1-2},
abstract = {The subclassification of incidental prostatic carcinoma into the categories
{T}1a and {T}1b is of major prognostic and therapeutic relevance.
{I}n this paper an attempt was made to find out which properties
mainly predispose to these two tumor categories, and whether it is
possible to predict the category from a battery of clinical and histopathological
variables using newer methods of multivariate data analysis. {T}he
incidental prostatic carcinomas of the decade 1990-99 diagnosed at
our department were reexamined. {B}esides acquisition of routine
clinical and pathological data, the tumours were scored by immunohistochemistry
for proliferative activity and p53-overexpression. {T}umour vascularization
(angiogenesis) and epithelial texture were investigated by quantitative
stereology. {L}earning vector quantization ({LVQ}) and support vector
machines ({SVM}) were used for the purpose of prediction of tumour
category from a set of 10 input variables (age, {G}leason score,
preoperative {PSA} value, immunohistochemical scores for proliferation
and p53-overexpression, 3 stereological parameters of angiogenesis,
2 stereological parameters of epithelial texture). {I}n a stepwise
logistic regression analysis with the tumour categories {T}1a and
{T}1b as dependent variables, only the {G}leason score and the volume
fraction of epithelial cells proved to be significant as independent
predictor variables of the tumour category. {U}sing {LVQ} and {SVM}
with the information from all 10 input variables, more than 80 of
the cases could be correctly predicted as {T}1a or {T}1b category
with specificity, sensitivity, negative and positive predictive value
from 74-92\%. {U}sing only the two significant input variables {G}leason
score and epithelial volume fraction, the accuracy of prediction
was not worse. {T}hus, descriptive and quantitative texture parameters
of tumour cells are of major importance for the extent of propagation
in the prostate gland in incidental prostatic adenocarcinomas. {C}lassical
statistical tools and neuronal approaches led to consistent conclusions.}
}
@article{Mattick2003Challenging,
author = {John S. Mattick},
title = {Challenging the dogma: the hidden layer of non-protein-coding {RNAs}
in complex organisms},
journal = {BioEssays},
year = {2003},
volume = {25},
pages = {930-939},
keywords = {csbcbook}
}
@article{Mattick2006Non,
author = {John S. Mattick and Igor V. Makunin},
title = {Non-coding {RNA}},
journal = {Hum. Mol. Genet.},
year = {2006},
volume = {15},
pages = {R17-R29},
keywords = {csbcbook}
}
@article{Mavroforakis2005Significance,
author = {Michael Mavroforakis and Harris Georgiou and Nikos Dimitropoulos
and Dionisis Cavouras and Sergios Theodoridis},
title = {Significance analysis of qualitative mammographic features, using
linear classifiers, neural networks and support vector machines.},
journal = {Eur {J} {R}adiol},
year = {2005},
volume = {54},
pages = {80-9},
number = {1},
month = {Apr},
abstract = {Advances in modern technologies and computers have enabled digital
image processing to become a vital tool in conventional clinical
practice, including mammography. {H}owever, the core problem of the
clinical evaluation of mammographic tumors remains a highly demanding
cognitive task. {I}n order for these automated diagnostic systems
to perform in levels of sensitivity and specificity similar to that
of human experts, it is essential that a robust framework on problem-specific
design parameters is formulated. {T}his study is focused on identifying
a robust set of clinical features that can be used as the base for
designing the input of any computer-aided diagnosis system for automatic
mammographic tumor evaluation. {A} thorough list of clinical features
was constructed and the diagnostic value of each feature was verified
against current clinical practices by an expert physician. {T}hese
features were directly or indirectly related to the overall morphological
properties of the mammographic tumor or the texture of the fine-scale
tissue structures as they appear in the digitized image, while others
contained external clinical data of outmost importance, like the
patient's age. {T}he entire feature set was used as an annotation
list for describing the clinical properties of mammographic tumor
cases in a quantitative way, such that subsequent objective analyses
were possible. {F}or the purposes of this study, a mammographic image
database was created, with complete clinical evaluation descriptions
and positive histological verification for each case. {A}ll tumors
contained in the database were characterized according to the identified
clinical features' set and the resulting dataset was used as input
for discrimination and diagnostic value analysis for each one of
these features. {S}pecifically, several standard methodologies of
statistical significance analysis were employed to create feature
rankings according to their discriminating power. {M}oreover, three
different classification models, namely linear classifiers, neural
networks and support vector machines, were employed to investigate
the true efficiency of each one of them, as well as the overall complexity
of the diagnostic task of mammographic tumor characterization. {B}oth
the statistical and the classification results have proven the explicit
correlation of all the selected features with the final diagnosis,
qualifying them as an adequate input base for any type of similar
automated diagnosis system. {T}he underlying complexity of the diagnostic
task has justified the high value of sophisticated pattern recognition
architectures.},
doi = {10.1016/j.ejrad.2004.12.015},
pdf = {../local/Mavroforakis2005Significance.pdf},
file = {Mavroforakis2005Significance.pdf:local/Mavroforakis2005Significance.pdf:PDF},
keywords = {Algorithms, Animals, Antibiotics, Antineoplastic, Artificial Intelligence,
Butadienes, Chloroplasts, Comparative Study, Computer Simulation,
Computer-Assisted, Diagnosis, Disinfectants, Dose-Response Relationship,
Drug, Drug Toxicity, Electrodes, Electroencephalography, Ethylamines,
Expert Systems, Feedback, Fungicides, Gene Expression Profiling,
Genes, Genetic Markers, Humans, Implanted, Industrial, Information
Storage and Retrieval, Kidney, Kidney Tubules, MEDLINE, Male, Mercuric
Chloride, Microarray Analysis, Molecular Biology, Motor Cortex, Movement,
Natural Language Processing, Neural Networks (Computer), Non-P.H.S.,
Non-U.S. Gov't, Plant Proteins, Predictive Value of Tests, Proteins,
Proteome, Proximal, Puromycin Aminonucleoside, Rats, Reproducibility
of Results, Research Support, Sprague-Dawley, Subcellular Fractions,
Terminology, Therapy, Time Factors, Toxicogenetics, U.S. Gov't, User-Computer
Interface, 15797296},
pii = {S0720-048X(05)00023-9},
url = {http://dx.doi.org/10.1016/j.ejrad.2004.12.015}
}
@article{Mayr2009Novel,
author = {Lorenz M Mayr and Dejan Bojanic},
title = {Novel trends in high-throughput screening.},
journal = {Curr Opin Pharmacol},
year = {2009},
volume = {9},
pages = {580--588},
number = {5},
month = {Oct},
abstract = {High-throughput screening (HTS) is a well-established process for
lead discovery in Pharma and Biotech companies and is now also being
used for basic and applied research in academia. It comprises the
screening of large chemical libraries for activity against biological
targets via the use of automation, miniaturized assays and large-scale
data analysis. Since its first advent in the early to mid 1990s,
the field of HTS has seen not only a continuous change in technology
and processes, but also an adaptation to various needs in lead discovery.
HTS has now evolved into a mature discipline that is a crucial source
of chemical starting points for drug discovery. Whereas in previous
years much emphasis has been put on a steady increase in screening
capacity ('quantitative increase') via automation and miniaturization,
the past years have seen a much greater emphasis on content and quality
('qualitative increase'). Today, many experts in the field see HTS
at a crossroad with the need to decide on either higher throughput/more
experimentation or a greater focus on assays of greater physiological
relevance, both of which may lead to higher productivity in pharmaceutical
R&D. In this paper, we describe the development of HTS over the past
decade and point out our own ideas for future directions of HTS in
biomedical research. We predict that the trend toward further miniaturization
will slow down with the balanced implementation of 384 well, 1536
well, and 384 low volume well plates. Furthermore, we envisage that
there will be much more emphasis on rigorous assay and chemical characterization,
particularly considering that novel and more difficult target classes
will be pursued. In recent years we have witnessed a clear trend
in the drug discovery community toward rigorous hit validation by
the use of orthogonal readout technologies, label free and biophysical
methodologies. We also see a trend toward a more flexible use of
the various screening approaches in lead discovery, that is, the
use of both full deck compound screening as well as the use of focused
screening and iterative screening approaches. Moreover, we expect
greater usage of target identification strategies downstream of phenotypic
screening and the more effective implementation of affinity selection
technologies as a result of advances in chemical diversity methodologies.
We predict that, ultimately, each hit finding strategy will be much
more project-related, tailor-made, and better integrated into the
broader drug discovery efforts.},
doi = {10.1016/j.coph.2009.08.004},
institution = {Novartis Institutes for BioMedical Research, Center of Proteomic
Chemistry, Protease Platform, Novartis Campus, CH-4002 Basel, Switzerland.
Lorenz.Mayr@novartis.com},
keywords = {Animals; Automation, Laboratory; Computer Simulation; Computer-Aided
Design, trends; Cost-Benefit Analysis; Drug Discovery, economics/standards/trends;
High-Throughput Screening Assays, economics/standards/trends; Humans;
Miniaturization; Models, Molecular; Quality Control; Small Molecule
Libraries; Structure-Activity Relationship; Systems Integration;
Time Factors},
language = {eng},
medline-pst = {ppublish},
owner = {philippe},
pii = {S1471-4892(09)00128-3},
pmid = {19775937},
timestamp = {2010.07.26},
url = {http://dx.doi.org/10.1016/j.coph.2009.08.004}
}
@article{Mayr2003Cross-reactive,
author = {Torsten Mayr and Christian Igel and Gregor Liebsch and Ingo Klimant
and Otto S Wolfbeis},
title = {Cross-reactive metal ion sensor array in a micro titer plate format.},
journal = {Anal {C}hem},
year = {2003},
volume = {75},
pages = {4389-96},
number = {17},
month = {Sep},
abstract = {A cross-reactive array in a micro titer plate ({MTP}) format is described
that is based on a versatile and highly flexible scheme. {I}t makes
use of rather unspecific metal ions probes having almost identical
fluorescence spectra, thus enabling (a) interrogation at identical
analytical wavelengths, and (b) imaging of the probes contained in
the wells of the {MTP} using a {CCD} camera and an array of blue-light-emitting
diodes as a light source. {T}he unselective response of the indicators
in the presence of mixtures of five divalent cations generates a
characteristic pattern that was analyzed by chemometric tools. {T}he
fluorescence intensity of the indicators was transferred into a time-dependent
parameter applying a scheme called dual lifetime referencing. {I}n
this method, the fluorescence decay profile of the indicator is referenced
against the phosphorescence of an inert reference dye added to the
system. {T}he intrinsically referenced measurements also were performed
using blue {LED}s as light sources and a {CCD} camera without intensifiers
as the detector. {T}he best performance was observed if each well
was excited by a single {LED}. {T}he assembly allows the detection
of dye concentrations in the nanomoles-per-liter range without amplification
and the acquisition of 96 wells simultaneously. {T}he pictures obtained
form the basis for evaluation by pattern recognition algorithms.
{S}upport vector machines are capable of predicting the presence
of significant concentrations of metal ions with high accuracy.},
keywords = {Agrochemicals, Air Pollutants, Aircraft, Algorithms, Artificial Intelligence,
Automated, Base Composition, Base Sequence, Bayes Theorem, Carbonic
Anhydrase Inhibitors, Cluster Analysis, Colonic Neoplasms, Comparative
Study, Computational Biology, Computer Simulation, Computer Systems,
Computer-Assisted, Computing Methodologies, Confidence Intervals,
Cytosine, DNA, Data Interpretation, Databases, Diagnosis, Drug Design,
Enhancer Elements (Genetics), Environmental Monitoring, Enzyme Inhibitors,
Ethanol, Exons, Forecasting, Fourier Transform Infrared, Gene Expression
Profiling, Gene Expression Regulation, Genetic, Genetic Screening,
Glucuronosyltransferase, Guanine, Humans, Image Interpretation, Isoenzymes,
Least-Squares Analysis, Leukemia, Linear Models, Lymphoma, Models,
Molecular, Molecular Conformation, Molecular Sequence Data, Natural
Disasters, Neoplasms, Neoplastic, Neural Networks (Computer), Non-P.H.S.,
Non-U.S. Gov't, Nonlinear Dynamics, Oligonucleotide Array Sequence
Analysis, Online Systems, P.H.S., Pattern Recognition, Pharmaceutical
Preparations, Phenotype, Photography, Probability, Pyrimidines, Quantitative
Structure-Activity Relationship, RNA Precursors, RNA Splice Sites,
RNA Splicing, Radiation, Reproducibility of Results, Research Support,
Sensitivity and Specificity, Sequence Alignment, Sequence Analysis,
Signal Processing, Software, Spectroscopy, Statistical, Subtraction
Technique, Terminology, Thermodynamics, Time Factors, U.S. Gov't,
Untranslated Regions, Video Recording, Walking, 14632041}
}
@article{Mazumder2012Exact,
author = {Mazumder, R. and Hastie, T.},
title = {Exact Covariance Thresholding into Connected Components for Large-Scale
Graphical Lasso},
journal = {J. Mach. Learn. Res.},
year = {2012},
volume = {13},
pages = {781--794},
month = {Mar},
pdf = {../local/Mazumder2012Exact.pdf},
file = {Mazumder2012Exact.pdf:Mazumder2012Exact.pdf:PDF},
owner = {jp},
timestamp = {2012.04.14},
url = {http://www.jmlr.org/papers/volume13/mazumder12a/mazumder12a.pdf}
}
@article{McAuliffe2004Multiple-sequence,
author = {McAuliffe, J. D. and Pachter, L. and Jordan, M. I.},
title = {Multiple-sequence functional annotation and the generalized hidden
{M}arkov phylogeny.},
journal = {Bioinformatics},
year = {2004},
volume = {20},
pages = {1850--1860},
number = {12},
month = {Aug},
abstract = {M{OTIVATION}: {P}hylogenetic shadowing is a comparative genomics principle
that allows for the discovery of conserved regions in sequences from
multiple closely related organisms. {W}e develop a formal probabilistic
framework for combining phylogenetic shadowing with feature-based
functional annotation methods. {T}he resulting model, a generalized
hidden {M}arkov phylogeny ({GHMP}), applies to a variety of situations
where functional regions are to be inferred from evolutionary constraints.
{RESULTS}: {W}e show how {GHMP}s can be used to predict complete
shared gene structures in multiple primate sequences. {W}e also describe
shadower, our implementation of such a prediction system. {W}e find
that shadower outperforms previously reported ab initio gene finders,
including comparative human-mouse approaches, on a small sample of
diverse exonic regions. {F}inally, we report on an empirical analysis
of shadower's performance which reveals that as few as five well-chosen
species may suffice to attain maximal sensitivity and specificity
in exon demarcation. {AVAILABILITY}: {A} {W}eb server is available
at http://bonaire.lbl.gov/shadower},
doi = {10.1093/bioinformatics/bth153},
pdf = {../local/McAuliffe2004Multiple-sequence.pdf},
file = {McAuliffe2004Multiple-sequence.pdf:local/McAuliffe2004Multiple-sequence.pdf:PDF},
keywords = {biogm},
owner = {vert},
pii = {bth153},
pmid = {14988105},
timestamp = {2006.01.18},
url = {http://dx.doi.org/10.1093/bioinformatics/bth153}
}
@article{McCarthy2010Third,
author = {Alice McCarthy},
title = {Third generation DNA sequencing: pacific biosciences' single molecule
real time technology.},
journal = {Chem Biol},
year = {2010},
volume = {17},
pages = {675--676},
number = {7},
month = {Jul},
doi = {10.1016/j.chembiol.2010.07.004},
owner = {phupe},
pii = {S1074-5521(10)00247-4},
pmid = {20659677},
timestamp = {2010.08.20},
url = {http://dx.doi.org/10.1016/j.chembiol.2010.07.004}
}
@article{Mcgann2003Shape,
author = {Mcgann, M. R. and Almond, H. R. and Nicholls, A. and Grant, A. J.
and Brown, F. K. },
title = {Gaussian docking functions},
journal = {Biopolymers},
year = {2003},
volume = {68},
pages = {76--90},
number = {1},
abstract = {A shape-based Gaussian docking function is constructed which uses
Gaussian functions to represent the shapes of individual atoms. A
set of 20 trypsin ligand-protein complexes are drawn from the Protein
Data Bank (PDB), the ligands are separated from the proteins, and
then are docked back into the active sites using numerical optimization
of this function. It is found that by employing this docking function,
quasi-Newton optimization is capable of moving ligands great distances
[on average 7 \r{A} root mean square distance (RMSD)] to locate the
correctly docked structure. It is also found that a ligand drawn
from one PDB file can be docked into a trypsin structure drawn from
any of the trypsin PDB files. This implies that this scoring function
is not limited to more accurate x-ray structures, as is the case
for many of the conventional docking methods, but could be extended
to homology models. {\copyright} 2002 Wiley Periodicals, Inc. Biopolymers
68: 76-90, 2003},
address = {Open Eye Scientific Software, Santa Fe, NM 87501, USA; Johnson \&
Johnson Pharmaceutical Research and Development LLC, Springhouse,
PA 19477, USA; Astra Zeneca Pharmaceuticals, EST(Chem) 26F17, Mereside,
Macclesfield, Cheshire, SK10 4TG UK; Johnson \& Johnson Pharmaceutical
Research and Development, LCC, 1000 Route 202, Raritan, NJ 08869},
citeulike-article-id = {1112505},
doi = {http://dx.doi.org/10.1002/bip.10207},
keywords = {docking, energyfunctions, openeye},
posted-at = {2007-02-19 11:10:26},
priority = {2},
url = {http://dx.doi.org/10.1002/bip.10207}
}
@article{McGinnis1983Implementation,
author = {L. F. McGinnis},
title = {Implementation and Testing of a Primal-Dual Algorithm for the Assignment
Problem},
journal = {Operations Research},
year = {1983},
volume = {31},
pages = {277--291},
number = {2}
}
@article{McGregor1997Clustering,
author = {M.J. McGregor and V. Pallai},
title = {Clustering of {L}arge {D}atabases of {C}ompounds: {U}sing the MDL
"{K}eys" as {S}tructural {D}escriptors},
journal = {J Chem Inf Comput Sci},
year = {1997},
volume = {37},
pages = {443-448},
owner = {mahe},
timestamp = {2006.08.22}
}
@article{McGregor1999Pharmacophore,
author = {M. J. McGregor and S. M. Muskal},
title = {{P}harmacophore fingerprinting. 1. {A}pplication to {QSAR} and focused
library design.},
journal = {J Chem Inf Comput Sci},
year = {1999},
volume = {39},
pages = {569--574},
number = {3},
abstract = {A new method of rapid pharmacophore fingerprinting (PharmPrint method)
has been developed. A basis set of 10,549 three-point pharmacophores
has been constructed by enumerating several distance ranges and pharmacophoric
features. Software has been developed to assign pharmacophoric types
to atoms in chemical structures, generate multiple conformations,
and construct the binary fingerprint according to the pharmacophores
that result. The fingerprint is used as a descriptor for developing
a quantitative structure-activity relationship (QSAR) model using
partial least squares. An example is given using sets of ligands
for the estrogen receptor (ER). The result is compared with previously
published results on the same data to show the superiority of a full
3D, conformationally flexible approach. The QSAR model can be readily
interpreted in structural/chemical terms. Further examples are given
using binary activity data and some of our novel in-house compounds,
which show the value of the model when crossing compound classes.},
keywords = {Chemistry, Combinatorial Chemistry Techniques, Drug Design, Drug Evaluation,
Estradiol Congeners, Estrogen, Least-Squares Analysis, Ligands, Models,
Molecular, Pharmaceutical, Preclinical, Receptors, Software, Structure-Activity
Relationship, 10361729},
owner = {mahe},
pmid = {10361729},
timestamp = {2006.08.22}
}
@article{McKernan2009Sequence,
author = {Kevin Judd McKernan and Heather E Peckham and Gina L Costa and Stephen
F McLaughlin and Yutao Fu and Eric F Tsung and Christopher R Clouser
and Cisyla Duncan and Jeffrey K Ichikawa and Clarence C Lee and Zheng
Zhang and Swati S Ranade and Eileen T Dimalanta and Fiona C Hyland
and Tanya D Sokolsky and Lei Zhang and Andrew Sheridan and Haoning
Fu and Cynthia L Hendrickson and Bin Li and Lev Kotler and Jeremy
R Stuart and Joel A Malek and Jonathan M Manning and Alena A Antipova
and Damon S Perez and Michael P Moore and Kathleen C Hayashibara
and Michael R Lyons and Robert E Beaudoin and Brittany E Coleman
and Michael W Laptewicz and Adam E Sannicandro and Michael D Rhodes
and Rajesh K Gottimukkala and Shan Yang and Vineet Bafna and Ali
Bashir and Andrew MacBride and Can Alkan and Jeffrey M Kidd and Evan
E Eichler and Martin G Reese and Francisco M De La Vega and Alan
P Blanchard},
title = {Sequence and structural variation in a human genome uncovered by
short-read, massively parallel ligation sequencing using two-base
encoding.},
journal = {Genome Res.},
year = {2009},
volume = {19},
pages = {1527--1541},
number = {9},
month = {Sep},
abstract = {We describe the genome sequencing of an anonymous individual of African
origin using a novel ligation-based sequencing assay that enables
a unique form of error correction that improves the raw accuracy
of the aligned reads to >99.9\%, allowing us to accurately call SNPs
with as few as two reads per allele. We collected several billion
mate-paired reads yielding approximately 18x haploid coverage of
aligned sequence and close to 300x clone coverage. Over 98\% of the
reference genome is covered with at least one uniquely placed read,
and 99.65\% is spanned by at least one uniquely placed mate-paired
clone. We identify over 3.8 million SNPs, 19\% of which are novel.
Mate-paired data are used to physically resolve haplotype phases
of nearly two-thirds of the genotypes obtained and produce phased
segments of up to 215 kb. We detect 226,529 intra-read indels, 5590
indels between mate-paired reads, 91 inversions, and four gene fusions.
We use a novel approach for detecting indels between mate-paired
reads that are smaller than the standard deviation of the insert
size of the library and discover deletions in common with those detected
with our intra-read approach. Dozens of mutations previously described
in OMIM and hundreds of nonsynonymous single-nucleotide and structural
variants in genes previously implicated in disease are identified
in this individual. There is more genetic variation in the human
genome still to be uncovered, and we provide guidance for future
surveys in populations and cancer biopsies.},
doi = {10.1101/gr.091868.109},
pdf = {../local/McKernan2009Sequence.pdf},
file = {McKernan2009Sequence.pdf:McKernan2009Sequence.pdf:PDF},
institution = {Life Technologies, Beverly, Massachusetts 01915, USA. Kevin.McKernan@appliedbiosystems.com},
keywords = {ngs},
owner = {jp},
pii = {gr.091868.109},
pmid = {19546169},
timestamp = {2009.10.09},
url = {http://dx.doi.org/10.1101/gr.091868.109}
}
@article{McKnight2003Categorization,
author = {Larry McKnight and Padmini Srinivasan},
title = {Categorization of sentence types in medical abstracts.},
journal = {A{MIA} {A}nnu {S}ymp {P}roc},
year = {2003},
pages = {440-4},
abstract = {This study evaluated the use of machine learning techniques in the
classification of sentence type. 7253 structured abstracts and 204
unstructured abstracts of {R}andomized {C}ontrolled {T}rials from
{M}ed{LINE} were parsed into sentences and each sentence was labeled
as one of four types ({I}ntroduction, {M}ethod, {R}esult, or {C}onclusion).
{S}upport {V}ector {M}achine ({SVM}) and {L}inear {C}lassifier models
were generated and evaluated on cross-validated data. {T}reating
sentences as a simple "bag of words", the {SVM} model had an average
{ROC} area of 0.92. {A}dding a feature of relative sentence location
improved performance markedly for some models and overall increasing
the average {ROC} to 0.95. {L}inear classifier performance was significantly
worse than the {SVM} in all datasets. {U}sing the {SVM} model trained
on structured abstracts to predict unstructured abstracts yielded
performance similar to that of models trained with unstructured abstracts
in 3 of the 4 types. {W}e conclude that classification of sentence
type seems feasible within the domain of {RCT}'s. {I}dentification
of sentence types may be helpful for providing context to end users
or other text summarization techniques.},
keywords = {biosvm},
pii = {D030003164}
}
@article{McKusick2007Mendelian,
author = {McKusick, V.A.},
title = {Mendelian Inheritance in Man and its online version, OMIM.},
journal = {Am. J. Hum. Genet.},
year = {2007},
volume = {80},
pages = {588--604},
number = {4},
month = {Apr},
doi = {10.1086/514346},
institution = {McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University
School of Medicine, Baltimore, MD, USA. mckusick@peas.welch.jhu.edu},
keywords = {Databases, Genetic; Epigenesis, Genetic; Genetic Predisposition to
Disease; Genetic Variation; History, 20th Century; History, 21st
Century; Internet; Phenotype; Terminology as Topic},
owner = {mordelet},
pii = {S0002-9297(07)61121-5},
pmid = {17357067},
timestamp = {2010.09.27},
url = {http://dx.doi.org/10.1086/514346}
}
@article{McManus2002Gene,
author = {McManus, M. T. and Sharp, P. A.},
title = {{G}ene silencing in mammals by small interfering {RNA}s.},
journal = {Nat. Rev. Genet.},
year = {2002},
volume = {3},
pages = {737--747},
number = {10},
month = {Oct},
abstract = {Among the 3 billion base pairs of the human genome, there are approximately
30,000-40,000 protein-coding genes, but the function of at least
half of them remains unknown. A new tool - short interfering RNAs
(siRNAs) - has now been developed for systematically deciphering
the functions and interactions of these thousands of genes. siRNAs
are an intermediate of RNA interference, the process by which double-stranded
RNA silences homologous genes. Although the use of siRNAs to silence
genes in vertebrate cells was only reported a year ago, the emerging
literature indicates that most vertebrate genes can be studied with
this technology.},
doi = {10.1038/nrg908},
pdf = {../local/McManus2002Gene.pdf},
file = {McManus2002Gene.pdf:McManus2002Gene.pdf:PDF},
keywords = {sirna},
owner = {vert},
pii = {nrg908},
pmid = {12360232},
timestamp = {2006.03.28},
url = {http://dx.doi.org/10.1038/nrg908}
}
@article{McMichael2002quest,
author = {McMichael, A. and Hanke, T.},
title = {{T}he quest for an {AIDS} vaccine: is the {CD}8+ {T}-cell approach
feasible?},
journal = {Nat. Rev. Immunol.},
year = {2002},
volume = {2},
pages = {283--291},
number = {4},
month = {Apr},
abstract = {The rationale for developing anti-HIV vaccines that stimulate cytotoxic
T-lymphocyte responses is given. We argue that such vaccines will
work, provided that attention is paid to the development of memory
T-cell responses that are strong and preferably activated. Furthermore,
the vaccine should match the prevailing virus clade as closely as
possible. Vaccines will have to stimulate a wide range of responses,
but it is not clear how this can be achieved.},
doi = {10.1038/nri779},
keywords = {immunoinformatics},
pmid = {12001999},
timestamp = {2007.01.25},
url = {http://dx.doi.org/10.1038/nri779}
}
@article{Meier2008group,
author = {Meier, L. and van de Geer, S. and B{\"u}hlmann, P.},
title = {The group lasso for logistic regression},
journal = {J. R. Stat. Soc. Ser. B},
year = {2008},
volume = {70},
pages = {53-71},
number = {1},
doi = {10.1111/j.1467-9868.2007.00627.x},
pdf = {../local/Meier2008The.pdf},
file = {Meier2008The.pdf:Meier2008The.pdf:PDF},
keywords = {lasso},
url = {http://dx.doi.org/10.1111/j.1467-9868.2007.00627.x}
}
@article{Meinicke2004Oligo,
author = {Meinicke, P. and Tech, M. and Morgenstern, B. and Merkl, R.},
title = {Oligo kernels for datamining on biological sequences: a case study
on prokaryotic translation initiation sites.},
journal = {B{MC} {B}ioinformatics},
year = {2004},
volume = {5},
number = {169},
abstract = {Background {K}ernel-based learning algorithms are among the most advanced
machine learning methods and have been successfully applied to a
variety of sequence classification tasks within the field of bioinformatics.
{C}onventional kernels utilized so far do not provide an easy interpretation
of the learnt representations in terms of positional and compositional
variability of the underlying biological signals. {R}esults {W}e
propose a kernel-based approach to datamining on biological sequences.
{W}ith our method it is possible to model and analyze positional
variability of oligomers of any length in a natural way. {O}n one
hand this is achieved by mapping the sequences to an intuitive but
high-dimensional feature space, well-suited for interpretation of
the learnt models. {O}n the other hand, by means of the kernel trick
we can provide a general learning algorithm for that high-dimensional
representation because all required statistics can be computed without
performing an explicit feature space mapping of the sequences. {B}y
introducing a kernel parameter that controls the degree of position-dependency,
our feature space representation can be tailored to the characteristics
of the biological problem at hand. {A} regularized learning scheme
enables application even to biological problems for which only small
sets of example sequences are available. {O}ur approach includes
a visualization method for transparent representation of characteristic
sequence features. {T}hereby importance of features can be measured
in terms of discriminative strength with respect to classification
of the underlying sequences. {T}o demonstrate and validate our concept
on a biochemically well-defined case, we analyze {E}. coli translation
initiation sites in order to show that we can find biologically relevant
signals. {F}or that case, our results clearly show that the {S}hine-{D}algarno
sequence is the most important signal upstream a start codon. {T}he
variability in position and composition we found for that signal
is in accordance with previous biological knowledge. {W}e also find
evidence for signals downstream of the start codon, previously introduced
as transcriptional enhancers. {T}hese signals are mainly characterized
by occurrences of adenine in a region of about 4 nucleotides next
to the start codon. {C}onclusions {W}e showed that the oligo kernel
can provide a valuable tool for the analysis of relevant signals
in biological sequences. {I}n the case of translation initiation
sites we could clearly deduce the most discriminative motifs and
their positional variation from example sequences. {A}ttractive features
of our approach are its flexibility with respect to oligomer length
and position conservation. {B}y means of these two parameters oligo
kernels can easily be adapted to different biological problems.},
doi = {10.1186/1471-2105-5-169},
pdf = {../local/Meinicke2004Oligo.pdf},
file = {Meinicke2004Oligo.pdf:local/Meinicke2004Oligo.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://www.biomedcentral.com/1471-2105/5/169}
}
@misc{Meinshausen2009Stability,
author = {Meinshausen, Nicolai and Buehlmann, Peter},
title = {Stability Selection},
month = {May},
year = {2009},
abstract = {Estimation of structure, such as in variable selection, graphical
modelling or cluster analysis is notoriously difficult, especially
for high-dimensional data. We introduce stability selection. It is
based on subsampling in combination with (high-dimensional) selection
algorithms. As such, the method is extremely general and has a very
wide range of applicability. Stability selection provides finite
sample control for some error rates of false discoveries and hence
a transparent principle to choose a proper amount of regularisation
for structure estimation. Variable selection and structure estimation
improve markedly for a range of selection methods if stability selection
is applied. We prove for randomised Lasso that stability selection
will be variable selection consistent even if the necessary conditions
needed for consistency of the original Lasso method are violated.
We demonstrate stability selection for variable selection and Gaussian
graphical modelling, using real and simulated data.},
comment = {How to choose the right amount of regularization.},
eprint = {0809.2932},
keywords = {model\_selection, regularization, resampling, sensitivity},
url = {http://arxiv.org/abs/0809.2932}
}
@article{Meinshausen2010Stability,
author = {Meinshausen, N. and B{\"u}hlmann, P.},
title = {Stability selection},
journal = {J. R. Stat. Soc. Ser. B},
year = {2010},
volume = {72},
pages = {417--473},
number = {4},
doi = {10.1111/j.1467-9868.2010.00740.x},
pdf = {../local/Meinshausen2010Stability.pdf},
file = {Meinshausen2010Stability.pdf:Meinshausen2010Stability.pdf:PDF},
owner = {jp},
timestamp = {2010.11.15},
url = {http://dx.doi.org/10.1111/j.1467-9868.2010.00740.x}
}
@article{Meinshausen2006High,
author = {Meinshausen, N. and B{\"u}hlmann, P.},
title = {High dimensional graphs and variable selection with the Lasso},
journal = {Ann. Stat.},
year = {2006},
volume = {34},
pages = {1436--1462},
abstract = {The pattern of zero entries in the inverse covariance matrix of a
multivariate normal distribution corresponds to conditional independence
restrictions between variables. Covariance selection aims at estimating
those structural zeros from data. We show that neighborhood selection
with the Lasso is a computationally attractive alternative to standard
covariance selection for sparse high-dimensional graphs. Neighborhood
selection estimates the conditional independence restrictions separately
for each node in the graph and is hence equivalent to variable selection
for Gaussian linear models. We show that the proposed neighborhood
selection scheme is consistent for sparse high-dimensional graphs.
Consistency hinges on the choice of the penalty parameter. The oracle
value for optimal prediction does not lead to a consistent neighborhood
estimate. Controlling instead the probability of falsely joining
some distinct connectivity components of the graph, consistent estimation
for sparse graphs is achieved (with exponential rates), even when
the number of variables grows as the number of observations raised
to an arbitrary power.},
doi = {10.1214/009053606000000281},
pdf = {../local/Meinshausen2006High.pdf},
file = {Meinshausen2006High.pdf:Meinshausen2006High.pdf:PDF},
owner = {jp},
timestamp = {2008.12.09},
url = {http://dx.doi.org/10.1214/009053606000000281}
}
@article{Meinshausen2007Discussion,
author = {N. Meinshausen and G. Rocha and B. Yu},
title = {Discussion: A tale of three cousins: Lasso, L2Boosting and Dantzig},
journal = {ANNALS OF STATISTICS},
year = {2007},
volume = {35},
pages = {2373},
url = {http://doi:10.1214/009053607000000460}
}
@article{Meireles2003Differentially,
author = {Meireles, S.I. and Carvalho, A.F. and Hirata, R. and Montagnini,
A.L. and Martins, W.K. and Runza, F.B. and Stolf, B.S. and Termini,
L. and Neto, C.E. and Silva, R.L. and Soares, F.A. and Neves, E.J.
and Reis, L.F.},
title = {Differentially expressed genes in gastric tumors identified by c{DNA}
array.},
journal = {Cancer {L}ett.},
year = {2003},
volume = {190},
pages = {199-211},
number = {2},
month = {Feb},
abstract = {Using c{DNA} fragments from the {FAPESP}/l{ICR} {C}ancer {G}enome
{P}roject, we constructed a c{DNA} array having 4512 elements and
determined gene expression in six normal and six tumor gastric tissues.
{U}sing t-statistics, we identified 80 c{DNA}s whose expression in
normal and tumor samples differed more than 3.5 sample standard deviations.
{U}sing {S}elf-{O}rganizing {M}ap, the expression profile of these
c{DNA}s allowed perfect separation of malignant and non-malignant
samples. {U}sing the supervised learning procedure {S}upport {V}ector
{M}achine, we identified trios of c{DNA}s that could be used to classify
samples as normal or tumor, based on single-array analysis. {F}inally,
we identified genes with altered linear correlation when their expression
in normal and tumor samples were compared. {F}urther investigation
concerning the function of these genes could contribute to the understanding
of gastric carcinogenesis and may prove useful in molecular diagnostics.},
doi = {10.1016/S0304-3835(02)00587},
pdf = {../local/Meireles2003Differentially.pdf},
file = {Meireles2003Differentially.pdf:local/Meireles2003Differentially.pdf:PDF},
keywords = {biosvm microarray},
owner = {jeanphilippevert},
url = {http://dx.doi.org/10.1016/S0304-3835(02)00587-6}
}
@article{Meister2004Mechanisms,
author = {Meister, G. and Tuschl, T.},
title = {Mechanisms of gene silencing by double-stranded {RNA}.},
journal = {Nature},
year = {2004},
volume = {431},
pages = {343-9},
number = {7006},
month = {Sep},
abstract = {Double-stranded {RNA} (ds{RNA}) is an important regulator of gene
expression in many eukaryotes. {I}t triggers different types of gene
silencing that are collectively referred to as {RNA} silencing or
{RNA} interference. {A} key step in known silencing pathways is the
processing of ds{RNA}s into short {RNA} duplexes of characteristic
size and structure. {T}hese short ds{RNA}s guide {RNA} silencing
by specific and distinct mechanisms. {M}any components of the {RNA}
silencing machinery still need to be identified and characterized,
but a more complete understanding of the process is imminent.},
doi = {10.1038/nature02873},
pdf = {../local/Meister2004Mechanisms.pdf},
file = {Meister2004Mechanisms.pdf:local/Meister2004Mechanisms.pdf:PDF},
keywords = {sirna},
pii = {nature02873},
url = {http://dx.doi.org/10.1038/nature02873}
}
@article{Mello2004Revealing,
author = {Craig C. Mello and Darryl {Conte Jr}},
title = {Revealing the world of {RNA} interference},
journal = {Nature},
year = {2004},
volume = {43},
pages = {338-342},
keywords = {csbcbook}
}
@book{Menard2001Applied,
title = {Applied logistic regression analysis},
publisher = {Sage Publications, Incorporated},
year = {2001},
author = {Menard, S.},
volume = {106}
}
@inproceedings{Menchetti2005Weighted,
author = {S. Menchetti and F. Costa and P. Frasconi},
title = {Weighted decomposition kernels},
booktitle = {Proceedings of the {T}wenty-{S}econd {I}nternational {C}onference
on {M}achine {L}earning ({ICML} 2005)},
year = {2005},
editor = {De Raedt, L. and Wrobel, S.},
pages = {585-592},
publisher = {ACM Press},
owner = {pmahe},
timestamp = {2007.11.21}
}
@unpublished{Mendelson2003Geometric,
author = {S. Mendelson},
title = {Geometric parameters in {L}earning {T}heory},
note = {Lecture notes},
year = {2003}
}
@article{Puijalon2004Malaria,
author = {O. Mercereau-Puijalon},
title = {Malaria research in the post-genomic era},
journal = {J. Soc. Biol.},
year = {2004},
volume = {198},
pages = {193--197},
number = {3},
abstract = {Genomic sequence determination of Plasmodium falciparum and other
species of the genus, as well as that of Anopheles gambiae, and human,
rat and mouse genome sequencing have completely changed the landscape
of fundamental research about malaria. These data should urgently
be exploited, in order to develop new tools to combat the disease:
new drugs, fine dissection of the cascade of events following infection
of the various vector species and vertebrate host, analysis of the
complex interaction leading to the pathology or, inversely, contributing
to sustained protection. Powerful population biology tools are now
available, allowing to investigate genetic exchanges within natural
population and to identify factors structuring parasitic and vector
populations. Nevertheless, important impediments persist, including
the complexity of experimental systems and the unclear relevance
of animals models. Numerous challenges are to be faced; they call
upon a more efficient organisation of research efforts in the systematic
explorations using the powerful novel post-genomic technologies,
as well as the development of new tools and experimental models required
by functional genomics and integrative biology.},
keywords = {plasmodium},
pmid = {15662935},
timestamp = {2006.04.13}
}
@article{Mercier2004Biological,
author = {Mercier, G. and Berthault, N. and Mary, J. and Peyre, J. and Antoniadis,
A. and Comet, J.-P. and Cornuejols, A. and Froidevaux, C. and Dutreix,
M.},
title = {Biological detection of low radiation doses by combining results
of two microarray analysis methods.},
journal = {Nucleic {A}cids {R}es.},
year = {2004},
volume = {32},
pages = {e12},
number = {1},
abstract = {The accurate determination of the biological effects of low doses
of pollutants is a major public health challenge. {DNA} microarrays
are a powerful tool for investigating small intracellular changes.
{H}owever, the inherent low reliability of this technique, the small
number of replicates and the lack of suitable statistical methods
for the analysis of such a large number of attributes (genes) impair
accurate data interpretation. {T}o overcome this problem, we combined
results of two independent analysis methods ({ANOVA} and {RELIEF}).
{W}e applied this analysis protocol to compare gene expression patterns
in {S}accharomyces cerevisiae growing in the absence and continuous
presence of varying low doses of radiation. {G}lobal distribution
analysis highlights the importance of mitochondrial membrane functions
in the response. {W}e demonstrate that microarrays detect cellular
changes induced by irradiation at doses that are 1000-fold lower
than the minimal dose associated with mutagenic effects.},
doi = {10.1093/nar/gnh002},
owner = {vert},
pii = {32/1/e12},
pmid = {14722227},
timestamp = {2006.02.27},
url = {http://dx.doi.org/10.1093/nar/gnh002}
}
@article{Mercier2001Transcriptional,
author = {Mercier, G. and Denis, Y. and Marc, P. and Picard, L. and Dutreix,
M.},
title = {Transcriptional induction of repair genes during slowing of replication
in irradiated {{S}}accharomyces cerevisiae.},
journal = {Mutat. {R}es.},
year = {2001},
volume = {487},
pages = {157--172},
number = {3-4},
month = {Dec},
abstract = {We investigated the inhibition of cell-cycle progression and replication
and the induction of the transcriptional response in diploid budding
yeast populations exposed to two different doses of gamma-rays resulting
in 15 and 85\% survival respectively. {W}e studied the kinetics of
the cellular response to ionizing treatment during the period required
for all of the surviving cells to achieve at least one cell division.
{T}he length of these periods increased with the dose. {I}rradiated
populations arrested as large-budded cells containing partially replicated
chromosomes. {T}he extent of the {S}-phase was proportional to the
amount of damage and lasted 3 or 7h depending on the irradiation
dose. {I}n parallel to the division study, we carried out a kinetic
analysis of the expression of 126 selected genes by use of dedicated
microarrays. {A}bout 26 genes were induced by irradiation and displayed
various pattern of expression. {I}nterestingly, 10 repair genes ({RAD}51,
{RAD}54, {CDC}8, {MSH}2, {RFA}2, {RFA}3, {UBC}5, {SRS}2, {SPO}12
and {TOP}1), involved in recombination and {DNA} synthesis, display
similar regulation of expression in the two irradiated populations.
{T}heir pattern of expression were confirmed by {N}orthern analysis.
{A}t the two doses, the expression of this group of genes closely
followed the extended replication period, and their expression resumed
when replication restarted. {T}hese results suggest that the damage-induced
response and {DNA} synthesis are closely regulated during repair.
{T}he analysis of the promoter regions indicates a high occurrence
of the three {MCB}, {HAP} and {UASH} regulatory boxes in the promoters
of this group of genes. {T}he association of the three boxes could
confer an irradiation-replication specific regulation.},
owner = {vert},
pii = {S0921877701001161},
pmid = {11738942},
timestamp = {2006.02.27}
}
@article{Merhav1998Universal,
author = {Merhav, N. and Feder, M. },
title = {Universal prediction},
journal = {I{EEE} {T}rans. {I}nform. {T}heory},
year = {1998},
volume = {44},
pages = {2124-2147},
number = {6},
month = {Oct},
abstract = {This paper consists of an overview on universal prediction from an
information-theoretic perspective. {S}pecial attention is given to
the notion of probability assignment under the self-information loss
function, which is directly related to the theory of universal data
compression. {B}oth the probabilistic setting and the deterministic
setting of the universal prediction problem are described with emphasis
on the analogy and the differences between results in the two settings},
pdf = {../local/Merhav1998Universal.pdf},
file = {Merhav1998Universal.pdf:local/Merhav1998Universal.pdf:PDF},
owner = {vert}
}
@article{Merhav1995strong,
author = {Merhav, N. and Feder, M.},
title = {A strong version of the redundancy-capacity theorem of universal},
journal = {I{EEE} {T}rans. {I}nform. {T}heory},
year = {1995},
volume = {41},
pages = {714-722},
number = {3},
month = {May},
abstract = {The capacity of the channel induced by a given class of sources is
well known to be an attainable lower bound on the redundancy of universal
codes with respect to this class, both in the minimax sense and in
the {B}ayesian (maximin) sense. {W}e show that this capacity is essentially
a lower bound also in a stronger sense, that is, for ?most? sources
in the class. {T}his result extends {R}issanen's (1984, 1986) lower
bound for parametric families. {W}e demonstrate the applicability
of this result in several examples, e.g., parametric families with
growing dimensionality, piecewise-fixed sources, arbitrarily varying
sources, and noisy samples of learnable functions. {F}inally, we
discuss implications of our results to statistical inference },
pdf = {../local/Merhav1995strong.pdf},
file = {Merhav1995strong.pdf:local/Merhav1995strong.pdf:PDF},
keywords = {information-theory},
owner = {vert}
}
@article{Merhav1993Universal,
author = {Merhav, N. and Feder, M. },
title = {Universal schemes for sequential decision from individual data sequences},
journal = {I{EEE} {T}rans. {I}nform. {T}heory},
year = {1993},
volume = {39},
pages = {1280-1292},
number = {4},
month = {Jul},
abstract = {Sequential decision algorithms are investigated in relation to a family
of additive performance criteria for individual data sequences. {S}imple
universal sequential schemes are known, under certain conditions,
to approach optimality uniformly as fast as n-1 log n, where n is
the sample size. {F}or the case of finite-alphabet observations,
the class of schemes that can be implemented by finite-state machines
({FSM}s) is studied. {I}t is shown that {M}arkovian machines with
sufficiently long memory exist, which are asymptotically nearly as
good as any given deterministic or randomized {FSM} for the purpose
of sequential decision. {F}or the continuous-valued observation case,
a useful class of parametric schemes is discussed with special attention
to the recursive least squares algorithm },
pdf = {../local/Merhav1993Universal.pdf},
file = {Merhav1993Universal.pdf:local/Merhav1993Universal.pdf:PDF},
keywords = {information-theory source-coding},
owner = {vert}
}
@article{Merhav1993Some,
author = {Merhav, N. and Feder, M. and Gutman, M. },
title = {Some properties of sequential predictors for binary {M}arkov sources},
journal = {I{EEE} {T}rans. {I}nform. {T}heory},
year = {1993},
volume = {39},
pages = {887-892},
number = {3},
month = {May},
abstract = {Universal predictions of the next outcome of a binary sequence drawn
from a {M}arkov source with unknown parameters is considered. {F}or
a given source, the predictability is defined as the least attainable
expected fraction of prediction errors. {A} lower bound is derived
on the maximum rate at which the predictability is asymptotically
approached uniformly over all sources in the {M}arkov class. {T}his
bound is achieved by a simple majority predictor. {F}or {B}ernoulli
sources, bounds on the large deviations performance are investigated.
{A} lower bound is derived for the probability that the fraction
of errors will exceed the predictability by a prescribed amount ?>0.
{T}his bound is achieved by the same predictor if ? is sufficiently
small },
pdf = {../local/Merhav1993Some.pdf},
file = {Merhav1993Some.pdf:local/Merhav1993Some.pdf:PDF},
keywords = {information-theory source-coding},
owner = {vert}
}
@article{Mering2002Comparative,
author = {von Mering, C. and Krause, R. and Snel, B. and Cornell, M. and Oliver,
S. G. and Fields, S. and Bork, P.},
title = {{C}omparative assessment of large-scale data sets of protein-protein
interactions.},
journal = {Nature},
year = {2002},
volume = {417},
pages = {399--403},
number = {6887},
month = {May},
abstract = {Comprehensive protein protein interaction maps promise to reveal many
aspects of the complex regulatory network underlying cellular function.
Recently, large-scale approaches have predicted many new protein
interactions in yeast. To measure their accuracy and potential as
well as to identify biases, strengths and weaknesses, we compare
the methods with each other and with a reference set of previously
reported protein interactions.},
doi = {10.1038/nature750},
pii = {nature750},
pmid = {12000970},
timestamp = {2007.02.01},
url = {http://dx.doi.org/10.1038/nature750}
}
@article{Merkwirth2004Ensemble,
author = {Christian Merkwirth and Harald Mauser and Tanja Schulz-Gasch and
Olivier Roche and Martin Stahl and Thomas Lengauer},
title = {Ensemble methods for classification in cheminformatics.},
journal = {J {C}hem {I}nf {C}omput {S}ci},
year = {2004},
volume = {44},
pages = {1971-8},
number = {6},
abstract = {We describe the application of ensemble methods to binary classification
problems on two pharmaceutical compound data sets. {S}everal variants
of single and ensembles models of k-nearest neighbors classifiers,
support vector machines ({SVM}s), and single ridge regression models
are compared. {A}ll methods exhibit robust classification even when
more features are given than observations. {O}n two data sets dealing
with specific properties of drug-like substances (cytochrome {P}450
inhibition and "{F}requent {H}itters", i.e., unspecific protein inhibition),
we achieve classification rates above 90\%. {W}e are able to reduce
the cross-validated misclassification rate for the {F}requent {H}itters
problem by a factor of 2 compared to previous results obtained for
the same data set with different modeling techniques.},
doi = {10.1021/ci049850e},
pdf = {../local/Merkwirth2004Ensemble.pdf},
file = {Merkwirth2004Ensemble.pdf:local/Merkwirth2004Ensemble.pdf:PDF},
keywords = {chemoinformatics},
url = {http://dx.doi.org/10.1021/ci049850e}
}
@article{Meron2004Finite-memory,
author = {Meron, E. and Feder, M.},
title = {Finite-memory universal prediction of individual sequences},
journal = {I{EEE} {T}rans. {I}nform. {T}heory},
year = {2004},
volume = {50},
pages = {1506-1523},
number = {7},
month = {Jul},
abstract = {The problem of predicting the next outcome of an individual binary
sequence under the constraint that the universal predictor has a
finite memory, is explored. {I}n this analysis, the finite-memory
universal predictors are either deterministic or random time-invariant
finite-state ({FS}) machines with {K} states ({K}-state machines).
{T}he paper provides bounds on the asymptotic achievable regret of
these constrained universal predictors as a function of {K}, the
number of their states, for long enough sequences. {T}he specific
results are as follows. {W}hen the universal predictors are deterministic
machines, the comparison class consists of constant predictors, and
prediction is with respect to the 0-1 loss function ({H}amming distance),
we get tight bounds indicating that the optimal asymptotic regret
is 1/(2{K}). {I}n that case of {K}-state deterministic universal
predictors, the constant predictors comparison class, but prediction
is with respect to the self-information (code length) and the square-error
loss functions, we show an upper bound on the regret (coding redundancy)
of {O}({K}/sup -2/3/) and a lower bound of /spl {T}heta/({K}/sup
-4/5/). {F}or these loss functions, if the predictor is allowed to
be a random {K}-state machine, i.e., a machine with random state
transitions, we get a lower bound of /spl {T}heta/(1/{K}) on the
regret, with a matching upper bound of {O}(1/{K}) for the square-error
loss, and an upper bound of {O}(log{K}/{K}) {T}hroughout the paper
for the self-information loss. {I}n addition, we provide results
for all these loss functions in the case where the comparison class
consists of all predictors that are order-{L} {M}arkov machines.},
pdf = {../local/Meron2004Finite-memory.pdf},
file = {Meron2004Finite-memory.pdf:local/Meron2004Finite-memory.pdf:PDF},
keywords = {information-theory source-coding},
owner = {vert}
}
@article{Mestres2004Computational,
author = {Jordi Mestres},
title = {Computational chemogenomics approaches to systematic knowledge-based
drug discovery.},
journal = {Curr Opin Drug Discov Devel},
year = {2004},
volume = {7},
pages = {304--313},
number = {3},
month = {May},
abstract = {Chemogenomics, the identification of all possible drugs for all possible
targets, has recently emerged as a new paradigm in drug discovery
in which efficiency in the compound design and optimization process
is achieved through the gain and reuse of targeted knowledge. As
targeted knowledge resides at the interface between chemistry and
biology, computational tools aimed at integrating the chemical and
biological spaces play a central role in chemogenomics. This review
covers the recent progress made in integrative computational approaches
to data annotation and knowledge generation for the systematic knowledge-based
design and screening of chemical libraries.},
keywords = {Chemistry, Pharmaceutical; Combinatorial Chemistry Techniques; Computational
Biology; Drug Design; Genomics; Ligands; Proteins; Receptors, G-Protein-Coupled},
owner = {vert},
pmid = {15216933},
timestamp = {2007.08.02}
}
@article{Metzker2010Sequencing,
author = {Metzker, M. L.},
title = {Sequencing technologies - the next generation.},
journal = {Nat. Rev. Genet.},
year = {2010},
volume = {11},
pages = {31--46},
number = {1},
month = {Jan},
abstract = {Demand has never been greater for revolutionary technologies that
deliver fast, inexpensive and accurate genome information. This challenge
has catalysed the development of next-generation sequencing (NGS)
technologies. The inexpensive production of large volumes of sequence
data is the primary advantage over conventional methods. Here, I
present a technical review of template preparation, sequencing and
imaging, genome alignment and assembly approaches, and recent advances
in current and near-term commercially available NGS instruments.
I also outline the broad range of applications for NGS technologies,
in addition to providing guidelines for platform selection to address
biological questions of interest.},
doi = {10.1038/nrg2626},
institution = { Human Genetics, Baylor College of Medicine, Houston, Texas 77030,
USA. mmetzker@bcm.edu},
keywords = {ngs},
language = {eng},
medline-pst = {ppublish},
owner = {philippe},
pii = {nrg2626},
pmid = {19997069},
timestamp = {2010.07.27},
url = {http://dx.doi.org/10.1038/nrg2626}
}
@article{Mewes2002MIPS:,
author = {H.W. Mewes and D. Frishman and U. G{\"u}ldener and G. Mannhaupt and
K. Mayer and M. Mokrejs and B. Morgenstern and M. M{\"u}nsterkoetter
and S. Rudd and B. Weil},
title = {M{IPS}: a database for genomes and protein sequences},
journal = {Nucleic {A}cids {R}es.},
year = {2002},
volume = {30},
pages = {31--34},
number = {1},
pdf = {../local/Mewes2002MIPS.pdf},
file = {Mewes2002MIPS.pdf:local/Mewes2002MIPS.pdf:PDF},
url = {http://nar.oxfordjournals.org/cgi/content/abstract/30/1/31}
}
@article{Mezlini2013iReckon,
author = {Mezlini, A. M. and Smith, E. J. M. and Fiume, M. and Buske, O. and
Savich, G. L. and Shah, S. and Aparicio, S. and Chiang, D. Y. and
Goldenberg, A. and Brudno, M.},
title = {{iReckon}: Simultaneous isoform discovery and abundance estimation
from {RNA}-seq data.},
journal = {Genome Res},
year = {2013},
volume = {23},
pages = {519--529},
number = {3},
month = {Mar},
abstract = {High-throughput RNA sequencing (RNA-seq) promises to revolutionize
our understanding of genes and their role in human disease by characterizing
the RNA content of tissues and cells. The realization of this promise,
however, is conditional on the development of effective computational
methods for the identification and quantification of transcripts
from incomplete and noisy data. In this article, we introduce iReckon,
a method for simultaneous determination of the isoforms and estimation
of their abundances. Our probabilistic approach incorporates multiple
biological and technical phenomena, including novel isoforms, intron
retention, unspliced pre-mRNA, PCR amplification biases, and multimapped
reads. iReckon utilizes regularized expectation-maximization to accurately
estimate the abundances of known and novel isoforms. Our results
on simulated and real data demonstrate a superior ability to discover
novel isoforms with a significantly reduced number of false-positive
predictions, and our abundance accuracy prediction outmatches that
of other state-of-the-art tools. Furthermore, we have applied iReckon
to two cancer transcriptome data sets, a triple-negative breast cancer
patient sample and the MCF7 breast cancer cell line, and show that
iReckon is able to reconstruct the complex splicing changes that
were not previously identified. QT-PCR validations of the isoforms
detected in the MCF7 cell line confirmed all of iReckon's predictions
and also showed strong agreement (r = 0.94) with the predicted abundances.},
doi = {10.1101/gr.142232.112},
pdf = {../local/Mezlini2013iReckon.pdf},
file = {Mezlini2013iReckon.pdf:Mezlini2013iReckon.pdf:PDF},
institution = {Department of Computer Science, University of Toronto, Ontario M5S
2E4, Canada;},
keywords = {ngs, rnaseq},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {gr.142232.112},
pmid = {23204306},
timestamp = {2013.03.29},
url = {http://dx.doi.org/10.1101/gr.142232.112}
}
@article{Micchelli2005On,
author = {Charles A Micchelli and Massimiliano Pontil},
title = {On learning vector-valued functions.},
journal = {Neural {C}omput},
year = {2005},
volume = {17},
pages = {177-204},
number = {1},
month = {Jan},
abstract = {In this letter, we provide a study of learning in a {H}ilbert space
of vectorvalued functions. {W}e motivate the need for extending learning
theory of scalar-valued functions by practical considerations and
establish some basic results for learning vector-valued functions
that should prove useful in applications. {S}pecifically, we allow
an output space {Y} to be a {H}ilbert space, and we consider a reproducing
kernel {H}ilbert space of functions whose values lie in {Y}. {I}n
this setting, we derive the form of the minimal norm interpolant
to a finite set of data and apply it to study some regularization
functionals that are important in learning theory. {W}e consider
specific examples of such functionals corresponding to multiple-output
regularization networks and support vector machines, for both regression
and classification. {F}inally, we provide classes of operator-valued
kernels of the dot product and translation-invariant type.},
doi = {10.1162/0899766052530802},
keywords = {Algorithms, Amino Acid, Amino Acids, Artificial Intelligence, Ascomycota,
Automated, Base Sequence, Chromosome Mapping, Codon, Colonic Neoplasms,
Comparative Study, Computer Simulation, Computer-Assisted, Computing
Methodologies, Crystallography, DNA, DNA Primers, Databases, Decision
Support Techniques, Diagnostic Imaging, Enzymes, Feedback, Fixation,
Gene Expression Profiling, Genetic, Hordeum, Host-Parasite Relations,
Humans, Image Enhancement, Image Interpretation, Informatics, Information
Storage and Retrieval, Kinetics, Logistic Models, Magnetic Resonance
Spectroscopy, Mathematical Computing, Models, Nanotechnology, Neural
Networks (Computer), Non-P.H.S., Non-U.S. Gov't, Nonlinear Dynamics,
Ocular, Oligonucleotide Array Sequence Analysis, P.H.S., Pattern
Recognition, Plant, Plants, Predictive Value of Tests, Protein, Protein
Conformation, Regression Analysis, Research Support, Sample Size,
Selection (Genetics), Sequence Alignment, Sequence Analysis, Sequence
Homology, Signal Processing, Skin, Software, Statistical, Subtraction
Technique, Theoretical, Thermodynamics, U.S. Gov't, Viral Proteins,
X-Ray, 15563752},
url = {http://dx.doi.org/10.1162/0899766052530802}
}
@article{Michiels2005Prediction,
author = {Michiels, S. and Koscielny, S. and Hill, C.},
title = {Prediction of cancer outcome with microarrays: a multiple random
validation strategy},
journal = {Lancet},
year = {2005},
volume = {365},
pages = {488--492},
number = {9458},
abstract = {BACKGROUND: General studies of microarray gene-expression profiling
have been undertaken to predict cancer outcome. Knowledge of this
gene-expression profile or molecular signature should improve treatment
of patients by allowing treatment to be tailored to the severity
of the disease. We reanalysed data from the seven largest published
studies that have attempted to predict prognosis of cancer patients
on the basis of DNA microarray analysis. METHODS: The standard strategy
is to identify a molecular signature (ie, the subset of genes most
differentially expressed in patients with different outcomes) in
a training set of patients and to estimate the proportion of misclassifications
with this signature on an independent validation set of patients.
We expanded this strategy (based on unique training and validation
sets) by using multiple random sets, to study the stability of the
molecular signature and the proportion of misclassifications. FINDINGS:
The list of genes identified as predictors of prognosis was highly
unstable; molecular signatures strongly depended on the selection
of patients in the training sets. For all but one study, the proportion
misclassified decreased as the number of patients in the training
set increased. Because of inadequate validation, our chosen studies
published overoptimistic results compared with those from our own
analyses. Five of the seven studies did not classify patients better
than chance. INTERPRETATION: The prognostic value of published microarray
results in cancer studies should be considered with caution. We advocate
the use of validation by repeated random sampling.},
doi = {10.1016/S0140-6736(05)17866-0},
institution = {Biostatistics and Epidemiology Unit, Institut Gustave Roussy, Villejuif,
France.},
keywords = {featureselection, breastcancer, microarray},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {S0140-6736(05)17866-0},
pmid = {15705458},
timestamp = {2010.10.12},
url = {http://dx.doi.org/10.1016/S0140-6736(05)17866-0}
}
@article{Middendorf2004Discriminative,
author = {Middendorf, M. and Ziv, E. and Adams, C. and Hom, J. and Koytcheff,
R. and Levovitz, C. and Woods, G. and Chen, L. and Wiggins, C.},
title = {Discriminative topological features reveal biological network mechanisms.},
journal = {B{MC} {B}ioinformatics},
year = {2004},
volume = {5},
number = {181},
abstract = {B{ACKGROUND}: {R}ecent genomic and bioinformatic advances have motivated
the development of numerous network models intending to describe
graphs of biological, technological, and sociological origin. {I}n
most cases the success of a model has been evaluated by how well
it reproduces a few key features of the real-world data, such as
degree distributions, mean geodesic lengths, and clustering coefficients.
{O}ften pairs of models can reproduce these features with indistinguishable
fidelity despite being generated by vastly different mechanisms.
{I}n such cases, these few target features are insufficient to distinguish
which of the different models best describes real world networks
of interest; moreover, it is not clear a priori that any of the presently-existing
algorithms for network generation offers a predictive description
of the networks inspiring them. {RESULTS}: {W}e present a method
to assess systematically which of a set of proposed network generation
algorithms gives the most accurate description of a given biological
network. {T}o derive discriminative classifiers, we construct a mapping
from the set of all graphs to a high-dimensional (in principle infinite-dimensional)
"word space". {T}his map defines an input space for classification
schemes which allow us to state unambiguously which models are most
descriptive of a given network of interest. {O}ur training sets include
networks generated from 17 models either drawn from the literature
or introduced in this work. {W}e show that different duplication-mutation
schemes best describe the {E}. coli genetic network, the {S}. cerevisiae
protein interaction network, and the {C}. elegans neuronal network,
out of a set of network models including a linear preferential attachment
model and a small-world model. {CONCLUSIONS}: {O}ur method is a first
step towards systematizing network models and assessing their predictability,
and we anticipate its usefulness for a number of communities.},
doi = {10.1186/1471-2105-5-181},
pdf = {../local/Middendorf2004Discriminative.pdf},
file = {Middendorf2004Discriminative.pdf:local/Middendorf2004Discriminative.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://www.biomedcentral.com/1471-2105/5/181}
}
@article{Miertus2000Concepts,
author = {S. Miertus and G. Fassina and P.F. Seneci},
title = {Concepts of {C}ombinatorial {C}hemistry and {C}ombinatorial {T}echnologies},
journal = {Chemick{\'e} {L}isty},
year = {2000},
volume = {94},
pages = {1104-1110},
pdf = {../local/stat.php?id=pdf10:http\},
file = {stat.php?id=pdf10:http\://www.combichemistry.com/statdir/stat.php?id=pdf10:PDF},
keywords = {chemoinformatics},
owner = {mahe},
timestamp = {2006.08.22}
}
@article{Mika2004NLProt,
author = {Sven Mika and Burkhard Rost},
title = {N{LP}rot: extracting protein names and sequences from papers.},
journal = {Nucleic {A}cids {R}es},
year = {2004},
volume = {32},
pages = {W634-7},
number = {Web Server issue},
month = {Jul},
abstract = {Automatically extracting protein names from the literature and linking
these names to the associated entries in sequence databases is becoming
increasingly important for annotating biological databases. {NLP}rot
is a novel system that combines dictionary- and rule-based filtering
with several support vector machines ({SVM}s) to tag protein names
in {P}ub{M}ed abstracts. {W}hen considering partially tagged names
as errors, {NLP}rot still reached a precision of 75\% at a recall
of 76\%. {B}y many criteria our system outperformed other tagging
methods significantly; in particular, it proved very reliable even
for novel names. {N}ames encountered particularly frequently in {D}rosophila,
such as white, wing and bizarre, constitute an obvious limitation
of {NLP}rot. {O}ur method is available both as an {I}nternet server
and as a program for download (http://cubic.bioc.columbia.edu/services/{NLP}rot/).
{I}nput can be {P}ub{M}ed/{MEDLINE} identifiers, authors, titles
and journals, as well as collections of abstracts, or entire papers.},
doi = {10.1093/nar/gkh427},
keywords = {biosvm nlp},
pii = {32/suppl_2/W634},
url = {http://dx.doi.org/10.1093/nar/gkh427}
}
@article{Mika2004Protein,
author = {Mika, Sven and Rost, Burkhard},
title = {Protein names precisely peeled off free text},
journal = {Bioinformatics},
year = {2004},
volume = {20},
pages = {i241-i247},
number = {Suppl. 1},
abstract = {Motivation: {A}utomatically identifying protein names from the scientific
literature is a pre-requisite for the increasing demand in data-mining
this wealth of information. {E}xisting approaches are based on dictionaries,
rules and machine-learning. {H}ere, we introduced a novel system
that combines a pre-processing dictionary- and rule-based filtering
step with several separately trained support vector machines ({SVM}s)
to identify protein names in the {MEDLINE} abstracts. {R}esults:
{O}ur new tagging-system {NLP}rot is capable of extracting protein
names with a precision (accuracy) of 75% at a recall (coverage) of
76% after training on a corpus, which was used before by other groups
and contains 200 annotated abstracts. {F}or our estimate of sustained
performance, we considered partially identified names as false positives.
{O}ne important issue frequently ignored in the literature is the
redundancy in evaluation sets. {W}e suggested some guidelines for
removing overly inadequate overlaps between training and testing
sets. {A}pplying these new guidelines, our program appeared to significantly
out-perform other methods tagging protein names. {NLP}rot was so
successful due to the {SVM}-building blocks that succeeded in utilizing
the local context of protein names in the scientific literature.
{W}e challenge that our system may constitute the most general and
precise method for tagging protein names. {A}vailability: http://cubic.bioc.columbia.edu/services/nlprot/},
pdf = {../local/Mika2004Protein.pdf},
file = {Mika2004Protein.pdf:Mika2004Protein.pdf:PDF},
keywords = {biosvm nlp},
owner = {jeanphilippevert},
url = {http://bioinformatics.oupjournals.org/cgi/content/abstract/20/suppl_1/i241}
}
@inproceedings{Mika1999Fisher,
author = {S. Mika and G. R{\"a}tsch and J. Weston and B. Sch{\"o}lkopf and
K.R. M{\"u}ller},
title = {Fisher discriminant analysis with kernels},
booktitle = {Neural {N}etworks for {S}ignal {P}rocessing {IX}},
year = {1999},
editor = {Y.-H. Hu and J. Larsen and E. Wilson and S. Douglas},
pages = {41--48},
publisher = {IEEE},
pdf = {../local/mika99.pdf},
file = {mika99.pdf:local/mika99.pdf:PDF},
subject = {kernel},
url = {http://ida.first.gmd.de/~mika/PS/MikRaeWesSchMue99.ps}
}
@article{Milik1998Application,
author = {Milik, M. and Sauer, D. and Brunmark, A. P. and Yuan, L. and Vitiello,
A. and Jackson, M. R. and Peterson, P. A. and Skolnick, J. and Glass,
C. A.},
title = {{A}pplication of an artificial neural network to predict specific
class {I} {MHC} binding peptide sequences.},
journal = {Nat. Biotechnol.},
year = {1998},
volume = {16},
pages = {753--756},
number = {8},
month = {Aug},
abstract = {Computational methods were used to predict the sequences of peptides
that bind to the MHC class I molecule, K(b). The rules for predicting
binding sequences, which are limited, are based on preferences for
certain amino acids in certain positions of the peptide. It is apparent
though, that binding can be influenced by the amino acids in all
of the positions of the peptide. An artificial neural network (ANN)
has the ability to simultaneously analyze the influence of all of
the amino acids of the peptide and thus may improve binding predictions.
ANNs were compared to statistically analyzed peptides for their abilities
to predict the sequences of K(b) binding peptides. ANN systems were
trained on a library of binding and nonbinding peptide sequences
from a phage display library. Statistical and ANN methods identified
strong binding peptides with preferred amino acids. ANNs detected
more subtle binding preferences, enabling them to predict medium
binding peptides. The ability to predict class I MHC molecule binding
peptides is useful for immunolological therapies involving cytotoxic-T
cells.},
doi = {10.1038/nbt0898-753},
keywords = {immunoinformatics},
pmid = {9702766},
timestamp = {2007.01.25},
url = {http://dx.doi.org/10.1038/nbt0898-753}
}
@article{Miller1993On,
author = {Miller, J.W. and Goodman, R. and Smyth, P. },
title = {On loss functions which minimize to conditional expected values and
posterior probabilities},
journal = {I{EEE} {T}rans. {I}nform. {T}heory},
year = {1993},
volume = {39},
pages = {1404-1408},
number = {4},
month = {Jul},
abstract = {A loss function, or objective function, is a function used to compare
parameters when fitting a model to data. {T}he loss function gives
a distance between the model output and the desired output. {T}wo
common examples are the squared-error loss function and the cross
entropy loss function. {M}inimizing the mean-square error loss function
is equivalent to minimizing the mean square difference between the
model output and the expected value of the output given a particular
input. {T}his property of minimization to the expected value is formalized
as {P}-admissibility. {T}he necessary and sufficient conditions for
{P}-admissibility, leading to a parametric description of all {P}-admissible
loss functions, are found. {I}n particular, it is shown that two
of the simplest members of this class of functions are the squared
error and the cross entropy loss functions. {O}ne application of
this work is in the choice of a loss function for training neural
networks to provide probability estimates },
pdf = {../local/Miller1993On.pdf},
file = {Miller1993On.pdf:local/Miller1993On.pdf:PDF},
owner = {vert}
}
@article{Miller2007Expression,
author = {Miller, L.D. and Liu, E.T.},
title = {Expression genomics in breast cancer research: microarrays at the
crossroads of biology and medicine},
journal = {Breast Cancer Res.},
year = {2007},
volume = {9},
pages = {206},
abstract = {Genome-wide expression microarray studies have revealed that the biological
and clinical heterogeneity of breast cancer can be partly explained
by information embedded within a complex but ordered transcriptional
architecture. Comprising this architecture are gene expression networks,
or signatures, reflecting biochemical and behavioral properties of
tumors that might be harnessed to improve disease subtyping, patient
prognosis and prediction of therapeutic response. Emerging 'hypothesis-driven'
strategies that incorporate knowledge of pathways and other biological
phenomena in the signature discovery process are linking prognosis
and therapy prediction with transcriptional readouts of tumorigenic
mechanisms that better inform therapeutic options.},
doi = {10.1186/bcr1662},
pdf = {../local/Miller2007Expression.pdf},
file = {Miller2007Expression.pdf:Miller2007Expression.pdf:PDF},
keywords = {csbcbook, csbcbook-ch3},
url = {http://dx.doi.org/10.1186/bcr1662}
}
@book{Milnor1969Topology,
title = {Topology from the Differentiable Viewpoint},
publisher = {Univ. Press of Virginia},
year = {1969},
author = {J.W. Milnor},
isbn = {978-0-691-04833-8}
}
@article{Miranda2006pattern-based,
author = {Kevin C Miranda and Tien Huynh and Yvonne Tay and Yen-Sin Ang and
Wai-Leong Tam and Andrew M Thomson and Bing Lim and Isidore Rigoutsos},
title = {A pattern-based method for the identification of MicroRNA binding
sites and their corresponding heteroduplexes.},
journal = {Cell},
year = {2006},
volume = {126},
pages = {1203--1217},
number = {6},
month = {Sep},
abstract = {We present rna22, a method for identifying microRNA binding sites
and their corresponding heteroduplexes. Rna22 does not rely upon
cross-species conservation, is resilient to noise, and, unlike previous
methods, it first finds putative microRNA binding sites in the sequence
of interest, then identifies the targeting microRNA. Computationally,
we show that rna22 identifies most of the currently known heteroduplexes.
Experimentally, with luciferase assays, we demonstrate average repressions
of 30\% or more for 168 of 226 tested targets. The analysis suggests
that some microRNAs may have as many as a few thousand targets, and
that between 74\% and 92\% of the gene transcripts in four model
genomes are likely under microRNA control through their untranslated
and amino acid coding regions. We also extended the method's key
idea to a low-error microRNA-precursor-discovery scheme; our studies
suggest that the number of microRNA precursors in mammalian genomes
likely ranges in the tens of thousands.},
doi = {10.1016/j.cell.2006.07.031},
pdf = {../local/Miranda2006pattern-based.pdf},
file = {Miranda2006pattern-based.pdf:Miranda2006pattern-based.pdf:PDF},
institution = {Bioinformatics and Pattern Discovery Group, IBM Thomas J. Watson
Research Center, Yorktown Heights, P.O. Box 218, NY 10598, USA.},
keywords = {sirna},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {S0092-8674(06)01099-3},
pmid = {16990141},
timestamp = {2009.10.28},
url = {http://dx.doi.org/10.1016/j.cell.2006.07.031}
}
@article{Mirzadegan2003Sequence,
author = {Mirzadegan, T. and Benk{\"o}, G. and Filipek, S. and Palczewski,
K.},
title = {Sequence analyses of {G}-protein-coupled receptors: similarities
to rhodopsin},
journal = {Biochemistry},
year = {2003},
volume = {42},
pages = {2759--2767},
number = {10},
month = {Mar},
doi = {10.1021/bi027224+},
keywords = {chemogenomics},
owner = {laurent},
pmid = {12627940},
timestamp = {2008.07.16}
}
@article{Mishra2006Human,
author = {Mishra, G.R. and Suresh, M. and Kumaran, K. and Kannabiran, N. and
Suresh, S. and Bala, P. and Shivakumar, K. and Anuradha, N. and Reddy,
R. and Raghavan, T.M. and Menon, S. and Hanumanthu, G. and Gupta,
M. and Upendran, S. and Gupta, S. and Mahesh, M. and Jacob, B. and
Mathew, P. and Chatterjee, P. and Arun, K.S. and Sharma, S. and Chandrika,
K.N. and Deshpande, N. and Palvankar, K. and Raghavnath, R. and Krishnakanth,
R. and Karathia, H. and Rekha, B. and Nayak, R. and Vishnupriya,
G. and Kumar, H.G.M. and Nagini, M. and Kumar, G.S.S. and Jose, R.
and Deepthi, P. and Mohan, S.S. and GandhiT.K.B. and Harsha, H.C.
and Deshpande, K.S. and Sarker, M. and Prasad, T.S.K. and Pandey,
A.},
title = {Human protein reference database--2006 update.},
journal = {Nucleic Acids Res},
year = {2006},
volume = {34},
pages = {D411--D414},
number = {Database issue},
month = {Jan},
abstract = {Human Protein Reference Database (HPRD) (http://www.hprd.org) was
developed to serve as a comprehensive collection of protein features,
post-translational modifications (PTMs) and protein-protein interactions.
Since the original report, this database has increased to >20 000
proteins entries and has become the largest database for literature-derived
protein-protein interactions (>30 000) and PTMs (>8000) for human
proteins. We have also introduced several new features in HPRD including:
(i) protein isoforms, (ii) enhanced search options, (iii) linking
of pathway annotations and (iv) integration of a novel browser, GenProt
Viewer (http://www.genprot.org), developed by us that allows integration
of genomic and proteomic information. With the continued support
and active participation by the biomedical community, we expect HPRD
to become a unique source of curated information for the human proteome
and spur biomedical discoveries based on integration of genomic,
transcriptomic and proteomic data.},
doi = {10.1093/nar/gkj141},
institution = {Institute of Bioinformatics, International Tech Park, Bangalore 560
066, India.},
keywords = {Databases, Protein; Genomics; Humans; Internet; Protein Interaction
Mapping; Protein Isoforms; Protein Processing, Post-Translational;
Proteins; Proteome; Proteomics; Signal Transduction; Systems Integration;
User-Computer Interface},
owner = {fantine},
pii = {34/suppl_1/D411},
pmid = {16381900},
timestamp = {2010.10.21},
url = {http://dx.doi.org/10.1093/nar/gkj141}
}
@article{Mishra2008Review,
author = {K. P. Mishra and L. Ganju and M. Sairam and P. K. Banerjee and R.
C. Sawhney},
title = {A review of high throughput technology for the screening of natural
products.},
journal = {Biomed Pharmacother},
year = {2008},
volume = {62},
pages = {94--98},
number = {2},
month = {Feb},
abstract = {High throughput screening is commonly defined as automatic testing
of potential drug candidates at a rate in excess of 10,000 compounds
per week. The aim of high throughput drug discovery is to test large
compound collections for potentially active compounds ('hits') in
order to allow further development of compounds for pre-clinical
testing ('leads'). High throughput technology has emerged over the
last few years as an important tool for drug discovery and lead optimisation.
In this approach, the molecular diversity and range of biological
properties displayed by secondary metabolites constitutes a challenge
to combinatorial strategies for natural products synthesis and derivatization.
This article reviews the approach of High throughput technique for
the screening of natural products for drug discovery.},
doi = {10.1016/j.biopha.2007.06.012},
institution = {Defence Institute of Physiology and Allied Sciences, Lucknow Road,
Timarpur, Delhi 110054, India.},
keywords = {Automation; Biological Products, pharmacology; Combinatorial Chemistry
Techniques; Drug Design; Drug Evaluation, Preclinical; Technology,
Pharmaceutical, methods},
language = {eng},
medline-pst = {ppublish},
owner = {philippe},
pii = {S0753-3322(07)00127-8},
pmid = {17692498},
timestamp = {2010.07.26},
url = {http://dx.doi.org/10.1016/j.biopha.2007.06.012}
}
@article{Misra2002Interactive,
author = {Misra, J. and Schmitt, W. and Hwang, D. and Hsiao, L.-L. and Gullans,
S. and Stephanopoulos, G. and Stephanopoulos, G.},
title = {Interactive exploration of microarray gene expression patterns in
a reduced dimensional space.},
journal = {Genome Res.},
year = {2002},
volume = {12},
pages = {1112--1120},
number = {7},
month = {Jul},
abstract = {The very high dimensional space of gene expression measurements obtained
by DNA microarrays impedes the detection of underlying patterns in
gene expression data and the identification of discriminatory genes.
In this paper we show the use of projection methods such as principal
components analysis (PCA) to obtain a direct link between patterns
in the genes and patterns in samples. This feature is useful in the
initial interactive pattern exploration of gene expression data and
data-driven learning of the nature and types of samples. Using oligonucleotide
microarray measurements of 40 samples from different normal human
tissues, we show that distinct patterns are obtained when the genes
are projected on a two-dimensional plane spanned by the loadings
of the two major principal components. These patterns define the
particular genes associated with a sample class (i.e., tissue). When
used separately from the other genes, these class-specific (i.e.,
tissue-specific) genes in turn define distinct tissue patterns in
the projection space spanned by the scores of the two major principal
components. In this study, PCA projection facilitated discriminatory
gene selection for different tissues and identified tissue-specific
gene expression signatures for liver, skeletal muscle, and brain
samples. Furthermore, it allowed the classification of nine new samples
belonging to these three types using the linear combination of the
expression levels of the tissue-specific genes determined from the
first set of samples. The application of the technique to other published
data sets is also discussed.},
doi = {10.1101/gr.225302},
pdf = {../local/Misra2002Interactive.pdf},
file = {Misra2002Interactive.pdf:Misra2002Interactive.pdf:PDF},
institution = {Department of Chemical Engineering, Massachusetts Institute of Technology,
Cambridge, Massachusetts 02139, USA.},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pmid = {12097349},
timestamp = {2011.12.30},
url = {http://dx.doi.org/10.1101/gr.225302}
}
@article{Mitelman2007Impact,
author = {Mitelman, F. and Johansson, B. and Mertens, F.},
title = {{{T}he impact of translocations and gene fusions on cancer causation}},
journal = {Nat. Rev. Cancer},
year = {2007},
volume = {7},
pages = {233--245},
keywords = {csbcbook}
}
@article{Miteva2005Fast,
author = {M. A. Miteva and W. H. Lee and M. O. Montes and B. O. Villoutreix},
title = {{F}ast structure-based virtual ligand screening combining {FRED},
{DOCK}, and {S}urflex.},
journal = {J. Med. Chem.},
year = {2005},
volume = {48},
pages = {6012--6022},
number = {19},
month = {Sep},
abstract = {A protocol was devised in which FRED, DOCK, and Surflex were combined
in a multistep virtual ligand screening (VLS) procedure to screen
the pocket of four different proteins. One goal was to evaluate the
impact of chaining "freely available packages to academic users"
on docking/scoring accuracy and CPU time consumption. A bank of 65
660 compounds including 49 known actives was generated. Our procedure
is successful because docking/scoring parameters are tuned according
to the nature of the binding pocket and because a shape-based filtering
tool is applied prior to flexible docking. The obtained enrichment
factors are in line with those reported in recent studies. We suggest
that consensus docking/scoring could be valuable to some drug discovery
projects. The present protocol could process the entire bank for
one receptor in less than a week on one processor, suggesting that
VLS experiments could be performed even without large computer resources.},
doi = {10.1021/jm050262h},
keywords = {Binding Sites, Databases, Estrogen, Factor VIIa, Factual, Ligands,
Molecular Structure, Neuraminidase, Non-U.S. Gov't, Protein Binding,
Quantitative Structure-Activity Relationship, Receptors, Research
Support, Thymidine Kinase, 16162004},
owner = {mahe},
pmid = {16162004},
timestamp = {2006.09.07},
url = {http://dx.doi.org/10.1021/jm050262h}
}
@article{Mitra2007p53,
author = {Mitra, A.P. and Birkhahn, M. and Cote, RJ.},
title = {p53 and retinoblastoma pathways in bladder cancer.},
journal = {World J Urol.},
year = {2007},
volume = {25},
pages = {563-571},
owner = {lcalzone},
timestamp = {2010.04.27}
}
@article{Mitra2004probabilistic,
author = {Pabitra Mitra and C. A. Murthy and Sankar K Pal},
title = {A probabilistic active support vector learning algorithm.},
journal = {I{EEE} {T}rans {P}attern {A}nal {M}ach {I}ntell},
year = {2004},
volume = {26},
pages = {413-8},
number = {3},
month = {Mar},
abstract = {The paper describes a probabilistic active learning strategy for support
vector machine ({SVM}) design in large data applications. {T}he learning
strategy is motivated by the statistical query model. {W}hile most
existing methods of active {SVM} learning query for points based
on their proximity to the current separating hyperplane, the proposed
method queries for a set of points according to a distribution as
determined by the current separating hyperplane and a newly defined
concept of an adaptive confidence factor. {T}his enables the algorithm
to have more robust and efficient learning capabilities. {T}he confidence
factor is estimated from local information using the k nearest neighbor
principle. {T}he effectiveness of the method is demonstrated on real-life
data sets both in terms of generalization performance, query complexity,
and training time.}
}
@article{Mitsumori2005Gene,
author = {Tomohiro Mitsumori and Sevrani Fation and Masaki Murata and Kouichi
Doi and Hirohumi Doi},
title = {Gene/protein name recognition based on support vector machine using
dictionary as features.},
journal = {B{MC} {B}ioinformatics},
year = {2005},
volume = {6 Suppl 1},
pages = {S8},
abstract = {B{ACKGROUND}: {A}utomated information extraction from biomedical literature
is important because a vast amount of biomedical literature has been
published. {R}ecognition of the biomedical named entities is the
first step in information extraction. {W}e developed an automated
recognition system based on the {SVM} algorithm and evaluated it
in {T}ask 1.{A} of {B}io{C}re{A}t{I}v{E}, a competition for automated
gene/protein name recognition. {RESULTS}: {I}n the work presented
here, our recognition system uses the feature set of the word, the
part-of-speech ({POS}), the orthography, the prefix, the suffix,
and the preceding class. {W}e call these features "internal resource
features", i.e., features that can be found in the training data.
{A}dditionally, we consider the features of matching against dictionaries
to be external resource features. {W}e investigated and evaluated
the effect of these features as well as the effect of tuning the
parameters of the {SVM} algorithm. {W}e found that the dictionary
matching features contributed slightly to the improvement in the
performance of the f-score. {W}e attribute this to the possibility
that the dictionary matching features might overlap with other features
in the current multiple feature setting. {CONCLUSION}: {D}uring {SVM}
learning, each feature alone had a marginally positive effect on
system performance. {T}his supports the fact that the {SVM} algorithm
is robust on the high dimensionality of the feature vector space
and means that feature selection is not required.},
doi = {10.1186/1471-2105-6-S1-S8},
pdf = {../local/Mitsumori2005Gene.pdf},
file = {Mitsumori2005Gene.pdf:local/Mitsumori2005Gene.pdf:PDF},
keywords = {biosvm nlp},
pii = {1471-2105-6-S1-S8},
url = {http://dx.doi.org/10.1186/1471-2105-6-S1-S8}
}
@article{Mittal2004Improving,
author = {Mittal, V.},
title = {Improving the efficiency of {RNA} interference in mammals.},
journal = {Nat. {R}ev. {G}enet.},
year = {2004},
volume = {5},
pages = {355-65},
number = {5},
month = {May},
doi = {10.1038/nrg1323},
keywords = {sirna},
pii = {nrg1323},
url = {http://dx.doi.org/10.1038/nrg1323}
}
@article{Miwakeichi2001comparison,
author = {F. Miwakeichi and R. Ramirez-Padron and P. A. Valdes-Sosa and T.
Ozaki},
title = {A comparison of non-linear non-parametric models for epilepsy data.},
journal = {Comput. {B}iol. {M}ed.},
year = {2001},
volume = {31},
pages = {41-57},
number = {1},
month = {Jan},
abstract = {E{EG} spike and wave ({SW}) activity has been described through a
non-parametric stochastic model estimated by the {N}adaraya-{W}atson
({NW}) method. {I}n this paper the performance of the {NW}, the local
linear polynomial regression and support vector machines ({SVM})
methods were compared. {T}he noise-free realizations obtained by
the {NW} and {SVM} methods reproduced {SW} better than as reported
in previous works. {T}he tuning parameters had to be estimated manually.
{A}dding dynamical noise, only the {NW} method was capable of generating
{SW} similar to training data. {T}he standard deviation of the dynamical
noise was estimated by means of the correlation dimension.},
keywords = {Acute, Acute Disease, Adenocarcinoma, Algorithms, Amino Acid Sequence,
Animals, Artificial Intelligence, Automated, B-Lymphocytes, Bacterial
Proteins, Base Pair Mismatch, Base Sequence, Bayes Theorem, Binding
Sites, Biological, Bone Marrow Cells, Brachyura, Cell Compartmentation,
Chemistry, Child, Chromosome Aberrations, Classification, Codon,
Colonic Neoplasms, Comparative Study, Computational Biology, Computer
Simulation, Computer-Assisted, DNA, Data Interpretation, Databases,
Decision Trees, Diabetes Mellitus, Diagnosis, Discriminant Analysis,
Discrimination Learning, Electric Conductivity, Electroencephalography,
Electrophysiology, Epilepsy, Escherichia coli Proteins, Factual,
Feedback, Female, Fungal, Gastric Emptying, Gene Expression Profiling,
Gene Expression Regulation, Genes, Genetic, Genetic Markers, Genetic
Predisposition to Disease, Genomics, Hemolysins, Humans, Indians,
Information Storage and Retrieval, Initiator, Ion Channels, Kinetics,
Leukemia, Likelihood Functions, Linear Models, Lipid Bilayers, Logistic
Models, Lymphocytic, MEDLINE, Male, Markov Chains, Melanoma, Models,
Molecular, Myeloid, Neoplasm, Neoplasms, Neoplastic, Neural Networks
(Computer), Neurological, Nevus, Non-P.H.S., Non-U.S. Gov't, Nonlinear
Dynamics, Normal Distribution, North American, Nucleic Acid Conformation,
Oligonucleotide Array Sequence Analysis, Organ Specificity, Organelles,
Ovarian Neoplasms, Ovary, P.H.S., Pattern Recognition, Physical,
Pigmented, Predictive Value of Tests, Promoter Regions (Genetics),
Protein Biosynthesis, Protein Folding, Protein Structure, Proteins,
Proteome, RNA, Reproducibility of Results, Research Support, Saccharomyces
cerevisiae, Secondary, Sensitivity and Specificity, Sequence Alignment,
Sequence Analysis, Sex Characteristics, Skin Diseases, Skin Neoplasms,
Skin Pigmentation, Software, Sound Spectrography, Statistical, Stochastic
Processes, Stomach Diseases, T-Lymphocytes, Thermodynamics, Transcription,
Transcription Factors, Tumor Markers, Type 2, U.S. Gov't, Vertebrates,
11058693},
pii = {S0010482500000214}
}
@article{Model2001Feature,
author = {Model, F. and Adorjan, P. and Olek, A. and Piepenbrock, C.},
title = {Feature selection for {DNA} methylation based cancer classification},
journal = {Bioinformatics},
year = {2001},
volume = {17},
pages = {S157-S164},
number = {Supp. 1},
abstract = {Molecular portraits, such as m{RNA} expression or {DNA} methylation
patterns, have been shown to be strongly correlated with phenotypical
parameters. {T}hese molecular patterns can be revealed routinely
on a genomic scale. {H}owever, class prediction based on these patterns
is an under-determined problem, due to the extreme high dimensionality
of the data compared to the usually small number of available samples.
{T}his makes a reduction of the data dimensionality necessary. {H}ere
we demonstrate how phenotypic classes can be predicted by combining
feature selection and discriminant analysis. {B}y comparing several
feature selection methods we show that the right dimension reduction
strategy is of crucial importance for the classification performance.
{T}he techniques are demonstrated by methylation pattern based discrimination
between acute lymphoblastic leukemia and acute myeloid leukemia.
{C}ontact: {F}abian.{M}odel@epigenomics.com},
pdf = {../local/Model2001Feature.pdf},
file = {Model2001Feature.pdf:local/Model2001Feature.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://bioinformatics.oupjournals.org/cgi/content/abstract/17/suppl_1/S157}
}
@article{Modha1998Memory-universal,
author = {Modha, D.S. and Masry, E.},
title = {Memory-universal prediction of stationary random processes},
journal = {I{EEE} {T}rans. {I}nform. {T}heory},
year = {1998},
volume = {44},
pages = {117-133},
number = {1},
month = {Jan},
abstract = {We consider the problem of one-step-ahead prediction of a real-valued,
stationary, strongly mixing random process ({X}i)i=-??. {T}he best
mean-square predictor of {X}0 is its conditional mean given the entire
infinite past ({X}i)i=-?-1. {G}iven a sequence of observations {X}1,
{X}2, {XN}, we propose estimators for the conditional mean based
on sequences of parametric models of increasing memory and of increasing
dimension, for example, neural networks and {L}egendre polynomials.
{T}he proposed estimators select both the model memory and the model
dimension, in a data-driven fashion, by minimizing certain complexity
regularized least squares criteria. {W}hen the underlying predictor
function has a finite memory, we establish that the proposed estimators
are memory-universal: the proposed estimators, which do not know
the true memory, deliver the same statistical performance (rates
of integrated mean-squared error) as that delivered by estimators
that know the true memory. {F}urthermore, when the underlying predictor
function does not have a finite memory, we establish that the estimator
based on {L}egendre polynomials is consistent },
pdf = {../local/Modha1998Memory-universal.pdf},
file = {Modha1998Memory-universal.pdf:local/Modha1998Memory-universal.pdf:PDF},
keywords = {information-theory},
owner = {vert}
}
@article{Modha1996Minimum,
author = {Modha, D.S. and Masry, E. },
title = {Minimum complexity regression estimation with weakly dependent observations},
journal = {I{EEE} {T}rans. {I}nform. {T}heory},
year = {1996},
volume = {42},
pages = {2133-2145},
number = {6},
month = {Nov},
abstract = {The minimum complexity regression estimation framework ({B}arron,
1991; {B}arron and {C}over, 1991 and {R}issanen, 1989) is a general
data-driven methodology for estimating a regression function from
a given list of parametric models using independent and identically
distributed (i.i.d.) observations. {W}e extend {B}arron's regression
estimation framework to m-dependent observations and to strongly
mixing observations. {I}n particular, we propose abstract minimum
complexity regression estimators for dependent observations, which
may be adapted to a particular list of parametric models, and establish
upper bounds on the statistical risks of the proposed estimators
in terms of certain deterministic indices of resolvability. {A}ssuming
that the regression function satisfies a certain {F}ourier-transform-type
representation, we examine minimum complexity regression estimators
adapted to a list of parametric models based on neural networks and
by using the upper bounds for the abstract estimators, we establish
rates of convergence for the statistical risks of these estimators.
{A}lso, as a key tool, we extend the classical {B}ernstein inequality
from i.i.d. random variables to m-dependent processes and to strongly
mixing processes },
doi = {10.1109/WITS.1994.513898},
pdf = {../local/Modha1996Minimum.pdf},
file = {Modha1996Minimum.pdf:local/Modha1996Minimum.pdf:PDF},
keywords = {information-theory},
owner = {vert},
url = {http://dx.doi.org/10.1109/WITS.1994.513898}
}
@article{Mohamed2005Prostate,
author = {S. S. Mohamed and M. M A Salama and M. Kamel and E. F. El-Saadany
and K. Rizkalla and J. Chin},
title = {Prostate cancer multi-feature analysis using trans-rectal ultrasound
images.},
journal = {Phys {M}ed {B}iol},
year = {2005},
volume = {50},
pages = {N175-85},
number = {15},
month = {Aug},
abstract = {This note focuses on extracting and analysing prostate texture features
from trans-rectal ultrasound ({TRUS}) images for tissue characterization.
{O}ne of the principal contributions of this investigation is the
use of the information of the images' frequency domain features and
spatial domain features to attain a more accurate diagnosis. {E}ach
image is divided into regions of interest ({ROI}s) by the {G}abor
multi-resolution analysis, a crucial stage, in which segmentation
is achieved according to the frequency response of the image pixels.
{T}he pixels with a similar response to the same filter are grouped
to form one {ROI}. {N}ext, from each {ROI} two different statistical
feature sets are constructed; the first set includes four grey level
dependence matrix ({GLDM}) features and the second set consists of
five grey level difference vector ({GLDV}) features. {T}hese constructed
feature sets are then ranked by the mutual information feature selection
({MIFS}) algorithm. {H}ere, the features that provide the maximum
mutual information of each feature and class (cancerous and non-cancerous)
and the minimum mutual information of the selected features are chosen,
yeilding a reduced feature subset. {T}he two constructed feature
sets, {GLDM} and {GLDV}, as well as the reduced feature subset, are
examined in terms of three different classifiers: the condensed k-nearest
neighbour ({CNN}), the decision tree ({DT}) and the support vector
machine ({SVM}). {T}he accuracy classification results range from
87.5\% to 93.75\%, where the performance of the {SVM} and that of
the {DT} are significantly better than the performance of the {CNN}.},
doi = {10.1088/0031-9155/50/15/N02},
pdf = {../local/Mohamed2005Prostate.pdf},
file = {Mohamed2005Prostate.pdf:local/Mohamed2005Prostate.pdf:PDF},
keywords = {, , 16030375},
pii = {S0031-9155(05)89652-6},
url = {http://dx.doi.org/10.1088/0031-9155/50/15/N02}
}
@inproceedings{Mohar1991Laplacian,
author = {B. Mohar},
title = {The {L}aplacian spectrum of graphs},
booktitle = {Graph theory, combinatorics, and applications},
year = {1991},
editor = {Y. Alavi and G. Chartrand and O. Ollermann and A. Schwenk},
pages = {871--898},
address = {New-York},
publisher = {John Wiley and Sons, Inc.},
pdf = {../local/moha91.pdf},
file = {moha91.pdf:local/moha91.pdf:PDF},
subject = {net},
url = {http://www.fmf.uni-lj.si/~mohar/Papers/Spec.pdf}
}
@incollection{Mohar1997Some,
author = {B. Mohar},
title = {Some applications of {L}aplace eigenvalues of graphs},
booktitle = {Graph {S}ymmetry: {A}lgebraic {M}ethods and {A}pplications},
publisher = {Kluwer},
year = {1997},
editor = {G. Hahn and G. Sabidussi},
volume = {497},
series = {NATO ASI Series C},
pages = {227--275},
address = {Dordrecht},
pdf = {../local/moha97.pdf},
file = {moha97.pdf:local/moha97.pdf:PDF},
subject = {net},
url = {http://citeseer.nj.nec.com/mohar97some.html}
}
@article{Moitessier2008Towards,
author = {N. Moitessier and P. Englebienne and D. Lee and J. Lawandi and C.
R. Corbeil},
title = {Towards the development of universal, fast and highly accurate docking/scoring
methods: a long way to go.},
journal = {Br. J. Pharmacol.},
year = {2008},
volume = {153 Suppl 1},
pages = {S7--26},
month = {Mar},
abstract = {Accelerating the drug discovery process requires predictive computational
protocols capable of reducing or simplifying the synthetic and/or
combinatorial challenge. Docking-based virtual screening methods
have been developed and successfully applied to a number of pharmaceutical
targets. In this review, we first present the current status of docking
and scoring methods, with exhaustive lists of these. We next discuss
reported comparative studies, outlining criteria for their interpretation.
In the final section, we describe some of the remaining developments
that would potentially lead to a universally applicable docking/scoring
method.},
doi = {10.1038/sj.bjp.0707515},
institution = {Department of Chemistry, McGill University, Montréal, Québec, Canada.
nicolas.moitessier@mcgill.ca},
keywords = {Algorithms; Animals; Artificial Intelligence; Computer Simulation;
Drug Evaluation, Preclinical, methods; Humans; Metals, chemistry;
Models, Molecular; Molecular Conformation; Nucleic Acids, chemistry/drug
effects; Proteins, chemistry/drug effects; Reproducibility of Results;
Stochastic Processes},
owner = {bricehoffmann},
pii = {0707515},
pmid = {18037925},
timestamp = {2009.02.13},
url = {http://dx.doi.org/10.1038/sj.bjp.0707515}
}
@article{Moler2000Analysis,
author = {Moler, E. J. and Chow, M. L. and Mian, I. S.},
title = {Analysis of molecular profile data using generative and discriminative
methods},
journal = {Physiol. {G}enomics},
year = {2000},
volume = {4},
pages = {109-126},
number = {2},
month = {Dec},
abstract = {A modular framework is proposed for modeling and understanding the
relationships between molecular profile data and other domain knowledge
using a combination of generative (here, graphical models) and discriminative
[{S}upport {V}ector {M}achines ({SVM}s)] methods. {A}s illustration,
naive {B}ayes models, simple graphical models, and {SVM}s were applied
to published transcription profile data for 1,988 genes in 62 colon
adenocarcinoma tissue specimens labeled as tumor or nontumor. {T}hese
unsupervised and supervised learning methods identified three classes
or subtypes of specimens, assigned tumor or nontumor labels to new
specimens and detected six potentially mislabeled specimens. {T}he
probability parameters of the three classes were utilized to develop
a novel gene relevance, ranking, and selection method. {SVM}s trained
to discriminate nontumor from tumor specimens using only the 50-200
top-ranked genes had the same or better generalization performance
than the full repertoire of 1,988 genes. {A}pproximately 90 marker
genes were pinpointed for use in understanding the basic biology
of colon adenocarcinoma, defining targets for therapeutic intervention
and developing diagnostic tools. {T}hese potential markers highlight
the importance of tissue biology in the etiology of cancer. {C}omparative
analysis of molecular profile data is proposed as a mechanism for
predicting the physiological function of genes in instances when
comparative sequence analysis proves uninformative, such as with
human and yeast translationally controlled tumour protein. {G}raphical
models and {SVM}s hold promise as the foundations for developing
decision support systems for diagnosis, prognosis, and monitoring
as well as inferring biological networks.},
pdf = {../local/Moler2000Analysis.pdf},
file = {Moler2000Analysis.pdf:local/Moler2000Analysis.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://physiolgenomics.physiology.org/cgi/content/abstract/4/2/109}
}
@article{Molloy1998size,
author = {Molloy, M. and Reed, B.},
title = {The size of the giant component of a random graph with a given degree
sequence},
journal = {Combinator. {P}robab. {C}omput.},
year = {1998},
volume = {7},
pages = {295--305},
pdf = {../local/moll98.pdf},
file = {moll98.pdf:local/moll98.pdf:PDF},
subject = {compnet},
url = {http://www.cs.toronto.edu/~molloy/webpapers/size.ps}
}
@article{Molloy1995critical,
author = {Molloy, M. and Reed, B.},
title = {A critical point for random graphs with a given degree sequence},
journal = {Random {S}truct. {A}lgorithm.},
year = {1995},
volume = {6},
pages = {161--179},
pdf = {../local/moll95.pdf},
file = {moll95.pdf:local/moll95.pdf:PDF},
subject = {compnet},
url = {http://www.cs.toronto.edu/~molloy/webpapers/gc2.ps}
}
@article{Mootha2003PGC,
author = {Mootha, V. K. and Lindgren, C. M. and Eriksson, K.-F. and Subramanian,
A. and Sihag, S. and Lehar, J. and Puigserver, P. and Carlsson, E.
and Ridderstr\r{a}le, M. and Laurila, E. and Houstis, N. and Daly,
M. J. and Patterson, N. and Mesirov, J. P. and Golub, T. R. and Tamayo,
P. and Spiegelman, B. and Lander, E. S. and Hirschhorn, J. N. and
Altshuler, D. and Groop, L. C.},
title = {{PGC-1$\alpha$}-responsive genes involved in oxidative phosphorylation
are coordinately downregulated in human diabetes.},
journal = {Nat. Genet.},
year = {2003},
volume = {34},
pages = {267--273},
number = {3},
month = {Jul},
abstract = {DNA microarrays can be used to identify gene expression changes characteristic
of human disease. This is challenging, however, when relevant differences
are subtle at the level of individual genes. We introduce an analytical
strategy, Gene Set Enrichment Analysis, designed to detect modest
but coordinate changes in the expression of groups of functionally
related genes. Using this approach, we identify a set of genes involved
in oxidative phosphorylation whose expression is coordinately decreased
in human diabetic muscle. Expression of these genes is high at sites
of insulin-mediated glucose disposal, activated by PGC-1alpha and
correlated with total-body aerobic capacity. Our results associate
this gene set with clinically important variation in human metabolism
and illustrate the value of pathway relationships in the analysis
of genomic profiling experiments.},
doi = {10.1038/ng1180},
pdf = {../local/Mootha2003PGC.pdf},
file = {Mootha2003PGC.pdf:Mootha2003PGC.pdf:PDF},
institution = {Whitehead Institute/MIT Center for Genome Research, Cambridge, Massachusetts,
USA.},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {ng1180},
pmid = {12808457},
timestamp = {2011.09.21},
url = {http://dx.doi.org/10.1038/ng1180}
}
@phdthesis{Mordelet2010Learning,
author = {Mordelet, F.},
title = {Learning from positive and unlabeled examples in biology},
school = {Mines ParisTech},
year = {2010},
owner = {jp},
timestamp = {2011.01.24}
}
@article{Mordelet2011ProDiGe,
author = {Fantine Mordelet and Jean-Philippe Vert},
title = {{ProDiGe}: Prioritization Of Disease Genes with multitask machine
learning from positive and unlabeled examples.},
journal = {BMC Bioinformatics},
year = {2011},
volume = {12},
pages = {389},
__markedentry = {[jp]},
abstract = {Elucidating the genetic basis of human diseases is a central goal
of genetics and molecular biology. While traditional linkage analysis
and modern high-throughput techniques often provide long lists of
tens or hundreds of disease gene candidates, the identification of
disease genes among the candidates remains time-consuming and expensive.
Efficient computational methods are therefore needed to prioritize
genes within the list of candidates, by exploiting the wealth of
information available about the genes in various databases.We propose
ProDiGe, a novel algorithm for Prioritization of Disease Genes. ProDiGe
implements a novel machine learning strategy based on learning from
positive and unlabeled examples, which allows to integrate various
sources of information about the genes, to share information about
known disease genes across diseases, and to perform genome-wide searches
for new disease genes. Experiments on real data show that ProDiGe
outperforms state-of-the-art methods for the prioritization of genes
in human diseases.ProDiGe implements a new machine learning paradigm
for gene prioritization, which could help the identification of new
disease genes. It is freely available at http://cbio.ensmp.fr/prodige.},
doi = {10.1186/1471-2105-12-389},
pdf = {../local/Mordelet2011ProDiGe.pdf},
file = {Mordelet2011ProDiGe.pdf:Mordelet2011ProDiGe.pdf:PDF},
institution = {Centre for Computational Biology, Mines ParisTech, Fontainebleau,
F-77300 France.},
language = {eng},
medline-pst = {epublish},
owner = {jp},
pii = {1471-2105-12-389},
pmid = {21977986},
timestamp = {2012.03.12},
url = {http://dx.doi.org/10.1186/1471-2105-12-389}
}
@techreport{Mordelet2010bagging,
author = {Mordelet, F. and Vert, J-P.},
title = {A bagging {SVM} to learn from positive and unlabeled examples},
institution = {HAL},
year = {2010},
number = {00523336},
month = {October},
abstract = {We consider the problem of learning a binary classifier from a training
set of positive and unlabeled examples, both in the inductive and
in the transductive setting. This problem, often referred to as \emph{PU
learning}, differs from the standard supervised classification problem
by the lack of negative examples in the training set. It corresponds
to an ubiquitous situation in many applications such as information
retrieval or gene ranking, when we have identified a set of data
of interest sharing a particular property, and we wish to automatically
retrieve additional data sharing the same property among a large
and easily available pool of unlabeled data. We propose a conceptually
simple method, akin to bagging, to approach both inductive and transductive
PU learning problems, by converting them into series of supervised
binary classification problems discriminating the known positive
examples from random subsamples of the unlabeled set. We empirically
demonstrate the relevance of the method on simulated and real data,
where it performs at least as well as existing methods while being
faster.},
owner = {jp},
timestamp = {2010.11.01},
url = {http://hal.archives-ouvertes.fr/hal-00523336}
}
@article{Mordelet2008SIRENE,
author = {Mordelet, F. and Vert, J.-P.},
title = {{SIRENE}: Supervised inference of regulatory networks},
journal = {Bioinformatics},
year = {2008},
volume = {24},
pages = {i76--i82},
number = {16},
doi = {10.1093/bioinformatics/btn273},
pdf = {../local/Mordelet2008SIRENE.pdf},
file = {Mordelet2008SIRENE.pdf:Mordelet2008SIRENE.pdf:PDF},
timestamp = {2008.05.26},
url = {http://dx.doi.org/10.1093/bioinformatics/btn273}
}
@article{Moreau1980Autocorrelation,
author = {G. Moreau and P. Broto},
title = {Autocorrelation of molecular structures: Application
to {SAR} studies},
journal = {Nouv. J. Chim.},
year = {1980},
volume = {757},
pages = {764},
keywords = {chemoinformatics},
owner = {mahe},
timestamp = {2006.09.08}
}
@article{moreau1963inf,
author = {Moreau, JJ},
title = {Inf-convolution des fonctions numeriques sur un espace vectoriel
proximite et dualite dans un espace hilbertien},
journal = {Comptes Rendus de l’Academie des Sciences de Paris},
year = {1963},
volume = {256},
pages = {125--129}
}
@article{Moreau1965Proximite,
author = {Moreau, J.-J.},
title = {Proximit{\'e} et dualit{\'e} dans un espace hilbertien},
journal = {Bulletin de la S.M.F.},
year = {1965},
volume = {93},
pages = {273-299},
owner = {anne-clairehaury},
timestamp = {2012.10.14}
}
@article{Morgan1965Generation,
author = {Morgan, H.L.},
title = {The {G}eneration of {U}nique {M}achine {D}escription for {C}hemical
{S}tructures - {A} {T}echnique {D}eveloped at {C}hemical {A}bstracts
{S}ervice},
journal = {J {C}hem {D}oc},
year = {1965},
volume = {5},
pages = {107-113},
owner = {mahe},
timestamp = {2006.08.09}
}
@article{Morin2008Application,
author = {Ryan D Morin and Michael D O'Connor and Malachi Griffith and Florian
Kuchenbauer and Allen Delaney and Anna-Liisa Prabhu and Yongjun Zhao
and Helen McDonald and Thomas Zeng and Martin Hirst and Connie J
Eaves and Marco A Marra},
title = {Application of massively parallel sequencing to microRNA profiling
and discovery in human embryonic stem cells.},
journal = {Genome Res},
year = {2008},
volume = {18},
pages = {610--621},
number = {4},
month = {Apr},
abstract = {MicroRNAs (miRNAs) are emerging as important, albeit poorly characterized,
regulators of biological processes. Key to further elucidation of
their roles is the generation of more complete lists of their numbers
and expression changes in different cell states. Here, we report
a new method for surveying the expression of small RNAs, including
microRNAs, using Illumina sequencing technology. We also present
a set of methods for annotating sequences deriving from known miRNAs,
identifying variability in mature miRNA sequences, and identifying
sequences belonging to previously unidentified miRNA genes. Application
of this approach to RNA from human embryonic stem cells obtained
before and after their differentiation into embryoid bodies revealed
the sequences and expression levels of 334 known plus 104 novel miRNA
genes. One hundred seventy-one known and 23 novel microRNA sequences
exhibited significant expression differences between these two developmental
states. Owing to the increased number of sequence reads, these libraries
represent the deepest miRNA sampling to date, spanning nearly six
orders of magnitude of expression. The predicted targets of those
miRNAs enriched in either sample shared common features. Included
among the high-ranked predicted gene targets are those implicated
in differentiation, cell cycle control, programmed cell death, and
transcriptional regulation.},
doi = {10.1101/gr.7179508},
pdf = {../local/Morin2008Application.pdf},
file = {Morin2008Application.pdf:Morin2008Application.pdf:PDF},
institution = {Genome Sciences Centre, BC Cancer Agency, Vancouver, British Columbia
V5Z 1L3, Canada.},
keywords = {ngs, sirna},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {gr.7179508},
pmid = {18285502},
timestamp = {2009.10.28},
url = {http://dx.doi.org/10.1101/gr.7179508}
}
@article{Morley2004Genetic,
author = {Morley, Michael and Molony, Cliona M. and Weber, Teresa M. and Devlin,
James L. and Ewens, Kathryn G. and Spielman, Richard S. and Cheung,
Vivian G.},
title = {Genetic analysis of genome-wide variation in human gene expression.},
journal = {Nature},
year = {2004},
volume = {430},
pages = {743--747},
number = {7001},
month = {Aug},
abstract = {Natural variation in gene expression is extensive in humans and other
organisms, and variation in the baseline expression level of many
genes has a heritable component. To localize the genetic determinants
of these quantitative traits (expression phenotypes) in humans, we
used microarrays to measure gene expression levels and performed
genome-wide linkage analysis for expression levels of 3,554 genes
in 14 large families. For approximately 1,000 expression phenotypes,
there was significant evidence of linkage to specific chromosomal
regions. Both cis- and trans-acting loci regulate variation in the
expression levels of genes, although most act in trans. Many gene
expression phenotypes are influenced by several genetic determinants.
Furthermore, we found hotspots of transcriptional regulation where
significant evidence of linkage for several expression phenotypes
(up to 31) coincides, and expression levels of many genes that share
the same regulatory region are significantly correlated. The combination
of microarray techniques for phenotyping and linkage analysis for
quantitative traits allows the genetic mapping of determinants that
contribute to variation in human gene expression.},
doi = {10.1038/nature02797},
institution = {Department of Pediatrics, University of Pennsylvania, The Children's
Hospital of Philadelphia, Philadelphia, Pennsylvania 19104, USA.},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {nature02797},
pmid = {15269782},
timestamp = {2012.02.29},
url = {http://dx.doi.org/10.1038/nature02797}
}
@article{Morris2005Real,
author = {Morris, R. J. and Najmanovich, R.J. and Kahraman, A. and Thornton,
J.M.},
title = {Real spherical harmonic expansion coefficients as 3D shape descriptors
for protein binding pocket and ligand comparisons.},
journal = {Bioinformatics},
year = {2005},
volume = {21},
pages = {2347--2355},
number = {10},
month = {May},
abstract = {MOTIVATION: An increasing number of protein structures are being determined
for which no biochemical characterization is available. The analysis
of protein structure and function assignment is becoming an unexpected
challenge and a major bottleneck towards the goal of well-annotated
genomes. As shape plays a crucial role in biomolecular recognition
and function, the examination and development of shape description
and comparison techniques is likely to be of prime importance for
understanding protein structure-function relationships. RESULTS:
A novel technique is presented for the comparison of protein binding
pockets. The method uses the coefficients of a real spherical harmonics
expansion to describe the shape of a protein's binding pocket. Shape
similarity is computed as the L2 distance in coefficient space. Such
comparisons in several thousands per second can be carried out on
a standard linux PC. Other properties such as the electrostatic potential
fit seamlessly into the same framework. The method can also be used
directly for describing the shape of proteins and other molecules.
AVAILABILITY: A limited version of the software for the real spherical
harmonics expansion of a set of points in PDB format is freely available
upon request from the authors. Binding pocket comparisons and ligand
prediction will be made available through the protein structure annotation
pipeline Profunc (written by Roman Laskowski) which will be accessible
from the EBI website shortly.},
owner = {vero},
pmid = {15728116},
timestamp = {2009.02.04}
}
@article{Morris2007Identification,
author = {Stephanie A Morris and Bhargavi Rao and Benjamin A Garcia and Sandra
B Hake and Robert L Diaz and Jeffrey Shabanowitz and Donald F Hunt
and C. David Allis and Jason D Lieb and Brian D Strahl},
title = {Identification of histone H3 lysine 36 acetylation as a highly conserved
histone modification.},
journal = {J Biol Chem},
year = {2007},
volume = {282},
pages = {7632--7640},
number = {10},
month = {Mar},
abstract = {Histone lysine acetylation is a major mechanism by which cells regulate
the structure and function of chromatin, and new sites of acetylation
continue to be discovered. Here we identify and characterize histone
H3K36 acetylation (H3K36ac). By mass spectrometric analyses of H3
purified from Tetrahymena thermophila and Saccharomyces cerevisiae
(yeast), we find that H3K36 can be acetylated or methylated. Using
an antibody specific to H3K36ac, we show that this modification is
conserved in mammals. In yeast, genome-wide ChIP-chip experiments
show that H3K36ac is localized predominantly to the promoters of
RNA polymerase II-transcribed genes, a pattern inversely related
to that of H3K36 methylation. The pattern of H3K36ac localization
is similar to that of other sites of H3 acetylation, including H3K9ac
and H3K14ac. Using histone acetyltransferase complexes purified from
yeast, we show that the Gcn5-containing SAGA complex that regulates
transcription specifically acetylates H3K36 in vitro. Deletion of
GCN5 completely abolishes H3K36ac in vivo. These data expand our
knowledge of the genomic targets of Gcn5, show H3K36ac is highly
conserved, and raise the intriguing possibility that the transition
between H3K36ac and H3K36me acts as an "acetyl/methyl switch" governing
chromatin function along transcription units.},
doi = {10.1074/jbc.M607909200},
institution = {Department of Biochemistry and Biophysics, University of North Carolina
School of Medicine, Chapel Hill, North Carolina 27599, USA.},
keywords = {Acetylation; Amino Acid Sequence; Animals; Chromatin Immunoprecipitation;
Conserved Sequence; Histone Acetyltransferases, physiology; Histones,
chemistry; Humans; Lysine; Methylation; Mice; Molecular Sequence
Data; Promoter Regions, Genetic; Saccharomyces cerevisiae Proteins,
physiology; Saccharomyces cerevisiae, chemistry; Tetrahymena, chemistry},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {M607909200},
pmid = {17189264},
timestamp = {2010.11.23},
url = {http://dx.doi.org/10.1074/jbc.M607909200}
}
@article{Mortazavi2008Mapping,
author = {Mortazavi, A. and Williams, B. A. and McCue, K. and Schaeffer, L.
and Wold, B.},
title = {Mapping and quantifying mammalian transcriptomes by {RNA-Seq}},
journal = {Nat. Methods},
year = {2008},
volume = {5},
pages = {621--628},
number = {7},
month = {Jul},
abstract = {We have mapped and quantified mouse transcriptomes by deeply sequencing
them and recording how frequently each gene is represented in the
sequence sample (RNA-Seq). This provides a digital measure of the
presence and prevalence of transcripts from known and previously
unknown genes. We report reference measurements composed of 41-52
million mapped 25-base-pair reads for poly(A)-selected RNA from adult
mouse brain, liver and skeletal muscle tissues. We used RNA standards
to quantify transcript prevalence and to test the linear range of
transcript detection, which spanned five orders of magnitude. Although
>90\% of uniquely mapped reads fell within known exons, the remaining
data suggest new and revised gene models, including changed or additional
promoters, exons and 3' untranscribed regions, as well as new candidate
microRNA precursors. RNA splice events, which are not readily measured
by standard gene expression microarray or serial analysis of gene
expression methods, were detected directly by mapping splice-crossing
sequence reads. We observed 1.45 x 10(5) distinct splices, and alternative
splices were prominent, with 3,500 different genes expressing one
or more alternate internal splices.},
doi = {10.1038/nmeth.1226},
pdf = {../local/Mortazavi2008Mapping.pdf},
file = {Mortazavi2008Mapping.pdf:Mortazavi2008Mapping.pdf:PDF},
institution = {Division of Biology, MC 156-29, California Institute of Technology,
Pasadena, California 91125, USA.},
owner = {jp},
pii = {nmeth.1226},
pmid = {18516045},
timestamp = {2008.12.09},
url = {http://dx.doi.org/10.1038/nmeth.1226}
}
@article{Morvai1997Weakly,
author = {Morvai, G. and Yakowitz, S.J. and Algoet, P. },
title = {Weakly convergent nonparametric forecasting of stationary time series},
journal = {I{EEE} {T}rans. {I}nform. {T}heory},
year = {1997},
volume = {43},
pages = {483-498},
number = {2},
month = {Mar},
abstract = {The conditional distribution of the next outcome given the infinite
past of a stationary process can be inferred from finite but growing
segments of the past. {S}everal schemes are known for constructing
pointwise consistent estimates, but they all demand prohibitive amounts
of input data. {W}e consider real-valued time series and construct
conditional distribution estimates that make much more efficient
use of the input data. {T}he estimates are consistent in a weak sense,
and the question whether they are pointwise-consistent is still open.
{F}or finite-alphabet processes one may rely on a universal data
compression scheme like the {L}empel-{Z}iv (1978) algorithm to construct
conditional probability mass function estimates that are consistent
in expected information divergence. {C}onsistency in this strong
sense cannot be attained in a universal sense for all stationary
processes with values in an infinite alphabet, but weak consistency
can. {S}ome applications of the estimates to on-line forecasting,
regression, and classification are discussed },
pdf = {../local/Morvai1997Weakly.pdf},
file = {Morvai1997Weakly.pdf:local/Morvai1997Weakly.pdf:PDF},
keywords = {information-theory},
owner = {vert}
}
@article{Mosley2008Cell,
author = {Mosley, J.D. and Keri, R.A.},
title = {Cell cycle correlated genes dictate the prognostic power of breast
cancer gene lists},
journal = {BMC Medical Genomics},
year = {2008},
volume = {1},
pages = {11},
number = {1},
publisher = {BioMed Central Ltd}
}
@techreport{Mukherjee1998Support,
author = {S. Mukherjee and P. Tamayo and J. P. Mesirov and D. Slonim and A.
Verri and T. Poggio},
title = {Support vector machine classification of microarray data},
institution = {C.B.L.C.},
year = {1998},
number = {182},
note = {A.I. Memo 1677},
pdf = {../local/Mukherjee1998Support.pdf},
file = {Mukherjee1998Support.pdf:local/Mukherjee1998Support.pdf:PDF},
keywords = {biosvm microarray},
subject = {biokernel},
url = {http://citeseer.nj.nec.com/437379.html}
}
@techreport{Murphy1999Modelling,
author = {Murphy, K. and Mian, S.},
title = {Modelling gene expression data using dynamic {B}ayesian networks},
institution = {Computer Science Division, University of California, Berkeley, CA.},
year = {1999},
pdf = {../local/Murphy1999Modelling.pdf},
file = {Murphy1999Modelling.pdf:local/Murphy1999Modelling.pdf:PDF},
keywords = {biogm},
owner = {vert},
timestamp = {2006.01.18}
}
@inproceedings{Murphy2003Using,
author = {Murphy, Kevin and Torralba, Antonio and Freeman, William T.F.},
title = {Using the forest to see the trees: a graphical model relating features,
objects and scenes},
booktitle = {Adv. {N}eural {I}nform. {P}rocess. {S}yst.},
year = {2003},
address = {Vancouver, BC},
publisher = {MIT Press},
pdf = {../local/Murphy2003Using.pdf},
file = {Murphy2003Using.pdf:local/Murphy2003Using.pdf:PDF},
keywords = {conditional-random-field},
owner = {vert}
}
@article{Murzin1995SCOP,
author = {Murzin, A. G. and Brenner, S. E. and Hubbard, T. and Chothia, C.},
title = {S{COP}: {A} structural classification of proteins database for the
investigation of sequences and structures},
journal = {J. {M}ol. {B}iol.},
year = {1995},
volume = {247},
pages = {536--540}
}
@article{Mustafi2009Topology,
author = {Debarshi Mustafi and Krzysztof Palczewski},
title = {Topology of class A G protein-coupled receptors: insights gained
from crystal structures of rhodopsins, adrenergic and adenosine receptors.},
journal = {Mol Pharmacol},
year = {2009},
volume = {75},
pages = {1--12},
number = {1},
month = {Jan},
abstract = {Biological membranes are densely packed with membrane proteins that
occupy approximately half of their volume. In almost all cases, membrane
proteins in the native state lack the higher-order symmetry required
for their direct study by diffraction methods. Despite many technical
difficulties, numerous crystal structures of detergent solubilized
membrane proteins have been determined that illustrate their internal
organization. Among such proteins, class A G protein-coupled receptors
have become amenable to crystallization and high resolution X-ray
diffraction analyses. The derived structures of native and engineered
receptors not only provide insights into their molecular arrangements
but also furnish a framework for designing and testing potential
models of transformation from inactive to active receptor signaling
states and for initiating rational drug design.},
doi = {10.1124/mol.108.051938},
institution = {Department of Pharmacology, School of Medicine, Case Western Reserve
University, Cleveland, Ohio 44106-4965, USA.},
keywords = {Animals; Crystallography, X-Ray; Humans; Models, Molecular; Protein
Structure, Secondary; Receptors, Adrenergic; Receptors, G-Protein-Coupled;
Receptors, Purinergic P1; Rhodopsin},
owner = {ljacob},
pii = {mol.108.051938},
pmid = {18945819},
timestamp = {2009.11.09},
url = {http://dx.doi.org/10.1124/mol.108.051938}
}
@article{Myasnikova2002Support,
author = {Myasnikova, E. and Samsonova, A. and Samsonova, M. and Reinitz, J.},
title = {Support vector regression applied to the determination of the developmental
age of a {D}rosophila embryo from its segmentation gene expression
patterns},
journal = {Bioinformatics},
year = {2002},
volume = {18},
pages = {S87-S95},
number = {Suppl. 1},
abstract = {Motivation: {I}n this paper we address the problem of the determination
of developmental age of an embryo from its segmentation gene expression
patterns in {D}rosophila. {R}esults: {B}y applying support vector
regression we have developed a fast method for automated staging
of an embryo on the basis of its gene expression pattern. {S}upport
vector regression is a statistical method for creating regression
functions of arbitrary type from a set of training data. {T}he training
set is composed of embryos for which the precise developmental age
was determined by measuring the degree of membrane invagination.
{T}esting the quality of regression on the training set showed good
prediction accuracy. {T}he optimal regression function was then used
for the prediction of the gene expression based age of embryos in
which the precise age has not been measured by membrane morphology.
{M}oreover, we show that the same accuracy of prediction can be achieved
when the dimensionality of the feature vector was reduced by applying
factor analysis. {T}he data reduction allowed us to avoid over-fitting
and to increase the efficiency of the algorithm. {A}vailability:
{T}his software may be obtained from the authors. {C}ontact: samson@fn.csa.ru
{K}eywords: gene expression patterns; development; embryo staging;
support vector regression; segmentation genes; {D}rosophila.},
pdf = {../local/Myasnikova2002Support.pdf},
file = {Myasnikova2002Support.pdf:local/Myasnikova2002Support.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://bioinformatics.oupjournals.org/cgi/content/abstract/18/suppl_1/S87}
}
@article{Mueller2005Classifying,
author = {K.-R. M{\"u}ller and G. R{\"a}tsch and S. Sonnenburg and S. Mika
and M. Grimm and N. Heinrich},
title = {Classifying 'drug-likeness' with {K}ernel-based learning methods.},
journal = {J {C}hem {I}nf {M}odel},
year = {2005},
volume = {45},
pages = {249-53},
number = {2},
abstract = {In this article we report about a successful application of modern
machine learning technology, namely {S}upport {V}ector {M}achines,
to the problem of assessing the 'drug-likeness' of a chemical from
a given set of descriptors of the substance. {W}e were able to drastically
improve the recent result by {B}yvatov et al. (2003) on this task
and achieved an error rate of about 7\% on unseen compounds using
{S}upport {V}ector {M}achines. {W}e see a very high potential of
such machine learning techniques for a variety of computational chemistry
problems that occur in the drug discovery and drug design process.},
doi = {10.1021/ci049737o},
pdf = {../local/Mueller2005Classifying.pdf},
file = {Mueller2005Classifying.pdf:local/Mueller2005Classifying.pdf:PDF},
keywords = {biosvm chemoinformatics},
url = {http://dx.doi.org/10.1021/ci049737o}
}
@article{Nabieva2005Whole-proteome,
author = {Elena Nabieva and Kam Jim and Amit Agarwal and Bernard Chazelle and
Mona Singh},
title = {Whole-proteome prediction of protein function via graph-theoretic
analysis of interaction maps.},
journal = {Bioinformatics},
year = {2005},
volume = {21 Suppl 1},
pages = {i302--i310},
month = {Jun},
abstract = {MOTIVATION: Determining protein function is one of the most important
problems in the post-genomic era. For the typical proteome, there
are no functional annotations for one-third or more of its proteins.
Recent high-throughput experiments have determined proteome-scale
protein physical interaction maps for several organisms. These physical
interactions are complemented by an abundance of data about other
types of functional relationships between proteins, including genetic
interactions, knowledge about co-expression and shared evolutionary
history. Taken together, these pairwise linkages can be used to build
whole-proteome protein interaction maps. RESULTS: We develop a network-flow
based algorithm, FunctionalFlow, that exploits the underlying structure
of protein interaction maps in order to predict protein function.
In cross-validation testing on the yeast proteome, we show that FunctionalFlow
has improved performance over previous methods in predicting the
function of proteins with few (or no) annotated protein neighbors.
By comparing several methods that use protein interaction maps to
predict protein function, we demonstrate that FunctionalFlow performs
well because it takes advantage of both network topology and some
measure of locality. Finally, we show that performance can be improved
substantially as we consider multiple data sources and use them to
create weighted interaction networks. AVAILABILITY: http://compbio.cs.princeton.edu/function},
doi = {10.1093/bioinformatics/bti1054},
institution = {Computer Science Department, Princeton University Princeton, NJ 08544,
USA.},
keywords = {Algorithms; Computational Biology, methods; Evolution, Molecular;
Fungal Proteins, chemistry; Genomics; Models, Statistical; Models,
Theoretical; Protein Interaction Mapping, methods; Proteins, chemistry;
Proteomics, methods},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {21/suppl_1/i302},
pmid = {15961472},
timestamp = {2010.04.03},
url = {http://dx.doi.org/10.1093/bioinformatics/bti1054}
}
@article{Nacu2007Gene,
author = {Nacu, S. and Critchley-Thorne, R. and Lee, P. and Holmes, S.},
title = {Gene expression network analysis and applications to immunology},
journal = {Bioinformatics},
year = {2007},
volume = {23},
pages = {850--858},
number = {7},
month = {Apr},
abstract = {We address the problem of using expression data and prior biological
knowledge to identify differentially expressed pathways or groups
of genes. Following an idea of Ideker et al. (2002), we construct
a gene interaction network and search for high-scoring subnetworks.
We make several improvements in terms of scoring functions and algorithms,
resulting in higher speed and accuracy and easier biological interpretation.
We also assign significance levels to our results, adjusted for multiple
testing. Our methods are successfully applied to three human microarray
data sets, related to cancer and the immune system, retrieving several
known and potential pathways. The method, denoted by the acronym
GXNA (Gene eXpression Network Analysis) is implemented in software
that is publicly available and can be used on virtually any microarray
data set. SUPPLEMENTARY INFORMATION: The source code and executable
for the software, as well as certain supplemental materials, can
be downloaded from http://stat.stanford.edu/~serban/gxna.},
doi = {10.1093/bioinformatics/btm019},
pdf = {../local/Nacu2007Gene.pdf},
file = {Nacu2007Gene.pdf:Nacu2007Gene.pdf:PDF},
institution = {Department of Statistics, Stanford University, Stanford, CA 94305,
USA. serban@stat.stanford.edu},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {btm019},
pmid = {17267429},
timestamp = {2011.10.04},
url = {http://dx.doi.org/10.1093/bioinformatics/btm019}
}
@article{Naderi2006gene-expression,
author = {Naderi, A. and Teschendorff, A. E. and Barbosa-Morais, N. L. and
Pinder, S. E. and Green, A. R. and Powe, D. G. and Robertson, J.
F. R. and Aparicio, S. and Ellis, I. O. and Brenton, J. D. and Caldas,
C.},
title = {A gene-expression signature to predict survival in breast cancer
across independent data sets},
journal = {Oncogene},
year = {2006},
volume = {26},
pages = {1507--1516},
number = {10},
doi = {10.1038/sj.onc.1209920},
owner = {jp},
timestamp = {2011.01.14},
url = {http://dx.doi.org/10.1038/sj.onc.1209920}
}
@article{Nair2005Mimicking,
author = {Rajesh Nair and Burkhard Rost},
title = {Mimicking cellular sorting improves prediction of subcellular localization.},
journal = {J {M}ol {B}iol},
year = {2005},
volume = {348},
pages = {85-100},
number = {1},
month = {Apr},
abstract = {Predicting the native subcellular compartment of a protein is an important
step toward elucidating its function. {H}ere we introduce {LOC}tree,
a hierarchical system combining support vector machines ({SVM}s)
and other prediction methods. {LOC}tree predicts the subcellular
compartment of a protein by mimicking the mechanism of cellular sorting
and exploiting a variety of sequence and predicted structural features
in its input. {C}urrently {LOC}tree does not predict localization
for membrane proteins, since the compositional properties of membrane
proteins significantly differ from those of non-membrane proteins.
{W}hile any information about function can be used by the system,
we present estimates of performance that are valid when only the
amino acid sequence of a protein is known. {W}hen evaluated on a
non-redundant test set, {LOC}tree achieved sustained levels of 74\%
accuracy for non-plant eukaryotes, 70\% for plants, and 84\% for
prokaryotes. {W}e rigorously benchmarked {LOC}tree in comparison
to the best alternative methods for localization prediction. {LOC}tree
outperformed all other methods in nearly all benchmarks. {L}ocalization
assignments using {LOC}tree agreed quite well with data from recent
large-scale experiments. {O}ur preliminary analysis of a few entirely
sequenced organisms, namely human ({H}omo sapiens), yeast ({S}accharomyces
cerevisiae), and weed ({A}rabidopsis thaliana) suggested that over
35\% of all non-membrane proteins are nuclear, about 20\% are retained
in the cytosol, and that every fifth protein in the weed resides
in the chloroplast.},
doi = {10.1016/j.jmb.2005.02.025},
pdf = {../local/Nair2005Mimicking.pdf},
file = {Nair2005Mimicking.pdf:local/Nair2005Mimicking.pdf:PDF},
keywords = {biosvm},
pii = {S0022-2836(05)00177-4},
url = {http://dx.doi.org/10.1016/j.jmb.2005.02.025}
}
@article{Najmanovich2008Detection,
author = {Najmanovich, R. and Kurbatova, N. and Thornton, J.},
title = {Detection of 3D atomic similarities and their use in the discrimination
of small molecule protein-binding sites.},
journal = {Bioinformatics},
year = {2008},
volume = {24},
pages = {i105--i111},
number = {16},
month = {Aug},
abstract = {MOTIVATION: Current computational methods for the prediction of function
from structure are restricted to the detection of similarities and
subsequent transfer of functional annotation. In a significant minority
of cases, global sequence or structural (fold) similarities do not
provide clues about protein function. In these cases, one alternative
is to detect local binding site similarities. These may still reflect
more distant evolutionary relationships as well as unique physico-chemical
constraints necessary for binding similar ligands, thus helping pinpoint
the function. In the present work, we ask the following question:
is it possible to discriminate within a dataset of non-homologous
proteins those that bind similar ligands based on their binding site
similarities? METHODS: We implement a graph-matching-based method
for the detection of 3D atomic similarities introducing some simplifications
that allow us to extend its applicability to the analysis of large
allatom binding site models. This method, called IsoCleft, does not
require atoms to be connected either in sequence or space. We apply
the method to a cognate-ligand bound dataset of non-homologous proteins.
We define a family of binding site models with decreasing knowledge
about the identity of the ligand-interacting atoms to uncouple the
questions of predicting the location of the binding site and detecting
binding site similarities. Furthermore, we calculate the individual
contributions of binding site size, chemical composition and geometry
to prediction performance. RESULTS: We find that it is possible to
discriminate between different ligand-binding sites. In other words,
there is a certain uniqueness in the set of atoms that are in contact
to specific ligand scaffolds. This uniqueness is restricted to the
atoms in close proximity of the ligand in which case, size and chemical
composition alone are sufficient to discriminate binding sites. Discrimination
ability decreases with decreasing knowledge about the identity of
the ligand-interacting binding site atoms. The decrease is quite
abrupt when considering size and chemical composition alone, but
much slower when including geometry. We also observe that certain
ligands are easier to discriminate. Interestingly, the subset of
binding site atoms belonging to highly conserved residues is not
sufficient to discriminate binding sites, implying that convergently
evolved binding sites arrived at dissimilar solutions. AVAILABILITY:
IsoCleft can be obtained from the authors.},
owner = {vero},
pmid = {18689810},
timestamp = {2009.02.04}
}
@article{Nakabayashi2006JTB,
author = {Nakabayashi, J. and Sasaki, A.},
title = {A mathematical model for apoptosome assembly: The optimal cytochrome
c/Apaf-1 ratio},
journal = {Journal of Theoretical Biology},
year = {2006},
volume = {242},
pages = {280 - 287},
number = {2},
abstract = {Apoptosis, a highly conserved form of cell suicide, is regulated by
apoptotic signals and their transduction with caspases, a family
of cystein proteases. Caspases are constantly expressed in the normal
cells as inactive pro-enzymes. The activity of caspase is regulated
by the proteolysis. Sequential proteolytic reactions of caspases
are needed to execute apoptosis. Mitochondrial pathway is one of
these apoptotic signal pathways, in which caspases are oligomerized
into characteristic heptamer structure, called apoptosome, with caspase-9
that activate the effector caspases for apoptosis. To investigate
the dynamics of signal transduction pathway regulated by oligomerization,
we construct a mathematical model for Apaf-1 heptamer assembly process.
The model first reveals that intermediate products can remain unconverted
even after all assemble reactions are completed. The second result
of the model is that the conversion efficiency of Apaf-1 heptamer
assembly is maximized when the initial concentration of cytochrome
c is equal to that of Apaf-1. When the concentration of cytochrome
c is sufficiently larger or smaller than that of Apaf-1, the final
Apaf-1 heptamer production is decreased, because intermediate Apaf-1
oligomers (tetramers and bigger oligomers), which themselves are
unable to form active heptamer, accumulate too fast in the cells,
choking a smooth production of Apaf-1 heptamer. Slow activation of
Apaf-1 monomers and small oligomers increase the conversion efficiency.
We also study the optimal number of subunits comprising an active
oligomer that maximize the conversion efficiency in assembly process,
and found that the tetramer is the optimum.},
doi = {DOI: 10.1016/j.jtbi.2006.02.022},
issn = {0022-5193},
keywords = {csbcbook},
url = {http://www.sciencedirect.com/science/article/B6WMD-4JVT1Y9-1/2/88df2dfdc8df3b8d6a17c602e71aeb74}
}
@article{Nakabayashi2006Mathematical,
author = {Nakabayashi, J. and Sasaki, A.},
title = {A mathematical model for apoptosome assembly: The optimal cytochrome
c/Apaf-1 ratio},
journal = {J. Theor. Biol.},
year = {2006},
volume = {242},
pages = {280--287},
number = {2},
abstract = {Apoptosis, a highly conserved form of cell suicide, is regulated by
apoptotic signals and their transduction with caspases, a family
of cystein proteases. Caspases are constantly expressed in the normal
cells as inactive pro-enzymes. The activity of caspase is regulated
by the proteolysis. Sequential proteolytic reactions of caspases
are needed to execute apoptosis. Mitochondrial pathway is one of
these apoptotic signal pathways, in which caspases are oligomerized
into characteristic heptamer structure, called apoptosome, with caspase-9
that activate the effector caspases for apoptosis. To investigate
the dynamics of signal transduction pathway regulated by oligomerization,
we construct a mathematical model for Apaf-1 heptamer assembly process.
The model first reveals that intermediate products can remain unconverted
even after all assemble reactions are completed. The second result
of the model is that the conversion efficiency of Apaf-1 heptamer
assembly is maximized when the initial concentration of cytochrome
c is equal to that of Apaf-1. When the concentration of cytochrome
c is sufficiently larger or smaller than that of Apaf-1, the final
Apaf-1 heptamer production is decreased, because intermediate Apaf-1
oligomers (tetramers and bigger oligomers), which themselves are
unable to form active heptamer, accumulate too fast in the cells,
choking a smooth production of Apaf-1 heptamer. Slow activation of
Apaf-1 monomers and small oligomers increase the conversion efficiency.
We also study the optimal number of subunits comprising an active
oligomer that maximize the conversion efficiency in assembly process,
and found that the tetramer is the optimum.},
doi = {DOI: 10.1016/j.jtbi.2006.02.022},
issn = {0022-5193},
keywords = {csbcbook},
owner = {jp},
timestamp = {2012.05.11},
url = {http://www.sciencedirect.com/science/article/B6WMD-4JVT1Y9-1/2/88df2dfdc8df3b8d6a17c602e71aeb74}
}
@article{Nakamura1998ATM,
author = {Yusuke Nakamura},
title = {{ATM}: the p53 booster},
journal = {Nature Medicine},
year = {1998},
volume = {4},
pages = {1231-1232},
keywords = {csbcbook}
}
@incollection{Nakaya2001Extraction,
author = {A. Nakaya and S. Goto and M. Kanehisa},
title = {Extraction of correlated gene clusters by multiple graph comparison},
booktitle = {Genome {I}nformatics 2001},
publisher = {Universal Academy Press},
year = {2001},
pages = {44--53},
address = {Tokyo, Japan},
pdf = {../local/Nakaya2001Extraction.pdf},
file = {Nakaya2001Extraction.pdf:local/Nakaya2001Extraction.pdf:PDF},
url = {http://www.jsbi.org/journal/GIW01/GIW01F05.html}
}
@article{Nakayama2001Role,
author = {J. Nakayama and J. C. Rice and B. D. Strahl and C. D. Allis and S.
I. Grewal},
title = {Role of histone H3 lysine 9 methylation in epigenetic control of
heterochromatin assembly.},
journal = {Science},
year = {2001},
volume = {292},
pages = {110--113},
number = {5514},
month = {Apr},
abstract = {The assembly of higher order chromatin structures has been linked
to the covalent modifications of histone tails. We provide in vivo
evidence that lysine 9 of histone H3 (H3 Lys9) is preferentially
methylated by the Clr4 protein at heterochromatin-associated regions
in fission yeast. Both the conserved chromo- and SET domains of Clr4
are required for H3 Lys9 methylation in vivo. Localization of Swi6,
a homolog of Drosophila HP1, to heterochomatic regions is dependent
on H3 Lys9 methylation. Moreover, an H3-specific deacetylase Clr3
and a beta-propeller domain protein Rik1 are required for H3 Lys9
methylation by Clr4 and Swi6 localization. These data define a conserved
pathway wherein sequential histone modifications establish a "histone
code" essential for the epigenetic inheritance of heterochromatin
assembly.},
doi = {10.1126/science.1060118},
institution = {Cold Spring Harbor Laboratory, Post Office Box 100, Cold Spring Harbor,
NY 11724, USA.},
keywords = {Acetylation; Cell Cycle Proteins, chemistry/genetics/metabolism; Centromere,
metabolism; Chromosomes, Fungal, metabolism; Fungal Proteins, genetics/metabolism;
Gene Silencing; Genes, Fungal; Heterochromatin, metabolism; Histone
Deacetylases, genetics/metabolism; Histone-Lysine N-Methyltransferase;
Histones, chemistry/metabolism; Lysine, metabolism; Methylation;
Methyltransferases, chemistry/genetics/metabolism; Mutation; Protein
Methyltransferases; Protein Structure, Tertiary; Recombinant Proteins,
chemistry/metabolism; Saccharomyces cerevisiae Proteins; Schizosaccharomyces
pombe Proteins; Schizosaccharomyces, genetics/metabolism; Transcription
Factors, metabolism},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {1060118},
pmid = {11283354},
timestamp = {2010.11.23},
url = {http://dx.doi.org/10.1126/science.1060118}
}
@article{Narendra1977branch,
author = {Narendra, P.M. and Fukunaga, K.},
title = {A branch and bound algorithm for feature subset selection},
journal = {Computers, IEEE Transactions on},
year = {1977},
volume = {100},
pages = {917--922},
number = {9},
publisher = {IEEE}
}
@article{Natarajan1995Sparse,
author = {Natarajan, B. K.},
title = {Sparse Approximate Solutions to Linear Systems},
journal = {SIAM J. Comput.},
year = {1995},
volume = {24},
pages = {227--234},
number = {2},
address = {Philadelphia, PA, USA},
doi = {http://dx.doi.org/10.1137/S0097539792240406},
issn = {0097-5397},
publisher = {Society for Industrial and Applied Mathematics}
}
@article{Natsoulis2005Classification,
author = {Georges Natsoulis and Laurent El Ghaoui and Gert R G Lanckriet and
Alexander M Tolley and Fabrice Leroy and Shane Dunlea and Barrett
P Eynon and Cecelia I Pearson and Stuart Tugendreich and Kurt Jarnagin},
title = {Classification of a large microarray data set: algorithm comparison
and analysis of drug signatures.},
journal = {Genome {R}es.},
year = {2005},
volume = {15},
pages = {724-36},
number = {5},
month = {May},
abstract = {A large gene expression database has been produced that characterizes
the gene expression and physiological effects of hundreds of approved
and withdrawn drugs, toxicants, and biochemical standards in various
organs of live rats. {I}n order to derive useful biological knowledge
from this large database, a variety of supervised classification
algorithms were compared using a 597-microarray subset of the data.
{O}ur studies show that several types of linear classifiers based
on {S}upport {V}ector {M}achines ({SVM}s) and {L}ogistic {R}egression
can be used to derive readily interpretable drug signatures with
high classification performance. {B}oth methods can be tuned to produce
classifiers of drug treatments in the form of short, weighted gene
lists which upon analysis reveal that some of the signature genes
have a positive contribution (act as "rewards" for the class-of-interest)
while others have a negative contribution (act as "penalties") to
the classification decision. {T}he combination of reward and penalty
genes enhances performance by keeping the number of false positive
treatments low. {T}he results of these algorithms are combined with
feature selection techniques that further reduce the length of the
drug signatures, an important step towards the development of useful
diagnostic biomarkers and low-cost assays. {M}ultiple signatures
with no genes in common can be generated for the same classification
end-point. {C}omparison of these gene lists identifies biological
processes characteristic of a given class.},
doi = {10.1101/gr.2807605},
pdf = {../local/Natsoulis2005Classification.pdf},
file = {Natsoulis2005Classification.pdf:local/Natsoulis2005Classification.pdf:PDF},
pii = {15/5/724},
url = {http://dx.doi.org/10.1101/gr.2807605}
}
@article{Natt2004Prediction,
author = {Natt, N.K. and Kaur, H. and Raghava, G.P.},
title = {Prediction of transmembrane regions of beta-barrel proteins using
{ANN}- and {SVM}-based methods.},
journal = {Proteins},
year = {2004},
volume = {56},
pages = {11-18},
number = {1},
abstract = {This article describes a method developed for predicting transmembrane
beta-barrel regions in membrane proteins using machine learning techniques:
artificial neural network ({ANN}) and support vector machine ({SVM}).
{T}he {ANN} used in this study is a feed-forward neural network with
a standard back-propagation training algorithm. {T}he accuracy of
the {ANN}-based method improved significantly, from 70.4% to 80.5%,
when evolutionary information was added to a single sequence as a
multiple sequence alignment obtained from {PSI}-{BLAST}. {W}e have
also developed an {SVM}-based method using a primary sequence as
input and achieved an accuracy of 77.4%. {T}he {SVM} model was modified
by adding 36 physicochemical parameters to the amino acid sequence
information. {F}inally, {ANN}- and {SVM}-based methods were combined
to utilize the full potential of both techniques. {T}he accuracy
and {M}atthews correlation coefficient ({MCC}) value of {SVM}, {ANN},
and combined method are 78.5%, 80.5%, and 81.8%, and 0.55, 0.63,
and 0.64, respectively. {T}hese methods were trained and tested on
a nonredundant data set of 16 proteins, and performance was evaluated
using "leave one out cross-validation" ({LOOCV}). {B}ased on this
study, we have developed a {W}eb server, {TBBP}red, for predicting
transmembrane beta-barrel regions in proteins (available at http://www.imtech.res.in/raghava/tbbpred).},
doi = {10.1002/prot.20092},
pdf = {../local/Natt2004Prediction.pdf},
file = {Natt2004Prediction.pdf:local/Natt2004Prediction.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://dx.doi.org/10.1002/prot.20092}
}
@article{Nattkemper2005Evaluation,
author = {Tim W Nattkemper and Bert Arnrich and Oliver Lichte and Wiebke Timm
and Andreas Degenhard and Linda Pointon and Carmel Hayes and Martin
O Leach and The UK MARIBS Breast Screening Study},
title = {Evaluation of radiological features for breast tumour classification
in clinical screening with machine learning methods.},
journal = {Artif. {I}ntell. {M}ed.},
year = {2005},
volume = {34},
pages = {129-39},
number = {2},
month = {Jun},
abstract = {O{BJECTIVE}: {I}n this work, methods utilizing supervised and unsupervised
machine learning are applied to analyze radiologically derived morphological
and calculated kinetic tumour features. {T}he features are extracted
from dynamic contrast enhanced magnetic resonance imaging ({DCE}-{MRI})
time-course data. {MATERIAL}: {T}he {DCE}-{MRI} data of the female
breast are obtained within the {UK} {M}ulticenter {B}reast {S}creening
{S}tudy. {T}he group of patients imaged in this study is selected
on the basis of an increased genetic risk for developing breast cancer.
{METHODS}: {T}he k-means clustering and self-organizing maps ({SOM})
are applied to analyze the signal structure in terms of visualization.
{W}e employ k-nearest neighbor classifiers (k-nn), support vector
machines ({SVM}) and decision trees ({DT}) to classify features using
a computer aided diagnosis ({CAD}) approach. {RESULTS}: {R}egarding
the unsupervised techniques, clustering according to features indicating
benign and malignant characteristics is observed to a limited extend.
{T}he supervised approaches classified the data with 74\% accuracy
({DT}) and providing an area under the receiver-operator-characteristics
({ROC}) curve ({AUC}) of 0.88 ({SVM}). {CONCLUSION}: {I}t was found
that contour and wash-out type ({WOT}) features determined by the
radiologists lead to the best {SVM} classification results. {A}lthough
a fast signal uptake in early time-point measurements is an important
feature for malignant/benign classification of tumours, our results
indicate that the wash-out characteristics might be considered as
important.},
keywords = {breastcancer}
}
@article{Npg2007DNA,
author = {{Nature Publishing Group}},
title = {{DNA} {T}echnologies - {M}ilestones timeline},
journal = {Nature Milestones},
year = {2007},
note = {{http://www.nature.com/milestones/miledna/timeline.html}},
keywords = {csbcbook, csbcbook-ch2}
}
@article{Needleman1970general,
author = {S.B. Needleman and C.D. Wunsch},
title = {A general method applicable to the search for similarities in the
amino acid sequences of two proteins},
journal = {J. {M}ol. {B}iol.},
year = {1970},
volume = {48},
pages = {443--453}
}
@article{Nesterov1994Interior,
author = {Y. Nesterov and A. Nemirovsky},
title = {Interior Point Polynomial Methods in Convex Programming: Theory and
Applications.},
journal = {SIAM},
year = {1994}
}
@article{Netterwald20101000,
author = {Netterwald, J},
title = {The \$1000 Genome: Coming Soon?},
journal = {Drug Discovery \& Development},
year = {2010},
volume = {13},
pages = {14-15},
owner = {philippe},
timestamp = {2010.07.28}
}
@article{Network2008Comprehensive,
author = {Cancer Genome Atlas Research Network},
title = {Comprehensive genomic characterization defines human glioblastoma
genes and core pathways},
journal = {Nature},
year = {2008},
volume = {455},
pages = {1061--1068},
number = {7216},
month = {Oct},
abstract = {Human cancer cells typically harbour multiple chromosomal aberrations,
nucleotide substitutions and epigenetic modifications that drive
malignant transformation. The Cancer Genome Atlas (TCGA) pilot project
aims to assess the value of large-scale multi-dimensional analysis
of these molecular characteristics in human cancer and to provide
the data rapidly to the research community. Here we report the interim
integrative analysis of DNA copy number, gene expression and DNA
methylation aberrations in 206 glioblastomas--the most common type
of adult brain cancer--and nucleotide sequence aberrations in 91
of the 206 glioblastomas. This analysis provides new insights into
the roles of ERBB2, NF1 and TP53, uncovers frequent mutations of
the phosphatidylinositol-3-OH kinase regulatory subunit gene PIK3R1,
and provides a network view of the pathways altered in the development
of glioblastoma. Furthermore, integration of mutation, DNA methylation
and clinical treatment data reveals a link between MGMT promoter
methylation and a hypermutator phenotype consequent to mismatch repair
deficiency in treated glioblastomas, an observation with potential
clinical implications. Together, these findings establish the feasibility
and power of TCGA, demonstrating that it can rapidly expand knowledge
of the molecular basis of cancer.},
doi = {10.1038/nature07385},
pdf = {../local/Network2008Comprehensive.pdf},
file = {Network2008Comprehensive.pdf:Network2008Comprehensive.pdf:PDF},
owner = {jp},
pii = {nature07385},
pmid = {18772890},
timestamp = {2008.11.26},
url = {http://dx.doi.org/10.1038/nature07385}
}
@book{Neuhaus2007Bridging,
title = {Bridging the Gap Between Graph Edit Distance and Kernel Machines},
publisher = {World Scientific},
year = {2007},
author = {Neuhaus, M. and Bunke, H.},
month = {September},
bibsource = {http://www.visionbib.com/bibliography/match575.html#TT47912}
}
@article{Neuhaus2006Edit,
author = {Neuhaus, M. and Bunke, H.},
title = {Edit distance-based kernel functions for structural pattern classification},
journal = {Pattern Recognition},
year = {2006},
volume = {39},
pages = {1852--1863},
number = {10},
month = {Oct},
abstract = {A common approach in structural pattern classification is to define
a dissimilarity measure on patterns and apply a distance-based nearest-neighbor
classifier. In this paper, we introduce an alternative method for
classification using kernel functions based on edit distance. The
proposed approach is applicable to both string and graph representations
of patterns. By means of the kernel functions introduced in this
paper, string and graph classification can be performed in an implicit
vector space using powerful statistical algorithms. The validity
of the kernel method cannot be established for edit distance in general.
However, by evaluating theoretical criteria we show that the kernel
functions are nevertheless suitable for classification, and experiments
on various string and graph datasets clearly demonstrate that nearest-neighbor
classifiers can be outperformed by support vector machines using
the proposed kernel functions.},
doi = {10.1016/j.patcog.2006.04.012},
pdf = {../local/Neuhaus2006Edit.pdf},
file = {Neuhaus2006Edit.pdf:local/Neuhaus2006Edit.pdf:PDF},
keywords = {image},
timestamp = {2008.07.29},
url = {http://dx.doi.org/10.1016/j.patcog.2006.04.012}
}
@inproceedings{Neuhaus06Fast,
author = {M. Neuhaus and K. Riesen and H. Bunke},
title = {Fast Suboptimal Algorithms for the Computation of Graph Edit Distance.},
booktitle = {SSPR/SPR},
year = {2006},
editor = {Dit-Yan Yeung and James T. Kwok and Ana L. N. Fred and Fabio Roli
and Dick de Ridder},
volume = {4109},
series = {Lecture Notes in Computer Science},
pages = {163-172},
publisher = {Springer},
date = {2006-08-22},
ee = {http://dx.doi.org/10.1007/11815921_17},
interhash = {e36316a00d394d5ad024646e7a6d53f0},
intrahash = {b05f82717fe7c777540d3510f641f9cb},
isbn = {3-540-37236-9},
url = {http://dblp.uni-trier.de/db/conf/sspr/sspr2006.html#NeuhausRB06}
}
@article{Neuvial2006Spatial,
author = {Pierre Neuvial and Philippe Hupé and Isabel Brito and Stéphane Liva
and Elodie Manié and Caroline Brennetot and François Radvanyi and
Alain Aurias and Emmanuel Barillot},
title = {Spatial normalization of array-CGH data.},
journal = {BMC Bioinformatics},
year = {2006},
volume = {7},
pages = {264},
abstract = {BACKGROUND: Array-based comparative genomic hybridization (array-CGH)
is a recently developed technique for analyzing changes in DNA copy
number. As in all microarray analyses, normalization is required
to correct for experimental artifacts while preserving the true biological
signal. We investigated various sources of systematic variation in
array-CGH data and identified two distinct types of spatial effect
of no biological relevance as the predominant experimental artifacts:
continuous spatial gradients and local spatial bias. Local spatial
bias affects a large proportion of arrays, and has not previously
been considered in array-CGH experiments. RESULTS: We show that existing
normalization techniques do not correct these spatial effects properly.
We therefore developed an automatic method for the spatial normalization
of array-CGH data. This method makes it possible to delineate and
to eliminate and/or correct areas affected by spatial bias. It is
based on the combination of a spatial segmentation algorithm called
NEM (Neighborhood Expectation Maximization) and spatial trend estimation.
We defined quality criteria for array-CGH data, demonstrating significant
improvements in data quality with our method for three data sets
coming from two different platforms (198, 175 and 26 BAC-arrays).
CONCLUSION: We have designed an automatic algorithm for the spatial
normalization of BAC CGH-array data, preventing the misinterpretation
of experimental artifacts as biologically relevant outliers in the
genomic profile. This algorithm is implemented in the R package MANOR
(Micro-Array NORmalization), which is described at http://bioinfo.curie.fr/projects/manor
and available from the Bioconductor site http://www.bioconductor.org.
It can also be tested on the CAPweb bioinformatics platform at http://bioinfo.curie.fr/CAPweb.},
doi = {10.1186/1471-2105-7-264},
institution = {Institut Curie, Service de Bioinformatique, 26, rue d'Ulm, Paris,
75248 cedex 05, France. pierre.neuvial@curie.fr},
keywords = {Algorithms; Artifacts; Base Sequence; Chromosome Mapping, methods;
Computer Simulation; Data Interpretation, Statistical; Gene Dosage;
In Situ Hybridization, methods; Models, Genetic; Models, Statistical;
Molecular Sequence Data; Oligonucleotide Array Sequence Analysis,
methods; Sequence Analysis, DNA, methods},
language = {eng},
medline-pst = {epublish},
owner = {philippe},
pii = {1471-2105-7-264},
pmid = {16716215},
timestamp = {2010.08.04},
url = {http://dx.doi.org/10.1186/1471-2105-7-264}
}
@incollection{Newman2002Random,
author = {M. E. J. Newman},
title = {Random graphs as models of networks},
booktitle = {Handbook of {G}raphs and {N}etworks},
publisher = {Wiley-VCH},
year = {2002},
editor = {S. Bornholdt and H. G. Schuster},
address = {Berlin},
note = {To appear},
pdf = {../local/newm02.pdf},
file = {newm02.pdf:local/newm02.pdf:PDF},
subject = {compnet},
url = {http://arxiv.org/abs/cond-mat/0202208/}
}
@article{Newman2001Random,
author = {M. E. J. Newman and S. H. Strogatz and D. J. Watts},
title = {Random graphs with arbitrary degree distributions and their applications},
journal = {Phys. {R}ev. {E}},
year = {2001},
volume = {64},
pages = {26118},
pdf = {../local/newm01.pdf},
file = {newm01.pdf:local/newm01.pdf:PDF},
subject = {compnet},
url = {http://xxx.lanl.gov/abs/cond-mat/0007235}
}
@inproceedings{Ng2001On,
author = {Andrew Y. Ng and Michael I. Jordan and Yair Weiss},
title = {On Spectral Clustering: Analysis and an algorithm},
booktitle = {Advances in Neural Information Processing Systems 14},
year = {2001},
pages = {849--856},
publisher = {MIT Press}
}
@article{Nguyen2005Prediction,
author = {Minh N Nguyen and Jagath C Rajapakse},
title = {Prediction of protein relative solvent accessibility with a two-stage
{SVM} approach.},
journal = {Proteins},
year = {2005},
volume = {59},
pages = {30-7},
number = {1},
month = {Apr},
abstract = {Information on relative solvent accessibility ({RSA}) of amino acid
residues in proteins provides valuable clues to the prediction of
protein structure and function. {A} two-stage approach with support
vector machines ({SVM}s) is proposed, where an {SVM} predictor is
introduced to the output of the single-stage {SVM} approach to take
into account the contextual relationships among solvent accessibilities
for the prediction. {B}y using the position-specific scoring matrices
({PSSM}s) generated by {PSI}-{BLAST}, the two-stage {SVM} approach
achieves accuracies up to 90.4\% and 90.2\% on the {M}anesh data
set of 215 protein structures and the {RS}126 data set of 126 nonhomologous
globular proteins, respectively, which are better than the highest
published scores on both data sets to date. {A} {W}eb server for
protein {RSA} prediction using a two-stage {SVM} method has been
developed and is available (http://birc.ntu.edu.sg/~pas0186457/rsa.html).},
doi = {10.1002/prot.20404},
pdf = {../local/Nguyen2005Prediction.pdf},
file = {Nguyen2005Prediction.pdf:local/Nguyen2005Prediction.pdf:PDF},
keywords = {biosvm},
url = {http://dx.doi.org/10.1002/prot.20404}
}
@article{Nguyen2005Two-stage,
author = {M. N. Nguyen and J. C. Rajapakse},
title = {Two-stage multi-class support vector machines to protein secondary
structure prediction.},
journal = {Pac {S}ymp {B}iocomput},
year = {2005},
pages = {346-57},
abstract = {Bioinformatics techniques to protein secondary structure ({PSS}) prediction
are mostly single-stage approaches in the sense that they predict
secondary structures of proteins by taking into account only the
contextual information in amino acid sequences. {I}n this paper,
we propose two-stage {M}ulti-class {S}upport {V}ector {M}achine ({MSVM})
approach where a {MSVM} predictor is introduced to the output of
the first stage {MSVM} to capture the sequential relationship among
secondary structure elements for the prediction. {B}y using position
specific scoring matrices, generated by {PSI}-{BLAST}, the two-stage
{MSVM} approach achieves {Q}3 accuracies of 78.0\% and 76.3\% on
the {RS}126 dataset of 126 nonhomologous globular proteins and the
{CB}396 dataset of 396 nonhomologous proteins, respectively, which
are better than the highest scores published on both datasets to
date.},
keywords = {biosvm}
}
@article{Nguyen2003Multi-class,
author = {Minh N Nguyen and Jagath C Rajapakse},
title = {Multi-class support vector machines for protein secondary structure
prediction.},
journal = {Genome {I}nform {S}er {W}orkshop {G}enome {I}nform},
year = {2003},
volume = {14},
pages = {218-27},
abstract = {The solution of binary classification problems using the {S}upport
{V}ector {M}achine ({SVM}) method has been well developed. {T}hough
multi-class classification is typically solved by combining several
binary classifiers, recently, several multi-class methods that consider
all classes at once have been proposed. {H}owever, these methods
require resolving a much larger optimization problem and are applicable
to small datasets. {T}hree methods based on binary classifications:
one-against-all ({OAA}), one-against-one ({OAO}), and directed acyclic
graph ({DAG}), and two approaches for multi-class problem by solving
one single optimization problem, are implemented to predict protein
secondary structure. {O}ur experiments indicate that multi-class
{SVM} methods are more suitable for protein secondary structure ({PSS})
prediction than the other methods, including binary {SVM}s, because
their capacity to solve an optimization problem in one step. {F}urthermore,
in this paper, we argue that it is feasible to extend the prediction
accuracy by adding a second-stage multi-class {SVM} to capture the
contextual information among secondary structural elements and thereby
further improving the accuracies. {W}e demonstrate that two-stage
{SVM}s perform better than single-stage {SVM} techniques for {PSS}
prediction using two datasets and report a maximum accuracy of 79.5\%.},
keywords = {biosvm}
}
@article{Nguyen2006Experimental,
author = {Nam-Ky Nguyen and E. R. Williams},
title = {Experimental designs for 2-colour cDNA microarray experiments},
journal = {Applied Stochastic Models in Business and Industry},
year = {2006},
volume = {22},
pages = {631–638},
owner = {phupe},
timestamp = {2011.04.08}
}
@misc{Nicholls2005Openeye,
author = {A. Nicholls},
title = {OEChem, version 1.3.4, OpenEye Scientific Software},
howpublished = {website},
year = {2005},
owner = {bricehoffmann},
timestamp = {2009.02.13}
}
@article{Niedringhaus2011Landscape,
author = {Thomas P Niedringhaus and Denitsa Milanova and Matthew B Kerby and
Michael P Snyder and Annelise E Barron},
title = {Landscape of Next-Generation Sequencing Technologies.},
journal = {Anal Chem},
year = {2011},
month = {May},
doi = {10.1021/ac2010857},
institution = {Department of Chemical Engineering, Stanford University, Palo Alto,
California, United States.},
language = {eng},
medline-pst = {aheadofprint},
owner = {phupe},
pmid = {21612267},
timestamp = {2011.06.01},
url = {http://dx.doi.org/10.1021/ac2010857}
}
@article{Nielsen2010Visualizing,
author = {Cydney B Nielsen and Michael Cantor and Inna Dubchak and David Gordon
and Ting Wang},
title = {Visualizing genomes: techniques and challenges.},
journal = {Nat Methods},
year = {2010},
volume = {7},
pages = {S5--S15},
number = {3 Suppl},
month = {Mar},
abstract = {As our ability to generate sequencing data continues to increase,
data analysis is replacing data generation as the rate-limiting step
in genomics studies. Here we provide a guide to genomic data visualization
tools that facilitate analysis tasks by enabling researchers to explore,
interpret and manipulate their data, and in some cases perform on-the-fly
computations. We will discuss graphical methods designed for the
analysis of de novo sequencing assemblies and read alignments, genome
browsing, and comparative genomics, highlighting the strengths and
limitations of these approaches and the challenges ahead.},
doi = {10.1038/nmeth.1422},
institution = {British Columbia Cancer Agency, Genome Sciences Centre, Vancouver,
Canada. cydneyn@bcgsc.ca},
language = {eng},
medline-pst = {ppublish},
owner = {philippe},
pii = {nmeth.1422},
pmid = {20195257},
timestamp = {2010.07.27},
url = {http://dx.doi.org/10.1038/nmeth.1422}
}
@article{Nielsen1997Identification,
author = {Nielsen, H. and Engelbrecht, J. and Brunak, S. and von Heijne, G.},
title = {Identification of prokaryotic and eukaryotic signal peptides and
prediction of their cleavage sites},
journal = {Protein {E}ng.},
year = {1997},
volume = {10},
pages = {1--6},
number = {1},
pdf = {../local/niel97.pdf},
file = {niel97.pdf:local/niel97.pdf:PDF},
subject = {bioprot},
url = {http://protein.oupjournals.org/cgi/content/abstract/10/1/1}
}
@article{Nielsen2004Improved,
author = {Nielsen, M. and Lundegaard, C. and Worning, P. and Hvid, C. S. and
Lamberth, K. and Buus, S. and Brunak, S. and Lund, O.},
title = {{I}mproved prediction of {MHC} class {I} and class {II} epitopes
using a novel {G}ibbs sampling approach.},
journal = {Bioinformatics},
year = {2004},
volume = {20},
pages = {1388--1397},
number = {9},
month = {Jun},
abstract = {MOTIVATION: Prediction of which peptides will bind a specific major
histocompatibility complex (MHC) constitutes an important step in
identifying potential T-cell epitopes suitable as vaccine candidates.
MHC class II binding peptides have a broad length distribution complicating
such predictions. Thus, identifying the correct alignment is a crucial
part of identifying the core of an MHC class II binding motif. In
this context, we wish to describe a novel Gibbs motif sampler method
ideally suited for recognizing such weak sequence motifs. The method
is based on the Gibbs sampling method, and it incorporates novel
features optimized for the task of recognizing the binding motif
of MHC classes I and II. The method locates the binding motif in
a set of sequences and characterizes the motif in terms of a weight-matrix.
Subsequently, the weight-matrix can be applied to identifying effectively
potential MHC binding peptides and to guiding the process of rational
vaccine design. RESULTS: We apply the motif sampler method to the
complex problem of MHC class II binding. The input to the method
is amino acid peptide sequences extracted from the public databases
of SYFPEITHI and MHCPEP and known to bind to the MHC class II complex
HLA-DR4(B1*0401). Prior identification of information-rich (anchor)
positions in the binding motif is shown to improve the predictive
performance of the Gibbs sampler. Similarly, a consensus solution
obtained from an ensemble average over suboptimal solutions is shown
to outperform the use of a single optimal solution. In a large-scale
benchmark calculation, the performance is quantified using relative
operating characteristics curve (ROC) plots and we make a detailed
comparison of the performance with that of both the TEPITOPE method
and a weight-matrix derived using the conventional alignment algorithm
of ClustalW. The calculation demonstrates that the predictive performance
of the Gibbs sampler is higher than that of ClustalW and in most
cases also higher than that of the TEPITOPE method.},
doi = {10.1093/bioinformatics/bth100},
keywords = {immunoinformatics},
pii = {bth100},
pmid = {14962912},
timestamp = {2007.01.25},
url = {http://dx.doi.org/10.1093/bioinformatics/bth100}
}
@article{Nielsen2003Reliable,
author = {Nielsen, M. and Lundegaard, C. and Worning, P. and Lauem{\o}ller,
S. L. and Lamberth, K. and Buus, S. and Brunak, S. and Lund, O.},
title = {{R}eliable prediction of {T}-cell epitopes using neural networks
with novel sequence representations.},
journal = {Protein Sci.},
year = {2003},
volume = {12},
pages = {1007--1017},
number = {5},
month = {May},
abstract = {In this paper we describe an improved neural network method to predict
T-cell class I epitopes. A novel input representation has been developed
consisting of a combination of sparse encoding, Blosum encoding,
and input derived from hidden Markov models. We demonstrate that
the combination of several neural networks derived using different
sequence-encoding schemes has a performance superior to neural networks
derived using a single sequence-encoding scheme. The new method is
shown to have a performance that is substantially higher than that
of other methods. By use of mutual information calculations we show
that peptides that bind to the HLA A*0204 complex display signal
of higher order sequence correlations. Neural networks are ideally
suited to integrate such higher order correlations when predicting
the binding affinity. It is this feature combined with the use of
several neural networks derived from different and novel sequence-encoding
schemes and the ability of the neural network to be trained on data
consisting of continuous binding affinities that gives the new method
an improved performance. The difference in predictive performance
between the neural network methods and that of the matrix-driven
methods is found to be most significant for peptides that bind strongly
to the HLA molecule, confirming that the signal of higher order sequence
correlation is most strongly present in high-binding peptides. Finally,
we use the method to predict T-cell epitopes for the genome of hepatitis
C virus and discuss possible applications of the prediction method
to guide the process of rational vaccine design.},
keywords = {immunoinformatics},
pmid = {12717023},
timestamp = {2007.01.25}
}
@article{Nielsen2011Genotype,
author = {Nielsen, R. and Paul, J. S. and Albrechtsen, A. and Song, Y. S.},
title = {Genotype and {SNP} calling from next-generation sequencing data.},
journal = {Nat. Rev. Genet.},
year = {2011},
volume = {12},
pages = {443--451},
number = {6},
month = {Jun},
abstract = {Meaningful analysis of next-generation sequencing (NGS) data, which
are produced extensively by genetics and genomics studies, relies
crucially on the accurate calling of SNPs and genotypes. Recently
developed statistical methods both improve and quantify the considerable
uncertainty associated with genotype calling, and will especially
benefit the growing number of studies using low- to medium-coverage
data. We review these methods and provide a guide for their use in
NGS studies.},
doi = {10.1038/nrg2986},
pdf = {../local/Nielsen2011Genotype.pdf},
file = {Nielsen2011Genotype.pdf:Nielsen2011Genotype.pdf:PDF},
institution = {Department of Integrative Biology, University of California, Berkeley,
CA 94720, USA. rasmus_nielsen@berkeley.edu},
keywords = {ngs},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {nrg2986},
pmid = {21587300},
timestamp = {2011.10.27},
url = {http://dx.doi.org/10.1038/nrg2986}
}
@article{Nigam2000Text,
author = {Nigam, K. and Mccallum, A.K. and Thrun, S. and Mitchell, T.M.},
title = {Text Classification from Labeled and Unlabeled Documents using EM},
journal = {Mach. Learn.},
year = {2000},
volume = {39},
pages = {103--134},
number = {2/3},
citeulike-article-id = {224914},
citeulike-linkout-0 = {http://citeseer.ist.psu.edu/nigam99text.html},
citeulike-linkout-1 = {http://citeseer.lcs.mit.edu/nigam99text.html},
citeulike-linkout-2 = {http://citeseer.ifi.unizh.ch/nigam99text.html},
citeulike-linkout-3 = {http://citeseer.comp.nus.edu.sg/nigam99text.html},
keywords = {em, semi-supervised-learning, text-classification, unlabeled-data},
owner = {fantine},
timestamp = {2009.07.09},
url = {http://citeseer.ist.psu.edu/nigam99text.html}
}
@article{Nilsson2004Approximate,
author = {Nilsson, J. and Fioretos, T. and Höglund, M. and Fontes, M.},
title = {Approximate geodesic distances reveal biologically relevant structures
in microarray data.},
journal = {Bioinformatics},
year = {2004},
volume = {20},
pages = {874-80},
number = {6},
month = {Apr},
abstract = {M{OTIVATION}: {G}enome-wide gene expression measurements, as currently
determined by the microarray technology, can be represented mathematically
as points in a high-dimensional gene expression space. {G}enes interact
with each other in regulatory networks, restricting the cellular
gene expression profiles to a certain manifold, or surface, in gene
expression space. {T}o obtain knowledge about this manifold, various
dimensionality reduction methods and distance metrics are used. {F}or
data points distributed on curved manifolds, a sensible distance
measure would be the geodesic distance along the manifold. {I}n this
work, we examine whether an approximate geodesic distance measure
captures biological similarities better than the traditionally used
{E}uclidean distance. {RESULTS}: {W}e computed approximate geodesic
distances, determined by the {I}somap algorithm, for one set of lymphoma
and one set of lung cancer microarray samples. {C}ompared with the
ordinary {E}uclidean distance metric, this distance measure produced
more instructive, biologically relevant, visualizations when applying
multidimensional scaling. {T}his suggests the {I}somap algorithm
as a promising tool for the interpretation of microarray data. {F}urthermore,
the results demonstrate the benefit and importance of taking nonlinearities
in gene expression data into account.},
doi = {10.1093/bioinformatics/btg496},
pdf = {../local/Nilsson2004Approximate.pdf},
file = {Nilsson2004Approximate.pdf:local/Nilsson2004Approximate.pdf:PDF},
keywords = {dimred},
pii = {btg496},
url = {http://dx.doi.org/10.1093/bioinformatics/btg496}
}
@article{Nilsson2009reliable,
author = {Nilsson, R. and Bj{\"o}rkegren, J. and Tegn{\'e}r, J.},
title = {On reliable discovery of molecular signatures},
journal = {BMC Bioinformatics},
year = {2009},
volume = {10},
pages = {38},
abstract = {Background: Molecular signatures are sets of genes, proteins, genetic
variants or other variables that can be used as markers for a particular
phenotype. Reliable signature discovery methods could yield valuable
insight into cell biology and mechanisms of human disease. However,
it is currently not clear how to control error rates such as the
false discovery rate (FDR) in signature discovery. Moreover, signatures
for cancer gene expression have been shown to be unstable, that is,
difficult to replicate in independent studies, casting doubts on
their reliability.
Results: We demonstrate that with modern prediction methods, signatures
that yield accurate predictions may still have a high FDR. Further,
we show that even signatures with low FDR may fail to replicate in
independent studies due to limited statistical power. Thus, neither
stability nor predictive accuracy are relevant when FDR control is
the primary goal. We therefore develop a general statistical hypothesis
testing framework that for the first time provides FDR control for
signature discovery. Our method is demonstrated to be correct in
simulation studies. When applied to five cancer data sets, the method
was able to discover molecular signatures with 5% FDR in three cases,
while two data sets yielded no significant findings.
Conclusion: Our approach enables reliable discovery of molecular signatures
from genome-wide data with current sample sizes. The statistical
framework developed herein is potentially applicable to a wide range
of prediction problems in bioinformatics.},
doi = {10.1186/1471-2105-10-38},
pdf = {../local/Nilsson2009reliable.pdf},
file = {Nilsson2009reliable.pdf:Nilsson2009reliable.pdf:PDF},
owner = {jp},
timestamp = {2011.01.13},
url = {http://dx.doi.org/10.1186/1471-2105-10-38}
}
@incollection{Noble2004Support,
author = {Noble, W. S.},
title = {Support vector machine applications in computational biology},
booktitle = {Kernel {M}ethods in {C}omputational {B}iology},
publisher = {MIT Press},
year = {2004},
editor = {Schölkopf, B. and Tsuda, K. and Vert, J.P.},
pages = {71-92},
abstract = {During the past three years, the support vector machine learning algorithm
has been extensively applied within the field of computational biology.
{T}he algorithm has been used to detect patterns within and among
biological sequences, to classify genes and patients based upon gene
expression profiles, and has recently been applied to several new
biological problems. {T}his chapter reviews the state of the art
with respect to {SVM} applications in computational biology.},
pdf = {../local/Noble2004Support.pdf},
file = {Noble2004Support.pdf:local/Noble2004Support.pdf:PDF},
keywords = {biosvm},
owner = {vert}
}
@book{Nocedal2006Numerical,
title = {Numerical optimization},
publisher = {Springer},
year = {2006},
author = {Nocedal, J. and Wright, S.},
owner = {jp},
timestamp = {2011.05.02}
}
@article{Noon93Efficient,
author = {C. Noon and J.C. Bean},
title = {An efficient transformation of the generalized traveling salesman
problem},
journal = {INFOR},
year = {1993},
pages = {39--44}
}
@article{OConnor2006BiotechnolLett,
author = {O'Connor, K. C. and Muhitch, J. W. and Lacks, D. J. and Al-Rubeai,
M.},
title = {Modeling suppression of cell death by Bcl-2 over-expression in myeloma
NS0 6A1 cells},
journal = {Biotechnol Lett},
year = {2006},
volume = {28},
pages = {1919--24},
number = {23},
abstract = {A novel population-balance model was employed to evaluate the suppression
of cell death in myeloma NS0 6A1 cells metabolically engineered to
over-express the apoptotic suppressor Bcl-2. The model is robust
in its ability to simulate cell population dynamics in batch suspension
culture and in response to thymidine-induced growth inhibition: 89
percent of simulated cell concentrations are within two standard
deviations of experimental data. Kinetic rate constants in model
equations suggest that Bcl-2 over-expression extends culture longevity
from 6 days to at least 15 days by suppressing the specific rate
of early apoptotic cell formation by more than 6-fold and necrotic
cell formation by at least 3-fold, despite nearly a 3-fold decrease
in initial cell growth rate and no significant change in the specific
rate of late apoptotic cell formation. This computational analysis
supports a mechanism in which Bcl-2 is a common mediator of early
apoptotic and necrotic events occurring at rates that are dependent
on cellular factors accumulating over time. The model has current
application to the rational design of cell cultures through metabolic
engineering for the industrial production of biopharmaceuticals.},
keywords = {csbcbook}
}
@article{ODonnell2005Gene,
author = {Rebekah K O'Donnell and Michael Kupferman and S. Jack Wei and Sunil
Singhal and Randal Weber and Bert O'Malley and Yi Cheng and Mary
Putt and Michael Feldman and Barry Ziober and Ruth J Muschel},
title = {Gene expression signature predicts lymphatic metastasis in squamous
cell carcinoma of the oral cavity.},
journal = {Oncogene},
year = {2005},
volume = {24},
pages = {1244-51},
number = {7},
month = {Feb},
abstract = {Metastasis via the lymphatics is a major risk factor in squamous cell
carcinoma of the oral cavity ({OSCC}). {W}e sought to determine whether
the presence of metastasis in the regional lymph node could be predicted
by a gene expression signature of the primary tumor. {A} total of
18 {OSCC}s were characterized for gene expression by hybridizing
{RNA} to {A}ffymetrix {U}133{A} gene chips. {G}enes with differential
expression were identified using a permutation technique and verified
by quantitative {RT}-{PCR} and immunohistochemistry. {A} predictive
rule was built using a support vector machine, and the accuracy of
the rule was evaluated using crossvalidation on the original data
set and prediction of an independent set of four patients. {M}etastatic
primary tumors could be differentiated from nonmetastatic primary
tumors by a signature gene set of 116 genes. {T}his signature gene
set correctly predicted the four independent patients as well as
associating five lymph node metastases from the original patient
set with the metastatic primary tumor group. {W}e concluded that
lymph node metastasis could be predicted by gene expression profiles
of primary oral cavity squamous cell carcinomas. {T}he presence of
a gene expression signature for lymph node metastasis indicates that
clinical testing to assess risk for lymph node metastasis should
be possible.},
doi = {10.1038/sj.onc.1208285},
pdf = {../local/O'Donnell2005Gene.pdf},
file = {O'Donnell2005Gene.pdf:local/O'Donnell2005Gene.pdf:PDF},
keywords = {biosvm microarray},
pii = {1208285},
url = {http://dx.doi.org/10.1038/sj.onc.1208285}
}
@misc{ODriscoll2004Virtual,
author = {Cath O'Driscoll},
title = {A {V}irtual {S}pace {O}dyssey},
howpublished = {Nature Horizon : Charting Chemical Space},
year = {2004},
owner = {mahe},
timestamp = {2006.09.04}
}
@article{OFlanagan2005Non,
author = {R. A. O'Flanagan and G. Paillard and R. Lavery and A. M. Sengupta},
title = {Non-additivity in protein-{DNA} binding.},
journal = {Bioinformatics},
year = {2005},
volume = {21},
pages = {2254-63},
number = {10},
month = {May},
abstract = {M{OTIVATION}: {L}ocalizing protein binding sites within genomic {DNA}
is of considerable importance, but remains difficult for protein
families, such as transcription factors, which have loosely defined
target sequences. {I}t is generally assumed that protein affinity
for {DNA} involves additive contributions from successive nucleotide
pairs within the target sequence. {T}his is not necessarily true,
and non-additive effects have already been experimentally demonstrated
in a small number of cases. {T}he principal origin of non-additivity
involves the so-called indirect component of protein-{DNA} recognition
which is related to the sequence dependence of {DNA} deformation
induced during complex formation. {N}on-additive effects are difficult
to study because they require the identification of many more binding
sequences than are normally necessary for describing additive specificity
(typically via the construction of weight matrices). {RESULTS}: {I}n
the present work we will use theoretically estimated binding energies
as a basis for overcoming this problem. {O}ur approach enables us
to study the full combinatorial set of sequences for a variety of
{DNA}-binding proteins, make a detailed analysis of non-additive
effects and exploit this information to improve binding site predictions
using either weight matrices or support vector machines. {T}he results
underline the fact that, even in the presence of significant deformation,
non-additive effects may involve only a limited number of dinucleotide
steps. {T}his information helps to reduce the number of binding sites
which need to be identified for successful predictions and to avoid
problems of over-fitting. {AVAILABILITY}: {T}he {SVM} software is
available upon request from the authors.},
doi = {10.1093/bioinformatics/bti361},
pdf = {../local/OFlanagan2005Non.pdf},
file = {OFlanagan2005Non.pdf:local/OFlanagan2005Non.pdf:PDF},
keywords = {biosvm},
pii = {bti361},
url = {http://dx.doi.org/10.1093/bioinformatics/bti361}
}
@article{OHagan2003Array,
author = {O'Hagan, R. C. and Brennan, C. W. and Strahs, A. and Zhang, X. and
Kannan, K. and Donovan, M. and Cauwels, C. and Sharpless, N. E. and
Wong, W. H. and Chin, L.},
title = {Array comparative genome hybridization for tumor classification and
gene discovery in mouse models of malignant melanoma},
journal = {Cancer Res.},
year = {2003},
volume = {63},
pages = {5352--5356},
number = {17},
month = {Sep},
abstract = {Chromosomal numerical aberrations (CNAs), particularly regional amplifications
and deletions, are a hallmark of solid tumor genomes. These genomic
alterations carry the potential to convey etiologic and clinical
significance by virtue of their clonality within a tumor cell population,
their distinctive patterns in relation to tumor staging, and their
recurrence across different tumor types. In this study, we showed
that array-based comparative genomic hybridization (CGH) analysis
of genome-wide CNAs can classify tumors on the basis of differing
etiologies and provide mechanistic insights to specific biological
processes. In a RAS-induced p19(Arf-/-) mouse model that experienced
accelerated melanoma formation after UV exposure, array-CGH analysis
was effective in distinguishing phenotypically identical melanomas
that differed solely by previous UV exposure. Moreover, classification
by array-CGH identified key CNAs unique to each class, including
amplification of cyclin-dependent kinase 6 in UV-treated cohort,
a finding consistent with our recent report that UVB targets components
of the p16(INK4a)-cyclin-dependent kinase-RB pathway in melanoma
genesis (K. Kannan, et al., Proc. Natl. Acad. Sci. USA, 21: 2003).
These results are the first to establish the utility of array-CGH
as a means of etiology-based tumor classification in genetically
defined cancer-prone models.},
pdf = {../local/OHagan2003Array.pdf},
file = {OHagan2003Array.pdf:OHagan2003Array.pdf:PDF},
keywords = {cgh},
owner = {franck},
pmid = {14500367},
timestamp = {2007.10.31}
}
@techreport{Obozinski2011Group,
author = {G. Obozinski and L. Jacob and J.-P. Vert},
title = {Group Lasso with Overlaps: the Latent Group Lasso approach},
institution = {arXiv},
year = {2011}
}
@article{Obozinski2010Joint,
author = {Obozinski, G. and Taskar, B. and Jordan, M.I.},
title = {Joint covariate selection and joint subspace selection for multiple
classification problems},
journal = {Statistics and Computing},
year = {2010},
volume = {20},
pages = {231--252},
number = {2},
publisher = {Springer}
}
@techreport{Obozinski2007Multi-task,
author = {G. Obozinski and B. Taskar and M. I. Jordan},
title = {Multi-task Feature Selection},
institution = {arXiv},
year = {2007}
}
@article{Obozinski2011Support,
author = {Obozinski, G. and Wainwright, M. J. and Jordan, M. I.},
title = {Support union recovery in high-dimensional multivariate regression},
journal = {Ann. Stat.},
year = {2011},
volume = {39},
pages = {1--47},
number = {1},
pdf = {../local/Obozinski2011Support.pdf},
file = {Obozinski2011Support.pdf:Obozinski2011Support.pdf:PDF},
owner = {jp},
timestamp = {2013.01.01}
}
@techreport{Obozinski2008Union,
author = {Obozinski, G. and Wainwright, M. J. and Jordan, M. I.},
title = {Union support recovery in high-dimensional multivariate regression},
institution = {arXiv},
year = {2008},
number = {0808.0711v1},
month = {August},
pdf = {../local/Obozinski2008Union.pdf},
file = {Obozinski2008Union.pdf:Obozinski2008Union.pdf:PDF},
keywords = {lasso},
owner = {jp},
timestamp = {2009.05.02}
}
@article{Ogawa2000New,
author = {Nobuo Ogawa and Joseph DeRisi and Patrick O. Brown},
title = {New {C}omponents of a {S}ystem for {P}hosphate {A}ccumulation and
{P}olyphosphate {M}etabolism in {S}accharomyces cerevisiae {R}evealed
by {G}enomic {E}xpression {A}nalysis},
journal = {Mol. {B}iol. {C}ell},
year = {2000},
volume = {11},
pages = {4309--4321},
month = {Dec},
pdf = {../local/ogaw00.pdf},
file = {ogaw00.pdf:local/ogaw00.pdf:PDF},
subject = {microarray},
url = {http://www.molbiolcell.org/cgi/reprint/11/12/4309.pdf}
}
@article{Okada2004retinal,
author = {Okada, T. and Sugihara, M. and Bondar, A.-N. and Elstner, M. and
Entel, P. and Buss, V.},
title = {The retinal conformation and its environment in rhodopsin in light
of a new 2.2 A crystal structure.},
journal = {J. Mol. Biol.},
year = {2004},
volume = {342},
pages = {571--583},
number = {2},
month = {Sep},
abstract = {A new high-resolution structure is reported for bovine rhodopsin,
the visual pigment in rod photoreceptor cells. Substantial improvement
of the resolution limit to 2.2 A has been achieved by new crystallization
conditions, which also reduce significantly the probability of merohedral
twinning in the crystals. The new structure completely resolves the
polypeptide chain and provides further details of the chromophore
binding site including the configuration about the C6-C7 single bond
of the 11-cis-retinal Schiff base. Based on both an earlier structure
and the new improved model of the protein, a theoretical study of
the chromophore geometry has been carried out using combined quantum
mechanics/force field molecular dynamics. The consistency between
the experimental and calculated chromophore structures is found to
be significantly improved for the 2.2 A model, including the angle
of the negatively twisted 6-s-cis-bond. Importantly, the new crystal
structure refinement reveals significant negative pre-twist of the
C11-C12 double bond and this is also supported by the theoretical
calculation although the latter converges to a smaller value. Bond
alternation along the unsaturated chain is significant, but weaker
in the calculated structure than the one obtained from the X-ray
data. Other differences between the experimental and theoretical
structures in the chromophore binding site are discussed with respect
to the unique spectral properties and excited state reactivity of
the chromophore.},
doi = {10.1016/j.jmb.2004.07.044},
keywords = {chemogenomics},
owner = {laurent},
pii = {S0022-2836(04)00873-3},
pmid = {15327956},
timestamp = {2008.04.01},
url = {http://dx.doi.org/10.1016/j.jmb.2004.07.044}
}
@article{Okamoto2007Prediction,
author = {Okamoto, S. and Yamanishi, Y. and Ehira, S. and Kawashima, S. and
Tonomura, K. and Kanehisa, M.},
title = {Prediction of nitrogen metabolism-related genes in Anabaena by kernel-based
network analysis.},
journal = {Proteomics},
year = {2007},
volume = {7},
pages = {900--909},
number = {6},
month = {Mar},
abstract = {Prediction of molecular interaction networks from large-scale datasets
in genomics and other omics experiments is an important task in terms
of both developing bioinformatics methods and solving biological
problems. We have applied a kernel-based network inference method
for extracting functionally related genes to the response of nitrogen
deprivation in cyanobacteria Anabaena sp. PCC 7120 integrating three
heterogeneous datasets: microarray data, phylogenetic profiles, and
gene orders on the chromosome. We obtained 1348 predicted genes that
are somehow related to known genes in the Kyoto Encyclopedia of Genes
and Genomes (KEGG) pathways. While this dataset contained previously
known genes related to the nitrogen deprivation condition, it also
contained additional genes. Thus, we attempted to select any relevant
genes using the constraints of Pfam domains and NtcA-binding sites.
We found candidates of nitrogen metabolism-related genes, which are
depicted as extensions of existing KEGG pathways. The prediction
of functional relationships between proteins rather than functions
of individual proteins will thus assist the discovery from the large-scale
datasets.},
doi = {10.1002/pmic.200600862},
institution = {Bioinformatics Center, Institute for Chemical Research, Kyoto University,
Uji, Japan. so@kazusa.or.jp},
owner = {jp},
pmid = {17370268},
timestamp = {2008.06.01},
url = {http://dx.doi.org/10.1002/pmic.200600862}
}
@article{Okuno2007GLIDA,
author = {Okuno, Y. and Tamon, A. and Yabuuchi, H. and Niijima, S. and Minowa,
Y. and Tonomura, K. and Kunimoto, R. and Feng, C.},
title = {{GLIDA}: {GPCR} ligand database for chemical genomics drug discovery
database and tools update.},
journal = {Nucleic Acids Res.},
year = {2007},
volume = {36},
pages = {D907--D912},
number = {Database issue},
month = {Nov},
abstract = {G-protein coupled receptors (GPCRs) represent one of the most important
families of drug targets in pharmaceutical development. GLIDA is
a public GPCR-related Chemical Genomics database that is primarily
focused on the integration of information between GPCRs and their
ligands. It provides interaction data between GPCRs and their ligands,
along with chemical information on the ligands, as well as biological
information regarding GPCRs. These data are connected with each other
in a relational database, allowing users in the field of Chemical
Genomics research to easily retrieve such information from either
biological or chemical starting points. GLIDA includes a variety
of similarity search functions for the GPCRs and for their ligands.
Thus, GLIDA can provide correlation maps linking the searched homologous
GPCRs (or ligands) with their ligands (or GPCRs). By analyzing the
correlation patterns between GPCRs and ligands, we can gain more
detailed knowledge about their conserved molecular recognition patterns
and improve drug design efforts by focusing on inferred candidates
for GPCR-specific drugs. This article provides a summary of the GLIDA
database and user facilities, and describes recent improvements to
database design, data contents, ligand classification programs, similarity
search options and graphical interfaces. GLIDA is publicly available
at http://pharminfo.pharm.kyoto-u.ac.jp/services/glida/. We hope
that it will prove very useful for Chemical Genomics research and
GPCR-related drug discovery.},
doi = {10.1093/nar/gkm948},
keywords = {chemogenomics},
owner = {laurent},
pii = {gkm948},
pmid = {17986454},
timestamp = {2008.01.15},
url = {http://dx.doi.org/10.1093/nar/gkm948}
}
@article{Okuno2006GLIDA,
author = {Okuno, Y. and Yang, J. and Taneishi, K. and Yabuuchi, H. and Tsujimoto,
G.},
title = {{GLIDA}: {GPCR}-ligand database for chemical genomic drug discovery},
journal = {Nucleic Acids Res.},
year = {2006},
volume = {34},
pages = {D673--D677},
number = {Database issue},
month = {Jan},
abstract = {G-protein coupled receptors (GPCRs) represent one of the most important
families of drug targets in pharmaceutical development. GPCR-LIgand
DAtabase (GLIDA) is a novel public GPCR-related chemical genomic
database that is primarily focused on the correlation of information
between GPCRs and their ligands. It provides correlation data between
GPCRs and their ligands, along with chemical information on the ligands,
as well as access information to the various web databases regarding
GPCRs. These data are connected with each other in a relational database,
allowing users in the field of GPCR-related drug discovery to easily
retrieve such information from either biological or chemical starting
points. GLIDA includes structure similarity search functions for
the GPCRs and for their ligands. Thus, GLIDA can provide correlation
maps linking the searched homologous GPCRs (or ligands) with their
ligands (or GPCRs). By analyzing the correlation patterns between
GPCRs and ligands, we can gain more detailed knowledge about their
interactions and improve drug design efforts by focusing on inferred
candidates for GPCR-specific drugs. GLIDA is publicly available at
http://gdds.pharm.kyoto-u.ac.jp:8081/glida. We hope that it will
prove very useful for chemical genomic research and GPCR-related
drug discovery.},
doi = {10.1093/nar/gkj028},
keywords = {chemogenomics},
owner = {laurent},
pii = {34/suppl_1/D673},
pmid = {16381956},
timestamp = {2008.01.15},
url = {http://dx.doi.org/10.1093/nar/gkj028}
}
@article{Oloff2006Chemometric,
author = {Scott Oloff and Shuxing Zhang and Nagamani Sukumar and Curt Breneman
and Alexander Tropsha},
title = {Chemometric analysis of ligand receptor complementarity: identifying
Complementary Ligands Based on Receptor Information (CoLiBRI).},
journal = {J. Chem. Inf. Model.},
year = {2006},
volume = {46},
pages = {844--851},
number = {2},
abstract = {We have developed a novel structure-based chemoinformatics approach
to search for Complimentary Ligands Based on Receptor Information
(CoLiBRI). CoLiBRI is based on the representation of both receptor
binding sites and their respective ligands in a space of universal
chemical descriptors. The binding site atoms involved in the interaction
with ligands are identified by the means of a computational geometry
technique known as Delaunay tessellation as applied to X-ray characterized
ligand-receptor complexes. TAE/RECON multiple chemical descriptors
are calculated independently for each ligand as well as for its active
site atoms. The representation of both ligands and active sites using
chemical descriptors allows the application of well-known chemometric
techniques in order to correlate chemical similarities between active
sites and their respective ligands. We have established a protocol
to map patterns of nearest neighbor active site vectors in a multidimensional
TAE/RECON space onto those of their complementary ligands and vice
versa. This protocol affords the prediction of a virtual complementary
ligand vector in the ligand chemical space from the position of a
known active site vector. This prediction is followed by chemical
similarity calculations between this virtual ligand vector and those
calculated for molecules in a chemical database to identify real
compounds most similar to the virtual ligand. Consequently, the knowledge
of the receptor active site structure affords straightforward and
efficient identification of its complementary ligands in large databases
of chemical compounds using rapid chemical similarity searches. Conversely,
starting from the ligand chemical structure, one may identify possible
complementary receptor cavities as well. We have applied the CoLiBRI
approach to a data set of 800 X-ray characterized ligand-receptor
complexes in the PDBbind database. Using a k nearest neighbor (kNN)
pattern recognition approach and variable selection, we have shown
that knowledge of the active site structure affords identification
of its complimentary ligand among the top 1\% of a large chemical
database in over 90\% of all test active sites when a binding site
of the same protein family was present in the training set. In the
case where test receptors are highly dissimilar and not present among
the receptor families in the training set, the prediction accuracy
is decreased; however, CoLiBRI was still able to quickly eliminate
75\% of the chemical database as improbable ligands. CoLiBRI affords
rapid prefiltering of a large chemical database to eliminate compounds
that have little chance of binding to a receptor active site.},
doi = {10.1021/ci050065r},
keywords = {Algorithms; Binding Sites; Binding, Competitive; Computational Biology;
Databases, Factual; Drug Design; Drug Evaluation, Preclinical; Ligands;
Models, Biological; Structure-Activity Relationship},
owner = {laurent},
pmid = {16563016},
timestamp = {2007.09.22},
url = {http://dx.doi.org/10.1021/ci050065r}
}
@article{Olson2005Closed-loop,
author = {Byron P Olson and Jennie Si and Jing Hu and Jiping He},
title = {Closed-loop cortical control of direction using support vector machines.},
journal = {I{EEE} {T}rans {N}eural {S}yst {R}ehabil {E}ng},
year = {2005},
volume = {13},
pages = {72-80},
number = {1},
month = {Mar},
abstract = {Motor neuroprosthetics research has focused on reproducing natural
limb motions by correlating firing rates of cortical neurons to continuous
movement parameters. {W}e propose an alternative system where specific
spatial-temporal spike patterns, emerging in tasks, allow detection
of classes of behavior with the aid of sophisticated nonlinear classification
algorithms. {S}pecifically, we attempt to examine ensemble activity
from motor cortical neurons, not to reproduce the action this neural
activity normally precedes, but rather to predict an output supervisory
command to potentially control a vehicle. {T}o demonstrate the principle,
this design approach was implemented in a discrete directional task
taking a small number of motor cortical signals (8-10 single units)
fed into a support vector machine ({SVM}) to produce the commands
{L}eft and {R}ight. {I}n this study, rats were placed in a conditioning
chamber performing a binary paddle pressing task mimicking the control
of a wheelchair turning left or right. {F}our animal subjects (male
{S}prague-{D}awley rats) were able to use such a brain-machine interface
({BMI}) with an average accuracy of 78\% on their first day of exposure.
{A}dditionally, one animal continued to use the interface for three
consecutive days with an average accuracy over 90\%.},
keywords = {Algorithms, Animals, Artificial Intelligence, Computer-Assisted, Diagnosis,
Electrodes, Electroencephalography, Feedback, Implanted, Male, Motor
Cortex, Movement, Non-P.H.S., Non-U.S. Gov't, Rats, Research Support,
Sprague-Dawley, Therapy, U.S. Gov't, User-Computer Interface, 15813408}
}
@article{Ong2002Stable,
author = {Shao-En Ong and Blagoy Blagoev and Irina Kratchmarova and Dan Bach
Kristensen and Hanno Steen and Akhilesh Pandey and Matthias Mann},
title = {Stable isotope labeling by amino acids in cell culture, SILAC, as
a simple and accurate approach to expression proteomics.},
journal = {Mol Cell Proteomics},
year = {2002},
volume = {1},
pages = {376--386},
number = {5},
month = {May},
abstract = {Quantitative proteomics has traditionally been performed by two-dimensional
gel electrophoresis, but recently, mass spectrometric methods based
on stable isotope quantitation have shown great promise for the simultaneous
and automated identification and quantitation of complex protein
mixtures. Here we describe a method, termed SILAC, for stable isotope
labeling by amino acids in cell culture, for the in vivo incorporation
of specific amino acids into all mammalian proteins. Mammalian cell
lines are grown in media lacking a standard essential amino acid
but supplemented with a non-radioactive, isotopically labeled form
of that amino acid, in this case deuterated leucine (Leu-d3). We
find that growth of cells maintained in these media is no different
from growth in normal media as evidenced by cell morphology, doubling
time, and ability to differentiate. Complete incorporation of Leu-d3
occurred after five doublings in the cell lines and proteins studied.
Protein populations from experimental and control samples are mixed
directly after harvesting, and mass spectrometric identification
is straightforward as every leucine-containing peptide incorporates
either all normal leucine or all Leu-d3. We have applied this technique
to the relative quantitation of changes in protein expression during
the process of muscle cell differentiation. Proteins that were found
to be up-regulated during this process include glyceraldehyde-3-phosphate
dehydrogenase, fibronectin, and pyruvate kinase M2. SILAC is a simple,
inexpensive, and accurate procedure that can be used as a quantitative
proteomic approach in any cell culture system.},
institution = {Protein Interaction Laboratory, University of Southern Denmark, Odense,
Denmark.},
keywords = {3T3 Cells; Amino Acids; Animals; Cell Culture Techniques; Cell Differentiation;
Cell Line; Deuterium; Genetic Techniques; Hydrogen-Ion Concentration;
Leucine; Mice; Muscles; Peptides; Proteomics; Time Factors; Up-Regulation},
owner = {phupe},
pmid = {12118079},
timestamp = {2010.08.19}
}
@article{Opper2001Universal,
author = {M. Opper and R. Urbanczik},
title = {Universal learning curves of support vector machines.},
journal = {Phys {R}ev {L}ett},
year = {2001},
volume = {86},
pages = {4410-3},
number = {19},
month = {May},
abstract = {Using methods of statistical physics, we investigate the role of model
complexity in learning with support vector machines ({SVM}s), which
are an important alternative to neural networks. {W}e show the advantages
of using {SVM}s with kernels of infinite complexity on noisy target
rules, which, in contrast to common theoretical beliefs, are found
to achieve optimal generalization error although the training error
does not converge to the generalization error. {M}oreover, we find
a universal asymptotics of the learning curves which depend only
on the target rule but not on the {SVM} kernel.},
keywords = {Algorithms, Amino Acid Sequence, Artificial Intelligence, Biological,
Cell Compartmentation, Chemistry, Comparative Study, Computational
Biology, Computer Simulation, Computer-Assisted, Databases, Decision
Trees, Diagnosis, Discriminant Analysis, Electrophysiology, Factual,
Gastric Emptying, Humans, Logistic Models, Melanoma, Models, Neural
Networks (Computer), Nevus, Non-U.S. Gov't, Organelles, P.H.S., Physical,
Pigmented, Predictive Value of Tests, Proteins, Proteome, Reproducibility
of Results, Research Support, Skin Diseases, Skin Neoplasms, Skin
Pigmentation, Software, Stomach Diseases, U.S. Gov't, 11328187}
}
@article{Opper2000Gaussian,
author = {M. Opper and O. Winther},
title = {Gaussian processes for classification: mean-field algorithms.},
journal = {Neural {C}omput},
year = {2000},
volume = {12},
pages = {2655-84},
number = {11},
month = {Nov},
abstract = {We derive a mean-field algorithm for binary classification with gaussian
processes that is based on the {TAP} approach originally proposed
in statistical physics of disordered systems. {T}he theory also yields
an approximate leave-one-out estimator for the generalization error,
which is computed with no extra computational cost. {W}e show that
from the {TAP} approach, it is possible to derive both a simpler
"naive" mean-field theory and support vector machines ({SVM}s) as
limiting cases. {F}or both mean-field algorithms and support vector
machines, simulation results for three small benchmark data sets
are presented. {T}hey show that one may get state-of-the-art performance
by using the leave-one-out estimator for model selection and the
built-in leave-one-out estimators are extremely precise when compared
to the exact leave-one-out estimate. {T}he second result is taken
as strong support for the internal consistency of the mean-field
approach.},
keywords = {Acute, Acute Disease, Adenocarcinoma, Algorithms, Amino Acid Sequence,
Animals, Artificial Intelligence, Automated, B-Lymphocytes, Bacterial
Proteins, Base Pair Mismatch, Base Sequence, Bayes Theorem, Binding
Sites, Biological, Bone Marrow Cells, Brachyura, Cell Compartmentation,
Chemistry, Child, Chromosome Aberrations, Classification, Colonic
Neoplasms, Comparative Study, Computational Biology, Computer Simulation,
Computer-Assisted, DNA, Data Interpretation, Databases, Decision
Trees, Diabetes Mellitus, Diagnosis, Discriminant Analysis, Discrimination
Learning, Electric Conductivity, Electrophysiology, Escherichia coli
Proteins, Factual, Feedback, Female, Fungal, Gastric Emptying, Gene
Expression Profiling, Gene Expression Regulation, Genes, Genetic,
Genetic Markers, Genetic Predisposition to Disease, Hemolysins, Humans,
Indians, Ion Channels, Kinetics, Leukemia, Likelihood Functions,
Lipid Bilayers, Logistic Models, Lymphocytic, Male, Markov Chains,
Melanoma, Models, Molecular, Myeloid, Neoplasm, Neoplasms, Neoplastic,
Neural Networks (Computer), Neurological, Nevus, Non-P.H.S., Non-U.S.
Gov't, Nonlinear Dynamics, Normal Distribution, North American, Nucleic
Acid Conformation, Oligonucleotide Array Sequence Analysis, Organ
Specificity, Organelles, Ovarian Neoplasms, Ovary, P.H.S., Pattern
Recognition, Physical, Pigmented, Predictive Value of Tests, Promoter
Regions (Genetics), Protein Folding, Protein Structure, Proteins,
Proteome, RNA, Reproducibility of Results, Research Support, Saccharomyces
cerevisiae, Secondary, Sensitivity and Specificity, Sequence Alignment,
Sex Characteristics, Skin Diseases, Skin Neoplasms, Skin Pigmentation,
Software, Sound Spectrography, Statistical, Stomach Diseases, T-Lymphocytes,
Thermodynamics, Transcription, Transcription Factors, Tumor Markers,
Type 2, U.S. Gov't, 11110131}
}
@article{Ordonez2005Learning,
author = {Celestino Ordóñez and Javier Taboada and Fernando Bastante and
Jose MarÃa MatÃas and Angel Manuel FelicÃsimo},
title = {Learning machines applied to potential forest distribution.},
journal = {Environ {M}anage},
year = {2005},
volume = {35},
pages = {109-20},
number = {1},
month = {Jan},
abstract = {The clearing of forests to obtain land for pasture and agriculture
and the replacement of autochthonous species by other faster-growing
varieties of trees for timber have both led to the loss of vast areas
of forest worldwide. {A}t present, many developed countries are attempting
to reverse these effects, establishing policies for the restoration
of older woodland systems. {R}eforestation is a complex matter, planned
and carried out by experts who need objective information regarding
the type of forest that can be sustained in each area. {T}his information
is obtained by drawing up feasibility models constructed using statistical
methods that make use of the information provided by morphological
and environmental variables (height, gradient, rainfall, etc.) that
partially condition the presence or absence of a specific kind of
forestation in an area. {T}he aim of this work is to construct a
set of feasibility models for woodland located in the basin of the
{R}iver {L}iébana ({NW} {S}pain), to serve as a support tool for
the experts entrusted with carrying out the reforestation project.
{T}he techniques used are multilayer perceptron neural networks and
support vector machines. {T}heir results will be compared to the
results obtained by traditional techniques (such as discriminant
analysis and logistic regression) by measuring the degree of fit
between each model and the existing distribution of woodlands. {T}he
interpretation and problems of the feasibility models are commented
on in the {D}iscussion section.},
keywords = {Artificial Intelligence, Conservation of Natural Resources, Decision
Support Techniques, Ecosystem, Environment, Forestry, Regression
Analysis, Spain, 15984068}
}
@article{Ornstein1993Entropy,
author = {Ornstein, D.S. and Weiss, B. },
title = {Entropy and data compression schemes},
journal = {I{EEE} {T}rans. {I}nform. {T}heory},
year = {1993},
volume = {39},
pages = {78-83},
number = {1},
month = {Jan},
abstract = {Some new ways of defining the entropy of a process by observing a
single typical output sequence as well as a new kind of {S}hannon-{M}c{M}illan-{B}reiman
theorem are presented. {T}his provides a new and conceptually very
simple ways of estimating the entropy of an ergodic stationary source
as well as new insight into the workings of such well-known data
compression schemes as the {L}empel-{Z}iv algorithm },
pdf = {../local/Ornstein1993Entropy.pdf},
file = {Ornstein1993Entropy.pdf:local/Ornstein1993Entropy.pdf:PDF},
keywords = {information-theory},
owner = {vert}
}
@article{Ortiz-Estevez2010ACNE,
author = {Maria Ortiz-Estevez and Henrik Bengtsson and Angel Rubio},
title = {ACNE: a summarization method to estimate allele-specific copy numbers
for Affymetrix SNP arrays.},
journal = {Bioinformatics},
year = {2010},
volume = {26},
pages = {1827--1833},
number = {15},
month = {Aug},
abstract = {MOTIVATION: Current algorithms for estimating DNA copy numbers (CNs)
borrow concepts from gene expression analysis methods. However, single
nucleotide polymorphism (SNP) arrays have special characteristics
that, if taken into account, can improve the overall performance.
For example, cross hybridization between alleles occurs in SNP probe
pairs. In addition, most of the current CN methods are focused on
total CNs, while it has been shown that allele-specific CNs are of
paramount importance for some studies. Therefore, we have developed
a summarization method that estimates high-quality allele-specific
CNs. RESULTS: The proposed method estimates the allele-specific DNA
CNs for all Affymetrix SNP arrays dealing directly with the cross
hybridization between probes within SNP probesets. This algorithm
outperforms (or at least it performs as well as) other state-of-the-art
algorithms for computing DNA CNs. It better discerns an aberration
from a normal state and it also gives more precise allele-specific
CNs. AVAILABILITY: The method is available in the open-source R package
ACNE, which also includes an add on to the aroma.affymetrix framework
(http://www.aroma-project.org/).},
doi = {10.1093/bioinformatics/btq300},
institution = {Group of Bioinformatics, CEIT and TECNUN, University of Navarra,
San Sebastian, Spain.},
language = {eng},
medline-pst = {ppublish},
owner = {philippe},
pii = {btq300},
pmid = {20529889},
timestamp = {2010.08.05},
url = {http://dx.doi.org/10.1093/bioinformatics/btq300}
}
@article{Osborne1999A,
author = {M. R. Osborne and Brett Presnell and B.A. Turlach},
title = {A New Approach to Variable Selection in Least Squares Problems},
journal = {{IMA} {J}ournal of {N}umerical {A}nalysis},
year = {1999},
volume = {20},
pages = {389--404}
}
@article{Osborne1999On,
author = {Michael R. Osborne and Brett Presnell and Berwin A. Turlach},
title = {On the LASSO and Its Dual},
journal = {Journal of Computational and Graphical Statistics},
year = {1999},
volume = {9},
pages = {319--337}
}
@article{Osowski2004Support,
author = {Stanislaw Osowski and Linh Tran Hoai and Tomasz Markiewicz},
title = {Support vector machine-based expert system for reliable heartbeat
recognition.},
journal = {I{EEE} {T}rans {B}iomed {E}ng},
year = {2004},
volume = {51},
pages = {582-9},
number = {4},
month = {Apr},
abstract = {This paper presents a new solution to the expert system for reliable
heartbeat recognition. {T}he recognition system uses the support
vector machine ({SVM}) working in the classification mode. {T}wo
different preprocessing methods for generation of features are applied.
{O}ne method involves the higher order statistics ({HOS}) while the
second the {H}ermite characterization of {QRS} complex of the registered
electrocardiogram ({ECG}) waveform. {C}ombining the {SVM} network
with these preprocessing methods yields two neural classifiers, which
have been combined into one final expert system. {T}he combination
of classifiers utilizes the least mean square method to optimize
the weights of the weighted voting integrating scheme. {T}he results
of the performed numerical experiments for the recognition of 13
heart rhythm types on the basis of {ECG} waveforms confirmed the
reliability and advantage of the proposed approach.}
}
@article{Oti2008Conserved,
author = {Oti, M. and van Reeuwijk, J. and Huynen, M.A. and Brunner, H.G.},
title = {Conserved co-expression for candidate disease gene prioritization.},
journal = {BMC Bioinformatics},
year = {2008},
volume = {9},
pages = {208},
abstract = {BACKGROUND: Genes that are co-expressed tend to be involved in the
same biological process. However, co-expression is not a very reliable
predictor of functional links between genes. The evolutionary conservation
of co-expression between species can be used to predict protein function
more reliably than co-expression in a single species. Here we examine
whether co-expression across multiple species is also a better prioritizer
of disease genes than is co-expression between human genes alone.
RESULTS: We use co-expression data from yeast (S. cerevisiae), nematode
worm (C. elegans), fruit fly (D. melanogaster), mouse and human and
find that the use of evolutionary conservation can indeed improve
the predictive value of co-expression. The effect that genes causing
the same disease have higher co-expression than do other genes from
their associated disease loci, is significantly enhanced when co-expression
data are combined across evolutionarily distant species. We also
find that performance can vary significantly depending on the co-expression
datasets used, and just using more data does not necessarily lead
to better prioritization. Instead, we find that dataset quality is
more important than quantity, and using a consistent microarray platform
per species leads to better performance than using more inclusive
datasets pooled from various platforms. CONCLUSION: We find that
evolutionarily conserved gene co-expression prioritizes disease candidate
genes better than human gene co-expression alone, and provide the
integrated data as a new resource for disease gene prioritization
tools.},
doi = {10.1186/1471-2105-9-208},
institution = {Centre for Molecular and Biomolecular Informatics, Nijmegen Centre
for Molecular Life Sciences, Radboud University Nijmegen Medical
Centre, Geert Grooteplein 26-28, 6525 GA, Nijmegen, The Netherlands.
m.oti@cmbi.ru.nl},
keywords = {Animals; Base Sequence; Caenorhabditis elegans; Conserved Sequence;
Databases, Genetic; Disease; Drosophila melanogaster; Evolution,
Molecular; Gene Dosage; Gene Expression; Gene Expression Profiling;
Gene Frequency; Genetic Predisposition to Disease; Humans; Mice;
Oligonucleotide Array Sequence Analysis; Penetrance; Predictive Value
of Tests; Saccharomyces cerevisiae; Sample Size; Species Specificity},
owner = {mordelet},
pii = {1471-2105-9-208},
pmid = {18433471},
timestamp = {2010.09.27},
url = {http://dx.doi.org/10.1186/1471-2105-9-208}
}
@article{Oti2006Predicting,
author = {M. Oti and B. Snel and M. A. Huynen and H. G. Brunner},
title = {Predicting disease genes using protein-protein interactions.},
journal = {J Med Genet},
year = {2006},
volume = {43},
pages = {691--698},
number = {8},
month = {Aug},
abstract = {BACKGROUND: The responsible genes have not yet been identified for
many genetically mapped disease loci. Physically interacting proteins
tend to be involved in the same cellular process, and mutations in
their genes may lead to similar disease phenotypes. OBJECTIVE: To
investigate whether protein-protein interactions can predict genes
for genetically heterogeneous diseases. METHODS: 72,940 protein-protein
interactions between 10,894 human proteins were used to search 432
loci for candidate disease genes representing 383 genetically heterogeneous
hereditary diseases. For each disease, the protein interaction partners
of its known causative genes were compared with the disease associated
loci lacking identified causative genes. Interaction partners located
within such loci were considered candidate disease gene predictions.
Prediction accuracy was tested using a benchmark set of known disease
genes. RESULTS: Almost 300 candidate disease gene predictions were
made. Some of these have since been confirmed. On average, 10\% or
more are expected to be genuine disease genes, representing a 10-fold
enrichment compared with positional information only. Examples of
interesting candidates are AKAP6 for arrythmogenic right ventricular
dysplasia 3 and SYN3 for familial partial epilepsy with variable
foci. CONCLUSIONS: Exploiting protein-protein interactions can greatly
increase the likelihood of finding positional candidate disease genes.
When applied on a large scale they can lead to novel candidate gene
predictions.},
doi = {10.1136/jmg.2006.041376},
keywords = {Animals; Benchmarking; Databases, Protein; Disease; Genetic Predisposition
to Disease; Humans; Protein Binding; Proteins},
owner = {mordelet},
pii = {jmg.2006.041376},
pmid = {16611749},
timestamp = {2010.09.28},
url = {http://dx.doi.org/10.1136/jmg.2006.041376}
}
@article{Overbeek2000WIT,
author = {Overbeek, R. and Larsen, N. and Pusch, G. D. and D'Souza, M. and
Selkov, E. Jr. and Kyrpides, N. and Fonstein, M. and Maltsev, N.
and Selkov, E.},
title = {W{IT}: integrated system for high-throughput genome sequence analysis
and metabolic reconstruction},
journal = {Nucleic {A}cids {R}es.},
year = {2000},
volume = {28},
pages = {123--125},
pdf = {../local/Overbeek2000WIT.pdf},
file = {Overbeek2000WIT.pdf:local/Overbeek2000WIT.pdf:PDF},
url = {http://nar.oxfordjournals.org/cgi/content/abstract/28/1/123}
}
@article{Overhoff2005Local,
author = {Overhoff, M. and Alken, M. and Far, R. K. and Lemaitre, M. and Lebleu,
B. and Sczakiel, G. and Robbins, I.},
title = {{L}ocal {RNA} target structure influences si{RNA} efficacy: a systematic
global analysis.},
journal = {J. Mol. Biol.},
year = {2005},
volume = {348},
pages = {871--881},
number = {4},
month = {May},
abstract = {The efficiency with which small interfering RNAs (siRNAs) down-regulate
specific gene expression in living cells is variable and a number
of sequence-governed, biochemical parameters of the siRNA duplex
have been proposed for the design of an efficient siRNA. Some of
these parameters have been clearly identified to influence the assembly
of the RNA-induced silencing complex (RISC), or to favour the sequence
preferences of the RISC endonuclease. For other parameters, it is
difficult to ascertain whether the influence is a determinant of
the siRNA per se, or a determinant of the target RNA, especially
its local structural characteristics. In order to gain an insight
into the effects of local target structure on the biological activity
of siRNA, we have used large sets of siRNAs directed against local
targets of the mRNAs of ICAM-1 and survivin. Target structures were
classified as accessible or inaccessible using an original, iterative
computational approach and by experimental RNase H mapping. The effectiveness
of siRNA was characterized by measuring the IC50 values in cell culture
and the maximal extent of target suppression. Mean IC50 values were
tenfold lower for accessible local target sites, with respect to
inaccessible ones. Mean maximal target suppression was improved.
These data illustrate that local target structure does, indeed, influence
the activity of siRNA. We suggest that local target screening can
significantly improve the hit rate in the design of biologically
active siRNAs.},
doi = {10.1016/j.jmb.2005.03.012},
keywords = {sirna},
owner = {vert},
pii = {S0022-2836(05)00270-6},
pmid = {15843019},
timestamp = {2006.03.28},
url = {http://dx.doi.org/10.1016/j.jmb.2005.03.012}
}
@article{Oyang2005Data,
author = {Yen-Jen Oyang and Shien-Ching Hwang and Yu-Yen Ou and Chien-Yu Chen
and Zhi-Wei Chen},
title = {Data classification with radial basis function networks based on
a novel kernel density estimation algorithm.},
journal = {I{EEE} {T}rans {N}eural {N}etw},
year = {2005},
volume = {16},
pages = {225-36},
number = {1},
month = {Jan},
abstract = {This paper presents a novel learning algorithm for efficient construction
of the radial basis function ({RBF}) networks that can deliver the
same level of accuracy as the support vector machines ({SVM}s) in
data classification applications. {T}he proposed learning algorithm
works by constructing one {RBF} subnetwork to approximate the probability
density function of each class of objects in the training data set.
{W}ith respect to algorithm design, the main distinction of the proposed
learning algorithm is the novel kernel density estimation algorithm
that features an average time complexity of {O}(n log n), where n
is the number of samples in the training data set. {O}ne important
advantage of the proposed learning algorithm, in comparison with
the {SVM}, is that the proposed learning algorithm generally takes
far less time to construct a data classifier with an optimized parameter
setting. {T}his feature is of significance for many contemporary
applications, in particular, for those applications in which new
objects are continuously added into an already large database. {A}nother
desirable feature of the proposed learning algorithm is that the
{RBF} networks constructed are capable of carrying out data classification
with more than two classes of objects in one single run. {I}n other
words, unlike with the {SVM}, there is no need to resort to mechanisms
such as one-against-one or one-against-all for handling datasets
with more than two classes of objects. {T}he comparison with {SVM}
is of particular interest, because it has been shown in a number
of recent studies that {SVM} generally are able to deliver higher
classification accuracy than the other existing data classification
algorithms. {A}s the proposed learning algorithm is instance-based,
the data reduction issue is also addressed in this paper. {O}ne interesting
observation in this regard is that, for all three data sets used
in data reduction experiments, the number of training samples remaining
after a naive data reduction mechanism is applied is quite close
to the number of support vectors identified by the {SVM} software.
{T}his paper also compares the performance of the {RBF} networks
constructed with the proposed learning algorithm and those constructed
with a conventional cluster-based learning algorithm. {T}he most
interesting observation learned is that, with respect to data classification,
the distributions of training samples near the boundaries between
different classes of objects carry more crucial information than
the distributions of samples in the inner parts of the clusters.}
}
@article{Polya1934,
author = {G. P\'olya},
title = {Algebraische Berechnung der Anzhal der Isomeren einiger organischer
Verbindungen},
journal = {Z. Kristal.},
year = {1936},
volume = {93},
pages = {415--443}
}
@article{Paatero1994Positive,
author = {Paatero, P. and Tapper, U.},
title = {Positive matrix factorization: A non-negative factor model with optimal
utilization of error estimates of data values},
journal = {Environmetrics},
year = {1994},
volume = {5},
pages = {111--126},
number = {2},
doi = {10.1002/env.3170050203},
owner = {jp},
timestamp = {2011.12.30},
url = {http://dx.doi.org/10.1002/env.3170050203}
}
@techreport{Pachter2011Models,
author = {Pachter, L.},
title = {Models for transcript quantification from {RNA}-Seq},
institution = {arXiv},
year = {2011},
number = {1104-3889},
pdf = {../local/Pachter2011Models.pdf},
file = {Pachter2011Models.pdf:Pachter2011Models.pdf:PDF},
owner = {jp},
timestamp = {2013.03.29}
}
@article{Pahikkala2005Contextual,
author = {Tapio Pahikkala and Filip Ginter and Jorma Boberg and Jouni Jarvinen
and Tapio Salakoski},
title = {Contextual weighting for {S}upport {V}ector {M}achines in literature
mining: an application to gene versus protein name disambiguation.},
journal = {B{MC} {B}ioinformatics},
year = {2005},
volume = {6},
pages = {157},
number = {1},
month = {Jun},
abstract = {B{ACKGROUND}: {T}he ability to distinguish between genes and proteins
is essential for understanding biological text. {S}upport {V}ector
{M}achines ({SVM}s) have been proven to be very efficient in general
data mining tasks. {W}e explore their capability for the gene versus
protein name disambiguation task. {RESULTS}: {W}e incorporated into
the conventional {SVM} a weighting scheme based on distances of context
words from the word to be disambiguated. {T}his weighting scheme
increased the performance of {SVM}s by five percentage points giving
performance better than 85\% as measured by the area under {ROC}
curve and outperformed the {W}eighted {A}dditive {C}lassifier, which
also incorporates the weighting, and the {N}aive {B}ayes classifier.
{CONCLUSIONS}: {W}e show that the performance of {SVM}s can be improved
by the proposed weighting scheme. {F}urthermore, our results suggest
that in this study the increase of the classification performance
due to the weighting is greater than that obtained by selecting the
underlying classifier or the kernel part of the {SVM}.},
doi = {10.1186/1471-2105-6-157},
pdf = {../local/Pahikkala2005Contextual.pdf},
file = {Pahikkala2005Contextual.pdf:local/Pahikkala2005Contextual.pdf:PDF},
keywords = {biosvm},
pii = {1471-2105-6-157},
url = {http://dx.doi.org/10.1186/1471-2105-6-157}
}
@article{Pai2006Prospects,
author = {Pai, S. I. and Lin, Y.-Y. and Macaes, B. and Meneshian, A. and HungC.-F.
and Wu, T.-C.},
title = {{P}rospects of {RNA} interference therapy for cancer.},
journal = {Gene Ther.},
year = {2006},
volume = {13},
pages = {464--477},
number = {6},
month = {Mar},
abstract = {RNA interference (RNAi) is a powerful gene-silencing process that
holds great promise in the field of cancer therapy. The discovery
of RNAi has generated enthusiasm within the scientific community,
not only because it has been used to rapidly identify key molecules
involved in many disease processes including cancer, but also because
RNAi has the potential to be translated into a technology with major
therapeutic applications. Our evolving understanding of the molecular
pathways important for carcinogenesis has created opportunities for
cancer therapy employing RNAi technology to target the key molecules
within these pathways. Many gene products involved in carcinogenesis
have already been explored as targets for RNAi intervention, and
RNAi targeting of molecules crucial for tumor-host interactions and
tumor resistance to chemo- or radiotherapy has also been investigated.
In most of these studies, the silencing of critical gene products
by RNAi technology has generated significant antiproliferative and/or
proapoptotic effects in cell-culture systems or in preclinical animal
models. Nevertheless, significant obstacles, such as in vivo delivery,
incomplete suppression of target genes, nonspecific immune responses
and the so-called off-target effects, need to be overcome before
this technology can be successfully translated into the clinical
arena. Significant progress has already been made in addressing some
of these issues, and it is foreseen that early phase clinical trials
will be initiated in the very near future.},
doi = {10.1038/sj.gt.3302694},
keywords = {sirna},
owner = {vert},
pii = {3302694},
pmid = {16341059},
timestamp = {2006.03.28},
url = {http://dx.doi.org/10.1038/sj.gt.3302694}
}
@article{Paik2007Development,
author = {Paik, Soonmyung},
title = {Development and clinical utility of a 21-gene recurrence score prognostic
assay in patients with early breast cancer treated with tamoxifen.},
journal = {Oncologist},
year = {2007},
volume = {12},
pages = {631--635},
number = {6},
month = {Jun},
abstract = {Although patients diagnosed with axillary node-negative estrogen receptor-positive
breast cancer have an excellent prognosis, about 15\% of them fail
after 5 years of tamoxifen treatment. Clinical trials have provided
evidence that there is a significant benefit from chemotherapy for
these patients, but it would be significant overtreatment if all
of them were treated with chemotherapy. Therefore, context-specific
prognostic assays that can identify those who need chemotherapy in
addition to tamoxifen, or those who are essentially cured by tamoxifen
alone, and can be performed using routinely processed tumor biopsy
tissue would be clinically useful. Using a stepwise approach of going
through independent model-building and validation sets, a 21-gene
recurrence score (RS), based on monitoring of mRNA expression levels
of 16 cancer-related genes in relation to five reference genes, has
been developed. The RS identified approximately 50\% of the patients
who had excellent prognosis after tamoxifen alone. Subsequent study
suggested that high-risk patients identified with the RS preferentially
benefit from chemotherapy. Ideally the RS should be used as a continuous
variable. A prospective study-the Trial Assigning Individualized
Options for Treatment (Rx) (TAILORx)-to examine whether chemotherapy
is required for the intermediate-risk group defined by the RS is
accruing in North America.},
doi = {10.1634/theoncologist.12-6-631},
institution = {Division of Pathology, National Surgical Adjuvant Breast and Bowel
Project, Pittsburgh, Pennsylvania 15206, USA. soon.paik@nsabp.org},
keywords = {Breast Neoplasms, drug therapy/genetics/pathology; Estrogen Antagonists,
therapeutic use; Female; Gene Expression Profiling; Gene Expression
Regulation, Neoplastic; Genetic Predisposition to Disease; Humans;
Neoplasm Recurrence, Local; Prognosis; Risk Factors; Tamoxifen, therapeutic
use; Time Factors; Treatment Outcome},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {12/6/631},
pmid = {17602054},
timestamp = {2012.03.09},
url = {http://dx.doi.org/10.1634/theoncologist.12-6-631}
}
@article{Paik2006Gene,
author = {Paik, Soonmyung and Tang, Gong and Shak, Steven and Kim, Chungyeul
and Baker, Joffre and Kim, Wanseop and Cronin, Maureen and Baehner,
Frederick L. and Watson, Drew and Bryant, John and Costantino, Joseph
P. and Geyer, Jr, Charles E and Wickerham, D Lawrence and Wolmark,
Norman},
title = {Gene expression and benefit of chemotherapy in women with node-negative,
estrogen receptor-positive breast cancer.},
journal = {J Clin Oncol},
year = {2006},
volume = {24},
pages = {3726--3734},
number = {23},
month = {Aug},
abstract = {The 21-gene recurrence score (RS) assay quantifies the likelihood
of distant recurrence in women with estrogen receptor-positive, lymph
node-negative breast cancer treated with adjuvant tamoxifen. The
relationship between the RS and chemotherapy benefit is not known.The
RS was measured in tumors from the tamoxifen-treated and tamoxifen
plus chemotherapy-treated patients in the National Surgical Adjuvant
Breast and Bowel Project (NSABP) B20 trial. Cox proportional hazards
models were utilized to test for interaction between chemotherapy
treatment and the RS.A total of 651 patients were assessable (227
randomly assigned to tamoxifen and 424 randomly assigned to tamoxifen
plus chemotherapy). The test for interaction between chemotherapy
treatment and RS was statistically significant (P = .038). Patients
with high-RS (> or = 31) tumors (ie, high risk of recurrence) had
a large benefit from chemotherapy (relative risk, 0.26; 95\% CI,
0.13 to 0.53; absolute decrease in 10-year distant recurrence rate:
mean, 27.6\%; SE, 8.0\%). Patients with low-RS (< 18) tumors derived
minimal, if any, benefit from chemotherapy treatment (relative risk,
1.31; 95\% CI, 0.46 to 3.78; absolute decrease in distant recurrence
rate at 10 years: mean, -1.1\%; SE, 2.2\%). Patients with intermediate-RS
tumors did not appear to have a large benefit, but the uncertainty
in the estimate can not exclude a clinically important benefit.The
RS assay not only quantifies the likelihood of breast cancer recurrence
in women with node-negative, estrogen receptor-positive breast cancer,
but also predicts the magnitude of chemotherapy benefit.},
doi = {10.1200/JCO.2005.04.7985},
institution = {Division of Pathology, Operations Center, and Biostatistical Center,
National Surgical Adjuvant Breast and Bowel Project, Pittsburgh,
PA 15212, USA. soon.paik@nsabp.org},
keywords = {Adult; Aged; Antineoplastic Combined Chemotherapy Protocols, administration
/&/ dosage/therapeutic use; Breast Neoplasms, drug therapy/metabolism/pathology/prevention
/&/ control; Cisplatin, administration /&/ dosage; Female; Fluorouracil,
administration /&/ dosage; Gene Expression Regulation, Neoplastic;
Humans; Linear Models; Lymphatic Metastasis; Methotrexate, administration
/&/ dosage; Middle Aged; Mitomycins, administration /&/ dosage; Neoplasm
Proteins, metabolism; Neoplasm Recurrence, Local, metabolism/prevention
/&/ control; Odds Ratio; Predictive Value of Tests; Prognosis; Proportional
Hazards Models; Randomized Controlled Trials as Topic; Receptors,
Estrogen, metabolism; Recurrence, prevention /&/ control; Reverse
Transcriptase Polymerase Chain Reaction; Risk Assessment; Risk Factors;
Tamoxifen, administration /&/ dosage; Tumor Markers, Biological,
metabolism},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {JCO.2005.04.7985},
pmid = {16720680},
timestamp = {2012.03.09},
url = {http://dx.doi.org/10.1200/JCO.2005.04.7985}
}
@article{Pajares2004On,
author = {Gonzalo Pajares and Jesús M de la Cruz},
title = {On combining support vector machines and simulated annealing in stereovision
matching.},
journal = {I{EEE} {T}rans {S}yst {M}an {C}ybern {B} {C}ybern},
year = {2004},
volume = {34},
pages = {1646-57},
number = {4},
month = {Aug},
abstract = {This paper outlines a method for solving the stereovision matching
problem using edge segments as the primitives. {I}n stereovision
matching, the following constraints are commonly used: epipolar,
similarity, smoothness, ordering, and uniqueness. {W}e propose a
new strategy in which such constraints are sequentially combined.
{T}he goal is to achieve high performance in terms of correct matches
by combining several strategies. {T}he contributions of this paper
are reflected in the development of a similarity measure through
a support vector machines classification approach; the transformation
of the smoothness, ordering and epipolar constraints into the form
of an energy function, through an optimization simulated annealing
approach, whose minimum value corresponds to a good matching solution
and by introducing specific conditions to overcome the violation
of the smoothness and ordering constraints. {T}he performance of
the proposed method is illustrated by comparative analysis against
some recent global matching methods.}
}
@article{Palmer2003Comparison,
author = {Gregory M Palmer and Changfang Zhu and Tara M Breslin and Fushen
Xu and Kennedy W Gilchrist and Nirmala Ramanujam},
title = {Comparison of multiexcitation fluorescence and diffuse reflectance
spectroscopy for the diagnosis of breast cancer ({M}arch 2003).},
journal = {I{EEE} {T}rans {B}iomed {E}ng},
year = {2003},
volume = {50},
pages = {1233-42},
number = {11},
month = {Nov},
abstract = {Nonmalignant (n = 36) and malignant (n = 20) tissue samples were obtained
from breast cancer and breast reduction surgeries. {T}hese tissues
were characterized using multiple excitation wavelength fluorescence
spectroscopy and diffuse reflectance spectroscopy in the ultraviolet-visible
wavelength range, immediately after excision. {S}pectra were then
analyzed using principal component analysis ({PCA}) as a data reduction
technique. {PCA} was performed on each fluorescence spectrum, as
well as on the diffuse reflectance spectrum individually, to establish
a set of principal components for each spectrum. {A} {W}ilcoxon rank-sum
test was used to determine which principal components show statistically
significant differences between malignant and nonmalignant tissues.
{F}inally, a support vector machine ({SVM}) algorithm was utilized
to classify the samples based on the diagnostically useful principal
components. {C}ross-validation of this nonparametric algorithm was
carried out to determine its classification accuracy in an unbiased
manner. {M}ultiexcitation fluorescence spectroscopy was successful
in discriminating malignant and nonmalignant tissues, with a sensitivity
and specificity of 70\% and 92\%, respectively. {T}he sensitivity
(30\%) and specificity (78\%) of diffuse reflectance spectroscopy
alone was significantly lower. {C}ombining fluorescence and diffuse
reflectance spectra did not improve the classification accuracy of
an algorithm based on fluorescence spectra alone. {T}he fluorescence
excitation-emission wavelengths identified as being diagnostic from
the {PCA}-{SVM} algorithm suggest that the important fluorophores
for breast cancer diagnosis are most likely tryptophan, {NAD}({P}){H}
and flavoproteins.}
}
@article{Pan2004Comprehensive,
author = {Fei Pan and Baoying Wang and Xin Hu and William Perrizo},
title = {Comprehensive vertical sample-based {KNN}/{LSVM} classification for
gene expression analysis.},
journal = {J {B}iomed {I}nform},
year = {2004},
volume = {37},
pages = {240-8},
number = {4},
month = {Aug},
abstract = {Classification analysis of microarray gene expression data has been
widely used to uncover biological features and to distinguish closely
related cell types that often appear in the diagnosis of cancer.
{H}owever, the number of dimensions of gene expression data is often
very high, e.g., in the hundreds or thousands. {A}ccurate and efficient
classification of such high-dimensional data remains a contemporary
challenge. {I}n this paper, we propose a comprehensive vertical sample-based
{KNN}/{LSVM} classification approach with weights optimized by genetic
algorithms for high-dimensional data. {E}xperiments on common gene
expression datasets demonstrated that our approach can achieve high
accuracy and efficiency at the same time. {T}he improvement of speed
is mainly related to the vertical data representation, {P}-tree,{P}atents
are pending on the {P}-tree technology. {T}his work is partially
supported by {GSA} {G}rant {ACT}#:{K}96130308. and its optimized
logical algebra. {T}he high accuracy is due to the combination of
a {KNN} majority voting approach and a local support vector machine
approach that makes optimal decisions at the local level. {A}s a
result, our approach could be a powerful tool for high-dimensional
gene expression data analysis.},
doi = {10.1016/j.jbi.2004.07.003},
pdf = {../local/Pan2004Comprehensive.pdf},
file = {Pan2004Comprehensive.pdf:local/Pan2004Comprehensive.pdf:PDF},
keywords = {biosvm},
pii = {S1532-0464(04)00070-X},
url = {http://dx.doi.org/10.1016/j.jbi.2004.07.003}
}
@techreport{Pan2008A,
author = {Sinno Jialin Pan and Qiang Yang},
title = {A Survey on Transfer Learning},
institution = {Department of Computer Science and Engineering, Hong Kong University
of Science and Technology, Hong Kong, China},
year = {2008},
number = {HKUST-CS08-08},
month = {November},
url = {http://www.cse.ust.hk/~sinnopan/publications/TLsurvey\_0822.pdf}
}
@article{Pandey2000Proteomics,
author = {Pandey, A. and Mann, M.},
title = {Proteomics to study genes and genomes},
journal = {Nature},
year = {2000},
volume = {405},
pages = {837--846},
pdf = {../local/pand00.pdf},
file = {pand00.pdf:local/pand00.pdf:PDF},
subject = {bioprot},
url = {http://www.nature.com/cgi-taf/DynaPage.taf?file=/nature/journal/v405/n6788/full/405837a0_fs.html&content_filetype=pdf}
}
@article{Pang2005Face,
author = {Shaoning Pang and Daijin Kim and Sung Yang Bang},
title = {Face membership authentication using {SVM} classification tree generated
by membership-based {LLE} data partition.},
journal = {I{EEE} {T}rans {N}eural {N}etw},
year = {2005},
volume = {16},
pages = {436-46},
number = {2},
month = {Mar},
abstract = {This paper presents a new membership authentication method by face
classification using a support vector machine ({SVM}) classification
tree, in which the size of membership group and the members in the
membership group can be changed dynamically. {U}nlike our previous
{SVM} ensemble-based method, which performed only one face classification
in the whole feature space, the proposed method employed a divide
and conquer strategy that first performs a recursive data partition
by membership-based locally linear embedding ({LLE}) data clustering,
then does the {SVM} classification in each partitioned feature subset.
{O}ur experimental results show that the proposed {SVM} tree not
only keeps the good properties that the {SVM} ensemble method has,
such as a good authentication accuracy and the robustness to the
change of members, but also has a considerable improvement on the
stability under the change of membership group size.},
keywords = {80 and over, Aged, Algorithms, Area Under Curve, Cross-Sectional Studies,
Decision Trees, Diagnostic Imaging, Diagnostic Techniques, Face,
Glaucoma, Humans, Lasers, Least-Squares Analysis, Middle Aged, Nerve
Fibers, Non-U.S. Gov't, Ophthalmological, Optic Nerve Diseases, P.H.S.,
Photic Stimulation, ROC Curve, Research Support, Retinal Ganglion
Cells, Sensitivity and Specificity, Statistics, U.S. Gov't, 15787150}
}
@article{Papadakis2007Efficient,
author = {P. Papadakis and I. Pratikakis and S. Perantonis and T. Theoharis},
title = {Efficient 3D shape matching and retrieval using a concrete radialized
spherical projection representation},
journal = {Pattern Recogn.},
year = {2007},
volume = {40},
pages = {2437--2452},
number = {9},
address = {New York, NY, USA},
doi = {http://dx.doi.org/10.1016/j.patcog.2006.12.026},
issn = {0031-3203},
publisher = {Elsevier Science Inc.}
}
@article{Papadopoulos2005Characterization,
author = {A. Papadopoulos and D. I. Fotiadis and A. Likas},
title = {Characterization of clustered microcalcifications in digitized mammograms
using neural networks and support vector machines.},
journal = {Artif. {I}ntell. {M}ed.},
year = {2005},
volume = {34},
pages = {141-50},
number = {2},
month = {Jun},
abstract = {O{BJECTIVE}: {D}etection and characterization of microcalcification
clusters in mammograms is vital in daily clinical practice. {T}he
scope of this work is to present a novel computer-based automated
method for the characterization of microcalcification clusters in
digitized mammograms. {METHODS} {AND} {MATERIAL}: {T}he proposed
method has been implemented in three stages: (a) the cluster detection
stage to identify clusters of microcalcifications, (b) the feature
extraction stage to compute the important features of each cluster
and (c) the classification stage, which provides with the final characterization.
{I}n the classification stage, a rule-based system, an artificial
neural network ({ANN}) and a support vector machine ({SVM}) have
been implemented and evaluated using receiver operating characteristic
({ROC}) analysis. {T}he proposed method was evaluated using the {N}ijmegen
and {M}ammographic {I}mage {A}nalysis {S}ociety ({MIAS}) mammographic
databases. {T}he original feature set was enhanced by the addition
of four rule-based features. {RESULTS} {AND} {CONCLUSIONS}: {I}n
the case of {N}ijmegen dataset, the performance of the {SVM} was
{A}z=0.79 and 0.77 for the original and enhanced feature set, respectively,
while for the {MIAS} dataset the corresponding characterization scores
were {A}z=0.81 and 0.80. {U}tilizing neural network classification
methodology, the corresponding performance for the {N}ijmegen dataset
was {A}z=0.70 and 0.76 while for the {MIAS} dataset it was {A}z=0.73
and 0.78. {A}lthough the obtained high classification performance
can be successfully applied to microcalcification clusters characterization,
further studies must be carried out for the clinical evaluation of
the system using larger datasets. {T}he use of additional features
originating either from the image itself (such as cluster location
and orientation) or from the patient data may further improve the
diagnostic value of the system.},
doi = {10.1016/j.artmed.2004.10.001},
pdf = {../local/Papadopoulos2005Characterization.pdf},
file = {Papadopoulos2005Characterization.pdf:local/Papadopoulos2005Characterization.pdf:PDF},
keywords = {Apoptosis, Gene Expression Profiling, Humans, Neoplasms, Non-U.S.
Gov't, Oligonucleotide Array Sequence Analysis, Polymerase Chain
Reaction, Proteins, Research Support, Subcellular Fractions, Unknown
Primary, 15894178},
pii = {S0933-3657(04)00154-X},
url = {http://dx.doi.org/10.1016/j.artmed.2004.10.001}
}
@article{BLEU,
author = {Papineni, K. and Roukos, S. and Ward, T. and Zhu, W. J. },
title = {{BLEU: a Method for Automatic Evaluation of Machine Translation}},
journal = {IBM Research Report},
year = {2001},
volume = {RC22176},
organization = {IBM}
}
@inproceedings{Pardalos95aparallel,
author = {P.M. Pardalos and L. S. Pitsoulis and M. G. C. Resende},
title = {A Parallel GRASP Implementation for the Quadratic Assignment Problem},
booktitle = {Parallel Algorithms for Irregularly Structured Problems},
year = {1995},
pages = {111--130},
publisher = {Kluwer Academic Publishers}
}
@book{Parham2004The,
title = {The {I}mmune {S}ystem},
publisher = {Garland {S}cience {P}ublishing},
year = {2004},
author = {Peter Parham}
}
@article{Park2003Prediction,
author = {Park, K.-J. and Kanehisa, M.},
title = {Prediction of protein subcellular locations by support vector machines
using compositions of amino acids and amino acid pairs},
journal = {Bioinformatics},
year = {2003},
volume = {19},
pages = {1656-1663},
number = {13},
abstract = {Motivation: {T}he subcellular location of a protein is closely correlated
to its function. {T}hus, computational prediction of subcellular
locations from the amino acid sequence information would help annotation
and functional prediction of protein coding genes in complete genomes.
{W}e have developed a method based on support vector machines ({SVM}s).
{R}esults: {W}e considered 12 subcellular locations in eukaryotic
cells: chloroplast, cytoplasm, cytoskeleton, endoplasmic reticulum,
extracellular medium, {G}olgi apparatus, lysosome, mitochondrion,
nucleus, peroxisome, plasma membrane, and vacuole. {W}e constructed
a data set of proteins with known locations from the {SWISS}-{PROT}
database. {A} set of {SVM}s was trained to predict the subcellular
location of a given protein based on its amino acid, amino acid pair,
and gapped amino acid pair compositions. {T}he predictors based on
these different compositions were then combined using a voting scheme.
{R}esults obtained through 5-fold cross-validation tests showed an
improvement in prediction accuracy over the algorithm based on the
amino acid composition only. {T}his prediction method is available
via the {I}nternet. {A}vailability: http://www.genome.ad.jp/{SIT}/ploc.html
{S}upplementary information: http://web.kuicr.kyoto-u.ac.jp/~park/{S}eqdata/},
pdf = {../local/Park2003Prediction.pdf},
file = {Park2003Prediction.pdf:local/Park2003Prediction.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://bioinformatics.oupjournals.org/cgi/content/abstract/19/13/1656}
}
@article{Park2009ChIP,
author = {Peter J Park},
title = {ChIP-seq: advantages and challenges of a maturing technology.},
journal = {Nat Rev Genet},
year = {2009},
volume = {10},
pages = {669--680},
number = {10},
month = {Oct},
abstract = {Chromatin immunoprecipitation followed by sequencing (ChIP-seq) is
a technique for genome-wide profiling of DNA-binding proteins, histone
modifications or nucleosomes. Owing to the tremendous progress in
next-generation sequencing technology, ChIP-seq offers higher resolution,
less noise and greater coverage than its array-based predecessor
ChIP-chip. With the decreasing cost of sequencing, ChIP-seq has become
an indispensable tool for studying gene regulation and epigenetic
mechanisms. In this Review, I describe the benefits and challenges
in harnessing this technique with an emphasis on issues related to
experimental design and data analysis. ChIP-seq experiments generate
large quantities of data, and effective computational analysis will
be crucial for uncovering biological mechanisms.},
doi = {10.1038/nrg2641},
institution = {Harvard Medical School, 10 Shattuck Street, Boston, MA 02115, USA.
peter_park@harvard.edu},
keywords = {Animals; Chromatin Immunoprecipitation, methods; Computational Biology;
DNA-Binding Proteins, genetics; Epigenesis, Genetic; Humans; Nucleosomes,
genetics; Sequence Analysis, DNA, methods},
language = {eng},
medline-pst = {ppublish},
owner = {philippe},
pii = {nrg2641},
pmid = {19736561},
timestamp = {2010.08.05},
url = {http://dx.doi.org/10.1038/nrg2641}
}
@article{Parker2009Supervised,
author = {Joel S Parker and Michael Mullins and Maggie C U Cheang and Samuel
Leung and David Voduc and Tammi Vickery and Sherri Davies and Christiane
Fauron and Xiaping He and Zhiyuan Hu and John F Quackenbush and Inge
J Stijleman and Juan Palazzo and J. S. Marron and Andrew B Nobel
and Elaine Mardis and Torsten O Nielsen and Matthew J Ellis and Charles
M Perou and Philip S Bernard},
title = {Supervised risk predictor of breast cancer based on intrinsic subtypes.},
journal = {J Clin Oncol},
year = {2009},
volume = {27},
pages = {1160--1167},
number = {8},
month = {Mar},
abstract = {PURPOSE To improve on current standards for breast cancer prognosis
and prediction of chemotherapy benefit by developing a risk model
that incorporates the gene expression-based "intrinsic" subtypes
luminal A, luminal B, HER2-enriched, and basal-like. METHODS A 50-gene
subtype predictor was developed using microarray and quantitative
reverse transcriptase polymerase chain reaction data from 189 prototype
samples. Test sets from 761 patients (no systemic therapy) were evaluated
for prognosis, and 133 patients were evaluated for prediction of
pathologic complete response (pCR) to a taxane and anthracycline
regimen. RESULTS: The intrinsic subtypes as discrete entities showed
prognostic significance (P = 2.26E-12) and remained significant in
multivariable analyses that incorporated standard parameters (estrogen
receptor status, histologic grade, tumor size, and node status).
A prognostic model for node-negative breast cancer was built using
intrinsic subtype and clinical information. The C-index estimate
for the combined model (subtype and tumor size) was a significant
improvement on either the clinicopathologic model or subtype model
alone. The intrinsic subtype model predicted neoadjuvant chemotherapy
efficacy with a negative predictive value for pCR of 97\%. CONCLUSION
Diagnosis by intrinsic subtype adds significant prognostic and predictive
information to standard parameters for patients with breast cancer.
The prognostic properties of the continuous risk score will be of
value for the management of node-negative breast cancers. The subtypes
and risk score can also be used to assess the likelihood of efficacy
from neoadjuvant chemotherapy.},
doi = {10.1200/JCO.2008.18.1370},
institution = {Lineberger Comprehensive Cancer Center, Carolina Center for Genome
Sciences, University of North Carolina at Chapel Hill, Chapel Hill,
NC, USA.},
keywords = {Adult; Aged; Breast Neoplasms, classification/drug therapy/etiology/mortality;
Chemotherapy, Adjuvant; Female; Humans; Middle Aged; Neoplasm Recurrence,
Local, etiology; Prognosis; Receptor, erbB-2, analysis; Receptors,
Estrogen, analysis; Reverse Transcriptase Polymerase Chain Reaction;
Risk},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {JCO.2008.18.1370},
pmid = {19204204},
timestamp = {2012.02.29},
url = {http://dx.doi.org/10.1200/JCO.2008.18.1370}
}
@article{Parker1994Scheme,
author = {Parker, K. C. and Bednarek, M. A. and Coligan, J. E.},
title = {{S}cheme for ranking potential {HLA}-{A}2 binding peptides based
on independent binding of individual peptide side-chains},
journal = {J. Immunol.},
year = {1994},
volume = {152},
pages = {163--175},
number = {1},
month = {Jan},
abstract = {A method to predict the relative binding strengths of all possible
nonapeptides to the MHC class I molecule HLA-A2 has been developed
based on experimental peptide binding data. These data indicate that,
for most peptides, each side-chain of the peptide contributes a certain
amount to the stability of the HLA-A2 complex that is independent
of the sequence of the peptide. To quantify these contributions,
the binding data from a set of 154 peptides were combined together
to generate a table containing 180 coefficients (20 amino acids x
9 positions), each of which represents the contribution of one particular
amino acid residue at a specified position within the peptide to
binding to HLA-A2. Eighty peptides formed stable HLA-A2 complexes,
as assessed by measuring the rate of dissociation of beta 2m. The
remaining 74 peptides formed complexes that had a half-life of beta
2m dissociation of less than 5 min at 37 degrees C, or did not bind
to HLA-A2, and were included because they could be used to constrain
the values of some of the coefficients. The "theoretical" binding
stability (calculated by multiplying together the corresponding coefficients)
matched the experimental binding stability to within a factor of
5. The coefficients were then used to calculate the theoretical binding
stability for all the previously identified self or antigenic nonamer
peptides known to bind to HLA-A2. The binding stability for all other
nonamer peptides that could be generated from the proteins from which
these peptides were derived was also predicted. In every case, the
previously described HLA-A2 binding peptides were ranked in the top
2\% of all possible nonamers for each source protein. Therefore,
most biologically relevant nonamer peptides should be identifiable
using the table of coefficients. We conclude that the side-chains
of most nonamer peptides to the first approximation bind independently
of one another to the HLA-A2 molecule.},
keywords = {immunoinformatics},
pmid = {8254189},
timestamp = {2007.01.25}
}
@article{Parkhomenko2009Sparse,
author = {Parkhomenko, E. and Tritchler, D. and Beyene, J.},
title = {Sparse canonical correlation analysis with application to genomic
data integration.},
journal = {Stat Appl Genet Mol Biol},
year = {2009},
volume = {8},
pages = {Article 1},
number = {1},
month = {Jan},
abstract = {Large scale genomic studies with multiple phenotypic or genotypic
measures may require the identification of complex multivariate relationships.
In multivariate analysis a common way to inspect the relationship
between two sets of variables based on their correlation is canonical
correlation analysis, which determines linear combinations of all
variables of each type with maximal correlation between the two linear
combinations. However, in high dimensional data analysis, when the
number of variables under consideration exceeds tens of thousands,
linear combinations of the entire sets of features may lack biological
plausibility and interpretability. In addition, insufficient sample
size may lead to computational problems, inaccurate estimates of
parameters and non-generalizable results. These problems may be solved
by selecting sparse subsets of variables, i.e. obtaining sparse loadings
in the linear combinations of variables of each type. In this paper
we present Sparse Canonical Correlation Analysis (SCCA) which examines
the relationships between two types of variables and provides sparse
solutions that include only small subsets of variables of each type
by maximizing the correlation between the subsets of variables of
different types while performing variable selection. We also present
an extension of SCCA--adaptive SCCA. We evaluate their properties
using simulated data and illustrate practical use by applying both
methods to the study of natural variation in human gene expression.},
doi = {10.2202/1544-6115.1406},
institution = {Hospital for Sick Children Research Institute. elena@utstat.toronto.edu},
keywords = {Algorithms; Genomics, statistics /&/ numerical data; Humans; Models,
Statistical; Sample Size},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pmid = {19222376},
timestamp = {2012.02.29},
url = {http://dx.doi.org/10.2202/1544-6115.1406}
}
@article{Passerini2004Learning,
author = {Passerini, A. and Frasconi, P.},
title = {Learning to discriminate between ligand-bound and disulfide-bound
cysteines},
journal = {Protein {E}ng. {D}es. {S}el.},
year = {2004},
volume = {17},
pages = {367-373},
number = {4},
abstract = {We present a machine learning method to discriminate between cysteines
involved in ligand binding and cysteines forming disulfide bridges.
{O}ur method uses a window of multiple alignment profiles to represent
each instance and support vector machines with a polynomial kernel
as the learning algorithm. {W}e also report results obtained with
two new kernel functions based on similarity matrices. {E}xperimental
results indicate that binding type can be predicted at significantly
higher accuracy than using {PROSITE} patterns.},
doi = {10.1093/protein/gzh042},
pdf = {../local/Passerini2004Learning.pdf},
file = {Passerini2004Learning.pdf:local/Passerini2004Learning.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://dx.doi.org/10.1093/protein/gzh042}
}
@article{Passerini2004New,
author = {Andrea Passerini and Massimiliano Pontil and Paolo Frasconi},
title = {New results on error correcting output codes of kernel machines.},
journal = {I{EEE} {T}rans {N}eural {N}etw},
year = {2004},
volume = {15},
pages = {45-54},
number = {1},
month = {Jan},
abstract = {We study the problem of multiclass classification within the framework
of error correcting output codes ({ECOC}) using margin-based binary
classifiers. {S}pecifically, we address two important open problems
in this context: decoding and model selection. {T}he decoding problem
concerns how to map the outputs of the classifiers into class codewords.
{I}n this paper we introduce a new decoding function that combines
the margins through an estimate of their class conditional probabilities.
{C}oncerning model selection, we present new theoretical results
bounding the leave-one-out ({LOO}) error of {ECOC} of kernel machines,
which can be used to tune kernel hyperparameters. {W}e report experiments
using support vector machines as the base binary classifiers, showing
the advantage of the proposed decoding function over other functions
of the margin commonly used in practice. {M}oreover, our empirical
evaluations on model selection indicate that the bound leads to good
estimates of kernel parameters.},
keywords = {Neural Networks (Computer), Research Design, 15387246}
}
@techreport{Pastor-Satorras2002Evolving,
author = {Pastor-Satorras, R. and Smith, E. D. and Sol{\'e}, R. V.},
title = {Evolving protein interaction networks through gene duplication},
institution = {Santa Fe Institute},
year = {2002},
note = {Working paper 02-02-008},
pdf = {../local/past02.pdf},
file = {past02.pdf:local/past02.pdf:PDF},
subject = {bionetprot},
url = {http://www.santafe.edu/sfi/publications/Abstracts/02-02-008abs.html}
}
@article{Patil2005Uncovering,
author = {Patil, K. R. and Nielsen, J.},
title = {Uncovering transcriptional regulation of metabolism by using metabolic
network topology.},
journal = {Proc. Natl. Acad. Sci. U. S. A.},
year = {2005},
volume = {102},
pages = {2685--2689},
number = {8},
month = {Feb},
abstract = {Cellular response to genetic and environmental perturbations is often
reflected and/or mediated through changes in the metabolism, because
the latter plays a key role in providing Gibbs free energy and precursors
for biosynthesis. Such metabolic changes are often exerted through
transcriptional changes induced by complex regulatory mechanisms
coordinating the activity of different metabolic pathways. It is
difficult to map such global transcriptional responses by using traditional
methods, because many genes in the metabolic network have relatively
small changes at their transcription level. We therefore developed
an algorithm that is based on hypothesis-driven data analysis to
uncover the transcriptional regulatory architecture of metabolic
networks. By using information on the metabolic network topology
from genome-scale metabolic reconstruction, we show that it is possible
to reveal patterns in the metabolic network that follow a common
transcriptional response. Thus, the algorithm enables identification
of so-called reporter metabolites (metabolites around which the most
significant transcriptional changes occur) and a set of connected
genes with significant and coordinated response to genetic or environmental
perturbations. We find that cells respond to perturbations by changing
the expression pattern of several genes involved in the specific
part(s) of the metabolism in which a perturbation is introduced.
These changes then are propagated through the metabolic network because
of the highly connected nature of metabolism.},
doi = {10.1073/pnas.0406811102},
pdf = {../local/Patil2005Uncovering.pdf},
file = {Patil2005Uncovering.pdf:Patil2005Uncovering.pdf:PDF},
institution = {Center for Microbial Biotechnology, BioCentrum-DTU, Technical University
of Denmark, Building 223, DK-2800 Kgs. Lyngby, Denmark.},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {0406811102},
pmid = {15710883},
timestamp = {2011.10.08},
url = {http://dx.doi.org/10.1073/pnas.0406811102}
}
@inproceedings{Patterson2002Pre-mRNA,
author = {Patterson, D.J. and Yasuhara, K. and Ruzzo, W.L.},
title = {Pre-{m{RNA}} secondary structure prediction aids splice site prediction.},
booktitle = {Proceedings of the {P}acific {S}ymposium on {B}iocomputing 2002},
year = {2002},
editor = {Russ B. Altman and A. Keith Dunker and Lawrence Hunter and Kevin
Lauerdale and Teri E. Klein},
pages = {223-234},
publisher = {World Scientific},
abstract = {Accurate splice site prediction is a critical component of any computational
approach to gene prediction in higher organisms. {E}xisting approaches
generally use sequence-based models that capture local dependencies
among nucleotides in a small window around the splice site. {W}e
present evidence that computationally predicted secondary structure
of moderate length pre-m{RNA} subsequencies contains information
that can be exploited to improve acceptor splice site prediction
beyond that possible with conventional sequence-based approaches.
{B}oth decision tree and support vector machine classifiers, using
folding energy and structure metrics characterizing helix formation
near the splice site, achieve a 5-10% reduction in error rate with
a human data set. {B}ased on our data, we hypothesize that acceptors
preferentially exhibit short helices at the splice site.},
pdf = {../local/Patterson2002Pre-mRNA.pdf},
file = {Patterson2002Pre-mRNA.pdf:local/Patterson2002Pre-mRNA.pdf:PDF},
keywords = {biosvm},
subject = {biokernel},
url = {http://www.smi.stanford.edu/projects/helix/psb02/patterson.pdf}
}
@article{Patterson2003Proteomics,
author = {Scott D Patterson and Ruedi H Aebersold},
title = {Proteomics: the first decade and beyond.},
journal = {Nat Genet},
year = {2003},
volume = {33 Suppl},
pages = {311--323},
month = {Mar},
abstract = {Proteomics is the systematic study of the many and diverse properties
of proteins in a parallel manner with the aim of providing detailed
descriptions of the structure, function and control of biological
systems in health and disease. Advances in methods and technologies
have catalyzed an expansion of the scope of biological studies from
the reductionist biochemical analysis of single proteins to proteome-wide
measurements. Proteomics and other complementary analysis methods
are essential components of the emerging 'systems biology' approach
that seeks to comprehensively describe biological systems through
integration of diverse types of data and, in the future, to ultimately
allow computational simulations of complex biological systems.},
doi = {10.1038/ng1106},
institution = {Celera Genomics Corporation, 45 West Gude Drive, Rockville, Maryland
20850, USA. scottp@farmalbiomed.com},
keywords = {Amino Acid Sequence; Base Sequence; Chromatography, Liquid; Computational
Biology; DNA; Genetic Techniques; History, 20th Century; History,
21st Century; Mass Spectrometry; Oligonucleotide Array Sequence Analysis;
Proteins; Proteomics},
owner = {phupe},
pii = {ng1106},
pmid = {12610541},
timestamp = {2010.08.13},
url = {http://dx.doi.org/10.1038/ng1106}
}
@article{Pavey2004Microarray,
author = {Pavey, S. and Johansson, P. and Packer, L. and Taylor, J. and Stark,
M. and Pollock, P.M. and Walker, G.J. and Boyle, G.M. and Harper,
U. and Cozzi, S.J. and Hansen, K. and Yudt, L. and Schmidt, C. and
Hersey, P. and Ellem, K.A. and O'Rourke, M.G. and Parsons, P.G. and
Meltzer, P. and Ringner, M. and Hayward, N.K.},
title = {Microarray expression profiling in melanoma reveals a {BRAF} mutation
signature},
journal = {Oncogene},
year = {2004},
volume = {23},
pages = {4060-4067},
number = {23},
month = {May},
abstract = {We have used microarray gene expression profiling and machine learning
to predict the presence of {BRAF} mutations in a panel of 61 melanoma
cell lines. {T}he {BRAF} gene was found to be mutated in 42 samples
(69%) and intragenic mutations of the {NRAS} gene were detected in
seven samples (11%). {N}o cell line carried mutations of both genes.
{U}sing support vector machines, we have built a classifier that
differentiates between melanoma cell lines based on {BRAF} mutation
status. {A}s few as 83 genes are able to discriminate between {BRAF}
mutant and {BRAF} wild-type samples with clear separation observed
using hierarchical clustering. {M}ultidimensional scaling was used
to visualize the relationship between a {BRAF} mutation signature
and that of a generalized mitogen-activated protein kinase ({MAPK})
activation (either {BRAF} or {NRAS} mutation) in the context of the
discriminating gene list. {W}e observed that samples carrying {NRAS}
mutations lie somewhere between those with or without {BRAF} mutations.
{T}hese observations suggest that there are gene-specific mutation
signals in addition to a common {MAPK} activation that result from
the pleiotropic effects of either {BRAF} or {NRAS} on other signaling
pathways, leading to measurably different transcriptional changes.},
doi = {10.1038/sj.onc.1207563},
pdf = {../local/Pavey2004Microarray.pdf},
file = {Pavey2004Microarray.pdf:local/Pavey2004Microarray.pdf:PDF},
keywords = {biosvm microarray},
owner = {jeanphilippevert},
url = {http://dx.doi.org/10.1038/sj.onc.1207563}
}
@inproceedings{Pavlidis2001Promoter,
author = {P. Pavlidis and T. S. Furey and M. Liberto and D. Haussler and W.
N. Grundy},
title = {Promoter {R}egion-{B}ased {C}lassification of {G}enes},
booktitle = {Pacific {S}ymposium on {B}iocomputing},
year = {2001},
pages = {139--150},
pdf = {../local/pavl01b.pdf},
file = {pavl01b.pdf:local/pavl01b.pdf:PDF},
keywords = {biosvm},
subject = {biokernel},
url = {http://www.smi.stanford.edu/projects/helix/psb01/pavlidis.pdf}
}
@article{Pavlidis2002Exploring,
author = {Pavlidis, P. and Lewis, D. P. and Noble, W. S.},
title = {Exploring gene expression data with class scores.},
journal = {Pac. Symp. Biocomput.},
year = {2002},
pages = {474--485},
abstract = {We address a commonly asked question about gene expression data sets:
"What functional classes of genes are most interesting in the data?"
In the methods we present, expression data is partitioned into classes
based on existing annotation schemes. Each class is then given three
separately derived "interest" scores. The first score is based on
an assessment of the statistical significance of gene expression
changes experienced by members of the class, in the context of the
experimental design. The second is based on the co-expression of
genes in the class. The third score is based on the learnability
of the classification. We show that all three methods reveal significant
classes in each of three different gene expression data sets. Many
classes are identified by one method but not the others, indicating
that the methods are complementary. The classes identified are in
many cases of clear relevance to the experiment. Our results suggest
that these class scoring methods are useful tools for exploring gene
expression data.},
pdf = {../local/Pavlidis2002Exploring.pdf},
file = {Pavlidis2002Exploring.pdf:Pavlidis2002Exploring.pdf:PDF},
institution = {Columbia Genome Center, Columbia University, USA. pp175@columbia.edu},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pmid = {11928500},
timestamp = {2011.10.04}
}
@article{Pavlidis2004Support,
author = {Paul Pavlidis and Ilan Wapinski and William Stafford Noble},
title = {Support vector machine classification on the web.},
journal = {Bioinformatics},
year = {2004},
volume = {20},
pages = {586-7},
number = {4},
month = {Mar},
abstract = {The support vector machine ({SVM}) learning algorithm has been widely
applied in bioinformatics. {W}e have developed a simple web interface
to our implementation of the {SVM} algorithm, called {G}ist. {T}his
interface allows novice or occasional users to apply a sophisticated
machine learning algorithm easily to their data. {M}ore advanced
users can download the software and source code for local installation.
{T}he availability of these tools will permit more widespread application
of this powerful learning algorithm in bioinformatics.},
doi = {10.1093/bioinformatics/btg461},
pdf = {../local/Pavlidis2004Support.pdf},
file = {Pavlidis2004Support.pdf:local/Pavlidis2004Support.pdf:PDF},
keywords = {Adaptation, Algorithms, Ambergris, Amino Acid Sequence, Animals, Artifacts,
Artificial Intelligence, Automated, Cadmium, Candida, Candida albicans,
Capillary, Clinical, Cluster Analysis, Combinatorial Chemistry Techniques,
Comparative Study, Computational Biology, Computer Simulation, Computer-Assisted,
Computing Methodologies, Databases, Decision Support Systems, Electrophoresis,
Enzymes, Europe, Eye Enucleation, Humans, Image Interpretation, Image
Processing, Information Storage and Retrieval, Internet, Magnetic
Resonance Imaging, Magnetic Resonance Spectroscopy, Markov Chains,
Melanoma, Models, Molecular, Molecular Conformation, Molecular Sequence
Data, Molecular Structure, Neural Networks (Computer), Non-P.H.S.,
Non-U.S. Gov't, Nonlinear Dynamics, Odors, P.H.S., Pattern Recognition,
Perfume, Physiological, Predictive Value of Tests, Prognosis, Prospective
Studies, Protein, Protein Structure, Proteins, Proteomics, Quantitative
Structure-Activity Relationship, Rats, Reproducibility of Results,
Research Support, Saccharomyces cerevisiae, Saccharomyces cerevisiae
Proteins, Secondary, Sensitivity and Specificity, Signal Processing,
Single-Blind Method, Soft Tissue Neoplasms, Software, Statistical,
U.S. Gov't, Uveal Neoplasms, Visual, 14990457},
pii = {btg461},
url = {http://dx.doi.org/10.1093/bioinformatics/btg461}
}
@inproceedings{Pavlidis2001Gene,
author = {Pavlidis, P. and Weston, J. and Cai, J. and Grundy, W.N.},
title = {Gene functional classification from heterogeneous data},
booktitle = {Proceedings of the {F}ifth {A}nnual {I}nternational {C}onference
on {C}omputational {B}iology},
year = {2001},
pages = {249--255},
pdf = {../local/pavl01.pdf},
file = {pavl01.pdf:local/pavl01.pdf:PDF},
keywords = {biosvm},
subject = {biokernel},
url = {http://www.cs.columbia.edu/compbio/papers/exp-phylo.pdf}
}
@article{Pavlidis2002Learning,
author = {Pavlidis, P. and Weston, J. and Cai, J. and Noble, W.S.},
title = {Learning Gene Functional Classifications from Multiple Data Types},
journal = {J. Comput. Biol.},
year = {2002},
volume = {9},
pages = {401--411},
number = {2},
abstract = {In our attempts to understand cellular function at the molecular level,
we must be able to synthesize information from disparate types of
genomic data. {W}e consider the problem of inferring gene functional
classifications from a heterogeneous data set consisting of {DNA}
microarray expression measurements and phylogenetic profiles from
whole-genome sequence comparisons. {W}e demonstrate the application
of the support vector machine ({SVM}) learning algorithm to this
functional inference task. {O}ur results suggest the importance of
exploiting prior information about the heterogeneity of the data.
{I}n particular, we propose an {SVM} kernel function that is explicitly
heterogeneous. {I}n addition, we describe feature scaling methods
for further exploiting prior knowledge of heterogeneity by giving
each data type different weights.},
doi = {10.1089/10665270252935539},
pdf = {../local/Pavlidis2002Learning.pdf},
file = {Pavlidis2002Learning.pdf:local/Pavlidis2002Learning.pdf:PDF},
keywords = {biosvm},
owner = {vert}
}
@article{Pawitan2005Gene,
author = {Pawitan, Y. and Bj{\"o}hle, J. and Amler, L. and Borg, A.L. and Egyhazi,
S. and Hall, P. and Han, X. and Holmberg, L. and Huang, F. and Klaar,
S. and others},
title = {Gene expression profiling spares early breast cancer patients from
adjuvant therapy: derived and validated in two population-based cohorts},
journal = {Breast Cancer Research},
year = {2005},
volume = {7},
pages = {R953},
number = {6},
publisher = {BioMed Central Ltd}
}
@article{Pazos2001Similarity,
author = {Pazos, F. and Valencia, A.},
title = {Similarity of phylogenetic trees as indicator of protein-protein
interaction},
journal = {Protein {E}ng.},
year = {2001},
volume = {9},
pages = {609--614},
number = {14},
pdf = {../local/Pazos2001Similarity.pdf},
file = {Pazos2001Similarity.pdf:local/Pazos2001Similarity.pdf:PDF},
owner = {vert},
url = {http://peds.oxfordjournals.org/cgi/content/abstract/14/9/609}
}
@book{Pearl1988Probabilistic,
title = {Probabilistic {R}easoning in {I}ntelligent {S}ystems: {N}etworks
of {P}lausible {I}nference},
publisher = {Morgan Kaufmann},
year = {1988},
author = {Pearl, J. },
address = {San Mateo},
note = {The classic original book on belief networks, which was certainly
motivated by the idea that belief networks might have relevance to
brains},
owner = {vert}
}
@article{Pearlstein2003Characterization,
author = {Pearlstein, R. A. and Vaz, R. J. and Kang, J. and Chen, X.-L. and
Preobrazhenskaya, M. and Shchekotikhin, A. E. and Korolev, A. M.
and Lysenkova, L. N. and Miroshnikova, O. V. and Hendrix, J. and
Rampe, D.},
title = {{C}haracterization of {HERG} potassium channel inhibition using {C}o{MS}i{A}
3{D} {QSAR} and homology modeling approaches.},
journal = {Bioorg. Med. Chem. Lett.},
year = {2003},
volume = {13},
pages = {1829--1835},
number = {10},
month = {May},
abstract = {A data set consisting of twenty-two sertindole analogues and ten structurally
diverse inhibitors, spanning a wide range in potency, was analyzed
using CoMSiA. A homology model of HERG was constructed from the crystal
structure of the open MthK potassium channel. A complementary relationship
between our CoMSiA and homology models is apparent when the long
inhibitor axis is oriented parallel to the longitudinal axis of the
pore, with the tail region pointed toward the selectivity filter.
The key elements of the pharmacophore, the CoMSiA and the homology
model are: (1) The hydrophobic feature optimally consists of an aromatic
group that is capable of engaging in pi-stacking with a Phe656 side
chain. Optionally, a second aromatic or hydrophobic group present
in some inhibitors may contact an additional Phe656 side chain. (2)
The basic nitrogen appears to undergo a pi-cation interaction with
Tyr652. (3) The pore diameter (12A+), and depth of the selectivity
loop relative to the intracellular opening, act as constraints on
the conformation-dependent inhibitor dimensions.},
keywords = {chemoinformatics herg},
pii = {S0960894X03001963},
pmid = {12729675},
timestamp = {2006.10.06}
}
@article{Pearlstein2003Understanding,
author = {Pearlstein, R. and Vaz, R. and Rampe, D.},
title = {{U}nderstanding the structure-activity relationship of the human
ether-a-go-go-related gene cardiac {K}+ channel. {A} model for bad
behavior.},
journal = {J. Med. Chem.},
year = {2003},
volume = {46},
pages = {2017--2022},
number = {11},
month = {May},
doi = {10.1021/jm0205651},
keywords = {chemoinformatics herg},
pmid = {12747773},
timestamp = {2006.10.06},
url = {http://dx.doi.org/10.1021/jm0205651}
}
@article{Pearson1901On,
author = {Pearson, K.},
title = {{On lines and planes of closest fit to systems of points in space}},
journal = {Philos. Mag.},
year = {1901},
volume = {2},
pages = {559--572},
number = {6},
citeulike-article-id = {2013414},
keywords = {pca},
posted-at = {2007-11-29 10:41:36},
priority = {2}
}
@article{Pearson1990Rapid,
author = {Pearson, W. R.},
title = {Rapid and sensitive sequence comparisons with {FASTP} and {FASTA}},
journal = {Meth. {E}nzymol.},
year = {1990},
volume = {183},
pages = {63--98}
}
@inproceedings{Pelckmans2009Transductively,
author = {Pelckmans, K. and Suykens, J.A.K.},
title = {Transductively Learning from Positive Examples Only},
booktitle = {Proc. of the European Symposium on Artificial Neural Networks (ESANN
2009)},
year = {2009},
pdf = {../local/Pelckmans2009Transductively.pdf},
file = {Pelckmans2009Transductively.pdf:Pelckmans2009Transductively.pdf:PDF},
keywords = {PUlearning},
owner = {jp},
timestamp = {2010.01.29},
url = {ftp://ftp.esat.kuleuven.be/pub/SISTA/kpelckma/esann09_ssl.pdf}
}
@inproceedings{Pelleg2000X-means,
author = {Pelleg, D. and Moore, A.},
title = {X-means: Extending K-means with efficient estimation of the number
of clusters},
booktitle = {Proceedings of the Seventeenth International Conference on Machine
Learning},
year = {2000},
pages = {727--734},
address = {San Francisco},
publisher = {Morgan Kaufmann},
owner = {jp},
timestamp = {2011.12.29}
}
@article{Pellegrini1999Assigning,
author = {Pellegrini, M. and Marcotte, E. M. and Thompson, M. J. and Eisenberg,
D. and Yeates, T. O.},
title = {Assigning protein functions by comparative genome analysis: {P}rotein
phylogenetic profiles},
journal = {Proc. {N}atl. {A}cad. {S}ci. {USA}},
year = {1999},
volume = {96},
pages = {4285--4288},
month = {April},
pdf = {../local/pell99.pdf},
file = {pell99.pdf:local/pell99.pdf:PDF},
subject = {bio},
url = {http://www.pnas.org/cgi/reprint/96/8/4285.pdf}
}
@article{Peng2005Feature,
author = {Peng, H. and Long, F. and Ding, C.},
title = {Feature selection based on mutual information criteria of max-dependency,
max-relevance, and min-redundancy},
journal = {Pattern Analysis and Machine Intelligence, IEEE Transactions on},
year = {2005},
volume = {27},
pages = {1226--1238},
number = {8},
publisher = {IEEE}
}
@article{Peng2010Regularized,
author = {Jie Peng and Ji Zhu and Anna Bergamaschi and Wonshik Han and Dong-Young
Noh and Jonathan R.~ Pollack and Pei Wang},
title = {Regularized Multivariate Regression for Identifying Master Predictors
with Application to Integrative Genomics Study of Breast Cancer},
journal = {Ann. Appl. Stat.},
year = {2010},
volume = {4},
pages = {53--77},
number = {1},
doi = {doi:10.1214/09-AOAS271SUPP},
owner = {jp},
timestamp = {2012.02.29},
url = {http://dx.doi.org/doi:10.1214/09-AOAS271SUPP}
}
@article{Peng2003Splicing-site,
author = {Si-hua Peng and Long-jiang Fan and Xiao-ning Peng and Shu-lin Zhuang
and Wei Du and Liang-biao Chen},
title = {Splicing-site recognition of rice ({O}ryza sativa {L}.) {DNA} sequences
by support vector machines.},
journal = {J {Z}hejiang {U}niv {S}ci},
year = {2003},
volume = {4},
pages = {573-7},
number = {5},
abstract = {M{OTIVATION}: {I}t was found that high accuracy splicing-site recognition
of rice ({O}ryza sativa {L}.) {DNA} sequence is especially difficult.
{W}e described a new method for the splicing-site recognition of
rice {DNA} sequences. {METHOD}: {B}ased on the intron in eukaryotic
organisms conforming to the principle of {GT}-{AG}, we used support
vector machines ({SVM}) to predict the splicing sites. {B}y machine
learning, we built a model and used it to test the effect of the
test data set of true and pseudo splicing sites. {RESULTS}: {T}he
prediction accuracy we obtained was 87.53\% at the true 5' end splicing
site and 87.37\% at the true 3' end splicing sites. {T}he results
suggested that the {SVM} approach could achieve higher accuracy than
the previous approaches.}
}
@article{Peng2003Molecular,
author = {Peng, S. and Xu, Q. and Ling, X.B. and Peng, X. and Du, W. and Chen,
L.},
title = {Molecular classification of cancer types from microarray data using
the combination of genetic algorithms and support vector machines.},
journal = {F{EBS} {L}ett.},
year = {2003},
volume = {555},
pages = {358-362},
number = {2},
abstract = {Simultaneous multiclass classification of tumor types is essential
for future clinical implementations of microarray-based cancer diagnosis.
{I}n this study, we have combined genetic algorithms ({GA}s) and
all paired support vector machines ({SVM}s) for multiclass cancer
identification. {T}he predictive features have been selected through
iterative {SVM}s/{GA}s, and recursive feature elimination post-processing
steps, leading to a very compact cancer-related predictive gene set.
{L}eave-one-out cross-validations yielded accuracies of 87.93% for
the eight-class and 85.19% for the fourteen-class cancer classifications,
outperforming the results derived from previously published methods.},
doi = {10.1016/S0014-5793(03)01275-4},
pdf = {../local/Peng2003Molecular.pdf},
file = {Peng2003Molecular.pdf:local/Peng2003Molecular.pdf:PDF},
keywords = {biosvm microarray},
owner = {jeanphilippevert},
url = {http://dx.doi.org/10.1016/S0014-5793(03)01275-4}
}
@article{Pepperrell1991Techniques,
author = {C. A. Pepperrell and P. Willett},
title = {{T}echniques for the calculation of three-dimensional structural
similarity using inter-atomic distances.},
journal = {J Comput Aided Mol Des},
year = {1991},
volume = {5},
pages = {455--474},
number = {5},
month = {Oct},
abstract = {This paper reports a comparison of several methods for measuring the
degree of similarity between pairs of 3-D chemical structures that
are represented by inter-atomic distance matrices. The methods that
have been tested use the distance information in very different ways
and have very different computational requirements. Experiments with
10 small datasets, for which both structural and biological activity
data are available, suggest that the most cost-effective technique
is based on a mapping procedure that tries to match pairs of atoms,
one from each of the molecules that are being compared, that have
neighbouring atoms at approximately the same distances.},
keywords = {Algorithms, Binding Sites, Chemical, Chemistry, Comparative Study,
Computer Simulation, Databases, Factual, Macromolecular Substances,
Models, Molecular Conformation, Molecular Structure, Non-U.S. Gov't,
Physical, Protein Conformation, Protein Structure, Proteins, Research
Support, Structure-Activity Relationship, Tertiary, 1770381},
owner = {mahe},
pmid = {1770381},
timestamp = {2006.08.22}
}
@article{Perez-Iratxeta2002Association,
author = {Perez-Iratxeta, C. and Bork, P. and Andrade, M. A.},
title = {Association of genes to genetically inherited diseases using data
mining},
journal = {Nat. Genet.},
year = {2002},
volume = {31},
pages = {316--319},
number = {3},
month = {Jul},
abstract = {Although approximately one-quarter of the roughly 4,000 genetically
inherited diseases currently recorded in respective databases (LocusLink,
OMIM) are already linked to a region of the human genome, about 450
have no known associated gene. Finding disease-related genes requires
laborious examination of hundreds of possible candidate genes (sometimes,
these are not even annotated; see, for example, refs 3,4). The public
availability of the human genome draft sequence has fostered new
strategies to map molecular functional features of gene products
to complex phenotypic descriptions, such as those of genetically
inherited diseases. Owing to recent progress in the systematic annotation
of genes using controlled vocabularies, we have developed a scoring
system for the possible functional relationships of human genes to
455 genetically inherited diseases that have been mapped to chromosomal
regions without assignment of a particular gene. In a benchmark of
the system with 100 known disease-associated genes, the disease-associated
gene was among the 8 best-scoring genes with a 25\% chance, and among
the best 30 genes with a 50\% chance, showing that there is a relationship
between the score of a gene and its likelihood of being associated
with a particular disease. The scoring also indicates that for some
diseases, the chance of identifying the underlying gene is higher.},
doi = {10.1038/ng895},
pdf = {../local/Perez-Iratxeta2002Association.pdf},
file = {Perez-Iratxeta2002Association.pdf:Perez-Iratxeta2002Association.pdf:PDF},
institution = {European Molecular Biology Laboratory, Meyerhofstr.1, Heidelberg
69012, Germany.},
owner = {jp},
pii = {ng895},
pmid = {12006977},
timestamp = {2009.03.18},
url = {http://dx.doi.org/10.1038/ng895}
}
@article{Perez-Iratxeta2005,
author = {Carolina Perez-Iratxeta and Matthias Wjst and Peer Bork and Miguel
A Andrade},
title = {G2D: a tool for mining genes associated with disease.},
journal = {BMC Genet},
year = {2005},
volume = {6},
pages = {45},
abstract = {BACKGROUND: Human inherited diseases can be associated by genetic
linkage with one or more genomic regions. The availability of the
complete sequence of the human genome allows examining those locations
for an associated gene. We previously developed an algorithm to prioritize
genes on a chromosomal region according to their possible relation
to an inherited disease using a combination of data mining on biomedical
databases and gene sequence analysis. RESULTS: We have implemented
this method as a web application in our site G2D (Genes to Diseases).
It allows users to inspect any region of the human genome to find
candidate genes related to a genetic disease of their interest. In
addition, the G2D server includes pre-computed analyses of candidate
genes for 552 linked monogenic diseases without an associated gene,
and the analysis of 18 asthma loci. CONCLUSION: G2D can be publicly
accessed at http://www.ogic.ca/projects/g2d_2/.},
doi = {10.1186/1471-2156-6-45},
institution = {Ontario Genomics Innovation Centre, Ottawa Health Research Institute,
ON K1H 8L6, Ottawa, Canada. cperez-iratxeta@ohri.ca},
keywords = {Algorithms; Alzheimer Disease; Asthma; Genetic Diseases, Inborn; Genetic
Predisposition to Disease; Humans; Internet; Linkage (Genetics)},
owner = {mordelet},
pii = {1471-2156-6-45},
pmid = {16115313},
timestamp = {2010.09.27},
url = {http://dx.doi.org/10.1186/1471-2156-6-45}
}
@article{Perkins2005Expanding,
author = {D O Perkins and C Jeffries and P Sullivan},
title = {Expanding the 'central dogma': the regulatory role of nonprotein
coding genes and implications for the genetic liability to schizophrenia},
journal = {Molecular Psychiatry},
year = {2005},
volume = {10},
pages = {69-78},
keywords = {csbcbook}
}
@article{Perlman2004Multidimensional,
author = {Perlman, Z. E. and Slack, M. D. and Feng, Y. and Mitchison, T. J.
and Wu, L. F. and Altschuler, S. J.},
title = {Multidimensional drug profiling by automated microscopy},
journal = {Science},
year = {2004},
volume = {306},
pages = {1194--1198},
number = {5699},
month = {Nov},
abstract = {We present a method for high-throughput cytological profiling by microscopy.
Our system provides quantitative multidimensional measures of individual
cell states over wide ranges of perturbations. We profile dose-dependent
phenotypic effects of drugs in human cell culture with a titration-invariant
similarity score (TISS). This method successfully categorized blinded
drugs and suggested targets for drugs of uncertain mechanism. Multivariate
single-cell analysis is a starting point for identifying relationships
among drug effects at a systems level and a step toward phenotypic
profiling at the single-cell level. Our methods will be useful for
discovering the mechanism and predicting the toxicity of new drugs.},
doi = {10.1126/science.1100709},
pdf = {../local/Perlman2004Multidimensional.pdf},
file = {Perlman2004Multidimensional.pdf:Perlman2004Multidimensional.pdf:PDF},
institution = {Institute of Chemistry and Cell Biology, Harvard Medical School,
Boston, MA 02115, USA.},
keywords = {chemogenomics, highcontentscreening},
owner = {jp},
pii = {306/5699/1194},
pmid = {15539606},
timestamp = {2009.03.26},
url = {http://dx.doi.org/10.1126/science.1100709}
}
@article{Perola2004Conformational,
author = {Emanuele Perola and Paul S Charifson},
title = {Conformational analysis of drug-like molecules bound to proteins:
an extensive study of ligand reorganization upon binding.},
journal = {J. Med. Chem.},
year = {2004},
volume = {47},
pages = {2499--2510},
number = {10},
month = {May},
abstract = {This paper describes a large-scale study on the nature and the energetics
of the conformational changes drug-like molecules experience upon
binding. Ligand strain energies and conformational reorganization
were analyzed with different computational methods on 150 crystal
structures of pharmaceutically relevant protein-ligand complexes.
The common knowledge that ligands rarely bind in their lowest calculated
energy conformation was confirmed. Additionally, we found that over
60\% of the ligands do not bind in a local minimum conformation.
While approximately 60\% of the ligands were calculated to bind with
strain energies lower than 5 kcal/mol, strain energies over 9 kcal/mol
were calculated in at least 10\% of the cases regardless of the method
used. A clear correlation was found between acceptable strain energy
and ligand flexibility, while there was no correlation between strain
energy and binding affinity, thus indicating that expensive conformational
rearrangements can be tolerated in some cases without overly penalizing
the tightness of binding. On the basis of the trends observed, thresholds
for the acceptable strain energies of bioactive conformations were
defined with consideration of the impact of ligand flexibility. An
analysis of the degree of folding of the bound ligands confirmed
the general tendency of small molecules to bind in an extended conformation.
The results suggest that the unfolding of hydrophobic ligands during
binding, which exposes hydrophobic surfaces to contact with protein
residues, could be one of the factors accounting for high reorganization
energies. Finally, different methods for conformational analysis
were evaluated, and guidelines were defined to maximize the prevalence
of bioactive conformations in computationally generated ensembles.},
doi = {10.1021/jm030563w},
keywords = {Drug Design; Endopeptidases; Ligands; Molecular Conformation; Pharmaceutical
Preparations; Phosphotransferases; Protein Binding; Protein Folding;
Proteins; Thermodynamics},
owner = {laurent},
pmid = {15115393},
timestamp = {2008.01.16},
url = {http://dx.doi.org/10.1021/jm030563w}
}
@article{Perou1999Distinctive,
author = {Perou, C. M. and Jeffrey, S. S. and {van de Rijn}, M. and Rees, C.
A. and Eisen, M. B. and Ross, D. T. and Pergamenschikov, A. and Williams,
C. F. and Zhu, S. X. and Lee, J. C. and Lashkari, D. and Shalon,
D. and Brown, P. O. and Botstein, D.},
title = {Distinctive gene expression patterns in human mammary epithelial
cells and breast cancers.},
journal = {Proc. Natl. Acad. Sci. U S A},
year = {1999},
volume = {96},
pages = {9212--9217},
number = {16},
month = {Aug},
abstract = {cDNA microarrays and a clustering algorithm were used to identify
patterns of gene expression in human mammary epithelial cells growing
in culture and in primary human breast tumors. Clusters of coexpressed
genes identified through manipulations of mammary epithelial cells
in vitro also showed consistent patterns of variation in expression
among breast tumor samples. By using immunohistochemistry with antibodies
against proteins encoded by a particular gene in a cluster, the identity
of the cell type within the tumor specimen that contributed the observed
gene expression pattern could be determined. Clusters of genes with
coherent expression patterns in cultured cells and in the breast
tumors samples could be related to specific features of biological
variation among the samples. Two such clusters were found to have
patterns that correlated with variation in cell proliferation rates
and with activation of the IFN-regulated signal transduction pathway,
respectively. Clusters of genes expressed by stromal cells and lymphocytes
in the breast tumors also were identified in this analysis. These
results support the feasibility and usefulness of this systematic
approach to studying variation in gene expression patterns in human
cancers as a means to dissect and classify solid tumors.},
doi = {10.1073/pnas.96.16.9212},
pdf = {../local/Perou1999Distinctive.pdf},
file = {Perou1999Distinctive.pdf:Perou1999Distinctive.pdf:PDF},
institution = {Department of Genetics, Stanford University School of Medicine, Stanford,
CA 94305, USA.},
keywords = {csbcbook, csbcbook-ch3},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pmid = {10430922},
timestamp = {2011.11.30},
url = {http://dx.doi.org/10.1073/pnas.96.16.9212}
}
@article{Perou2000Molecular,
author = {Perou, C M. and S{\o}rlie, T. and Eisen, M. B. and van de Rijn, M.
and Jeffrey, S. S. and Rees, C. A. and Pollack, J. R. and Ross, D.
T. and Johnsen, H. and Akslen, L. A. and Fluge, O. and Pergamenschikov,
A. and Williams, C. and Zhu, S. X. and L{\o}nning, P. E. and B{\o}rresen-Dale,
A. L. and Brown, P. O. and Botstein, D.},
title = {Molecular portraits of human breast tumours},
journal = {Nature},
year = {2000},
volume = {406},
pages = {747--752},
number = {6797},
month = {Aug},
abstract = {Human breast tumours are diverse in their natural history and in their
responsiveness to treatments. Variation in transcriptional programs
accounts for much of the biological diversity of human cells and
tumours. In each cell, signal transduction and regulatory systems
transduce information from the cell's identity to its environmental
status, thereby controlling the level of expression of every gene
in the genome. Here we have characterized variation in gene expression
patterns in a set of 65 surgical specimens of human breast tumours
from 42 different individuals, using complementary DNA microarrays
representing 8,102 human genes. These patterns provided a distinctive
molecular portrait of each tumour. Twenty of the tumours were sampled
twice, before and after a 16-week course of doxorubicin chemotherapy,
and two tumours were paired with a lymph node metastasis from the
same patient. Gene expression patterns in two tumour samples from
the same individual were almost always more similar to each other
than either was to any other sample. Sets of co-expressed genes were
identified for which variation in messenger RNA levels could be related
to specific features of physiological variation. The tumours could
be classified into subtypes distinguished by pervasive differences
in their gene expression patterns.},
doi = {10.1038/35021093},
pdf = {../local/Perou2000Molecular.pdf},
file = {Perou2000Molecular.pdf:Perou2000Molecular.pdf:PDF},
institution = {Department of Genetics, Stanford University School of Medicine, California
94305, USA.},
keywords = {breastcancer, csbcbook, csbcbook-ch3},
owner = {jp},
pmid = {10963602},
timestamp = {2009.02.04},
url = {http://dx.doi.org/10.1038/35021093}
}
@article{Perrin2003Gene,
author = {Perrin, B.E. and Ralaivola, L. and Mazurie, A. and Bottani, S. and
Mallet, J. and d'Alche-Buc, F.},
title = {Gene networks inference using dynamic Bayesian networks},
journal = {Bioinformatics},
year = {2003},
volume = {19},
pages = {ii138--ii148},
number = {suppl 2},
publisher = {Oxford Univ Press}
}
@article{Perry2007Diet,
author = {George H Perry and Nathaniel J Dominy and Katrina G Claw and Arthur
S Lee and Heike Fiegler and Richard Redon and John Werner and Fernando
A Villanea and Joanna L Mountain and Rajeev Misra and Nigel P Carter
and Charles Lee and Anne C Stone},
title = {Diet and the evolution of human amylase gene copy number variation.},
journal = {Nat Genet},
year = {2007},
volume = {39},
pages = {1256--1260},
number = {10},
month = {Oct},
abstract = {Starch consumption is a prominent characteristic of agricultural societies
and hunter-gatherers in arid environments. In contrast, rainforest
and circum-arctic hunter-gatherers and some pastoralists consume
much less starch. This behavioral variation raises the possibility
that different selective pressures have acted on amylase, the enzyme
responsible for starch hydrolysis. We found that copy number of the
salivary amylase gene (AMY1) is correlated positively with salivary
amylase protein level and that individuals from populations with
high-starch diets have, on average, more AMY1 copies than those with
traditionally low-starch diets. Comparisons with other loci in a
subset of these populations suggest that the extent of AMY1 copy
number differentiation is highly unusual. This example of positive
selection on a copy number-variable gene is, to our knowledge, one
of the first discovered in the human genome. Higher AMY1 copy numbers
and protein levels probably improve the digestion of starchy foods
and may buffer against the fitness-reducing effects of intestinal
disease.},
doi = {10.1038/ng2123},
institution = {School of Human Evolution and Social Change, Arizona State University,
Tempe, Arizona 85287, USA.},
keywords = {Animals; Diet; Evolution, Molecular; Gene Dosage; Genetic Variation;
Humans; Starch, metabolism; alpha-Amylases, genetics},
language = {eng},
medline-pst = {ppublish},
owner = {philippe},
pii = {ng2123},
pmid = {17828263},
timestamp = {2010.08.01},
url = {http://dx.doi.org/10.1038/ng2123}
}
@article{Perry1992Database,
author = {N. Perry and V. J. van Geerestein},
title = {Database {S}earching on the basis of {T}hree-{D}imensional {S}imilarity
{U}sing the SPERM {P}rogram},
journal = {J Chem Inf Comput Sci},
year = {1992},
volume = {32},
pages = {607-616},
owner = {mahe},
timestamp = {2006.08.22}
}
@article{Peters2006community,
author = {Bjoern Peters and Huynh-Hoa Bui and Sune Frankild and Morten Nielson
and Claus Lundegaard and Emrah Kostem and Derek Basch and Kasper
Lamberth and Mikkel Harndahl and Ward Fleri and Stephen S Wilson
and John Sidney and Ole Lund and Soren Buus and Alessandro Sette},
title = {A community resource benchmarking predictions of peptide binding
to {MHC-I} molecules.},
journal = {PLoS Comput. Biol.},
year = {2006},
volume = {2},
pages = {e65},
number = {6},
month = {Jun},
abstract = {Recognition of peptides bound to major histocompatibility complex
(MHC) class I molecules by T lymphocytes is an essential part of
immune surveillance. Each MHC allele has a characteristic peptide
binding preference, which can be captured in prediction algorithms,
allowing for the rapid scan of entire pathogen proteomes for peptide
likely to bind MHC. Here we make public a large set of 48,828 quantitative
peptide-binding affinity measurements relating to 48 different mouse,
human, macaque, and chimpanzee MHC class I alleles. We use this data
to establish a set of benchmark predictions with one neural network
method and two matrix-based prediction methods extensively utilized
in our groups. In general, the neural network outperforms the matrix-based
predictions mainly due to its ability to generalize even on a small
amount of data. We also retrieved predictions from tools publicly
available on the internet. While differences in the data used to
generate these predictions hamper direct comparisons, we do conclude
that tools based on combinatorial peptide libraries perform remarkably
well. The transparent prediction evaluation on this dataset provides
tool developers with a benchmark for comparison of newly developed
prediction methods. In addition, to generate and evaluate our own
prediction methods, we have established an easily extensible web-based
prediction framework that allows automated side-by-side comparisons
of prediction methods implemented by experts. This is an advance
over the current practice of tool developers having to generate reference
predictions themselves, which can lead to underestimating the performance
of prediction methods they are not as familiar with as their own.
The overall goal of this effort is to provide a transparent prediction
evaluation allowing bioinformaticians to identify promising features
of prediction methods and providing guidance to immunologists regarding
the reliability of prediction tools.},
doi = {10.1371/journal.pcbi.0020065},
keywords = {Animals; Databases, Factual; HLA Antigens; Histocompatibility Antigens
Class I; Humans; Inhibitory Concentration 50; Macaca; Mice; Neural
Networks (Computer); Pan troglodytes; Peptides; ROC Curve; Software},
owner = {laurent},
pii = {06-PLCB-RA-0058R2},
pmid = {16789818},
timestamp = {2007.01.30},
url = {http://dx.doi.org/10.1371/journal.pcbi.0020065}
}
@article{Peters2005Generating,
author = {Bjoern Peters and Alessandro Sette},
title = {Generating quantitative models describing the sequence specificity
of biological processes with the stabilized matrix method.},
journal = {BMC Bioinformatics},
year = {2005},
volume = {6},
pages = {132},
abstract = {BACKGROUND: Many processes in molecular biology involve the recognition
of short sequences of nucleic-or amino acids, such as the binding
of immunogenic peptides to major histocompatibility complex (MHC)
molecules. From experimental data, a model of the sequence specificity
of these processes can be constructed, such as a sequence motif,
a scoring matrix or an artificial neural network. The purpose of
these models is two-fold. First, they can provide a summary of experimental
results, allowing for a deeper understanding of the mechanisms involved
in sequence recognition. Second, such models can be used to predict
the experimental outcome for yet untested sequences. In the past
we reported the development of a method to generate such models called
the Stabilized Matrix Method (SMM). This method has been successfully
applied to predicting peptide binding to MHC molecules, peptide transport
by the transporter associated with antigen presentation (TAP) and
proteasomal cleavage of protein sequences. RESULTS: Herein we report
the implementation of the SMM algorithm as a publicly available software
package. Specific features determining the type of problems the method
is most appropriate for are discussed. Advantageous features of the
package are: (1) the output generated is easy to interpret, (2) input
and output are both quantitative, (3) specific computational strategies
to handle experimental noise are built in, (4) the algorithm is designed
to effectively handle bounded experimental data, (5) experimental
data from randomized peptide libraries and conventional peptides
can easily be combined, and (6) it is possible to incorporate pair
interactions between positions of a sequence. CONCLUSION: Making
the SMM method publicly available enables bioinformaticians and experimental
biologists to easily access it, to compare its performance to other
prediction methods, and to extend it to other applications.},
doi = {10.1186/1471-2105-6-132},
keywords = {Algorithms; Amino Acid Sequence; Biology; Computational Biology; Computer
Simulation; Data Interpretation, Statistical; Databases, Protein;
Models, Biological; Models, Statistical; Neural Networks (Computer);
Peptide Library; Peptides; Programming Languages; Prote; Sensitivity
and Specificity; Software; in Binding},
owner = {laurent},
pii = {1471-2105-6-132},
pmid = {15927070},
timestamp = {2007.07.12},
url = {http://dx.doi.org/10.1186/1471-2105-6-132}
}
@inproceedings{Pfeifer2008Multiple,
author = {Pfeifer, Nico and Kohlbacher, Oliver},
title = {Multiple Instance Learning Allows MHC Class II Epitope Predictions
Across Alleles},
booktitle = {WABI '08: Proceedings of the 8th international workshop on Algorithms
in Bioinformatics},
year = {2008},
pages = {210--221},
address = {Berlin, Heidelberg},
publisher = {Springer-Verlag},
doi = {http://dx.doi.org/10.1007/978-3-540-87361-7_18},
isbn = {978-3-540-87360-0},
location = {Karlsruhe, Germany}
}
@article{Pham2005Support,
author = {Tho Hoan Pham and Kenji Satou and Tu Bao Ho},
title = {Support vector machines for prediction and analysis of beta and gamma-turns
in proteins.},
journal = {J. {B}ioinform. {C}omput. {B}iol.},
year = {2005},
volume = {3},
pages = {343-58},
number = {2},
month = {Apr},
abstract = {Tight turns have long been recognized as one of the three important
features of proteins, together with alpha-helix and beta-sheet. {T}ight
turns play an important role in globular proteins from both the structural
and functional points of view. {M}ore than 90\% tight turns are beta-turns
and most of the rest are gamma-turns. {A}nalysis and prediction of
beta-turns and gamma-turns is very useful for design of new molecules
such as drugs, pesticides, and antigens. {I}n this paper we investigated
two aspects of applying support vector machine ({SVM}), a promising
machine learning method for bioinformatics, to prediction and analysis
of beta-turns and gamma-turns. {F}irst, we developed two {SVM}-based
methods, called {BTSVM} and {GTSVM}, which predict beta-turns and
gamma-turns in a protein from its sequence. {W}hen compared with
other methods, {BTSVM} has a superior performance and {GTSVM} is
competitive. {S}econd, we used {SVM}s with a linear kernel to estimate
the support of amino acids for the formation of beta-turns and gamma-turns
depending on their position in a protein. {O}ur analysis results
are more comprehensive and easier to use than the previous results
in designing turns in proteins.},
keywords = {biosvm},
pii = {S0219720005001089}
}
@article{Pham2003Prediction,
author = {Tho Hoan Pham and Kenji Satou and Tu Bao Ho},
title = {Prediction and analysis of beta-turns in proteins by support vector
machine.},
journal = {Genome {I}nform {S}er {W}orkshop {G}enome {I}nform},
year = {2003},
volume = {14},
pages = {196-205},
abstract = {Tight turn has long been recognized as one of the three important
features of proteins after the alpha-helix and beta-sheet. {T}ight
turns play an important role in globular proteins from both the structural
and functional points of view. {M}ore than 90\% tight turns are beta-turns.
{A}nalysis and prediction of beta-turns in particular and tight turns
in general are very useful for the design of new molecules such as
drugs, pesticides, and antigens. {I}n this paper, we introduce a
support vector machine ({SVM}) approach to prediction and analysis
of beta-turns. {W}e have investigated two aspects of applying {SVM}
to the prediction and analysis of beta-turns. {F}irst, we developed
a new {SVM} method, called {BTSVM}, which predicts beta-turns of
a protein from its sequence. {T}he prediction results on the dataset
of 426 non-homologous protein chains by sevenfold cross-validation
technique showed that our method is superior to the other previous
methods. {S}econd, we analyzed how amino acid positions support (or
prevent) the formation of beta-turns based on the "multivariable"
classification model of a linear {SVM}. {T}his model is more general
than the other ones of previous statistical methods. {O}ur analysis
results are more comprehensive and easier to use than previously
published analysis results.},
keywords = {biosvm}
}
@article{Philippi2009BMCSysBio,
author = {Philippi, N. and Walter, D. and Schlatter, R. and Ferreira, K. and
Ederer, M. and Sawodny, O. and Timmer, J. and Borner, C. and Dandekar,
T.},
title = {Modeling system states in liver cells: survival, apoptosis and their
modifications in response to viral infection},
journal = {BMC Syst Biol},
year = {2009},
volume = {3},
pages = {97},
abstract = {BACKGROUND: The decision pro- or contra apoptosis is complex, involves
a number of different inputs, and is central for the homeostasis
of an individual cell as well as for the maintenance and regeneration
of the complete organism. RESULTS: This study centers on Fas ligand
(FasL)-mediated apoptosis, and a complex and internally strongly
linked network is assembled around the central FasL-mediated apoptosis
cascade. Different bioinformatical techniques are employed and different
crosstalk possibilities including the integrin pathway are considered.
This network is translated into a Boolean network (74 nodes, 108
edges). System stability is dynamically sampled and investigated
using the software SQUAD. Testing a number of alternative crosstalk
possibilities and networks we find that there are four stable system
states, two states comprising cell survival and two states describing
apoptosis by the intrinsic and the extrinsic pathways, respectively.
The model is validated by comparing it to experimental data from
kinetics of cytochrome c release and caspase activation in wildtype
and Bid knockout cells grown on different substrates. Pathophysiological
modifications such as input from cytomegalovirus proteins M36 and
M45 again produces output behavior that well agrees with experimental
data. CONCLUSION: A network model for apoptosis and crosstalk in
hepatocytes shows four different system states and reproduces a number
of different conditions around apoptosis including effects of different
growth substrates and viral infections. It produces semi-quantitative
predictions on the activity of individual nodes, agreeing with experimental
data. The model (SBML format) and all data are available for further
predictions and development.},
keywords = {csbcbook}
}
@article{Philips1962A,
author = {D. L. Philips},
title = {A technique for the numerical solution of certain integral equations
of the first kind},
journal = {J. Assoc. Comput. Mach.},
year = {1962},
volume = {9},
pages = {84--97}
}
@article{Picard2005statistical,
author = {Picard, F. and Robin, S. and Lavielle, M. and Vaisse, C. and Daudin,
J.-J.},
title = {A statistical approach for array {CGH} data analysis.},
journal = {BMC Bioinformatics},
year = {2005},
volume = {6},
pages = {27},
abstract = {BACKGROUND: Microarray-CGH experiments are used to detect and map
chromosomal imbalances, by hybridizing targets of genomic DNA from
a test and a reference sample to sequences immobilized on a slide.
These probes are genomic DNA sequences (BACs) that are mapped on
the genome. The signal has a spatial coherence that can be handled
by specific statistical tools. Segmentation methods seem to be a
natural framework for this purpose. A CGH profile can be viewed as
a succession of segments that represent homogeneous regions in the
genome whose BACs share the same relative copy number on average.
We model a CGH profile by a random Gaussian process whose distribution
parameters are affected by abrupt changes at unknown coordinates.
Two major problems arise: to determine which parameters are affected
by the abrupt changes (the mean and the variance, or the mean only),
and the selection of the number of segments in the profile. RESULTS:
We demonstrate that existing methods for estimating the number of
segments are not well adapted in the case of array CGH data, and
we propose an adaptive criterion that detects previously mapped chromosomal
aberrations. The performances of this method are discussed based
on simulations and publicly available data sets. Then we discuss
the choice of modeling for array CGH data and show that the model
with a homogeneous variance is adapted to this context. CONCLUSIONS:
Array CGH data analysis is an emerging field that needs appropriate
statistical tools. Process segmentation and model selection provide
a theoretical framework that allows precise biological interpretations.
Adaptive methods for model selection give promising results concerning
the estimation of the number of altered regions on the genome.},
doi = {10.1186/1471-2105-6-27},
pdf = {../local/Picard2005statistical.pdf},
file = {Picard2005statistical.pdf:Picard2005statistical.pdf:PDF},
institution = {Institut National Agronomique Paris-Grignon, UMR INAPG/ENGREF/INRA
MIA 518, Paris, France. picard@inapg.fr},
language = {eng},
medline-pst = {epublish},
owner = {jp},
pii = {1471-2105-6-27},
pmid = {15705208},
timestamp = {2010.05.19},
url = {http://dx.doi.org/10.1186/1471-2105-6-27}
}
@article{Pickett1996Diversity,
author = {Pickett, S. D. and Mason, J. S. and McLay, I. M.},
title = {Diversity profiling and design using 3{D} pharmacophores : {P}harmacophores-{D}erived
{Q}ueries ({PQD})},
journal = {J. Chem. Inf. Comput. Sci.},
year = {1996},
volume = {36},
pages = {1214-1223},
number = {6},
abstract = {The current interest in combinatorial chemistry for lead generation
has necessitated the development of methods for design and evaluation
of the diversity of the resultant compound libraries. Such methods
also have application in selecting diverse sets of compounds for
general screening from corporate databases and in the analysis of
large sets of structures to identify common patterns. In this paper
we describe a novel methodology for calculating diversity and identifying
common features based on the three-point pharmacophores expressed
by a compound.1 The method has been implemented within the environment
of the Chem-X molecular modeling package (ChemDBS-3D), using a systematic
analysis of 3D distance space with three point combinations of six
pharmacophoric groups. The strategy used to define the pharmacophores
is discussed, including an in-house developed atom type parameterization.
The method is compared with the related approach being developed
into the ChemDiverse module of Chem-X. Results from an analysis of
a large corporate database and examples of combinatorial library
profiling with both methods are presented. The use of 3D pharmacophores
for assessing diversity, and the application of such methods to combinatorial
library design, is discussed.},
doi = {10.1021/ci960039g},
pdf = {../local/Pickett1996Diversity.pdf},
file = {Pickett1996Diversity.pdf:Pickett1996Diversity.pdf:PDF},
keywords = {chemoinformatics},
url = {http://dx.doi.org/10.1021/ci960039g}
}
@article{Piliouras2004Development,
author = {N. Piliouras and I. Kalatzis and N. Dimitropoulos and D. Cavouras},
title = {Development of the cubic least squares mapping linear-kernel support
vector machine classifier for improving the characterization of breast
lesions on ultrasound.},
journal = {Comput {M}ed {I}maging {G}raph},
year = {2004},
volume = {28},
pages = {247-55},
number = {5},
month = {Jul},
abstract = {An efficient classification algorithm is proposed for characterizing
breast lesions. {T}he algorithm is based on the cubic least squares
mapping and the linear-kernel support vector machine ({SVM}({LSM}))
classifier. {U}ltrasound images of 154 confirmed lesions (59 benign
and 52 malignant solid masses, 7 simple cysts, and 32 complicated
cysts) were manually segmented by a physician using a custom developed
software. {T}exture and outline features and the {SVM}({LSM}) algorithm
were used to design a hierarchical tree classification system. {C}lassification
accuracy was 98.7\%, misdiagnosing 1 malignant an 1 benign solid
lesions only. {T}his system may be used as a second opinion tool
to the radiologists.},
doi = {10.1016/j.compmedimag.2004.04.003},
pii = {S0895611104000515},
url = {http://dx.doi.org/10.1016/j.compmedimag.2004.04.003}
}
@article{Pilpel2001Identifying,
author = {Pilpel, Y. and Sudarsanam, P. and Church, G. M.},
title = {Identifying regulatory networks by combinatorial analysis of promoter
elements},
journal = {Nature},
year = {2001},
volume = {29},
pages = {153--159},
pdf = {../local/pilp01.pdf},
file = {pilp01.pdf:local/pilp01.pdf:PDF},
subject = {microarray},
url = {http://www.nature.com/cgi-taf/DynaPage.taf?file=/ng/journal/v29/n2/full/ng724.html&filetype=PDF}
}
@article{Pinkel2005Array,
author = {Pinkel, D. and Albertson, D. G.},
title = {Array comparative genomic hybridization and its applications in cancer},
journal = {Nat. Genet.},
year = {2005},
volume = {37 Suppl},
pages = {S11--S17},
month = {Jun},
abstract = {Alteration in DNA copy number is one of the many ways in which gene
expression and function may be modified. Some variations are found
among normal individuals, others occur in the course of normal processes
in some species and still others participate in causing various disease
states. For example, many defects in human development are due to
gains and losses of chromosomes and chromosomal segments that occur
before or shortly after fertilization, and DNA dosage-alteration
changes occurring in somatic cells are frequent contributors to cancer.
Detecting these aberrations and interpreting them in the context
of broader knowledge facilitates the identification of crucial genes
and pathways involved in biological processes and disease. Over the
past several years, array comparative genomic hybridization has proven
its value for analyzing DNA copy-number variations. Here, we discuss
the state of the art of array comparative genomic hybridization and
its applications in cancer, emphasizing general concepts rather than
specific results.},
doi = {10.1038/ng1569},
pdf = {../local/Pinkel2005Array.pdf},
file = {Pinkel2005Array.pdf:Pinkel2005Array.pdf:PDF},
institution = {Department of Laboratory Medicine and Comprehensive Cancer Center,
University of California San Francisco, Box 0808, San Francisco,
California 94143, USA. pinkel@cc.ucsf.edu},
keywords = {csbcbook, cgh, csbcbook-ch2},
owner = {jp},
pii = {ng1569},
pmid = {15920524},
timestamp = {2009.10.08},
url = {http://dx.doi.org/10.1038/ng1569}
}
@article{Pinkel1998High,
author = {Pinkel, D. and Segraves, R. and Sudar, D. and Clark, S. and Poole,
I. and Kowbel, D. and Collins, C. and Kuo, W. L. and Chen, C. and
Zhai, Y. and Dairkee, S. H. and Ljung, B. M. and Gray, J. W. and
Albertson, D. G.},
title = {High resolution analysis of {DNA} copy number variation using comparative
genomic hybridization to microarrays},
journal = {Nat. Genet.},
year = {1998},
volume = {20},
pages = {207--211},
number = {2},
month = {Oct},
abstract = {Gene dosage variations occur in many diseases. In cancer, deletions
and copy number increases contribute to alterations in the expression
of tumour-suppressor genes and oncogenes, respectively. Developmental
abnormalities, such as Down, Prader Willi, Angelman and Cri du Chat
syndromes, result from gain or loss of one copy of a chromosome or
chromosomal region. Thus, detection and mapping of copy number abnormalities
provide an approach for associating aberrations with disease phenotype
and for localizing critical genes. Comparative genomic hybridization
(CGH) was developed for genome-wide analysis of DNA sequence copy
number in a single experiment. In CGH, differentially labelled total
genomic DNA from a 'test' and a 'reference' cell population are cohybridized
to normal metaphase chromosomes, using blocking DNA to suppress signals
from repetitive sequences. The resulting ratio of the fluorescence
intensities at a location on the 'cytogenetic map', provided by the
chromosomes, is approximately proportional to the ratio of the copy
numbers of the corresponding DNA sequences in the test and reference
genomes. CGH has been broadly applied to human and mouse malignancies.
The use of metaphase chromosomes, however, limits detection of events
involving small regions (of less than 20 Mb) of the genome, resolution
of closely spaced aberrations and linking ratio changes to genomic/genetic
markers. Therefore, more laborious locus-by-locus techniques have
been required for higher resolution studies. Hybridization to an
array of mapped sequences instead of metaphase chromosomes could
overcome the limitations of conventional CGH (ref. 6) if adequate
performance could be achieved. Copy number would be related to the
test/reference fluorescence ratio on the array targets, and genomic
resolution could be determined by the map distance between the targets,
or by the length of the cloned DNA segments. We describe here our
implementation of array CGH. We demonstrate its ability to measure
copy number with high precision in the human genome, and to analyse
clinical specimens by obtaining new information on chromosome 20
aberrations in breast cancer.},
doi = {10.1038/2524},
pdf = {../local/Pinkel1998High.pdf},
file = {Pinkel1998High.pdf:Pinkel1998High.pdf:PDF},
keywords = {cgh, csbcbook},
owner = {franck},
pmid = {9771718},
timestamp = {2007.09.14},
url = {http://dx.doi.org/10.1038/2524}
}
@inproceedings{Platt1999Fast,
author = {J. Platt},
title = {Fast Training of Support Vector Machines using Sequential Minimal
Optimization},
booktitle = {Advances in Kernel Methods - Support Vector Learning},
year = {1999},
editor = {B. Schölkopf and C. Burges and A. Smola},
pages = {185-208},
publisher = {MIT Press, Cambridge, MA, USA},
keywords = {kernel-theory},
owner = {mahe},
timestamp = {2006.08.31}
}
@article{Plewczyski2005support,
author = {Dariusz Plewczynski and Adrian Tkacz and Adam Godzik and Leszek Rychlewski},
title = {A support vector machine approach to the identification of phosphorylation
sites.},
journal = {Cell {M}ol {B}iol {L}ett},
year = {2005},
volume = {10},
pages = {73-89},
number = {1},
abstract = {We describe a bioinformatics tool that can be used to predict the
position of phosphorylation sites in proteins based only on sequence
information. {T}he method uses the support vector machine ({SVM})
statistical learning theory. {T}he statistical models for phosphorylation
by various types of kinases are built using a dataset of short (9-amino
acid long) sequence fragments. {T}he sequence segments are dissected
around post-translationally modified sites of proteins that are on
the current release of the {S}wiss-{P}rot database, and that were
experimentally confirmed to be phosphorylated by any kinase. {W}e
represent them as vectors in a multidimensional abstract space of
short sequence fragments. {T}he prediction method is as follows.
{F}irst, a given query protein sequence is dissected into overlapping
short segments. {A}ll the fragments are then projected into the multidimensional
space of sequence fragments via a collection of different representations.
{T}hose points are classified with pre-built statistical models (the
{SVM} method with linear, polynomial and radial kernel functions)
either as phosphorylated or inactive ones. {T}he resulting list of
plausible sites for phosphorylation by various types of kinases in
the query protein is returned to the user. {T}he efficiency of the
method for each type of phosphorylation is estimated using leave-one-out
tests and presented here. {T}he sensitivities of the models can reach
over 70\%, depending on the type of kinase. {T}he additional information
from profile representations of short sequence fragments helps in
gaining a higher degree of accuracy in some phosphorylation types.
{T}he further development of an automatic phosphorylation site annotation
predictor based on our algorithm should yield a significant improvement
when using statistical algorithms in order to quantify the results.},
pdf = {../local/Plewczyski2005support.pdf},
file = {Plewczyski2005support.pdf:local/Plewczyski2005support.pdf:PDF},
keywords = {biosvm}
}
@article{Plewczynski2005AutoMotif,
author = {Dariusz Plewczynski and Adrian Tkacz and Lucjan Stanislaw Wyrwicz
and Leszek Rychlewski},
title = {Auto{M}otif server: prediction of single residue post-translational
modifications in proteins.},
journal = {Bioinformatics},
year = {2005},
volume = {21},
pages = {2525-7},
number = {10},
month = {May},
abstract = {The {A}uto{M}otif {S}erver allows for identification of post-translational
modification ({PTM}) sites in proteins based only on local sequence
information. {T}he local sequence preferences of short segments around
{PTM} residues are described here as linear functional motifs ({LFM}s).
{S}equence models for all types of {PTM}s are trained by support
vector machine on short-sequence fragments of proteins in the current
release of {S}wiss-{P}rot database (phosphorylation by various protein
kinases, sulfation, acetylation, methylation, amidation, etc.). {T}he
accuracy of the identification is estimated using the standard leave-one-out
procedure. {T}he sensitivities for all types of short {LFM}s are
in the range of 70\%. {AVAILABILITY}: {T}he {A}uto{M}otif {S}erver
is available free for academic use at http://automotif.bioinfo.pl/},
doi = {10.1093/bioinformatics/bti333},
pdf = {../local/Plewczynski2005AutoMotif.pdf},
file = {Plewczynski2005AutoMotif.pdf:local/Plewczynski2005AutoMotif.pdf:PDF},
keywords = {biosvm},
pii = {bti333},
url = {http://dx.doi.org/10.1093/bioinformatics/bti333}
}
@article{Pochet2004Systematic,
author = {Pochet, N. and De Smet, F. and Suykens, J. A. K. and De Moor, B.
L. R.},
title = {Systematic benchmarking of microarray data classification: assessing
the role of non-linearity and dimensionality reduction},
journal = {Bioinformatics},
year = {2004},
volume = {20},
pages = {3185-3195},
number = {17},
month = {Nov},
abstract = {Motivation: {M}icroarrays are capable of determining the expression
levels of thousands of genes simultaneously. {I}n combination with
classification methods, this technology can be useful to support
clinical management decisions for individual patients, e.g. in oncology.
{T}he aim of this paper is to systematically benchmark the role of
non-linear versus linear techniques and dimensionality reduction
methods. {R}esults: {A} systematic benchmarking study is performed
by comparing linear versions of standard classification and dimensionality
reduction techniques with their non-linear versions based on non-linear
kernel functions with a radial basis function ({RBF}) kernel. {A}
total of 9 binary cancer classification problems, derived from 7
publicly available microarray datasets, and 20 randomizations of
each problem are examined. {C}onclusions: {T}hree main conclusions
can be formulated based on the performances on independent test sets.
(1) {W}hen performing classification with least squares support vector
machines ({LS}-{SVM}s) (without dimensionality reduction), {RBF}
kernels can be used without risking too much overfitting. {T}he results
obtained with well-tuned {RBF} kernels are never worse and sometimes
even statistically significantly better compared to results obtained
with a linear kernel in terms of test set receiver operating characteristic
and test set accuracy performances. (2) {E}ven for classification
with linear classifiers like {LS}-{SVM} with linear kernel, using
regularization is very important. (3) {W}hen performing kernel principal
component analysis (kernel {PCA}) before classification, using an
{RBF} kernel for kernel {PCA} tends to result in overfitting, especially
when using supervised feature selection. {I}t has been observed that
an optimal selection of a large number of features is often an indication
for overfitting. {K}ernel {PCA} with linear kernel gives better results.
{A}vailability: {M}atlab scripts are available on request. {S}upplementary
information: http://www.esat.kuleuven.ac.be/~npochet/{B}ioinformatics/},
doi = {10.1093/bioinformatics/bth383},
pdf = {../local/Pochet2004Systematic.pdf},
file = {Pochet2004Systematic.pdf:local/Pochet2004Systematic.pdf:PDF},
keywords = {biosvm microarray},
owner = {jeanphilippevert},
url = {http://dx.doi.org/10.1093/bioinformatics/bth383}
}
@article{Podani2001Comparable,
author = {J. Podani and Z.N. Oltvai and H. Jeong and B. Tombor and A.-L. Barab{\'a}si
and E. Szathm{\'a}ry},
title = {Comparable system-level organization of {A}rchaea and {E}ukaryotes},
journal = {Nat. {G}enet.},
year = {2001},
volume = {29},
pages = {54--56},
pdf = {../local/poda01.pdf},
file = {poda01.pdf:local/poda01.pdf:PDF},
subject = {bionet},
url = {http://www.nature.com/cgi-taf/DynaPage.taf?file=/ng/journal/v29/n1/full/ng708.html&filetype=PDF}
}
@article{Poggio1998Sparse,
author = {Poggio and Girosi},
title = {A {S}parse {R}epresentation for {F}unction {A}pproximation.},
journal = {Neural {C}omput},
year = {1998},
volume = {10},
pages = {1445-54},
number = {6},
month = {Jul},
abstract = {We derive a new general representation for a function as a linear
combination of local correlation kernels at optimal sparse locations
(and scales) and characterize its relation to principal component
analysis, regularization, sparsity principles, and support vector
machines.},
keywords = {Algorithms, Automated, Biometry, Computers, DNA, Databases, Factual,
Fungal, Fungal Proteins, GTP-Binding Proteins, Gene Expression, Genes,
Learning, Markov Chains, Models, Neural Networks (Computer), Neurological,
Non-P.H.S., Non-U.S. Gov't, Nucleic Acid Hybridization, Open Reading
Frames, P.H.S., Pattern Recognition, Protein, Protein Structure,
Proteins, Reproducibility of Results, Research Support, Saccharomyces
cerevisiae, Sequence Alignment, Sequence Analysis, Software, Statistical,
Tertiary, U.S. Gov't, 9698352}
}
@article{Pollack1999Genome,
author = {Jonathan R. Pollack and Charles M. Perou and Ash A. Alizadeh and
Michael B. Eisen and Alexander Pergamenschikov and Cheryl F. Williams
and Stefanie S. Jeffrey and David Botstein and Patrick O. Brown},
title = {Genome-wide analysis of {DNA} copy-number changes using {cDNA} microarrays},
journal = {Nat. Genet.},
year = {1999},
volume = {23},
pages = {41-46},
keywords = {csbcbook, csbcbook-ch2}
}
@article{Pollack2002Microarray,
author = {Jonathan R Pollack and Therese Sørlie and Charles M Perou and Christian
A Rees and Stefanie S Jeffrey and Per E Lonning and Robert Tibshirani
and David Botstein and Anne-Lise Børresen-Dale and Patrick O Brown},
title = {Microarray analysis reveals a major direct role of DNA copy number
alteration in the transcriptional program of human breast tumors.},
journal = {Proc Natl Acad Sci U S A},
year = {2002},
volume = {99},
pages = {12963--12968},
number = {20},
month = {Oct},
abstract = {Genomic DNA copy number alterations are key genetic events in the
development and progression of human cancers. Here we report a genome-wide
microarray comparative genomic hybridization (array CGH) analysis
of DNA copy number variation in a series of primary human breast
tumors. We have profiled DNA copy number alteration across 6,691
mapped human genes, in 44 predominantly advanced, primary breast
tumors and 10 breast cancer cell lines. While the overall patterns
of DNA amplification and deletion corroborate previous cytogenetic
studies, the high-resolution (gene-by-gene) mapping of amplicon boundaries
and the quantitative analysis of amplicon shape provide significant
improvement in the localization of candidate oncogenes. Parallel
microarray measurements of mRNA levels reveal the remarkable degree
to which variation in gene copy number contributes to variation in
gene expression in tumor cells. Specifically, we find that 62\% of
highly amplified genes show moderately or highly elevated expression,
that DNA copy number influences gene expression across a wide range
of DNA copy number alterations (deletion, low-, mid- and high-level
amplification), that on average, a 2-fold change in DNA copy number
is associated with a corresponding 1.5-fold change in mRNA levels,
and that overall, at least 12\% of all the variation in gene expression
among the breast tumors is directly attributable to underlying variation
in gene copy number. These findings provide evidence that widespread
DNA copy number alteration can lead directly to global deregulation
of gene expression, which may contribute to the development or progression
of cancer.},
doi = {10.1073/pnas.162471999},
institution = {Departments of Pathology, Genetics, Surgery, Health Research and
Policy, and Biochemistry, and Howard Hughes Medical Institute, Stanford
University School of Medicine, Stanford, CA 94305, USA. pollack1@stanford.edu},
keywords = {Breast Neoplasms, genetics; Chromosome Aberrations; Disease Progression;
Gene Dosage; Genome; Humans; Oligonucleotide Array Sequence Analysis;
RNA, Messenger, metabolism; Transcription, Genetic; Tumor Cells,
Cultured},
language = {eng},
medline-pst = {ppublish},
owner = {philippe},
pii = {162471999},
pmid = {12297621},
timestamp = {2011.06.03},
url = {http://dx.doi.org/10.1073/pnas.162471999}
}
@article{Polonik1997Minimum,
author = {W. Polonik},
title = {Minimum volume sets and generalized quantile processes},
journal = {Stochastic {P}rocesses and their {A}pplications},
year = {1997},
volume = {69},
pages = {1-24},
publisher = {Elsevier}
}
@article{Polonik1995Measuring,
author = {W. Polonik},
title = {Measuring {M}ass {C}oncentrations and {E}stimating {D}ensity {C}ontour
{C}lusters-{A}n {E}xcess {M}ass {A}pproach},
journal = {Ann. {S}tat.},
year = {1995},
volume = {23},
pages = {855-881},
number = {3},
pdf = {../local/Polonik1995Measuring.pdf},
file = {Polonik1995Measuring.pdf:local/Polonik1995Measuring.pdf:PDF},
url = {http://links.jstor.org/sici?sici=0090-5364%28199506%2923%3A3%3C855%3AMMCAED%3E2.0.CO%3B2-K}
}
@article{Pontil1998Properties,
author = {M. Pontil and A. Verri},
title = {Properties of support vector machines.},
journal = {Neural {C}omput},
year = {1998},
volume = {10},
pages = {955-74},
number = {4},
month = {May},
abstract = {Support vector machines ({SVM}s) perform pattern recognition between
two point classes by finding a decision surface determined by certain
points of the training set, termed support vectors ({SV}). {T}his
surface, which in some feature space of possibly infinite dimension
can be regarded as a hyperplane, is obtained from the solution of
a problem of quadratic programming that depends on a regularization
parameter. {I}n this article, we study some mathematical properties
of support vectors and show that the decision surface can be written
as the sum of two orthogonal terms, the first depending on only the
margin vectors (which are {SV}s lying on the margin), the second
proportional to the regularization parameter. {F}or almost all values
of the parameter, this enables us to predict how the decision surface
varies for small parameter changes. {I}n the special but important
case of feature space of finite dimension m, we also show that m
+ 1 {SV}s are usually sufficient to determine the decision surface
fully. {F}or relatively small m, this latter result leads to a consistent
reduction of the {SV} number.},
keywords = {Algorithms, Artificial Intelligence, Automated, Biometry, Computers,
DNA, Databases, Factual, Fungal, Fungal Proteins, GTP-Binding Proteins,
Gene Expression, Genes, Learning, Linear Models, Markov Chains, Mathematics,
Models, Neural Networks (Computer), Neurological, Non-P.H.S., Non-U.S.
Gov't, Nonlinear Dynamics, Nucleic Acid Hybridization, Open Reading
Frames, P.H.S., Pattern Recognition, Protein, Protein Structure,
Proteins, Reproducibility of Results, Research Support, Saccharomyces
cerevisiae, Sequence Alignment, Sequence Analysis, Software, Statistical,
Tertiary, U.S. Gov't, 9573414}
}
@book{Poor2008Quickest,
title = {Quickest Detection},
publisher = {Cambridge University Press},
year = {2008},
author = {Poor, H. V. and Hadjiliadis, O.},
keywords = {segmentation},
owner = {jp},
timestamp = {2010.05.29}
}
@misc{Popat,
author = {K. Popat and D. H. Greene and J. K. Romberg and D. S. Bloomberg},
title = {Adding Linguistic Constraints to Document Image Decoding: Comparing
the Iterated Complete Path and Stack Algorithms},
year = {2001},
institution = {Xerox Parc}
}
@article{Popova2009Genome,
author = {Tatiana Popova and Elodie Manié and Dominique Stoppa-Lyonnet and
Guillem Rigaill and Emmanuel Barillot and Marc Henri Stern},
title = {Genome Alteration Print (GAP): a tool to visualize and mine complex
cancer genomic profiles obtained by SNP arrays.},
journal = {Genome Biol},
year = {2009},
volume = {10},
pages = {R128},
number = {11},
abstract = {We describe a method for automatic detection of absolute segmental
copy numbers and genotype status in complex cancer genome profiles
measured with single-nucleotide polymorphism (SNP) arrays. The method
is based on pattern recognition of segmented and smoothed copy number
and allelic imbalance profiles. Assignments were verified by DNA
indexes of primary tumors and karyotypes of cell lines. The method
performs well even for poor-quality data, low tumor content, and
highly rearranged tumor genomes.},
doi = {10.1186/gb-2009-10-11-r128},
institution = {Centre de Recherche, Institut Curie, 26 rue d'Ulm, Paris, 75248,
France. tatiana.popova@curie.fr},
keywords = {Allelic Imbalance; Automation; Breast Neoplasms, genetics; Gene Expression
Profiling; Gene Expression Regulation, Neoplastic; Genome; Genomics;
Genotype; Homozygote; Humans; Karyotyping; Loss of Heterozygosity;
Models, Genetic; Ploidies; Polymorphism, Single Nucleotide},
language = {eng},
medline-pst = {ppublish},
owner = {philippe},
pii = {gb-2009-10-11-r128},
pmid = {19903341},
timestamp = {2010.08.01},
url = {http://dx.doi.org/10.1186/gb-2009-10-11-r128}
}
@article{Popovici2010Effect,
author = {Popovici, V. and Chen, W. and Gallas, B.G. and Hatzis, C. and Shi,
W. and Samuelson, F.W. and Nikolsky, Y. and Tsyganova, M. and Ishkin,
A. and Nikolskaya, T. and others},
title = {Effect of training-sample size and classification difficulty on the
accuracy of genomic predictors},
journal = {Breast Cancer Res},
year = {2010},
volume = {12},
pages = {R5},
number = {1}
}
@article{Portela2010Epigenetic,
author = {Anna Portela and Manel Esteller},
title = {Epigenetic modifications and human disease.},
journal = {Nat Biotechnol},
year = {2010},
volume = {28},
pages = {1057--1068},
number = {10},
month = {Oct},
abstract = {Epigenetics is one of the most rapidly expanding fields in biology.
The recent characterization of a human DNA methylome at single nucleotide
resolution, the discovery of the CpG island shores, the finding of
new histone variants and modifications, and the unveiling of genome-wide
nucleosome positioning maps highlight the accelerating speed of discovery
over the past two years. Increasing interest in epigenetics has been
accompanied by technological breakthroughs that now make it possible
to undertake large-scale epigenomic studies. These allow the mapping
of epigenetic marks, such as DNA methylation, histone modifications
and nucleosome positioning, which are critical for regulating gene
and noncoding RNA expression. In turn, we are learning how aberrant
placement of these epigenetic marks and mutations in the epigenetic
machinery is involved in disease. Thus, a comprehensive understanding
of epigenetic mechanisms, their interactions and alterations in health
and disease, has become a priority in biomedical research.},
doi = {10.1038/nbt.1685},
institution = {Cancer Epigenetics and Biology Program, Bellvitge Biomedical Research
Institute, Barcelona, Catalonia, Spain.},
keywords = {Amino Acid Sequence; Autoimmune Diseases, genetics; DNA Methylation,
genetics; Disease, genetics; Epigenesis, Genetic; Histones, chemistry/metabolism;
Humans; Molecular Sequence Data; Nerve Degeneration, genetics},
language = {eng},
medline-pst = {ppublish},
owner = {philippe},
pii = {nbt.1685},
pmid = {20944598},
timestamp = {2011.06.04},
url = {http://dx.doi.org/10.1038/nbt.1685}
}
@article{Post2008Extensions,
author = {Post, T.M. and Freijer, J.I. and Ploeger, B.A.},
title = {{E}xtensions to the {V}isual {P}redictive {C}heck to facilitate model
performance evaluation.},
journal = {J Pharmacokinet Pharmacodyn},
year = {2008},
volume = {35},
pages = {185-02},
doi = {10.1007/s10928-007-9081-1},
owner = {kb}
}
@article{Prados2004Mining,
author = {Prados, J. and Kalousis, A. and Sanchez, J.C. and Allard, L. and
Carrette, O. and Hilario, M.},
title = {Mining mass spectra for diagnosis and biomarker discovery of cerebral
accidents.},
journal = {Proteomics},
year = {2004},
volume = {4},
pages = {2320-2332},
number = {8},
abstract = {In this paper we try to identify potential biomarkers for early stroke
diagnosis using surface-enhanced laser desorption/ionization mass
spectrometry coupled with analysis tools from machine learning and
data mining. {D}ata consist of 42 specimen samples, i.e., mass spectra
divided in two big categories, stroke and control specimens. {A}mong
the stroke specimens two further categories exist that correspond
to ischemic and hemorrhagic stroke; in this paper we limit our data
analysis to discriminating between control and stroke specimens.
{W}e performed two suites of experiments. {I}n the first one we simply
applied a number of different machine learning algorithms; in the
second one we have chosen the best performing algorithm as it was
determined from the first phase and coupled it with a number of different
feature selection methods. {T}he reason for this was 2-fold, first
to establish whether feature selection can indeed improve performance,
which in our case it did not seem to confirm, but more importantly
to acquire a small list of potentially interesting biomarkers. {O}f
the different methods explored the most promising one was support
vector machines which gave us high levels of sensitivity and specificity.
{F}inally, by analyzing the models constructed by support vector
machines we produced a small set of 13 features that could be used
as potential biomarkers, and which exhibited good performance both
in terms of sensitivity, specificity and model stability.},
doi = {10.1002/pmic.200400857},
pdf = {../local/Prados2004Mining.pdf},
file = {Prados2004Mining.pdf:local/Prados2004Mining.pdf:PDF},
keywords = {biosvm proteomics},
owner = {jeanphilippevert},
url = {http://dx.doi.org/10.1002/pmic.200400857}
}
@article{Prakash2002Fetal,
author = {K. N Bhanu Prakash and A. G. Ramakrishnan and S. Suresh and Teresa
W P Chow},
title = {Fetal lung maturity analysis using ultrasound image features.},
journal = {I{EEE} {T}rans {I}nf {T}echnol {B}iomed},
year = {2002},
volume = {6},
pages = {38-45},
number = {1},
month = {Mar},
abstract = {This pilot study was carried out to find the feasibility of analyzing
the maturity of the fetal lung using ultrasound images. {D}ata were
collected from normal pregnant women at intervals of two weeks from
the gestation age of 24 to 38 weeks. {I}mages were acquired at two
centers located at different geographical locations. {T}he total
data acquired consisted of 750 images of immature and 250 images
of mature class. {A} region of interest of 64 x 64 pixels was used
for extracting the features. {V}arious textural features were computed
from the fetal lung and liver images. {T}he ratios of fetal lung
to liver feature values were investigated as possible indexes for
classifying the images into those from mature (reduced pulmonary
risk) and immature (possible pulmonary risk) lung. {T}he features
used are fractal dimension, lacunarity, and features derived from
the histogram of the images. {T}he following classifiers were used
to classify the fetal lung images as belonging to mature or immature
lung: nearest neighbor, k-nearest neighbor, modified k-nearest neighbor,
multilayer perceptron, radial basis function network, and support
vector machines. {T}he classification accuracy obtained for the testing
set ranges from 73\% to 96\%.}
}
@article{Praz2009CleanEx:,
author = {Viviane Praz and Philipp Bucher},
title = {CleanEx: new data extraction and merging tools based on MeSH term
annotation.},
journal = {Nucleic Acids Res},
year = {2009},
volume = {37},
pages = {D880--D884},
number = {Database issue},
month = {Jan},
abstract = {The CleanEx expression database (http://www.cleanex.isb-sib.ch) provides
access to public gene expression data via unique gene names as well
as via experiments biomedical characteristics. To reach this, a dual
annotation of both sequences and experiments has been generated.
First, the system links official gene symbols to any kind of sequences
used for gene expression measurements (cDNA, Affymetrix, oligonucleotide
arrays, SAGE or MPSS tags, Expressed Sequence Tags or other mRNA
sequences, etc.). For the biomedical annotation, we re-annotate each
experiment from the CleanEx database with the MeSH (Medical Subject
Headings) terms, primarily used by NLM (National Library of Medicine)
for indexing articles for the MEDLINE/PubMED database. This annotation
allows a fast and easy retrieval of expression data with common biological
or medical features. The numerical data can then be exported as matrix-like
tab-delimited text files. Data can be extracted from either one dataset
or from heterogeneous datasets.},
doi = {10.1093/nar/gkn878},
institution = {ISREC, Swiss Institute of Bioinformatics, Boveresses 155, Epalinges,
VD 1066, Switzerland. viviane.praz@unil.ch},
keywords = {Animals; Chromosome Mapping; Databases, Genetic; Gene Expression Profiling;
Humans; Medical Subject Headings; Mice; Oligonucleotide Array Sequence
Analysis; Software},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {gkn878},
pmid = {19073704},
timestamp = {2011.09.21},
url = {http://dx.doi.org/10.1093/nar/gkn878}
}
@article{Praz2004CleanEx,
author = {Praz, V. and Jagannathan, V. and Bucher, P.},
title = {{CleanEx}: a database of heterogeneous gene expression data based
on a consistent gene nomenclature.},
journal = {Nucleic Acids Res.},
year = {2004},
volume = {32},
pages = {D542--D547},
number = {Database issue},
month = {Jan},
abstract = {The main goal of CleanEx is to provide access to public gene expression
data via unique gene names. A second objective is to represent heterogeneous
expression data produced by different technologies in a way that
facilitates joint analysis and cross-data set comparisons. A consistent
and up-to-date gene nomenclature is achieved by associating each
single experiment with a permanent target identifier consisting of
a physical description of the targeted RNA population or the hybridization
reagent used. These targets are then mapped at regular intervals
to the growing and evolving catalogues of human genes and genes from
model organisms. The completely automatic mapping procedure relies
partly on external genome information resources such as UniGene and
RefSeq. The central part of CleanEx is a weekly built gene index
containing cross-references to all public expression data already
incorporated into the system. In addition, the expression target
database of CleanEx provides gene mapping and quality control information
for various types of experimental resource, such as cDNA clones or
Affymetrix probe sets. The web-based query interfaces offer access
to individual entries via text string searches or quantitative expression
criteria. CleanEx is accessible at: http://www.cleanex.isb-sib.ch/.},
doi = {10.1093/nar/gkh107},
pdf = {../local/Praz2004CleanEx.pdf},
file = {Praz2004CleanEx.pdf:Praz2004CleanEx.pdf:PDF},
institution = {Swiss Insitiute of Bioinformatics, Ch. des Boveresses 155, 1066 Epalinges
s/Lausanne, Switzerland.},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {32/suppl_1/D542},
pmid = {14681477},
timestamp = {2011.09.21},
url = {http://dx.doi.org/10.1093/nar/gkh107}
}
@article{Prill2005PlosBiol,
author = {Robert J Prill and Pablo A Iglesias and Andre Levchenko},
title = {Dynamic properties of network motifs contribute to biological network
organization.},
journal = {PLoS Biol},
year = {2005},
volume = {3},
pages = {e343},
number = {11},
month = {Nov},
abstract = {Biological networks, such as those describing gene regulation, signal
transduction, and neural synapses, are representations of large-scale
dynamic systems. Discovery of organizing principles of biological
networks can be enhanced by embracing the notion that there is a
deep interplay between network structure and system dynamics. Recently,
many structural characteristics of these non-random networks have
been identified, but dynamical implications of the features have
not been explored comprehensively. We demonstrate by exhaustive computational
analysis that a dynamical property--stability or robustness to small
perturbations--is highly correlated with the relative abundance of
small subnetworks (network motifs) in several previously determined
biological networks. We propose that robust dynamical stability is
an influential property that can determine the non-random structure
of biological networks.},
doi = {10.1371/journal.pbio.0030343},
institution = {Department of Biomedical Engineering, Johns Hopkins University, Baltimore,
Maryland, USA.},
keywords = {Animals; Caenorhabditis elegans, physiology; Computational Biology,
methods; Computer Simulation; Drosophila melanogaster, physiology;
Escherichia coli, physiology; Models, Biological; Nerve Net; Saccharomyces
cerevisiae, physiology; Signal Transduction; Statistics as Topic;
Systems Theory; Transcription, Genetic},
language = {eng},
medline-pst = {ppublish},
owner = {Andrei Zinovyev},
pii = {05-PLBI-RA-0233R2},
pmid = {16187794},
timestamp = {2011.04.08},
url = {http://dx.doi.org/10.1371/journal.pbio.0030343}
}
@article{Puig2001tandem,
author = {O. Puig and F. Caspary and G. Rigaut and B. Rutz and E. Bouveret
and E. Bragado-Nilsson and M. Wilm and B. Séraphin},
title = {The tandem affinity purification (TAP) method: a general procedure
of protein complex purification.},
journal = {Methods},
year = {2001},
volume = {24},
pages = {218--229},
number = {3},
month = {Jul},
abstract = {Identification of components present in biological complexes requires
their purification to near homogeneity. Methods of purification vary
from protein to protein, making it impossible to design a general
purification strategy valid for all cases. We have developed the
tandem affinity purification (TAP) method as a tool that allows rapid
purification under native conditions of complexes, even when expressed
at their natural level. Prior knowledge of complex composition or
function is not required. The TAP method requires fusion of the TAP
tag, either N- or C-terminally, to the target protein of interest.
Starting from a relatively small number of cells, active macromolecular
complexes can be isolated and used for multiple applications. Variations
of the method to specifically purify complexes containing two given
components or to subtract undesired complexes can easily be implemented.
The TAP method was initially developed in yeast but can be successfully
adapted to various organisms. Its simplicity, high yield, and wide
applicability make the TAP method a very useful procedure for protein
purification and proteome exploration.},
doi = {10.1006/meth.2001.1183},
institution = {European Molecular Biology Laboratory Meyerhofstrasse 1, Heidelberg,
D-69117, Germany.},
keywords = {Bacterial Proteins; Blotting, Western; DNA, Bacterial; Fungal Proteins;
Genetic Vectors; Methods; Mutation; Polymerase Chain Reaction; Proteins;
Proteome; Ribonucleases; Ribonucleoproteins; Saccharomyces cerevisiae;
Saccharomyces cerevisiae Proteins; Staphylococcus aureus},
owner = {phupe},
pii = {S1046-2023(01)91183-1},
pmid = {11403571},
timestamp = {2010.08.31},
url = {http://dx.doi.org/10.1006/meth.2001.1183}
}
@article{Pushkarev2009Single,
author = {Dmitry Pushkarev and Norma F Neff and Stephen R Quake},
title = {Single-molecule sequencing of an individual human genome.},
journal = {Nat Biotechnol},
year = {2009},
volume = {27},
pages = {847--852},
number = {9},
month = {Sep},
abstract = {Recent advances in high-throughput DNA sequencing technologies have
enabled order-of-magnitude improvements in both cost and throughput.
Here we report the use of single-molecule methods to sequence an
individual human genome. We aligned billions of 24- to 70-bp reads
(32 bp average) to approximately 90\% of the National Center for
Biotechnology Information (NCBI) reference genome, with 28x average
coverage. Our results were obtained on one sequencing instrument
by a single operator with four data collection runs. Single-molecule
sequencing enabled analysis of human genomic information without
the need for cloning, amplification or ligation. We determined approximately
2.8 million single nucleotide polymorphisms (SNPs) with a false-positive
rate of less than 1\% as validated by Sanger sequencing and 99.8\%
concordance with SNP genotyping arrays. We identified 752 regions
of copy number variation by analyzing coverage depth alone and validated
27 of these using digital PCR. This milestone should allow widespread
application of genome sequencing to many aspects of genetics and
human health, including personal genomics.},
doi = {10.1038/nbt.1561},
institution = {Department of Bioengineering, Stanford University and Howard Hughes
Medical Institute, Stanford, California, USA.},
keywords = {Computer Simulation; Genome, Human; Genomics, methods; Humans; Polymorphism,
Single Nucleotide; Reproducibility of Results; Sequence Analysis,
DNA, methods},
language = {eng},
medline-pst = {ppublish},
owner = {philippe},
pii = {nbt.1561},
pmid = {19668243},
timestamp = {2010.07.28},
url = {http://dx.doi.org/10.1038/nbt.1561}
}
@article{Perez-Cruz2005Convergence,
author = {Fernando Pérez-Cruz and Carlos Bousoño-Calzón and Antonio Artés-RodrÃguez},
title = {Convergence of the {IRWLS} {P}rocedure to the {S}upport {V}ector
{M}achine {S}olution.},
journal = {Neural {C}omput},
year = {2005},
volume = {17},
pages = {7-18},
number = {1},
month = {Jan},
abstract = {An iterative reweighted least squares ({IRWLS}) procedure recently
proposed is shown to converge to the support vector machine solution.
{T}he convergence to a stationary point is ensured by modifying the
original {IRWLS} procedure.},
keywords = {80 and over, Aged, Algorithms, Amino Acids, Animals, Area Under Curve,
Automated, Brain Chemistry, Brain Neoplasms, Comparative Study, Computer-Assisted,
Cross-Sectional Studies, Decision Trees, Diagnosis, Diagnostic Imaging,
Diagnostic Techniques, Discriminant Analysis, Evolution, Face, Genetic,
Glaucoma, Humans, Lasers, Least-Squares Analysis, Magnetic Resonance
Imaging, Magnetic Resonance Spectroscopy, Middle Aged, Models, Molecular,
Nerve Fibers, Non-U.S. Gov't, Numerical Analysis, Ophthalmological,
Optic Nerve Diseases, P.H.S., Pattern Recognition, Photic Stimulation,
Protein, ROC Curve, Regression Analysis, Research Support, Retinal
Ganglion Cells, Sensitivity and Specificity, Sequence Analysis, Statistics,
U.S. Gov't, beta-Lactamases, 15779160}
}
@article{Qian2003Prediction,
author = {Qian, J. and Lin, J. and Luscombe, N. M. and Yu, H. and Gerstein,
M.},
title = {Prediction of regulatory networks: genome-wide identification of
transcription factor targets from gene expression data},
journal = {Bioinformatics},
year = {2003},
volume = {19},
pages = {1917-1926},
number = {15},
abstract = {Motivation: {D}efining regulatory networks, linking transcription
factors ({TF}s) to their targets, is a central problem in post-genomic
biology. {O}ne might imagine one could readily determine these networks
through inspection of gene expression data. {H}owever, the relationship
between the expression timecourse of a transcription factor and its
target is not obvious (e.g. simple correlation over the timecourse),
and current analysis methods, such as hierarchical clustering, have
not been very successful in deciphering them. {R}esults: {H}ere we
introduce an approach based on support vector machines ({SVM}s) to
predict the targets of a transcription factor by identifying subtle
relationships between their expression profiles. {I}n particular,
we used {SVM}s to predict the regulatory targets for 36 transcription
factors in the {S}accharomyces cerevisiae genome based on the microarray
expression data from many different physiological conditions. {W}e
trained and tested our {SVM} on a data set constructed to include
a significant number of both positive and negative examples, directly
addressing data imbalance issues. {T}his was non-trivial given that
most of the known experimental information is only for positives.
{O}verall, we found that 63% of our {TF}-target relationships were
confirmed through cross-validation. {W}e further assessed the performance
of our regulatory network identifications by comparing them with
the results from two recent genome-wide {C}h{IP}-chip experiments.
{O}verall, we find the agreement between our results and these experiments
is comparable to the agreement (albeit low) between the two experiments.
{W}e find that this network has a delocalized structure with respect
to chromosomal positioning, with a given transcription factor having
targets spread fairly uniformly across the genome. {A}vailability:
{T}he overall network of the relationships is available on the web
at http://bioinfo.mbb.yale.edu/expression/echipchip},
pdf = {../local/Qian2003Prediction.pdf},
file = {Qian2003Prediction.pdf:local/Qian2003Prediction.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://bioinformatics.oupjournals.org/cgi/content/abstract/19/15/1917}
}
@article{Qian2001Protein,
author = {Qian, J. and Luscombe, N. M. and Gerstein, M.},
title = {Protein {F}old and {F}amily {O}ccurrence in {G}enomes: {P}ower-{L}aw
{B}ehaviour and {E}volutionary {M}odel},
journal = {J. {M}ol. {B}iol.},
year = {2001},
volume = {313},
pages = {673--681},
pdf = {../local/qian01.pdf},
file = {qian01.pdf:local/qian01.pdf:PDF},
subject = {bionet},
url = {http://partslist.org/powerlaw}
}
@article{Qin2004[Automated,
author = {Dong-mei Qin and Zhan-yi Hu and Yong-heng Zhao},
title = {Automated classification of celestial spectra based on support vector
machines},
journal = {Guang {P}u {X}ue {Y}u {G}uang {P}u {F}en {X}i},
year = {2004},
volume = {24},
pages = {507-11},
number = {4},
month = {Apr},
abstract = {The main objective of an automatic recognition system of celestial
objects via their spectra is to classify celestial spectra and estimate
physical parameters automatically. {T}his paper proposes a new automatic
classification method based on support vector machines to separate
non-active objects from active objects via their spectra. {W}ith
low {SNR} and unknown red-shift value, it is difficult to extract
true spectral lines, and as a result, active objects can not be determined
by finding strong spectral lines and the spectral classification
between non-active and active objects becomes difficult. {T}he proposed
method in this paper combines the principal component analysis with
support vector machines, and can automatically recognize the spectra
of active objects with unknown red-shift values from non-active objects.
{I}t finds its applicability in the automatic processing of voluminous
observed data from large sky surveys in astronomy.},
keywords = {80 and over, Adult, Aged, Algorithms, Amino Acids, Animals, Area Under
Curve, Artifacts, Automated, Birefringence, Brain Chemistry, Brain
Neoplasms, Comparative Study, Computer-Assisted, Cornea, Cross-Sectional
Studies, Decision Trees, Diagnosis, Diagnostic Imaging, Diagnostic
Techniques, Discriminant Analysis, Evolution, Face, Female, Genetic,
Glaucoma, Humans, Intraocular Pressure, Lasers, Least-Squares Analysis,
Magnetic Resonance Imaging, Magnetic Resonance Spectroscopy, Male,
Middle Aged, Models, Molecular, Nerve Fibers, Non-U.S. Gov't, Numerical
Analysis, Ophthalmological, Optic Nerve Diseases, Optical Coherence,
P.H.S., Pattern Recognition, Photic Stimulation, Prospective Studies,
Protein, ROC Curve, Regression Analysis, Research Support, Retinal
Ganglion Cells, Sensitivity and Specificity, Sequence Analysis, Statistics,
Tomography, U.S. Gov't, Visual Fields, beta-Lactamases, 15766170}
}
@article{Qin2003Kernel,
author = {Qin, J. and Lewis, D. P. and Noble, W. S.},
title = {Kernel hierarchical gene clustering from microarray expression data},
journal = {Bioinformatics},
year = {2003},
volume = {19},
pages = {2097-2104},
number = {16},
abstract = {Motivation: {U}nsupervised analysis of microarray gene expression
data attempts to find biologically significant patterns within a
given collection of expression measurements. {F}or example, hierarchical
clustering can be applied to expression profiles of genes across
multiple experiments, identifying groups of genes that share similiar
expression profiles. {P}revious work using the support vector machine
supervised learning algorithm with microarray data suggests that
higher-order features, such as pairwise and tertiary correlations
across multiple experiments, may provide significant benefit in learning
to recognize classes of co-expressed genes. {R}esults: {W}e describe
a generalization of the hierarchical clustering algorithm that efficiently
incorporates these higher-order features by using a kernel function
to map the data into a high-dimensional feature space. {W}e then
evaluate the utility of the kernel hierarchical clustering algorithm
using both internal and external validation. {T}he experiments demonstrate
that the kernel representation itself is insufficient to provide
improved clustering performance. {W}e conclude that mapping gene
expression data into a high-dimensional feature space is only a good
idea when combined with a learning algorithm, such as the support
vector machine that does not suffer from the curse of dimensionality.
{A}vailability: {S}upplementary data at www.cs.columbia.edu/compbio/hiclust.
{S}oftware source code available by request.},
pdf = {../local/Qin2003Kernel.pdf},
file = {Qin2003Kernel.pdf:local/Qin2003Kernel.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://bioinformatics.oupjournals.org/cgi/content/abstract/19/16/2097}
}
@article{Qin2005Application,
author = {Zhong Qin and Qiang Yu and Jun Li and Zhi-Yi Wu and Bing-Min Hu},
title = {Application of least squares vector machines in modelling water vapor
and carbon dioxide fluxes over a cropland.},
journal = {J {Z}hejiang {U}niv {S}ci},
year = {2005},
volume = {6},
pages = {491-5},
number = {6},
month = {Jun},
abstract = {Least squares support vector machines ({LS}-{SVM}s), a nonlinear kemel
based machine was introduced to investigate the prospects of application
of this approach in modelling water vapor and carbon dioxide fluxes
above a summer maize field using the dataset obtained in the {N}orth
{C}hina {P}lain with eddy covariance technique. {T}he performances
of the {LS}-{SVM}s were compared to the corresponding models obtained
with radial basis function ({RBF}) neural networks. {T}he results
indicated the trained {LS}-{SVM}s with a radial basis function kernel
had satisfactory performance in modelling surface fluxes; its excellent
approximation and generalization property shed new light on the study
on complex processes in ecosystem.},
doi = {10.1631/jzus.2005.B0491},
pdf = {../local/Qin2005Application.pdf},
file = {Qin2005Application.pdf:local/Qin2005Application.pdf:PDF},
url = {http://dx.doi.org/10.1631/jzus.2005.B0491}
}
@article{Qiu2007structural,
author = {Qiu, J. and Hue, J. and Ben-Hur, A. and Vert, J.-P. and Noble, W.
S.},
title = {A structural alignment kernel for protein structures.},
journal = {Bioinformatics},
year = {2007},
volume = {23},
pages = {1090--1098},
number = {9},
month = {May},
abstract = {MOTIVATION: This work aims to develop computational methods to annotate
protein structures in an automated fashion. We employ a support vector
machine (SVM) classifier to map from a given class of structures
to their corresponding structural (SCOP) or functional (Gene Ontology)
annotation. In particular, we build upon recent work describing various
kernels for protein structures, where a kernel is a similarity function
that the classifier uses to compare pairs of structures. RESULTS:
We describe a kernel that is derived in a straightforward fashion
from an existing structural alignment program, MAMMOTH. We find in
our benchmark experiments that this kernel significantly out-performs
a variety of other kernels, including several previously described
kernels. Furthermore, in both benchmarks, classifying structures
using MAMMOTH alone does not work as well as using an SVM with the
MAMMOTH kernel. AVAILABILITY: http://noble.gs.washington.edu/proj/3dkernel},
doi = {10.1093/bioinformatics/btl642},
keywords = {Algorithms; Amino Acid Sequence; Artificial Intelligence; Molecular
Sequence Data; Pattern Recognition, Automated; Proteins; Sequence
Alignment; Sequence Analysis, Protein; Sequence Homology, Amino Acid},
owner = {laurent},
pii = {btl642},
pmid = {17234638},
timestamp = {2007.07.27},
url = {http://dx.doi.org/10.1093/bioinformatics/btl642}
}
@article{Qiu2005computational,
author = {Qiu, S. and Adema, C. M. and Lane, T.},
title = {{A} computational study of off-target effects of {RNA} interference.},
journal = {Nucleic Acids Res.},
year = {2005},
volume = {33},
pages = {1834--1847},
number = {6},
abstract = {RNA interference (RNAi) is an intracellular mechanism for post-transcriptional
gene silencing that is frequently used to study gene function. RNAi
is initiated by short interfering RNA (siRNA) of approximately 21
nt in length, either generated from the double-stranded RNA (dsRNA)
by using the enzyme Dicer or introduced experimentally. Following
association with an RNAi silencing complex, siRNA targets mRNA transcripts
that have sequence identity for destruction. A phenotype resulting
from this knockdown of expression may inform about the function of
the targeted gene. However, 'off-target effects' compromise the specificity
of RNAi if sequence identity between siRNA and random mRNA transcripts
causes RNAi to knockdown expression of non-targeted genes. The complete
off-target effects must be investigated systematically on each gene
in a genome by adjusting a group of parameters, which is too expensive
to conduct experimentally and motivates a study in silico. This computational
study examined the potential for off-target effects of RNAi, employing
the genome and transcriptome sequence data of Homo sapiens, Caenorhabditis
elegans and Schizosaccharomyces pombe. The chance for RNAi off-target
effects proved considerable, ranging from 5 to 80\% for each of the
organisms, when using as parameter the exact identity between any
possible siRNA sequences (arbitrary length ranging from 17 to 28
nt) derived from a dsRNA (range 100-400 nt) representing the coding
sequences of target genes and all other siRNAs within the genome.
Remarkably, high-sequence specificity and low probability for off-target
reactivity were optimally balanced for siRNA of 21 nt, the length
observed mostly in vivo. The chance for off-target RNAi increased
(although not always significantly) with greater length of the initial
dsRNA sequence, inclusion into the analysis of available untranslated
region sequences and allowing for mismatches between siRNA and target
sequences. siRNA sequences from within 100 nt of the 5' termini of
coding sequences had low chances for off-target reactivity. This
may be owing to coding constraints for signal peptide-encoding regions
of genes relative to regions that encode for mature proteins. Off-target
distribution varied along the chromosomes of C.elegans, apparently
owing to the use of more unique sequences in gene-dense regions.
Finally, biological and thermodynamical descriptors of effective
siRNA reduced the number of potential siRNAs compared with those
identified by sequence identity alone, but off-target RNAi remained
likely, with an off-target error rate of approximately 10\%. These
results also suggest a direction for future in vivo studies that
could both help in calibrating true off-target rates in living organisms
and also in contributing evidence toward the debate of whether siRNA
efficacy is correlated with, or independent of, the target molecule.
In summary, off-target effects present a real but not prohibitive
concern that should be considered for RNAi experiments.},
doi = {10.1093/nar/gki324},
keywords = {sirna},
owner = {vert},
pii = {33/6/1834},
pmid = {15800213},
timestamp = {2006.04.27},
url = {http://dx.doi.org/10.1093/nar/gki324}
}
@article{Qu2004Supervised,
author = {Yi Qu and Shizhong Xu},
title = {Supervised cluster analysis for microarray data based on multivariate
{G}aussian mixture.},
journal = {Bioinformatics},
year = {2004},
volume = {20},
pages = {1905-13},
number = {12},
month = {Aug},
abstract = {M{OTIVATION}: {G}rouping genes having similar expression patterns
is called gene clustering, which has been proved to be a useful tool
for extracting underlying biological information of gene expression
data. {M}any clustering procedures have shown success in microarray
gene clustering; most of them belong to the family of heuristic clustering
algorithms. {M}odel-based algorithms are alternative clustering algorithms,
which are based on the assumption that the whole set of microarray
data is a finite mixture of a certain type of distributions with
different parameters. {A}pplication of the model-based algorithms
to unsupervised clustering has been reported. {H}ere, for the first
time, we demonstrated the use of the model-based algorithm in supervised
clustering of microarray data. {RESULTS}: {W}e applied the proposed
methods to real gene expression data and simulated data. {W}e showed
that the supervised model-based algorithm is superior over the unsupervised
method and the support vector machines ({SVM}) method. {AVAILABILITY}:
{T}he program written in the {SAS} language implementing methods
{I}-{III} in this report is available upon request. {T}he software
of {SVM}s is available in the website http://svm.sdsc.edu/cgi-bin/nph-{SVM}submit.cgi},
doi = {10.1093/bioinformatics/bth177},
pdf = {../local/Qu2004Supervised.pdf},
file = {Qu2004Supervised.pdf:local/Qu2004Supervised.pdf:PDF},
pii = {bth177},
url = {http://dx.doi.org/10.1093/bioinformatics/bth177}
}
@article{Quackenbush2002Microarray,
author = {John Quackenbush},
title = {Microarray data normalization and transformation.},
journal = {Nat Genet},
year = {2002},
volume = {32 Suppl},
pages = {496--501},
month = {Dec},
doi = {10.1038/ng1032},
keywords = {Animals; Data Interpretation, Statistical; Forecasting; Gene Expression
Profiling, methods; Humans; Oligonucleotide Array Sequence Analysis,
methods; Research Design},
language = {eng},
medline-pst = {ppublish},
owner = {philippe},
pii = {ng1032},
pmid = {12454644},
timestamp = {2010.08.04},
url = {http://dx.doi.org/10.1038/ng1032}
}
@manual{RCoreTeam2012R,
title = {{R: A Language and Environment for Statistical Computing}},
author = {{R Core Team}},
organization = {{R Foundation for Statistical Computing}},
address = {{Vienna, Austria}},
year = {2012},
note = {{{ISBN} 3-900051-07-0}},
owner = {jp},
timestamp = {2012.07.31},
url = {http://www.R-project.org}
}
@article{Rucker1993Counts,
author = {R\"{u}cker, G. and R\"{u}cker, C.},
title = {Counts of {A}ll {W}alks as {A}tomic and {M}olecular {D}escriptors},
journal = {J {C}hem {I}nf {C}omput {S}ci},
year = {1993},
volume = {33},
pages = {683-695},
keywords = {chemoinformatics},
owner = {mahe},
timestamp = {2006.08.09}
}
@inproceedings{Radulescu2006ECCS,
author = {Radulescu, O. and Gorban, A. and Vakulenko, S. and Zinovyev, A.},
title = {Hierarchies and modules in complex biological systems},
booktitle = {Proceedings of European Conference on Complex Systems, Oxford, UK},
year = {2006},
owner = {Andrei Zinovyev},
timestamp = {2011.04.08}
}
@article{Radulescu2005JBSI,
author = {Radulescu, O and Lagarrigue, S and Siegel, A and Le Borgne, M and
Veber, P},
title = {Topology and static response of interaction networks in molecular
biology. },
journal = {J.{R}.{S}oc.{I}nterface},
year = {2005},
volume = {Published online}
}
@article{Raghava2005Correlation,
author = {Gajendra P S Raghava and Joon H Han},
title = {Correlation and prediction of gene expression level from amino acid
and dipeptide composition of its protein.},
journal = {B{MC} {B}ioinformatics},
year = {2005},
volume = {6},
pages = {59},
number = {1},
month = {Mar},
abstract = {B{ACKGROUND}: {A} large number of papers have been published on analysis
of microarray data with particular emphasis on normalization of data,
detection of differentially expressed genes, clustering of genes
and regulatory network. {O}n other hand there are only few studies
on relation between expression level and composition of nucleotide/protein
sequence, using expression data. {T}here is a need to understand
why particular genes/proteins express more in particular conditions.
{I}n this study, we analyze 3468 genes of {S}accharomyces cerevisiae
obtained from {H}olstege et al., (1998) to understand the relationship
between expression level and amino acid composition. {RESULTS}: {W}e
compute the correlation between expression of a gene and amino acid
composition of its protein. {I}t was observed that some residues
(like {A}la, {G}ly, {A}rg and {V}al) have significant positive correlation
(r > 0.20) and some other residues ({L}ike {A}sp, {L}eu, {A}sn and
{S}er) have negative correlation (r < -0.15) with the expression
of genes. {A} significant negative correlation (r = -0.18) was also
found between length and gene expression. {T}hese observations indicate
the relationship between percent composition and gene expression
level. {T}hus, attempts have been made to develop a {S}upport {V}ector
{M}achine ({SVM}) based method for predicting the expression level
of genes from its protein sequence. {I}n this method the {SVM} is
trained with proteins whose gene expression data is known in a given
condition. {T}hen trained {SVM} is used to predict the gene expression
of other proteins of the same organism in the same condition. {A}
correlation coefficient r = 0.70 was obtained between predicted and
experimentally determined expression of genes, which improves from
r = 0.70 to 0.72 when dipeptide composition was used instead of residue
composition. {T}he method was evaluated using 5-fold cross validation
test. {W}e also demonstrate that amino acid composition information
along with gene expression data can be used for improving the function
classification of proteins. {CONCLUSION}: {T}here is a correlation
between gene expression and amino acid composition that can be used
to predict the expression level of genes up to a certain extent.
{A} web server based on the above strategy has been developed for
calculating the correlation between amino acid composition and gene
expression and prediction of expression level http://kiwi.postech.ac.kr/raghava/lgepred/.
{T}his server will allow users to study the evolution from expression
data.},
doi = {10.1186/1471-2105-6-59},
keywords = {biosvm},
pii = {1471-2105-6-59},
url = {http://dx.doi.org/10.1186/1471-2105-6-59}
}
@article{Rahmann2004Mean,
author = {Rahmann, S. and Gr{\"a}fe, C.},
title = {Mean and variance of the {G}ibbs free energy of oligonucleotides
in the nearest neighbor model under varying conditions.},
journal = {Bioinformatics},
year = {2004},
volume = {20},
pages = {2928-33},
number = {17},
month = {Nov},
abstract = {M{OTIVATION}: {I}n order to assess the stability of {DNA}-{DNA} hybridizations-for
example during {PCR} primer design or oligonucleotide selection for
microarrays-one needs to predict the change in {G}ibbs free energy
{D}elta{G} during hybridization. {T}he nearest neighbor model provides
a good compromise between accuracy and computational simplicity for
this task. {T}o determine optimal combinations of reaction parameters
(temperature, salt concentration, oligonucleotide length and {GC}-content),
one would like to understand how {D}elta{G} depends on all of these
parameters simultaneously. {RESULTS}: {W}e derive analytic results
about the distribution of nearest neighbor {D}elta{G} values for
a {B}ernoulli random sequence model (specified by oligonucleotide
length and average {GC}-content) under given experimental conditions.
{W}e find that the distribution of {D}elta{G} values is approximately
{G}aussian and provide exact formulas for expectation and variance.},
doi = {10.1093/bioinformatics/bth334},
pii = {bth334},
url = {http://dx.doi.org/10.1093/bioinformatics/bth334}
}
@article{Rahnenfuhrer2004,
author = {Rahnenfuhrer, J and Domingues, FS and Maydt, J and Lengauer, T.},
title = {Calculating the statistical significance of changes in pathway activity
from gene expression data},
journal = {Statistical {A}pplications in {G}enetics and {M}olecular {B}iology},
year = {2004},
volume = {3},
pages = {Article 16},
number = {1}
}
@article{Rahnenfuehrer2004Calculating,
author = {Rahnenf{\"u}hrer, J. and Domingues, F. S. and Maydt, J. and Lengauer,
T.},
title = {Calculating the statistical significance of changes in pathway activity
from gene expression data.},
journal = {Stat. Appl. Genet. Mol. Biol.},
year = {2004},
volume = {3},
pages = {Article16},
abstract = {We present a statistical approach to scoring changes in activity of
metabolic pathways from gene expression data. The method identifies
the biologically relevant pathways with corresponding statistical
significance. Based on gene expression data alone, only local structures
of genetic networks can be recovered. Instead of inferring such a
network, we propose a hypothesis-based approach. We use given knowledge
about biological networks to improve sensitivity and interpretability
of findings from microarray experiments. Recently introduced methods
test if members of predefined gene sets are enriched in a list of
top-ranked genes in a microarray study. We improve this approach
by defining scores that depend on all members of the gene set and
that also take pairwise co-regulation of these genes into account.
We calculate the significance of co-regulation of gene sets with
a nonparametric permutation test. On two data sets the method is
validated and its biological relevance is discussed. It turns out
that useful measures for co-regulation of genes in a pathway can
be identified adaptively. We refine our method in two aspects specific
to pathways. First, to overcome the ambiguity of enzyme-to-gene mappings
for a fixed pathway, we introduce algorithms for selecting the best
fitting gene for a specific enzyme in a specific condition. In selected
cases, functional assignment of genes to pathways is feasible. Second,
the sensitivity of detecting relevant pathways is improved by integrating
information about pathway topology. The distance of two enzymes is
measured by the number of reactions needed to connect them, and enzyme
pairs with a smaller distance receive a higher weight in the score
calculation.},
doi = {10.2202/1544-6115.1055},
institution = {Max-Planck-Institute for Informatics, Saarbrücken, Germany. rahnenfj@mpi-sb.mpg.de},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pmid = {16646794},
timestamp = {2011.09.27},
url = {http://dx.doi.org/10.2202/1544-6115.1055}
}
@article{Rain2001protein-protein,
author = {Rain, J.-C. and Selig, L. and De Reuse, H. and Battaglia, V. and
Reverdy, C. and Simon, S. and Lenzen, G. and Petel, F. and Wojcik,
J. and Sch{\"a}chter, V. and Chemama, Y. and Labigne, A. and Legrain,
P.},
title = {The protein-protein interaction map of {H}elicobacter pylori},
journal = {Nature},
year = {2001},
volume = {409},
pages = {211--215},
pdf = {../local/rain01.pdf},
file = {rain01.pdf:local/rain01.pdf:PDF},
subject = {bionetprot},
url = {http://www.nature.com/cgi-taf/DynaPage.taf?file=/nature/journal/v409/n6817/full/409211a0_fs.html&content_filetype=pdf}
}
@article{Rajagopalan2005Inferring,
author = {Rajagopalan, D. and Agarwal, P.},
title = {Inferring pathways from gene lists using a literature-derived network
of biological relationships.},
journal = {Bioinformatics},
year = {2005},
volume = {21},
pages = {788--793},
number = {6},
month = {Mar},
abstract = {MOTIVATION: A number of omic technologies such as transcriptional
profiling, proteomics, literature searches, genetic association,
etc. help in the identification of sets of important genes. A subset
of these genes may act in a coordinated manner, possibly because
they are part of the same biological pathway. Interpreting such gene
lists and relating them to pathways is a challenging task. Databases
of biological relationships between thousands of mammalian genes
can help in deciphering omics data. The relationships between genes
can be assembled into a biological network with each protein as a
node and each relationship as an edge between two proteins (or nodes).
This network may then be searched for subnetworks consisting largely
of interesting genes from the omics experiment. The subset of genes
in the subnetwork along with the web of relationships between them
helps to decipher the underlying pathways. Finding such subnetworks
that maximally include all proteins from the query set but few others
is the focus for this paper. RESULTS: We present a heuristic algorithm
and a scoring function that work well both on simulated data and
on data from known pathways. The scoring function is an extension
of a previous study for a single biological experiment. We use a
simple set of heuristics that provide a more efficient solution than
the simulated annealing method. We find that our method works on
reasonably complex curated networks containing approximately 9000
biological entities (genes and metabolites), and approximately 30,000
biological relationships. We also show that our method can pick up
a pathway signal from a query list including a moderate number of
genes unrelated to the pathway. In addition, we quantify the sensitivity
and specificity of the technique.},
doi = {10.1093/bioinformatics/bti069},
pdf = {../local/Rajagopalan2005Inferring.pdf},
file = {Rajagopalan2005Inferring.pdf:Rajagopalan2005Inferring.pdf:PDF},
institution = {D, 709 Swedeland Road, UW2230, King of Prussia, PA 19406-0939, USA.
dilip_rajagopalan@gsk.com},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {bti069},
pmid = {15509611},
timestamp = {2011.10.08},
url = {http://dx.doi.org/10.1093/bioinformatics/bti069}
}
@inproceedings{Rajwan2000Universal,
author = {Rajwan, D. and Feder, M.},
title = {Universal finite memory machines for coding binary sequences},
booktitle = {Proceedings of the {D}ata {C}ompression {C}onference ({DCC} 2000)},
year = {2000},
pages = {113-122},
abstract = {In this work we consider the problem of universal sequential probability
assignment, under self-information loss, where the machine for performing
the universal probability assignment is constrained to have a finite
memory. {S}equential probability assignment is equivalent to lossless
source coding if we ignore the number of states required to convert
the probability estimate into code bits. {W}e consider both the probabilistic
setting where the sequence is generated by a probabilistic source
(either {B}ernoulli {IID} source or q-th order {M}arkov source),
and the deterministic setting where the sequence is a deterministic
individual sequence. {W}e also consider the case where the universal
machine is deterministic, randomized, time-invariant or time-variant.
{W}e provide in most cases lower bounds and describe finite memory
universal machines whose performance, in terms of the memory size,
is compared to these bounds },
pdf = {../local/Rajwan2000Universal.pdf},
file = {Rajwan2000Universal.pdf:local/Rajwan2000Universal.pdf:PDF},
owner = {vert}
}
@article{Rakotomamonjy2008SimpleMKL,
author = {Rakotomamonjy, A. and Bach, F. and Canu, S. and Grandvalet, Y.},
title = {{SimpleMKL}},
journal = {J. Mach. Learn. Res.},
year = {2008},
volume = {9},
pages = {2491--2521},
owner = {jp},
timestamp = {2008.12.08}
}
@inproceedings{Rakotomamonjy2007More,
author = {Rakotomamonjy, Alain and Bach, Francis and Canu, St\'{e}phane and
Grandvalet, Yves},
title = {More efficiency in multiple kernel learning},
booktitle = {ICML '07: Proceedings of the 24th international conference on Machine
learning},
year = {2007},
pages = {775--782},
address = {New York, NY, USA},
publisher = {ACM},
doi = {http://doi.acm.org/10.1145/1273496.1273594},
isbn = {978-1-59593-793-3},
location = {Corvalis, Oregon}
}
@article{Ralaivola2005Graph,
author = {Ralaivola, L. and Swamidass, S. J. and Saigo, H. and Baldi, P.},
title = {Graph kernels for chemical informatics.},
journal = {Neural {N}etw.},
year = {2005},
volume = {18},
pages = {1093--1110},
number = {8},
month = {Sep},
abstract = {Increased availability of large repositories of chemical compounds
is creating new challenges and opportunities for the application
of machine learning methods to problems in computational chemistry
and chemical informatics. {B}ecause chemical compounds are often
represented by the graph of their covalent bonds, machine learning
methods in this domain must be capable of processing graphical structures
with variable size. {H}ere, we first briefly review the literature
on graph kernels and then introduce three new kernels ({T}animoto,
{M}in{M}ax, {H}ybrid) based on the idea of molecular fingerprints
and counting labeled paths of depth up to d using depth-first search
from each possible vertex. {T}he kernels are applied to three classification
problems to predict mutagenicity, toxicity, and anti-cancer activity
on three publicly available data sets. {T}he kernels achieve performances
at least comparable, and most often superior, to those previously
reported in the literature reaching accuracies of 91.5\% on the {M}utag
dataset, 65-67\% on the {PTC} ({P}redictive {T}oxicology {C}hallenge)
dataset, and 72\% on the {NCI} ({N}ational {C}ancer {I}nstitute)
dataset. {P}roperties and tradeoffs of these kernels, as well as
other proposed kernels that leverage 1{D} or 3{D} representations
of molecules, are briefly discussed.},
doi = {10.1016/j.neunet.2005.07.009},
pdf = {../local/Ralaivola2005Graph.pdf},
file = {Ralaivola2005Graph.pdf:local/Ralaivola2005Graph.pdf:PDF},
keywords = {chemoinformatics},
pii = {S0893-6080(05)00169-3},
url = {http://dx.doi.org/10.1016/j.neunet.2005.07.009}
}
@article{Raliou2010Human,
author = {Mariam Raliou and Marta Grauso and Brice Hoffmann and Claude Nespoulous
and hélène Debat and Bano Singh and Didier Trotier and Jean-Claude
Pernollet and Jean-Pierre Montmayeur and Annick Faurion and Loïc
Briand},
title = {Human genetic polymorphisms in T1R1 an d T1R3 affect their function},
journal = {Chemical Senses},
year = {In Press},
owner = {bricehoffmann},
timestamp = {2010.06.09}
}
@article{Ramaswamy2003molecular,
author = {Ramaswamy, S. and Ross, K. N. and Lander, E. S. and Golub, T. R.},
title = {A molecular signature of metastasis in primary solid tumors.},
journal = {Nat. Genet.},
year = {2003},
volume = {33},
pages = {49--54},
number = {1},
month = {Jan},
abstract = {Metastasis is the principal event leading to death in individuals
with cancer, yet its molecular basis is poorly understood. To explore
the molecular differences between human primary tumors and metastases,
we compared the gene-expression profiles of adenocarcinoma metastases
of multiple tumor types to unmatched primary adenocarcinomas. We
found a gene-expression signature that distinguished primary from
metastatic adenocarcinomas. More notably, we found that a subset
of primary tumors resembled metastatic tumors with respect to this
gene-expression signature. We confirmed this finding by applying
the expression signature to data on 279 primary solid tumors of diverse
types. We found that solid tumors carrying the gene-expression signature
were most likely to be associated with metastasis and poor clinical
outcome (P < 0.03). These results suggest that the metastatic potential
of human tumors is encoded in the bulk of a primary tumor, thus challenging
the notion that metastases arise from rare cells within a primary
tumor that have the ability to metastasize.},
doi = {10.1038/ng1060},
pdf = {../local/Ramaswamy2003molecular.pdf},
file = {Ramaswamy2003molecular.pdf:Ramaswamy2003molecular.pdf:PDF},
institution = {Whitehead Institute/MIT Center for Genome Research, One Kendall Square,
Building 300, Cambridge, Massachusetts 02139, USA.},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {ng1060},
pmid = {12469122},
timestamp = {2011.09.20},
url = {http://dx.doi.org/10.1038/ng1060}
}
@article{Ramaswamy2001Multiclass,
author = {Ramaswamy, S. and Tamayo, P. and Rifkin, R. and Mukherjee, S. and
Yeang, C.H. and Angelo, M. and Ladd, C. and Reich, M. and Latulippe,
E. and Mesirov, J.P. and Poggio, T. and Gerald, W. and Loda, M. and
Lander, E.S. and Golub, T.R.},
title = {Multiclass cancer diagnosis using tumor gene expression signatures},
journal = {Proc. {N}atl. {A}cad. {S}ci. {USA}},
year = {2001},
volume = {98},
pages = {15149-15154},
number = {26},
month = {Dec},
abstract = {The optimal treatment of patients with cancer depends on establishing
accurate diagnoses by using a complex combination of clinical and
histopathological data. {I}n some instances, this task is difficult
or impossible because of atypical clinical presentation or histopathology.
{T}o determine whether the diagnosis of multiple common adult malignancies
could be achieved purely by molecular classification, we subjected
218 tumor samples, spanning 14 common tumor types, and 90 normal
tissue samples to oligonucleotide microarray gene expression analysis.
{T}he expression levels of 16,063 genes and expressed sequence tags
were used to evaluate the accuracy of a multiclass classifier based
on a support vector machine algorithm. {O}verall classification accuracy
was 78%, far exceeding the accuracy of random classification (9%).
{P}oorly differentiated cancers resulted in low-confidence predictions
and could not be accurately classified according to their tissue
of origin, indicating that they are molecularly distinct entities
with dramatically different gene expression patterns compared with
their well differentiated counterparts. {T}aken together, these results
demonstrate the feasibility of accurate, multiclass molecular cancer
classification and suggest a strategy for future clinical implementation
of molecular cancer diagnostics.},
doi = {10.1073/pnas.211566398},
pdf = {../local/Ramaswamy2001Multiclass.pdf},
file = {Ramaswamy2001Multiclass.pdf:local/Ramaswamy2001Multiclass.pdf:PDF},
keywords = {biosvm microarray},
owner = {vert},
url = {http://dx.doi.org/10.1073/pnas.211566398}
}
@article{Rammensee1999SYFPEITHI,
author = {H. Rammensee and J. Bachmann and N. P. Emmerich and O. A. Bachor
and S. Stevanovi\'c},
title = {SYFPEITHI: database for {MHC} ligands and peptide motifs.},
journal = {Immunogenetics},
year = {1999},
volume = {50},
pages = {213--219},
number = {3-4},
month = {Nov},
abstract = {The first version of the major histocompatibility complex (MHC) databank
SYFPEITHI: database for MHC ligands and peptide motifs, is now available
to the general public. It contains a collection of MHC class I and
class II ligands and peptide motifs of humans and other species,
such as apes, cattle, chicken, and mouse, for example, and is continuously
updated. All motifs currently available are accessible as individual
entries. Searches for MHC alleles, MHC motifs, natural ligands, T-cell
epitopes, source proteins/organisms and references are possible.
Hyperlinks to the EMBL and PubMed databases are included. In addition,
ligand predictions are available for a number of MHC allelic products.
The database content is restricted to published data only.},
keywords = {Amino Acid Motifs; Amino Acid Sequence; Animals; Databases, Factual;
Humans; Internet; Ligan; Major Histocompatibility Complex; Molecular
Sequence Data; Research Support, Non-U.S. Gov't; Sequence Homology,
Amino Acid; ds},
owner = {jacob},
pii = {90500213.251},
pmid = {10602881},
timestamp = {2006.08.30}
}
@article{Rammensee1995MHC,
author = {Rammensee, H. G. and Friede, T. and Stevanovi{\'i}c, S.},
title = {{MHC} ligands and peptide motifs: first listing},
journal = {Immunogenetics},
year = {1995},
volume = {41},
pages = {178--228},
number = {4},
keywords = {immunoinformatics},
pmid = {7890324},
timestamp = {2007.01.25}
}
@inproceedings{Ramon2003Expressivity,
author = {Ramon, J. and G\"{a}rtner, T.},
title = {{E}xpressivity versus efficiency of graph kernels},
booktitle = {{P}roceedings of the {F}irst {I}nternational {W}orkshop on {M}ining
{G}raphs, {T}rees and {S}equences},
year = {2003},
editor = {Washio, T. and De Raedt, L.},
pages = {65-74},
keywords = {kernel-theory chemoinformatics},
owner = {mahe},
timestamp = {2006.07.31}
}
@inproceedings{Rangarajan96lagrangian,
author = {A. Rangarajan and E. Mjolsness},
title = {A Lagrangian Relaxation Network for Graph Matching},
booktitle = {IEEE Trans. Neural Networks},
year = {1996},
pages = {4629--4634},
publisher = {IEEE Press}
}
@article{Rangwala2005Profile-based,
author = {Rangwala, H. and Karypis, G.},
title = {Profile-based direct kernels for remote homology detection and fold
recognition.},
journal = {Bioinformatics},
year = {2005},
volume = {21},
pages = {4239--4247},
number = {23},
month = {Dec},
abstract = {MOTIVATION: Protein remote homology detection is a central problem
in computational biology. Supervised learning algorithms based on
support vector machines are currently one of the most effective methods
for remote homology detection. The performance of these methods depends
on how the protein sequences are modeled and on the method used to
compute the kernel function between them. RESULTS: We introduce two
classes of kernel functions that are constructed by combining sequence
profiles with new and existing approaches for determining the similarity
between pairs of protein sequences. These kernels are constructed
directly from these explicit protein similarity measures and employ
effective profile-to-profile scoring schemes for measuring the similarity
between pairs of proteins. Experiments with remote homology detection
and fold recognition problems show that these kernels are capable
of producing results that are substantially better than those produced
by all of the existing state-of-the-art SVM-based methods. In addition,
the experiments show that these kernels, even when used in the absence
of profiles, produce results that are better than those produced
by existing non-profile-based schemes. AVAILABILITY: The programs
for computing the various kernel functions are available on request
from the authors.},
doi = {10.1093/bioinformatics/bti687},
keywords = {biosvm},
owner = {vert},
pii = {bti687},
pmid = {16188929},
timestamp = {2007.08.01},
url = {http://dx.doi.org/10.1093/bioinformatics/bti687}
}
@phdthesis{Rapaport2008Introduction,
author = {Rapaport, F.},
title = {Introduction de la connaissance a priori dans l’\'etude des puces
\`a {ADN}},
school = {Universit\'e Pierre et Marie Curie - Paris 6},
year = {2008},
owner = {jp},
timestamp = {2010.10.12}
}
@article{Rapaport2008Classification,
author = {Rapaport, F. and Barillot, E. and Vert, J.-P.},
title = {Classification of array{CGH} data using fused {SVM}},
journal = {Bioinformatics},
year = {2008},
volume = {24},
pages = {i375--i382},
number = {13},
month = {Jul},
abstract = {MOTIVATION: Array-based comparative genomic hybridization (arrayCGH)
has recently become a popular tool to identify DNA copy number variations
along the genome. These profiles are starting to be used as markers
to improve prognosis or diagnosis of cancer, which implies that methods
for automated supervised classification of arrayCGH data are needed.
Like gene expression profiles, arrayCGH profiles are characterized
by a large number of variables usually measured on a limited number
of samples. However, arrayCGH profiles have a particular structure
of correlations between variables, due to the spatial organization
of bacterial artificial chromosomes along the genome. This suggests
that classical classification methods, often based on the selection
of a small number of discriminative features, may not be the most
accurate methods and may not produce easily interpretable prediction
rules. RESULTS: We propose a new method for supervised classification
of arrayCGH data. The method is a variant of support vector machine
that incorporates the biological specificities of DNA copy number
variations along the genome as prior knowledge. The resulting classifier
is a sparse linear classifier based on a limited number of regions
automatically selected on the chromosomes, leading to easy interpretation
and identification of discriminative regions of the genome. We test
this method on three classification problems for bladder and uveal
cancer, involving both diagnosis and prognosis. We demonstrate that
the introduction of the new prior on the classifier leads not only
to more accurate predictions, but also to the identification of known
and new regions of interest in the genome. AVAILABILITY: All data
and algorithms are publicly available.},
doi = {10.1093/bioinformatics/btn188},
pdf = {../local/Rapaport2008Classification.pdf},
file = {Rapaport2008Classification.pdf:Rapaport2008Classification.pdf:PDF},
institution = {Institut Curie, Centre de Recherche, INSERM, U900, Paris, F-75248
France. franck.rapaport@curie.fr},
keywords = {cgh},
owner = {laurent},
pii = {btn188},
pmid = {18586737},
timestamp = {2008.10.26},
url = {http://dx.doi.org/10.1093/bioinformatics/btn188}
}
@article{Rapaport2007Classification,
author = {Rapaport, F. and Zynoviev, A. and Dutreix, M. and Barillot, E. and
Vert, J.-P.},
title = {Classification of microarray data using gene networks},
journal = {BMC Bioinformatics},
year = {2007},
volume = {8},
pages = {35},
timestamp = {2007.04.12}
}
@article{Rarey1996fast,
author = {M. Rarey and B. Kramer and T. Lengauer and G. Klebe},
title = {{A} fast flexible docking method using an incremental construction
algorithm.},
journal = {J. Mol. Biol.},
year = {1996},
volume = {261},
pages = {470--489},
number = {3},
month = {Aug},
abstract = {We present an automatic method for docking organic ligands into protein
binding sites. The method can be used in the design process of specific
protein ligands. It combines an appropriate model of the physico-chemical
properties of the docked molecules with efficient methods for sampling
the conformational space of the ligand. If the ligand is flexible,
it can adopt a large variety of different conformations. Each such
minimum in conformational space presents a potential candidate for
the conformation of the ligand in the complexed state. Our docking
method samples the conformation space of the ligand on the basis
of a discrete model and uses a tree-search technique for placing
the ligand incrementally into the active site. For placing the first
fragment of the ligand into the protein, we use hashing techniques
adapted from computer vision. The incremental construction algorithm
is based on a greedy strategy combined with efficient methods for
overlap detection and for the search of new interactions. We present
results on 19 complexes of which the binding geometry has been crystallographically
determined. All considered ligands are docked in at most three minutes
on a current workstation. The experimentally observed binding mode
of the ligand is reproduced with 0.5 to 1.2 A rms deviation. It is
almost always found among the highest-ranking conformations computed.},
doi = {10.1006/jmbi.1996.0477},
keywords = {Aldehyde Reductase, Algorithms, Amiloride, Aminoimidazole Carboxamide,
Animals, Arabinose, Automation, Binding Sites, Carbonic Anhydrases,
Computational Biology, Computer Simulation, Concanavalin A, Crystallography,
Databases, Drug Design, Drug Evaluation, Enzyme Inhibitors, Factual,
Folic Acid, Folic Acid Antagonists, Fructose-Bisphosphatase, Humans,
Internet, Ligands, Methotrexate, Models, Molecular, Non-U.S. Gov't,
Pancreatic Elastase, Pentamidine, Pliability, Point Mutation, Preclinical,
Protein Binding, Protein Conformation, Proteins, Reproducibility
of Results, Research Support, Ribonucleosides, Software, Tetrahydrofolate
Dehydrogenase, Thermolysin, Time Factors, Trypsin, X-Ray, 8780787},
owner = {mahe},
pii = {S0022-2836(96)90477-5},
pmid = {8780787},
timestamp = {2006.09.05},
url = {http://dx.doi.org/10.1006/jmbi.1996.0477}
}
@article{Rarey1996Placement,
author = {M. Rarey and S. Wefing and T. Lengauer},
title = {Placement of medium-sized molecular fragments into active sites of
proteins.},
journal = {J Comput Aided Mol Des},
year = {1996},
volume = {10},
pages = {41--54},
number = {1},
month = {Feb},
abstract = {We present an algorithm for placing molecular fragments into the active
site of a receptor. A molecular fragment is defined as a connected
part of a molecule containing only complete ring systems. The algorithm
is part of a docking tool, called FLEXX, which is currently under
development at GMD. The overall goal is to provide means of automatically
computing low-energy conformations of the ligand within the active
site, with an accuracy approaching the limitations of experimental
methods for resolving molecular structures and within a run time
that allows for docking large sets of ligands. The methods by which
we plan to achieve this goal are the explicit exploitation of molecular
flexibility of the ligand and the incorporation of physicochemical
properties of the molecules. The algorithm for fragment placement,
which is the topic of this paper, is based on pattern recognition
techniques and is able to predict a small set of possible positions
of a molecular fragment with low flexibility within seconds on a
workstation. In most cases, a placement with rms deviation below
1.0 A with respect to the X-ray structure is found among the 10 highest
ranking solutions, assuming that the receptor is given in the bound
conformation.},
institution = {German National Research Center for Information Technology (GMD),
Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin,
Germany.},
keywords = {Algorithms; Binding Sites; Databases, Factual; Ligands; Models, Chemical;
Peptide Fragments, chemistry; Proteins, chemistry; Software},
owner = {bricehoffmann},
pmid = {8786414},
timestamp = {2009.02.13}
}
@book{Rasmussen2005Gaussian,
title = {Gaussian Processes for Machine Learning (Adaptive Computation and
Machine Learning)},
publisher = {The MIT Press},
year = {2005},
author = {Rasmussen, Carl E. and Williams, Christopher K. I.},
month = {December},
abstract = {Gaussian processes (GPs) provide a principled, practical, probabilistic
approach to learning in kernel machines. GPs have received increased
attention in the machine-learning community over the past decade,
and this book provides a long-needed systematic and unified treatment
of theoretical and practical aspects of GPs in machine learning.
The treatment is comprehensive and self-contained, targeted at researchers
and students in machine learning and applied statistics.
The book deals with the supervised-learning problem for both regression
and classification, and includes detailed algorithms. A wide variety
of covariance (kernel) functions are presented and their properties
discussed. Model selection is discussed both from a Bayesian and
a classical perspective. Many connections to other well-known techniques
from machine learning and statistics are discussed, including support-vector
machines, neural networks, splines, regularization networks, relevance
vector machines and others. Theoretical issues including learning
curves and the PAC-Bayesian framework are treated, and several approximation
methods for learning with large datasets are discussed. The book
contains illustrative examples and exercises, and code and datasets
are available on the Web. Appendixes provide mathematical background
and a discussion of Gaussian Markov processes.},
howpublished = {Hardcover},
isbn = {026218253X},
keywords = {machine\_learning}
}
@article{Raudys2000How,
author = {S. Raudys},
title = {How good are support vector machines?},
journal = {Neural {N}etw},
year = {2000},
volume = {13},
pages = {17-9},
number = {1},
month = {Jan},
abstract = {Support vector ({SV}) machines are useful tools to classify populations
characterized by abrupt decreases in density functions. {A}t least
for one class of {G}aussian data model the {SV} classifier is not
an optimal one according to a mean generalization error criterion.
{I}n real world problems, we have neither {G}aussian populations
nor data with sharp linear boundaries. {T}hus, the {SV} (maximal
margin) classifiers can lose against other methods where more than
a fixed number of supporting vectors contribute in determining the
final weights of the classification and prediction rules. {A} good
alternative to the linear {SV} machine is a specially trained and
optimally stopped {SLP} in a transformed feature space obtained after
decorrelating and scaling the multivariate data.},
keywords = {Automated, Learning, Models, Neural Networks (Computer), Neurological,
Pattern Recognition, 10935455},
pii = {S0893608099000970}
}
@article{Ravdin1996computer,
author = {Ravdin, P. M.},
title = {A computer program to assist in making breast cancer adjuvant therapy
decisions.},
journal = {Semin Oncol},
year = {1996},
volume = {23},
pages = {43--50},
number = {1 Suppl 2},
month = {Feb},
abstract = {This report describes a computer program designed to assist health
care professionals in making projections of the average benefit of
systemic adjuvant therapy for individual breast cancer patients.
It requires as input patient age (used to make projections of natural
mortality), an estimate of breast cancer-related mortality at 5 years
(used to make projections of breast cancer-specific mortality), and
the proportional risk reduction for breast cancer mortality expected
for the adjuvant therapy (with included tables from the Early Breast
Cancer Trialist's 1992 meta-analysis). The program uses life table
analytical techniques to make projections of outcome in three scenarios:
that the breast cancer never occurred, that the breast cancer patient
received definitive local therapy but no adjuvant systemic therapy,
and that the patient received adjuvant therapy. The outcome projections
are given for total, natural (non-breast cancer-related), and breast
cancer-related mortality at several time points and also of total
remaining life expectancy. These estimates are currently widely made
by clinicians by nonnumerical techniques. Computer-based tools can
serve as valuable aids in physician and patient education and in
the process of informed decision making.},
institution = {Division of Oncology, University of Texas Health Sciences Center,
San Antonio, 78284, USA.},
keywords = {Adult; Age Factors; Aged; Aged, 80 and over; Breast Neoplasms, drug
therapy/mortality/surgery; Chemotherapy, Adjuvant; Decision Making;
Female; Humans; Life Expectancy; Life Tables; Middle Aged; Proportional
Hazards Models; Risk Factors; SEER Program; Software; Survival Rate;
Treatment Outcome},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pmid = {8614844},
timestamp = {2012.03.02}
}
@article{Ravdin1995computer,
author = {Ravdin, P. M.},
title = {A computer based program to assist in adjuvant therapy decisions
for individual breast cancer patients.},
journal = {Bull Cancer},
year = {1995},
volume = {82 Suppl 5},
pages = {561s--564s},
month = {Dec},
abstract = {This paper describes a personal computer based tool to aid in decision
making about whether a woman should receive adjuvant therapy for
breast cancer. This tool can assist in engaging women with primary
breast cancer in the discussion about: 1) her risk of breast cancer
related mortality if she receives only local control measures, but
no systemic adjuvant therapy, 2) how much receiving adjuvant therapy
may reduce this risk, and 3) what the impact of receiving the adjuvant
systemic therapy is in terms of survival. The tool utilizes life
table analytical techniques to project outcomes after entry of patient
age (used to calculate natural mortality rates), estimated risk of
breast cancer related mortality (with a help tool allowing the physician
to use estimates based on national database information), and estimate
of the efficacy of adjuvant chemotherapy (with included tables of
estimates based on the Early Breast Cancer Trialists' meta-analysis).
Computer based tools can serve as valuable aids in patient and physician
education, and the process of informed decision making.},
institution = {Division of Medical Oncology, University of Texas Health Sciences
Center, San Antonio 78284, USA.},
keywords = {Aged; Breast Neoplasms, drug therapy/mortality; Chemotherapy, Adjuvant;
Decision Making, Computer-Assisted; Education, Medical; Female; Humans;
Life Tables; Middle Aged; Patient Participation; Prognosis; Software;
Survival Rate},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pmid = {8680066},
timestamp = {2012.03.02}
}
@article{Ravdin2001Computer,
author = {Ravdin, P. M. and Siminoff, L. A. and Davis, G. J. and Mercer, M.
B. and Hewlett, J. and Gerson, N. and Parker, H. L.},
title = {Computer program to assist in making decisions about adjuvant therapy
for women with early breast cancer.},
journal = {J Clin Oncol},
year = {2001},
volume = {19},
pages = {980--991},
number = {4},
month = {Feb},
abstract = {The goal of the computer program Adjuvant! is to allow health professionals
and their patients with early breast cancer to make more informed
decisions about adjuvant therapy.Actuarial analysis was used to project
outcomes of patients with and without adjuvant therapy based on estimates
of prognosis largely derived from Surveillance, Epidemiology, and
End-Results data and estimates of the efficacy of adjuvant therapy
based on the 1998 overviews of randomized trials of adjuvant therapy.
These estimates can be refined using the Prognostic Factor Impact
Calculator, which uses a Bayesian method to make adjustments based
on relative risks conferred and prevalence of positive test results.From
the entries of patient information (age, menopausal status, comorbidity
estimate) and tumor staging and characteristics (tumor size, number
of positive axillary nodes, estrogen receptor status), baseline prognostic
estimates are made. Estimates for the efficacy of endocrine therapy
(5 years of tamoxifen) and of polychemotherapy (cyclophosphamide/methotrexate/fluorouracil-like
regimens, or anthracycline-based therapy, or therapy based on both
an anthracycline and a taxane) can then be used to project outcomes
presented in both numerical and graphical formats. Outcomes for overall
survival and disease-free survival and the improvement seen in clinical
trials, are reasonably modeled by Adjuvant!, although an ideal validation
for all patient subsets with all treatment options is not possible.
Additional speculative estimates of years of remaining life expectancy
and long-term survival curves can also be produced. Help files supply
general information about breast cancer. The program's Internet links
supply national treatment guidelines, cooperative group trial options,
and other related information.The computer program Adjuvant! can
play practical and educational roles in clinical settings.},
institution = {San Antonio, TX 78284, USA. pravdin@swog.org},
keywords = {Actuarial Analysis; Breast Neoplasms, mortality/therapy; Chemotherapy,
Adjuvant; Decision Making; Female; Humans; Prognosis; Software; Survival
Analysis},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pmid = {11181660},
timestamp = {2012.03.02}
}
@incollection{Ravikumar2008SpAM,
author = {Pradeep Ravikumar and Han Liu and John Lafferty and Larry Wasserman},
title = {SpAM: Sparse Additive Models},
booktitle = {Advances in Neural Information Processing Systems 20},
publisher = {MIT Press},
year = {2008},
editor = {J.C. Platt and D. Koller and Y. Singer and S. Roweis},
pages = {1201--1208},
address = {Cambridge, MA}
}
@incollection{Ravikumar2009Model,
author = {Pradeep Ravikumar and Garvesh Raskutti and Martin Wainwright and
Bin Yu},
title = {Model Selection in Gaussian Graphical Models: High-Dimensional Consistency
of {\boldmath$\ell_1$}-regularized MLE},
booktitle = {Advances in Neural Information Processing Systems 21},
publisher = {MIT Press},
year = {2009},
editor = {D. Koller and D. Schuurmans and Y. Bengio and L. Bottou},
pages = {1329--1336}
}
@article{Ravikumar2011High-dimensional,
author = {Ravikumar, P. and Wainwright, M. J. and Raskutti, G. and Yu, B.},
title = {High-dimensional covarince estimation by minimizing $\ell_1$-penalized
log-determinant divergence},
journal = {Electron. J. Statist.},
year = {2011},
volume = {5},
pages = {935--980},
doi = {10.1214/11-EJS631},
pdf = {../local/Ravikumar2011High-dimensional.pdf},
file = {Ravikumar2011High-dimensional.pdf:Ravikumar2011High-dimensional.pdf:PDF},
owner = {jp},
timestamp = {2012.03.20},
url = {http://dx.doi.org/10.1214/11-EJS631}
}
@article{Ray1957Finding,
author = {Ray, L. C. and Kirsch, R. A.},
title = {Finding Chemical Records by Digital Computers},
journal = {Science},
year = {1957},
volume = {126},
pages = {814}
}
@article{Raychaudhuri2000Principal,
author = {Raychaudhuri, S. and Stuart, J. M. and Altman, R. B.},
title = {Principal components analysis to summarize microarray experiments:
application to sporulation time series},
journal = {Pac. Symp. Biocomput.},
year = {2000},
pages = {455--466},
abstract = {A series of microarray experiments produces observations of differential
expression for thousands of genes across multiple conditions. It
is often not clear whether a set of experiments are measuring fundamentally
different gene expression states or are measuring similar states
created through different mechanisms. It is useful, therefore, to
define a core set of independent features for the expression states
that allow them to be compared directly. Principal components analysis
(PCA) is a statistical technique for determining the key variables
in a multidimensional data set that explain the differences in the
observations, and can be used to simplify the analysis and visualization
of multidimensional data sets. We show that application of PCA to
expression data (where the experimental conditions are the variables,
and the gene expression measurements are the observations) allows
us to summarize the ways in which gene responses vary under different
conditions. Examination of the components also provides insight into
the underlying factors that are measured in the experiments. We applied
PCA to the publicly released yeast sporulation data set (Chu et al.
1998). In that work, 7 different measurements of gene expression
were made over time. PCA on the time-points suggests that much of
the observed variability in the experiment can be summarized in just
2 components--i.e. 2 variables capture most of the information. These
components appear to represent (1) overall induction level and (2)
change in induction level over time. We also examined the clusters
proposed in the original paper, and show how they are manifested
in principal component space. Our results are available on the internet
at http:¿www.smi.stanford.edu/project/helix/PCArray .},
pdf = {../local/Raychaudhuri2000Principal.pdf},
file = {Raychaudhuri2000Principal.pdf:Raychaudhuri2000Principal.pdf:PDF},
institution = {Stanford Medical Informatics, Stanford University, CA 94305-5479,
USA. sxr@smi.stanford.edu},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pmid = {10902193},
timestamp = {2011.10.03},
url = {http://helix-web.stanford.edu/psb00/raychaudhuri.pdf}
}
@article{Rea2000Regulation,
author = {S. Rea and F. Eisenhaber and D. O'Carroll and B. D. Strahl and Z.
W. Sun and M. Schmid and S. Opravil and K. Mechtler and C. P. Ponting
and C. D. Allis and T. Jenuwein},
title = {Regulation of chromatin structure by site-specific histone H3 methyltransferases.},
journal = {Nature},
year = {2000},
volume = {406},
pages = {593--599},
number = {6796},
month = {Aug},
abstract = {The organization of chromatin into higher-order structures influences
chromosome function and epigenetic gene regulation. Higher-order
chromatin has been proposed to be nucleated by the covalent modification
of histone tails and the subsequent establishment of chromosomal
subdomains by non-histone modifier factors. Here we show that human
SUV39H1 and murine Suv39h1--mammalian homologues of Drosophila Su(var)3-9
and of Schizosaccharomyces pombe clr4--encode histone H3-specific
methyltransferases that selectively methylate lysine 9 of the amino
terminus of histone H3 in vitro. We mapped the catalytic motif to
the evolutionarily conserved SET domain, which requires adjacent
cysteine-rich regions to confer histone methyltransferase activity.
Methylation of lysine 9 interferes with phosphorylation of serine
10, but is also influenced by pre-existing modifications in the amino
terminus of H3. In vivo, deregulated SUV39H1 or disrupted Suv39h
activity modulate H3 serine 10 phosphorylation in native chromatin
and induce aberrant mitotic divisions. Our data reveal a functional
interdependence of site-specific H3 tail modifications and suggest
a dynamic mechanism for the regulation of higher-order chromatin.},
doi = {10.1038/35020506},
institution = {Research Institute of Molecular Pathology, The Vienna Biocenter,
Austria.},
keywords = {Amino Acid Sequence; Animals; Catalytic Domain; Chromatin, chemistry/metabolism;
Drosophila; Hela Cells; Histone-Lysine N-Methyltransferase; Humans;
Lysine, metabolism; Methylation; Methyltransferases, genetics/metabolism;
Mice; Molecular Sequence Data; Phosphorylation; Protein Conformation;
Protein Methyltransferases; Protein Structure, Tertiary; Recombinant
Proteins, metabolism; Repressor Proteins, genetics/metabolism; Sequence
Homology, Amino Acid; Serine, metabolism; Substrate Specificity},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pmid = {10949293},
timestamp = {2010.11.23},
url = {http://dx.doi.org/10.1038/35020506}
}
@article{Read1977Graph,
author = {R. C. Read and D. G. Corneil},
title = {The graph isomorphism disease},
journal = {J. Graph Theor.},
year = {1977},
volume = {1},
pages = {339-363},
number = {4},
publisher = {Wiley Periodicals}
}
@article{Reche2002Prediction,
author = {Reche, P. A. and Glutting, J.-P. and Reinherz, E. L.},
title = {{P}rediction of {MHC} class {I} binding peptides using profile motifs.},
journal = {Hum. Immunol.},
year = {2002},
volume = {63},
pages = {701--709},
number = {9},
month = {Sep},
abstract = {Peptides that bind to a given major histocompatibility complex (MHC)
molecule share sequence similarity. Therefore, a position specific
scoring matrix (PSSM) or profile derived from a set of peptides known
to bind to a specific MHC molecule would be a suitable predictor
of whether other peptides might bind, thus anticipating possible
T-cell epitopes within a protein. In this approach, the binding potential
of any peptide sequence (query) to a given MHC molecule is linked
to its similarity to a group of aligned peptides known to bind to
that MHC, and can be obtained by comparing the query to the PSSM.
This article describes the derivation of alignments and profiles
from a collection of peptides known to bind a specific MHC, compatible
with the structural and molecular basis of the peptide-MHC class
I (MHCI) interaction. Moreover, in order to apply these profiles
to the prediction of peptide-MHCI binding, we have developed a new
search algorithm (RANKPEP) that ranks all possible peptides from
an input protein using the PSSM coefficients. The predictive power
of the method was evaluated by running RANKPEP on proteins known
to bear MHCI K(b)- and D(b)-restricted T-cell epitopes. Analysis
of the results indicates that > 80\% of these epitopes are among
the top 2\% of scoring peptides. Prediction of peptide-MHC binding
using a variety of MHCI-specific PSSMs is available on line at our
RANKPEP web server (www.mifoundation.org/Tools/rankpep.html). In
addition, the RANKPEP server also allows the user to enter additional
profiles, making the server a powerful and versatile computational
biology benchmark for the prediction of peptide-MHC binding.},
keywords = {immunoinformatics},
pii = {S0198885902004329},
pmid = {12175724},
timestamp = {2007.01.25}
}
@article{Reche2004Enhancement,
author = {Pedro A Reche and John-Paul Glutting and Hong Zhang and Ellis L Reinherz},
title = {Enhancement to the {RANKPEP} resource for the prediction of peptide
binding to {MHC} molecules using profiles.},
journal = {Immunogenetics},
year = {2004},
volume = {56},
pages = {405--419},
number = {6},
month = {Sep},
abstract = {We introduced previously an on-line resource, RANKPEP that uses position
specific scoring matrices (PSSMs) or profiles for the prediction
of peptide-MHC class I (MHCI) binding as a basis for CD8 T-cell epitope
identification. Here, using PSSMs that are structurally consistent
with the binding mode of MHC class II (MHCII) ligands, we have extended
RANKPEP to prediction of peptide-MHCII binding and anticipation of
CD4 T-cell epitopes. Currently, 88 and 50 different MHCI and MHCII
molecules, respectively, can be targeted for peptide binding predictions
in RANKPEP. Because appropriate processing of antigenic peptides
must occur prior to major histocompatibility complex (MHC) binding,
cleavage site prediction methods are important adjuncts for T-cell
epitope discovery. Given that the C-terminus of most MHCI-restricted
epitopes results from proteasomal cleavage, we have modeled the cleavage
site from known MHCI-restricted epitopes using statistical language
models. The RANKPEP server now determines whether the C-terminus
of any predicted MHCI ligand may result from such proteasomal cleavage.
Also implemented is a variability masking function. This feature
focuses prediction on conserved rather than highly variable protein
segments encoded by infectious genomes, thereby offering identification
of invariant T-cell epitopes to thwart mutation as an immune evasion
mechanism.},
doi = {10.1007/s00251-004-0709-7},
keywords = {Algorithms; Amino Acid Motifs; Antigen Presentation; Epitopes, T-Lymphocyte;
Histocompatibility Antigens Class I; Histocompatibility Antigens
Class II; Humans; Models, Molecular; Peptide Fragments; Research
Support, Non-U.S. Gov't; Research Support, U.S. Gov't, P.H.S.; T-Lymphocytes},
owner = {jacob},
pmid = {15349703},
timestamp = {2006.08.30},
url = {http://dx.doi.org/10.1007/s00251-004-0709-7}
}
@article{Redfield1927Theory,
author = {J. H. Redfield},
title = {The theory of group-reduced distributions},
journal = {Amer. J. Math.},
year = {1927},
volume = {49},
pages = {433--455}
}
@article{Redon2006Global,
author = {Richard Redon and Shumpei Ishikawa and Karen R Fitch and Lars Feuk
and George H Perry and T. Daniel Andrews and Heike Fiegler and Michael
H Shapero and Andrew R Carson and Wenwei Chen and Eun Kyung Cho and
Stephanie Dallaire and Jennifer L Freeman and Juan R González and
Mònica Gratacòs and Jing Huang and Dimitrios Kalaitzopoulos and Daisuke
Komura and Jeffrey R MacDonald and Christian R Marshall and Rui Mei
and Lyndal Montgomery and Kunihiro Nishimura and Kohji Okamura and
Fan Shen and Martin J Somerville and Joelle Tchinda and Armand Valsesia
and Cara Woodwark and Fengtang Yang and Junjun Zhang and Tatiana
Zerjal and Jane Zhang and Lluis Armengol and Donald F Conrad and
Xavier Estivill and Chris Tyler-Smith and Nigel P Carter and Hiroyuki
Aburatani and Charles Lee and Keith W Jones and Stephen W Scherer
and Matthew E Hurles},
title = {Global variation in copy number in the human genome.},
journal = {Nature},
year = {2006},
volume = {444},
pages = {444--454},
number = {7118},
month = {Nov},
abstract = {Copy number variation (CNV) of DNA sequences is functionally significant
but has yet to be fully ascertained. We have constructed a first-generation
CNV map of the human genome through the study of 270 individuals
from four populations with ancestry in Europe, Africa or Asia (the
HapMap collection). DNA from these individuals was screened for CNV
using two complementary technologies: single-nucleotide polymorphism
(SNP) genotyping arrays, and clone-based comparative genomic hybridization.
A total of 1,447 copy number variable regions (CNVRs), which can
encompass overlapping or adjacent gains or losses, covering 360 megabases
(12\% of the genome) were identified in these populations. These
CNVRs contained hundreds of genes, disease loci, functional elements
and segmental duplications. Notably, the CNVRs encompassed more nucleotide
content per genome than SNPs, underscoring the importance of CNV
in genetic diversity and evolution. The data obtained delineate linkage
disequilibrium patterns for many CNVs, and reveal marked variation
in copy number among populations. We also demonstrate the utility
of this resource for genetic disease studies.},
doi = {10.1038/nature05329},
institution = {The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus,
Hinxton, Cambridge CB10 1SA, UK.},
keywords = {Chromosome Mapping; Gene Dosage; Genetic Variation; Genetics, Population;
Genome, Human; Genomics, methods; Genotype; Humans; Linkage Disequilibrium;
Molecular Diagnostic Techniques; Oligonucleotide Array Sequence Analysis,
methods; Polymorphism, Single Nucleotide},
language = {eng},
medline-pst = {ppublish},
owner = {philippe},
pii = {nature05329},
pmid = {17122850},
timestamp = {2010.08.01},
url = {http://dx.doi.org/10.1038/nature05329}
}
@article{Rege2004Parallel,
author = {Kaushal Rege and Asif Ladiwala and Nihal Tugcu and Curt M Breneman
and Steven M Cramer},
title = {Parallel screening of selective and high-affinity displacers for
proteins in ion-exchange systems.},
journal = {J {C}hromatogr {A}},
year = {2004},
volume = {1033},
pages = {19-28},
number = {1},
month = {Apr},
abstract = {This paper employs a parallel batch screening technique for the identification
of both selective and high-affinity displacers for a model binary
mixture of proteins in a cation-exchange system. {A} variety of molecules
were screened as possible displacers for the proteins ribonuclease
{A} ({RNA}se{A}) and alpha-chymotrypsinogen {A} (alpha-chy{A}) on
high performance {S}epharose {SP}. {T}he batch screening data for
each protein was used to select leads for selective and high-affinity
displacers and column experiments were carried out to evaluate the
performance of the selected leads. {T}he data from the batch displacements
was also employed to generate quantitative structure-efficacy relationship
({QSER}) models based on a support vector machine regression approach.
{T}he resulting models had high correlation coefficients and were
able to predict the behaviour of molecules not included in the training
set. {T}he descriptors selected in the {QSER} models for both proteins
were examined to provide insights into factors influencing displacer
selectivity in ion-exchange systems. {T}he results presented in this
paper demonstrate that this parallel batch screening-{QSER} approach
can be employed for the identification of selective and high-affinity
displacers for protein mixtures.}
}
@article{Rehm2009CellDeathDiff,
author = {Rehm, M. and Huber, H. J. and Hellwig, C. T. and Anguissola, S. and
Dussmann, H. and Prehn, J. H. M.},
title = {Dynamics of outer mitochondrial membrane permeabilization during
apoptosis},
journal = {Cell Death and Differentiation},
year = {2009},
volume = {16},
pages = {613 - 623},
issn = {1350-9047},
keywords = {csbcbook},
url = {http://www.nature.com/cdd/journal/v16/n4/suppinfo/cdd2008187s1.html}
}
@article{Rehm2009Dynamics,
author = {Rehm, M. and Huber, H. J. and Hellwig, C. T. and Anguissola, S. and
Dussmann, H. and Prehn, J. H. M.},
title = {Dynamics of outer mitochondrial membrane permeabilization during
apoptosis},
journal = {Cell Death Differ.},
year = {2009},
volume = {16},
pages = {613--623},
issn = {1350-9047},
keywords = {csbcbook},
owner = {jp},
timestamp = {2012.05.11},
url = {http://www.nature.com/cdd/journal/v16/n4/suppinfo/cdd2008187s1.html}
}
@article{Reinders2008Genome,
author = {Jon Reinders and Celine Delucinge Vivier and Gregory Theiler and
Didier Chollet and Patrick Descombes and Jerzy Paszkowski},
title = {Genome-wide, high-resolution {DNA} methylation profiling using bisulfite-mediated
cytosine conversion},
journal = {Genome Res.},
year = {2008},
volume = {18},
pages = {469-76},
keywords = {csbcbook, csbcbook-ch2}
}
@article{Remm2001Automatic,
author = {Remm, M. and Storm, C.E. and Sonnhammer, E.L.},
title = {Automatic clustering of orthologs and in-paralogs from pairwise species
comparisons},
journal = {J. Mol. Biol.},
year = {2001},
volume = {314},
pages = {1041--1052},
number = {5},
month = {Dec},
abstract = {Orthologs are genes in different species that originate from a single
gene in the last common ancestor of these species. Such genes have
often retained identical biological roles in the present-day organisms.
It is hence important to identify orthologs for transferring functional
information between genes in different organisms with a high degree
of reliability. For example, orthologs of human proteins are often
functionally characterized in model organisms. Unfortunately, orthology
analysis between human and e.g. invertebrates is often complex because
of large numbers of paralogs within protein families. Paralogs that
predate the species split, which we call out-paralogs, can easily
be confused with true orthologs. Paralogs that arose after the species
split, which we call in-paralogs, however, are bona fide orthologs
by definition.Orthologs and in-paralogs are typically detected with
phylogenetic methods, but these are slow and difficult to automate.
Automatic clustering methods based on two-way best genome-wide matches
on the other hand, have so far not separated in-paralogs from out-paralogs
effectively.We present a fully automatic method for finding orthologs
and in-paralogs from two species. Ortholog clusters are seeded with
a two-way best pairwise match, after which an algorithm for adding
in-paralogs is applied. The method bypasses multiple alignments and
phylogenetic trees, which can be slow and error-prone steps in classical
ortholog detection. Still, it robustly detects complex orthologous
relationships and assigns confidence values for both orthologs and
in-paralogs. The program, called INPARANOID, was tested on all completely
sequenced eukaryotic genomes. To assess the quality of INPARANOID
results, ortholog clusters were generated from a dataset of worm
and mammalian transmembrane proteins, and were compared to clusters
derived by manual tree-based ortholog detection methods. This study
led to the identification with a high degree of confidence of over
a dozen novel worm-mammalian ortholog assignments that were previously
undetected because of shortcomings of phylogenetic methods.A WWW
server that allows searching for orthologs between human and several
fully sequenced genomes is installed at http://www.cgb.ki.se/inparanoid/.
This is the first comprehensive resource with orthologs of all fully
sequenced eukaryotic genomes. Programs and tables of orthology assignments
are available from the same location.},
doi = {10.1006/jmbi.2000.5197},
pdf = {../local/Remm2001Automatic.pdf},
file = {Remm2001Automatic.pdf:Remm2001Automatic.pdf:PDF},
institution = {Center for Genomics and Bioinformatics, Karolinska Institutet, S-17177
Stockholm, Sweden.},
owner = {jp},
pii = {S0022-2836(00)95197-0},
pmid = {11743721},
timestamp = {2008.10.02},
url = {http://dx.doi.org/10.1006/jmbi.2000.5197}
}
@article{Ren2000Genome-wide,
author = {Ren, B. and Robert, F. and Wyrick, J. J. and Aparicio, O. and Jennings,
E. G. and Simon, I. and Zeitlinger, J. and Schreiber, J. and Hannett,
N. and Kanin, E. and Volkert, T. L. and Wilson, C. J. and Bell, S.
P. and Young, R. A.},
title = {Genome-wide location and function of {DNA} binding proteins.},
journal = {Science},
year = {2000},
volume = {290},
pages = {2306--2309},
number = {5500},
month = {Dec},
abstract = {Understanding how DNA binding proteins control global gene expression
and chromosomal maintenance requires knowledge of the chromosomal
locations at which these proteins function in vivo. We developed
a microarray method that reveals the genome-wide location of DNA-bound
proteins and used this method to monitor binding of gene-specific
transcription activators in yeast. A combination of location and
expression profiles was used to identify genes whose expression is
directly controlled by Gal4 and Ste12 as cells respond to changes
in carbon source and mating pheromone, respectively. The results
identify pathways that are coordinately regulated by each of the
two activators and reveal previously unknown functions for Gal4 and
Ste12. Genome-wide location analysis will facilitate investigation
of gene regulatory networks, gene function, and genome maintenance.},
doi = {10.1126/science.290.5500.2306},
pdf = {../local/Ren2000Genome-wide.pdf},
file = {Ren2000Genome-wide.pdf:Ren2000Genome-wide.pdf:PDF},
institution = {Whitehead Institute for Biomedical Research, Nine Cambridge Center,
Cambridge, MA 02142, USA.},
owner = {jp},
pii = {290/5500/2306},
pmid = {11125145},
timestamp = {2008.12.09},
url = {http://dx.doi.org/10.1126/science.290.5500.2306}
}
@article{Ren2005HIV,
author = {J. Ren and D.K. Stammers},
title = {{HIV} reverse transcriptase structures: designing new inhibitors
and understanding mechanisms of drug resistance},
journal = {Trends Pharmacol. Sci.},
year = {2005},
volume = {26},
pages = {4-7},
owner = {mahe},
timestamp = {2006.09.07}
}
@article{Ren2006siRecords,
author = {Ren, Y. and Gong, W. and Xu, Q. and Zheng, X. and Lin, D. and Wang,
Y. and Li, T.},
title = {si{R}ecords: an extensive database of mammalian si{RNA}s with efficacy
ratings.},
journal = {Bioinformatics},
year = {2006},
month = {Jan},
abstract = {SUMMARY: Short interfering RNAs have been gaining popularity as the
gene knock-down tool of choice by many researchers due to the clean
nature of their workings as well as the technical simplicity and
cost efficiency in their applications. We have constructed siRecords,
a database of siRNAs experimentally tested by researchers with consistent
efficacy ratings. This database will help siRNA researchers develop
more reliable siRNA design rules; in the mean time, benefit experimental
researchers directly by providing them with information about the
siRNAs that have been experimentally tested against the genes of
their interest. Currently, more than 4, 100 carefully annotated siRNA
sequences obtained from more than 1, 200 published siRNA studies
are hosted in siRecords. This database will continue to expand as
more experimentally tested siRNAs are published. AVAILABILITY: The
siRecords database can be accessed at http://siRecords.umn.edu/siRecords/.},
doi = {10.1093/bioinformatics/btl026},
keywords = {sirna},
owner = {vert},
pii = {btl026},
pmid = {16443930},
timestamp = {2006.03.28},
url = {http://dx.doi.org/10.1093/bioinformatics/btl026}
}
@inproceedings{Rennie2005Fast,
author = {Rennie, J. D. M. and Srebro, N.},
title = {Fast maximum margin matrix factorization for collaborative prediction},
booktitle = {Proceedings of the 22nd international conference on Machine learning},
year = {2005},
pages = {713--719},
address = {New York, NY, USA},
publisher = {ACM Press},
doi = {10.1145/1102351.1102441},
timestamp = {2007.10.22},
url = {http://dx.doi.org/10.1145/1102351.1102441}
}
@article{Rensing2005Protein,
author = {Stefan A Rensing and Dana Fritzowsky and Daniel Lang and Ralf Reski},
title = {Protein encoding genes in an ancient plant: analysis of codon usage,
retained genes and splice sites in a moss, {P}hyscomitrella patens.},
journal = {B{MC} {G}enomics},
year = {2005},
volume = {6},
pages = {43},
number = {1},
month = {Mar},
abstract = {B{ACKGROUND}: {T}he moss {P}hyscomitrella patens is an emerging plant
model system due to its high rate of homologous recombination, haploidy,
simple body plan, physiological properties as well as phylogenetic
position. {A}vailable {EST} data was clustered and assembled, and
provided the basis for a genome-wide analysis of protein encoding
genes. {RESULTS}: {W}e have clustered and assembled {P}hyscomitrella
patens {EST} and {CDS} data in order to represent the transcriptome
of this non-seed plant. {C}lustering of the publicly available data
and subsequent prediction resulted in a total of 19,081 non-redundant
{ORF}. {O}f these putative transcripts, approximately 30\% have a
homolog in both rice and {A}rabidopsis transcriptome. {M}ore than
130 transcripts are not present in seed plants but can be found in
other kingdoms. {T}hese potential "retained genes" might have been
lost during seed plant evolution. {F}unctional annotation of these
genes reveals unequal distribution among taxonomic groups and intriguing
putative functions such as cytotoxicity and nucleic acid repair.
{W}hereas introns in the moss are larger on average than in the seed
plant {A}rabidopsis thaliana, position and amount of introns are
approximately the same. {C}ontrary to {A}rabidopsis, where {CDS}
contain on average 44\% {G}/{C}, in {P}hyscomitrella the average
{G}/{C} content is 50\%. {I}nterestingly, moss orthologs of {A}rabidopsis
genes show a significant drift of codon fraction usage, towards the
seed plant. {W}hile averaged codon bias is the same in {P}hyscomitrella
and {A}rabidopsis, the distribution pattern is different, with 15\%
of moss genes being unbiased. {S}pecies-specific, sensitive and selective
splice site prediction for {P}hyscomitrella has been developed using
a dataset of 368 donor and acceptor sites, utilizing a support vector
machine. {T}he prediction accuracy is better than those achieved
with tools trained on {A}rabidopsis data. {CONCLUSION}: {A}nalysis
of the moss transcriptome displays differences in gene structure,
codon and splice site usage in comparison with the seed plant {A}rabidopsis.
{P}utative retained genes exhibit possible functions that might explain
the peculiar physiological properties of mosses. {B}oth the transcriptome
representation (including a {BLAST} and retrieval service) and splice
site prediction have been made available on http://www.cosmoss.org,
setting the basis for assembly and annotation of the {P}hyscomitrella
genome, of which draft shotgun sequences will become available in
2005.},
doi = {10.1186/1471-2164-6-43},
pdf = {../local/Rensing2005Protein.pdf},
file = {Rensing2005Protein.pdf:local/Rensing2005Protein.pdf:PDF},
keywords = {biosvm},
pii = {1471-2164-6-43},
url = {http://dx.doi.org/10.1186/1471-2164-6-43}
}
@article{Res2005evolution,
author = {I. Res and I. Mihalek and O. Lichtarge},
title = {An evolution based classifier for prediction of protein interfaces
without using protein structures.},
journal = {Bioinformatics},
year = {2005},
volume = {21},
pages = {2496-501},
number = {10},
month = {May},
abstract = {M{OTIVATION}: {T}he number of available protein structures still lags
far behind the number of known protein sequences. {T}his makes it
important to predict which residues participate in protein-protein
interactions using only sequence information. {F}ew studies have
tackled this problem until now. {RESULTS}: {W}e applied support vector
machines to sequences in order to generate a classification of all
protein residues into those that are part of a protein interface
and those that are not. {F}or the first time evolutionary information
was used as one of the attributes and this inclusion of evolutionary
importance rankings improves the classification. {L}eave-one-out
cross-validation experiments show that prediction accuracy reaches
64\%.},
doi = {10.1093/bioinformatics/bti340},
pdf = {../local/Res2005evolution.pdf},
file = {Res2005evolution.pdf:local/Res2005evolution.pdf:PDF},
keywords = {biosvm},
pii = {bti340},
url = {http://dx.doi.org/10.1093/bioinformatics/bti340}
}
@phdthesis{Reyal2009Analyse,
author = {Reyal, F.},
title = {Analyse du profil d'expression par la technique des puces {\`a} ADN.
Application \`a la caract\'erisation mol\'eculaire et \`a la d\'etermination
du pronostic des cancers canalaires infiltrants du sein.},
school = {Universit\'e Paris 11},
year = {2009},
keywords = {breastcancer, microarray},
owner = {jp},
timestamp = {2009.10.31}
}
@article{Reyal2008comprehensive,
author = {Reyal, F. and van Vliet, M. H. and Armstrong, N. J. and Horlings,
H. M. and de Visser, K. E. and Kok, M. and Teschendorff, A. E. and
Mook, S. and van't Veer, L. and Caldas, C. and Salmon, Remy, R. J.
and van de Vijver, M. J. and Wessels, L. F. A.},
title = {A comprehensive analysis of prognostic signatures reveals the high
predictive capacity of the proliferation, immune response and {RNA}
splicing modules in breast cancer},
journal = {Breast Cancer Res.},
year = {2008},
volume = {10},
pages = {R93},
number = {6},
doi = {10.1186/bcr2192},
pdf = {../local/Reyal2008comprehensive.pdf},
file = {Reyal2008comprehensive.pdf:Reyal2008comprehensive.pdf:PDF},
owner = {jp},
timestamp = {2011.01.13},
url = {http://dx.doi.org/10.1186/bcr2192}
}
@article{Reynolds2004Rational,
author = {Reynolds, A. and Leake, D. and Boese, Q. and Scaringe, S. and Marshall,
W. S. and Khvorova, A.},
title = {Rational si{RNA} design for {RNA} interference.},
journal = {Nat. {B}iotechnol.},
year = {2004},
volume = {22},
pages = {326-330},
number = {3},
month = {Mar},
abstract = {Short-interfering {RNA}s suppress gene expression through a highly
regulated enzyme-mediated process called {RNA} interference ({RNA}i)1,
2, 3, 4. {RNA}i involves multiple {RNA}-protein interactions characterized
by four major steps: assembly of si{RNA} with the {RNA}-induced silencing
complex ({RISC}), activation of the {RISC}, target recognition and
target cleavage. {T}hese interactions may bias strand selection during
si{RNA}-{RISC} assembly and activation, and contribute to the overall
efficiency of {RNA}i5, 6. {T}o identify si{RNA}-specific features
likely to contribute to efficient processing at each step, we performed
a systematic analysis of 180 si{RNA}s targeting the m{RNA} of two
genes. {E}ight characteristics associated with si{RNA} functionality
were identified: low {G}/{C} content, a bias towards low internal
stability at the sense strand 3'-terminus, lack of inverted repeats,
and sense strand base preferences (positions 3, 10, 13 and 19). {F}urther
analyses revealed that application of an algorithm incorporating
all eight criteria significantly improves potent si{RNA} selection.
{T}his highlights the utility of rational design for selecting potent
si{RNA}s and facilitating functional gene knockdown studies.},
doi = {10.1038/nbt936},
pdf = {../local/Reynolds2004Rational.pdf},
file = {Reynolds2004Rational.pdf:local/Reynolds2004Rational.pdf:PDF},
keywords = {sirna},
url = {http://dx.doi.org/10.1038/nbt936}
}
@article{Rhoades2002Prediction,
author = {Matthew W Rhoades and Brenda J Reinhart and Lee P Lim and Christopher
B Burge and Bonnie Bartel and David P Bartel},
title = {Prediction of plant microRNA targets.},
journal = {Cell},
year = {2002},
volume = {110},
pages = {513--520},
number = {4},
month = {Aug},
abstract = {We predict regulatory targets for 14 Arabidopsis microRNAs (miRNAs)
by identifying mRNAs with near complementarity. Complementary sites
within predicted targets are conserved in rice. Of the 49 predicted
targets, 34 are members of transcription factor gene families involved
in developmental patterning or cell differentiation. The near-perfect
complementarity between plant miRNAs and their targets suggests that
many plant miRNAs act similarly to small interfering RNAs and direct
mRNA cleavage. The targeting of developmental transcription factors
suggests that many plant miRNAs function during cellular differentiation
to clear key regulatory transcripts from daughter cell lineages.},
pdf = {../local/Rhoades2002Prediction.pdf},
file = {Rhoades2002Prediction.pdf:Rhoades2002Prediction.pdf:PDF},
institution = {Whitehead Institute for Biomedical Research, 9 Cambridge Center,
MA 02142, USA.},
keywords = {sirna},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {S0092867402008632},
pmid = {12202040},
timestamp = {2009.10.28}
}
@article{Rhodes2005Integrative,
author = {Rhodes, D. R. and Chinnaiyan, A. M.},
title = {Integrative analysis of the cancer transcriptome.},
journal = {Nat. Genet.},
year = {2005},
volume = {37 Suppl},
pages = {S31--S37},
month = {Jun},
abstract = {DNA microarrays have been widely applied to the study of human cancer,
delineating myriad molecular subtypes of cancer, many of which are
associated with distinct biological underpinnings, disease progression
and treatment response. These primary analyses have begun to decipher
the molecular heterogeneity of cancer, but integrative analyses that
evaluate cancer transcriptome data in the context of other data sources
are often capable of extracting deeper biological insight from the
data. Here we discuss several such integrative computational and
analytical approaches, including meta-analysis, functional enrichment
analysis, interactome analysis, transcriptional network analysis
and integrative model system analysis.},
doi = {10.1038/ng1570},
pdf = {../local/Rhodes2005Integrative.pdf},
file = {Rhodes2005Integrative.pdf:Rhodes2005Integrative.pdf:PDF},
institution = {Department of Pathology, Comprehensive Cancer Center, University
of Michigan Medical School, Ann Arbor, Michigan 48109, USA.},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {ng1570},
pmid = {15920528},
timestamp = {2011.09.20},
url = {http://dx.doi.org/10.1038/ng1570}
}
@article{Rhodes2005Mining,
author = {Rhodes, D. R. and Kalyana-Sundaram, S. and Mahavisno, V. and Barrette,
T. R. and Ghosh, D. and Chinnaiyan, A. M.},
title = {Mining for regulatory programs in the cancer transcriptome.},
journal = {Nat. Genet.},
year = {2005},
volume = {37},
pages = {579--583},
number = {6},
month = {Jun},
abstract = {DNA microarrays have been widely applied to cancer transcriptome analysis.
The Oncomine database contains a large collection of such data, as
well as hundreds of derived gene-expression signatures. We studied
the regulatory mechanisms responsible for gene deregulation in these
cancer signatures by searching for the coordinate regulation of genes
with common transcription factor binding sites. We found that genes
with binding sites for the archetypal cancer transcription factor,
E2F, were disproportionately overexpressed in a wide variety of cancers,
whereas genes with binding sites for other transcription factors,
such as Myc-Max, c-Rel and ATF, were disproportionately overexpressed
in specific cancer types. These results suggest that alterations
in pathways activating these transcription factors may be responsible
for the observed gene deregulation and cancer pathogenesis.},
doi = {10.1038/ng1578},
pdf = {../local/Rhodes2005Mining.pdf},
file = {Rhodes2005Mining.pdf:Rhodes2005Mining.pdf:PDF},
institution = {Department of Pathology, University of Michigan Medical School, Ann
Arbor, Michigan 48109, USA.},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {ng1578},
pmid = {15920519},
timestamp = {2011.09.21},
url = {http://dx.doi.org/10.1038/ng1578}
}
@article{Rhodes2007Oncomine,
author = {Rhodes, Daniel R. and Kalyana-Sundaram, Shanker and Mahavisno, Vasudeva
and Varambally, Radhika and Yu, Jianjun and Briggs, Benjamin B. and
Barrette, Terrence R. and Anstet, Matthew J. and Kincead-Beal, Colleen
and Kulkarni, Prakash and Varambally, Sooryanaryana and Ghosh, Debashis
and Chinnaiyan, Arul M.},
title = {Oncomine 3.0: genes, pathways, and networks in a collection of 18,000
cancer gene expression profiles.},
journal = {Neoplasia},
year = {2007},
volume = {9},
pages = {166--180},
number = {2},
month = {Feb},
abstract = {DNA microarrays have been widely applied to cancer transcriptome analysis;
however, the majority of such data are not easily accessible or comparable.
Furthermore, several important analytic approaches have been applied
to microarray analysis; however, their application is often limited.
To overcome these limitations, we have developed Oncomine, a bioinformatics
initiative aimed at collecting, standardizing, analyzing, and delivering
cancer transcriptome data to the biomedical research community. Our
analysis has identified the genes, pathways, and networks deregulated
across 18,000 cancer gene expression microarrays, spanning the majority
of cancer types and subtypes. Here, we provide an update on the initiative,
describe the database and analysis modules, and highlight several
notable observations. Results from this comprehensive analysis are
available at http://www.oncomine.org.},
institution = {Department of Pathology, University of Michigan Medical School, Ann
Arbor, MI 48109-0940, USA.},
keywords = {Antineoplastic Agents, pharmacology; Automatic Data Processing; Chromosome
Mapping; Chromosomes, Human, genetics; Computational Biology, organization
/&/ administration; Data Collection; Data Display; Data Interpretation,
Statistical; Databases, Genetic; Drug Design; Gene Expression Profiling,
statistics /&/ numerical data; Gene Expression Regulation, Neoplastic;
Genes, Neoplasm; Humans; Internet; Models, Biological; Neoplasm Proteins,
biosynthesis/chemistry/genetics; Neoplasms, classification/genetics/metabolism;
Oligonucleotide Array Sequence Analysis; Subtraction Technique; Transcription,
Genetic},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pmid = {17356713},
timestamp = {2012.03.10}
}
@article{Rhodes2005Probabilistic,
author = {Rhodes, D. R. and Tomlins, S. A. and Varambally, S. and Mahavisno,
V. and Barrette,T.and Kalyana-Sundaram, S. and Ghosh, D. and Pandey,
A. and Chinnaiyan, A. M.},
title = {Probabilistic model of the human protein-protein interaction network.},
journal = {Nat Biotechnol},
year = {2005},
volume = {23},
pages = {951--959},
number = {8},
month = {Aug},
abstract = {A catalog of all human protein-protein interactions would provide
scientists with a framework to study protein deregulation in complex
diseases such as cancer. Here we demonstrate that a probabilistic
analysis integrating model organism interactome data, protein domain
data, genome-wide gene expression data and functional annotation
data predicts nearly 40,000 protein-protein interactions in humans-a
result comparable to those obtained with experimental and computational
approaches in model organisms. We validated the accuracy of the predictive
model on an independent test set of known interactions and also experimentally
confirmed two predicted interactions relevant to human cancer, implicating
uncharacterized proteins into definitive pathways. We also applied
the human interactome network to cancer genomics data and identified
several interaction subnetworks activated in cancer. This integrative
analysis provides a comprehensive framework for exploring the human
protein interaction network.},
doi = {10.1038/nbt1103},
pdf = {../local/Rhodes2005Probabilistic.pdf},
file = {Rhodes2005Probabilistic.pdf:Rhodes2005Probabilistic.pdf:PDF},
institution = {Bioinformatics Program, Department of Pathology, University of Michigan
Medical School, Ann Arbor, Michigan 48109, USA.},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {nbt1103},
pmid = {16082366},
timestamp = {2011.08.07},
url = {http://dx.doi.org/10.1038/nbt1103}
}
@article{Rhodes2004Large-scale,
author = {Rhodes, D. R. and Yu, J. and Shanker, K. and Deshpande, N. and Varambally,
R. and Ghosh, D. and Barrette, T. and Pandey, A. and Chinnaiyan,
A. M.},
title = {Large-scale meta-analysis of cancer microarray data identifies common
transcriptional profiles of neoplastic transformation and progression.},
journal = {Proc. Natl. Acad. Sci. U. S. A.},
year = {2004},
volume = {101},
pages = {9309--9314},
number = {25},
month = {Jun},
abstract = {Many studies have used DNA microarrays to identify the gene expression
signatures of human cancer, yet the critical features of these often
unmanageably large signatures remain elusive. To address this, we
developed a statistical method, comparative metaprofiling, which
identifies and assesses the intersection of multiple gene expression
signatures from a diverse collection of microarray data sets. We
collected and analyzed 40 published cancer microarray data sets,
comprising 38 million gene expression measurements from >3,700 cancer
samples. From this, we characterized a common transcriptional profile
that is universally activated in most cancer types relative to the
normal tissues from which they arose, likely reflecting essential
transcriptional features of neoplastic transformation. In addition,
we characterized a transcriptional profile that is commonly activated
in various types of undifferentiated cancer, suggesting common molecular
mechanisms by which cancer cells progress and avoid differentiation.
Finally, we validated these transcriptional profiles on independent
data sets.},
doi = {10.1073/pnas.0401994101},
pdf = {../local/Rhodes2004Large-scale.pdf},
file = {Rhodes2004Large-scale.pdf:Rhodes2004Large-scale.pdf:PDF},
institution = {Department of Pathology, University of Michigan Medical School, Ann
Arbor, 48109, USA.},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {0401994101},
pmid = {15184677},
timestamp = {2011.09.20},
url = {http://dx.doi.org/10.1073/pnas.0401994101}
}
@article{Rice2005Reconstructing,
author = {Rice, J.J. and Tu, Y. and Stolovitzky, G.},
title = {Reconstructing biological networks using conditional correlation
analysis.},
journal = {Bioinformatics},
year = {2005},
volume = {21},
pages = {765--773},
number = {6},
month = {Mar},
abstract = {MOTIVATION: One of the present challenges in biological research is
the organization of the data originating from high-throughput technologies.
One way in which this information can be organized is in the form
of networks of influences, physical or statistical, between cellular
components. We propose an experimental method for probing biological
networks, analyzing the resulting data and reconstructing the network
architecture. METHODS: We use networks of known topology consisting
of nodes (genes), directed edges (gene-gene interactions) and a dynamics
for the genes' mRNA concentrations in terms of the gene-gene interactions.
We proposed a network reconstruction algorithm based on the conditional
correlation of the mRNA equilibrium concentration between two genes
given that one of them was knocked down. Using simulated gene expression
data on networks of known connectivity, we investigated how the reconstruction
error is affected by noise, network topology, size, sparseness and
dynamic parameters. RESULTS: Errors arise from correlation between
nodes connected through intermediate nodes (false positives) and
when the correlation between two directly connected nodes is obscured
by noise, non-linearity or multiple inputs to the target node (false
negatives). Two critical components of the method are as follows:
(1) the choice of an optimal correlation threshold for predicting
connections and (2) the reduction of errors arising from indirect
connections (for which a novel algorithm is proposed). With these
improvements, we can reconstruct networks with the topology of the
transcriptional regulatory network in Escherichia coli with a reasonably
low error rate.},
doi = {10.1093/bioinformatics/bti064},
institution = {Computational Biology Center, IBM T.J. Watson Research Center, PO
Box 218, Yorktown Heights, NY 10598, USA.},
keywords = {Algorithms; Computer Simulation; Gene Expression Profiling; Gene Expression
Regulation; Models, Biological; Models, Statistical; Oligonucleotide
Array Sequence Analysis; Protein Interaction Mapping; Signal Transduction;
Statistics as Topic; Transcription Factors},
owner = {fantine},
pii = {bti064},
pmid = {15486043},
timestamp = {2010.10.21},
url = {http://dx.doi.org/10.1093/bioinformatics/bti064}
}
@article{Rice2000EMBOSS,
author = {P. Rice and I. Longden and A. Bleasby},
title = {EMBOSS: the European Molecular Biology Open Software Suite.},
journal = {Trends Genet.},
year = {2000},
volume = {16},
pages = {276--277},
number = {6},
month = {Jun},
institution = {The Sanger Centre, Wellcome Trust Genome Campus, Hinxton, Cambridge,
UK CB10 1SA.},
keywords = {Internet; Molecular Biology; Sequence Alignment, methods; Software;
User-Computer Interface},
language = {eng},
medline-pst = {ppublish},
owner = {bricehoffmann},
pii = {S0168-9525(00)02024-2},
pmid = {10827456},
timestamp = {2009.07.29}
}
@article{Rice2005Mining,
author = {Simon B Rice and Goran Nenadic and Benjamin J Stapley},
title = {Mining protein function from text using term-based support vector
machines.},
journal = {B{MC} {B}ioinformatics},
year = {2005},
volume = {6 Suppl 1},
pages = {S22},
abstract = {B{ACKGROUND}: {T}ext mining has spurred huge interest in the domain
of biology. {T}he goal of the {B}io{C}re{A}t{I}v{E} exercise was
to evaluate the performance of current text mining systems. {W}e
participated in {T}ask 2, which addressed assigning {G}ene {O}ntology
terms to human proteins and selecting relevant evidence from full-text
documents. {W}e approached it as a modified form of the document
classification task. {W}e used a supervised machine-learning approach
(based on support vector machines) to assign protein function and
select passages that support the assignments. {A}s classification
features, we used a protein's co-occurring terms that were automatically
extracted from documents. {RESULTS}: {T}he results evaluated by curators
were modest, and quite variable for different problems: in many cases
we have relatively good assignment of {GO} terms to proteins, but
the selected supporting text was typically non-relevant (precision
spanning from 3\% to 50\%). {T}he method appears to work best when
a substantial set of relevant documents is obtained, while it works
poorly on single documents and/or short passages. {T}he initial results
suggest that our approach can also mine annotations from text even
when an explicit statement relating a protein to a {GO} term is absent.
{CONCLUSION}: {A} machine learning approach to mining protein function
predictions from text can yield good performance only if sufficient
training data is available, and significant amount of supporting
data is used for prediction. {T}he most promising results are for
combined document retrieval and {GO} term assignment, which calls
for the integration of methods developed in {B}io{C}re{A}t{I}v{E}
{T}ask 1 and {T}ask 2.},
doi = {10.1186/1471-2105-6-S1-S22},
pdf = {../local/Rice2005Mining.pdf},
file = {Rice2005Mining.pdf:local/Rice2005Mining.pdf:PDF},
keywords = {biosvm},
pii = {1471-2105-6-S1-S22},
url = {http://dx.doi.org/10.1186/1471-2105-6-S1-S22}
}
@article{Riedesel2004Peptide,
author = {Henning Riedesel and Björn Kolbeck and Oliver Schmetzer and Ernst-Walter
Knapp},
title = {Peptide binding at class {I} major histocompatibility complex scored
with linear functions and support vector machines.},
journal = {Genome {I}nform {S}er {W}orkshop {G}enome {I}nform},
year = {2004},
volume = {15},
pages = {198-212},
number = {1},
abstract = {We explore two different methods to predict the binding ability of
nonapeptides at the class {I} major histocompatibility complex using
a general linear scoring function that defines a separating hyperplane
in the feature space of sequences. {I}n absence of suitable data
on non-binding nonapeptides we generated sequences randomly from
a selected set of proteins from the protein data bank. {T}he parameters
of the scoring function were determined by a generalized least square
optimization ({LSM}) and alternatively by the support vector machine
({SVM}). {W}ith the generalized {LSM} impaired data for learning
with a small set of binding peptides and a large set of non-binding
peptides can be treated in a balanced way rendering {LSM} more successful
than {SVM}, while for symmetric data sets {SVM} has a slight advantage
compared to {LSM}.},
pdf = {../local/Riedesel2004Peptide.pdf},
file = {Riedesel2004Peptide.pdf:local/Riedesel2004Peptide.pdf:PDF},
keywords = {biosvm},
url = {http://www.jsbi.org/journal/IBSB04/IBSB04F004.html}
}
@article{Rigaut1999generic,
author = {G. Rigaut and A. Shevchenko and B. Rutz and M. Wilm and M. Mann and
B. Séraphin},
title = {A generic protein purification method for protein complex characterization
and proteome exploration.},
journal = {Nat Biotechnol},
year = {1999},
volume = {17},
pages = {1030--1032},
number = {10},
month = {Oct},
abstract = {We have developed a generic procedure to purify proteins expressed
at their natural level under native conditions using a novel tandem
affinity purification (TAP) tag. The TAP tag allows the rapid purification
of complexes from a relatively small number of cells without prior
knowledge of the complex composition, activity, or function. Combined
with mass spectrometry, the TAP strategy allows for the identification
of proteins interacting with a given target protein. The TAP method
has been tested in yeast but should be applicable to other cells
or organisms.},
doi = {10.1038/13732},
institution = {European Molecular Biology Laboratory, Meyerhofstrasse 1, D-69117
Heidelberg, Germany.},
keywords = {Affinity Labels; Amino Acid Sequence; Electrophoresis, Polyacrylamide
Gel; Methods; Molecular Sequence Data; Proteins; Proteome},
owner = {phupe},
pmid = {10504710},
timestamp = {2010.09.01},
url = {http://dx.doi.org/10.1038/13732}
}
@article{Rinaldo2009Properties,
author = {Rinaldo, A.},
title = {Properties and refinements of the fused lasso},
journal = {Ann. Stat.},
year = {2009},
volume = {37},
pages = {2922--2952},
number = {5B},
url = {http://doi:10.1214/08-AOS665}
}
@article{Risau-Gusman2000Generalization,
author = {Risau-Gusman and Gordon},
title = {Generalization properties of finite-size polynomial support vector
machines},
journal = {Phys {R}ev {E} {S}tat {P}hys {P}lasmas {F}luids {R}elat {I}nterdiscip
{T}opics},
year = {2000},
volume = {62},
pages = {7092-9},
number = {5 Pt B},
month = {Nov},
abstract = {The learning properties of finite-size polynomial support vector machines
are analyzed in the case of realizable classification tasks. {T}he
normalization of the high-order features acts as a squeezing factor,
introducing a strong anisotropy in the patterns distribution in feature
space. {A}s a function of the training set size, the corresponding
generalization error presents a crossover, more or less abrupt depending
on the distribution's anisotropy and on the task to be learned, between
a fast-decreasing and a slowly decreasing regime. {T}his behavior
corresponds to the stepwise decrease found by {D}ietrich et al. [{P}hys.
{R}ev. {L}ett. 82, 2975 (1999)] in the thermodynamic limit. {T}he
theoretical results are in excellent agreement with the numerical
simulations.},
keywords = {Acute, Acute Disease, Adenocarcinoma, Algorithms, Amino Acid Sequence,
Animals, Artificial Intelligence, Automated, B-Lymphocytes, Bacterial
Proteins, Base Pair Mismatch, Base Sequence, Bayes Theorem, Binding
Sites, Biological, Bone Marrow Cells, Brachyura, Cell Compartmentation,
Chemistry, Child, Chromosome Aberrations, Classification, Codon,
Colonic Neoplasms, Comparative Study, Computational Biology, Computer
Simulation, Computer-Assisted, DNA, Data Interpretation, Databases,
Decision Trees, Diabetes Mellitus, Diagnosis, Discriminant Analysis,
Discrimination Learning, Electric Conductivity, Electrophysiology,
Escherichia coli Proteins, Factual, Feedback, Female, Fungal, Gastric
Emptying, Gene Expression Profiling, Gene Expression Regulation,
Genes, Genetic, Genetic Markers, Genetic Predisposition to Disease,
Genomics, Hemolysins, Humans, Indians, Initiator, Ion Channels, Kinetics,
Leukemia, Likelihood Functions, Lipid Bilayers, Logistic Models,
Lymphocytic, Male, Markov Chains, Melanoma, Models, Molecular, Myeloid,
Neoplasm, Neoplasms, Neoplastic, Neural Networks (Computer), Neurological,
Nevus, Non-P.H.S., Non-U.S. Gov't, Nonlinear Dynamics, Normal Distribution,
North American, Nucleic Acid Conformation, Oligonucleotide Array
Sequence Analysis, Organ Specificity, Organelles, Ovarian Neoplasms,
Ovary, P.H.S., Pattern Recognition, Physical, Pigmented, Predictive
Value of Tests, Promoter Regions (Genetics), Protein Biosynthesis,
Protein Folding, Protein Structure, Proteins, Proteome, RNA, Reproducibility
of Results, Research Support, Saccharomyces cerevisiae, Secondary,
Sensitivity and Specificity, Sequence Alignment, Sequence Analysis,
Sex Characteristics, Skin Diseases, Skin Neoplasms, Skin Pigmentation,
Software, Sound Spectrography, Statistical, Stomach Diseases, T-Lymphocytes,
Thermodynamics, Transcription, Transcription Factors, Tumor Markers,
Type 2, U.S. Gov't, Vertebrates, 0011102066}
}
@article{Risau-Gusman2001Statistical,
author = {S. Risau-Gusman and M. B. Gordon},
title = {Statistical mechanics of learning with soft margin classifiers.},
journal = {Phys {R}ev {E} {S}tat {N}onlin {S}oft {M}atter {P}hys},
year = {2001},
volume = {64},
pages = {031907},
number = {3 Pt 1},
month = {Sep},
abstract = {We study the typical learning properties of the recently introduced
soft margin classifiers ({SMC}s), learning realizable and unrealizable
tasks, with the tools of statistical mechanics. {W}e derive analytically
the behavior of the learning curves in the regime of very large training
sets. {W}e obtain exponential and power laws for the decay of the
generalization error towards the asymptotic value, depending on the
task and on general characteristics of the distribution of stabilities
of the patterns to be learned. {T}he optimal learning curves of the
{SMC}s, which give the minimal generalization error, are obtained
by tuning the coefficient controlling the trade-off between the error
and the regularization terms in the cost function. {I}f the task
is realizable by the {SMC}, the optimal performance is better than
that of a hard margin support vector machine and is very close to
that of a {B}ayesian classifier.}
}
@article{Rissanen1984Universal,
author = {Rissanen, J. },
title = {Universal coding, information, prediction, and estimation},
journal = {I{EEE} {T}rans. {I}nform. {T}heory},
year = {1984},
volume = {30},
pages = {629-636},
number = {4},
month = {Jul},
abstract = {A connection between universal codes and the problems of prediction
and statistical estimation is established. {A} known lower bound
for the mean length of universal codes is sharpened and generalized,
and optimum universal codes constructed. {T}he bound is defined to
give the information in strings relative to the considered class
of processes. {T}he earlier derived minimum description length criterion
for estimation of parameters, including their number, is given a
fundamental information, theoretic justification by showing that
its estimators achieve the information in the strings. {I}t is also
shown that one cannot do prediction in {G}aussian autoregressive
moving average ({ARMA}) processes below a bound, which is determined
by the information in the data. },
pdf = {../local/Rissanen1984Universal.pdf},
file = {Rissanen1984Universal.pdf:local/Rissanen1984Universal.pdf:PDF},
keywords = {information-theory universal-coding},
owner = {vert}
}
@article{Rissanen1981Universal,
author = {Rissanen, J. and Langdon, G. Jr. },
title = {Universal modeling and coding},
journal = {I{EEE} {T}rans. {I}nform. {T}heory},
year = {1981},
volume = {27},
pages = {12-23},
number = {1},
month = {Jan},
pdf = {../local/Rissanen1981Universal.pdf},
file = {Rissanen1981Universal.pdf:local/Rissanen1981Universal.pdf:PDF},
keywords = {information-theory source-coding},
owner = {vert}
}
@article{Rissanen1992Density,
author = {Rissanen, J. and Speed, T. P. and Yu, B.},
title = {Density estimation by stochastic complexity},
journal = {I{EEE} {T}rans. {I}nform. {T}heory},
year = {1992},
volume = {38},
pages = {315-323},
number = {2},
month = {Mar},
abstract = {The results by {P}. {H}all and {E}.{J}. {H}annan (1988) on optimization
of histogram density estimators with equal bin widths by minimization
of the stochastic complexity are extended and sharpened in two separate
ways. {A}s the first contribution, two generalized histogram estimators
are constructed. {T}he first has unequal bin widths which, together
with the number of the bins, are determined by minimization of the
stochastic complexity using dynamic programming. {T}he other estimator
consists of a mixture of equal bin width estimators, each of which
is defined by the associated stochastic complexity. {A}s the main
contribution in the present work, two theorems are proved, which
together extend the universal coding theorems to a large class of
data generating densities. {T}he first gives an asymptotic upper
bound for the code redundancy in the order of magnitude, achieved
with a special predictive type of histogram estimator, which sharpens
a related bound. {T}he second theorem states that this bound cannot
be improved upon by any code whatsoever },
pdf = {../local/Rissanen1992Density.pdf},
file = {Rissanen1992Density.pdf:local/Rissanen1992Density.pdf:PDF},
keywords = {information-theory},
owner = {vert}
}
@article{Rissanen1996Fisher,
author = {Rissanen, J. J.},
title = {Fisher information and stochastic complexity},
journal = {I{EEE} {T}rans. {I}nform. {T}heory},
year = {1996},
volume = {42},
pages = {40-47},
number = {1},
month = {Jan},
abstract = {By taking into account the {F}isher information and removing an inherent
redundancy in earlier two-part codes, a sharper code length as the
stochastic complexity and the associated universal process are derived
for a class of parametric processes. {T}he main condition required
is that the maximum-likelihood estimates satisfy the central limit
theorem. {T}he same code length is also obtained from the so-called
maximum-likelihood code },
pdf = {../local/Rissanen1996Fisher.pdf},
file = {Rissanen1996Fisher.pdf:local/Rissanen1996Fisher.pdf:PDF},
keywords = {information-theory},
owner = {vert}
}
@article{Rivals2007Enrichment,
author = {Rivals, I. and Personnaz, L. and Taing, L. and Potier, M.-C.},
title = {Enrichment or depletion of a GO category within a class of genes:
which test?},
journal = {Bioinformatics},
year = {2007},
volume = {23},
pages = {401--407},
number = {4},
month = {Feb},
abstract = {A number of available program packages determine the significant enrichments
and/or depletions of GO categories among a class of genes of interest.
Whereas a correct formulation of the problem leads to a single exact
null distribution, these GO tools use a large variety of statistical
tests whose denominations often do not clarify the underlying P-value
computations.We review the different formulations of the problem
and the tests they lead to: the binomial, chi2, equality of two probabilities,
Fisher's exact and hypergeometric tests. We clarify the relationships
existing between these tests, in particular the equivalence between
the hypergeometric test and Fisher's exact test. We recall that the
other tests are valid only for large samples, the test of equality
of two probabilities and the chi2-test being equivalent. We discuss
the appropriateness of one- and two-sided P-values, as well as some
discreteness and conservatism issues.Supplementary data are available
at Bioinformatics online.},
doi = {10.1093/bioinformatics/btl633},
pdf = {../local/Rivals2007Enrichment.pdf},
file = {Rivals2007Enrichment.pdf:Rivals2007Enrichment.pdf:PDF},
institution = {Equipe de Statistique Appliquée, 10 rue Vauquelin, 75005 Paris, France.
isabelle.rivals@espci.fr},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {btl633},
pmid = {17182697},
timestamp = {2011.08.06},
url = {http://dx.doi.org/10.1093/bioinformatics/btl633}
}
@article{Roberts2011Identification,
author = {Roberts, A. and Pimentel, H. and Trapnell, C. and Pachter, L.},
title = {Identification of novel transcripts in annotated genomes using {RNA-Seq}.},
journal = {Bioinformatics},
year = {2011},
volume = {27},
pages = {2325--2329},
number = {17},
month = {Sep},
abstract = {We describe a new 'reference annotation based transcript assembly'
problem for RNA-Seq data that involves assembling novel transcripts
in the context of an existing annotation. This problem arises in
the analysis of expression in model organisms, where it is desirable
to leverage existing annotations for discovering novel transcripts.
We present an algorithm for reference annotation-based transcript
assembly and show how it can be used to rapidly investigate novel
transcripts revealed by RNA-Seq in comparison with a reference annotation.The
methods described in this article are implemented in the Cufflinks
suite of software for RNA-Seq, freely available from http://bio.math.berkeley.edu/cufflinks.
The software is released under the BOOST license.cole@broadinstitute.org;
lpachter@math.berkeley.eduSupplementary data are available at Bioinformatics
online.},
doi = {10.1093/bioinformatics/btr355},
pdf = {../local/Roberts2011Identification.pdf},
file = {Roberts2011Identification.pdf:Roberts2011Identification.pdf:PDF},
institution = {Department of Computer Science, UC Berkeley, Berkeley, CA, USA.},
keywords = {ngs, rnaseq},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {btr355},
pmid = {21697122},
timestamp = {2013.03.29},
url = {http://dx.doi.org/10.1093/bioinformatics/btr355}
}
@article{Roberts2011Improving,
author = {Roberts, A. and Trapnell, C. and Donaghey, J. and Rinn, J. L. and
Pachter, L.},
title = {Improving {RNA-Seq} expression estimates by correcting for fragment
bias.},
journal = {Genome Biol},
year = {2011},
volume = {12},
pages = {R22},
number = {3},
abstract = {The biochemistry of RNA-Seq library preparation results in cDNA fragments
that are not uniformly distributed within the transcripts they represent.
This non-uniformity must be accounted for when estimating expression
levels, and we show how to perform the needed corrections using a
likelihood based approach. We find improvements in expression estimates
as measured by correlation with independently performed qRT-PCR and
show that correction of bias leads to improved replicability of results
across libraries and sequencing technologies.},
doi = {10.1186/gb-2011-12-3-r22},
pdf = {../local/Roberts2011Improving.pdf},
file = {Roberts2011Improving.pdf:Roberts2011Improving.pdf:PDF},
institution = {Department of Computer Science, 387 Soda Hall, UC Berkeley, Berkeley,
CA 94720, USA.},
keywords = {ngs, rnaseq},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {gb-2011-12-3-r22},
pmid = {21410973},
timestamp = {2013.03.29},
url = {http://dx.doi.org/10.1186/gb-2011-12-3-r22}
}
@article{Robine2007Genome-wide,
author = {Robine, N. and Uematsu, N. and Amiot, F. and Gidrol, X. and Barillot,
E. and Nicolas, A. and Borde, V.},
title = {Genome-wide redistribution of meiotic double-strand breaks in Saccharomyces
cerevisiae},
journal = {Mol. Cell. Biol.},
year = {2007},
volume = {27},
pages = {1868--1880},
number = {5},
month = {Mar},
abstract = {Meiotic recombination is initiated by the formation of programmed
DNA double-strand breaks (DSBs) catalyzed by the Spo11 protein. DSBs
are not randomly distributed along chromosomes. To better understand
factors that control the distribution of DSBs in budding yeast, we
have examined the genome-wide binding and cleavage properties of
the Gal4 DNA binding domain (Gal4BD)-Spo11 fusion protein. We found
that Gal4BD-Spo11 cleaves only a subset of its binding sites, indicating
that the association of Spo11 with chromatin is not sufficient for
DSB formation. In centromere-associated regions, the centromere itself
prevents DSB cleavage by tethered Gal4BD-Spo11 since its displacement
restores targeted DSB formation. In addition, we observed that new
DSBs introduced by Gal4BD-Spo11 inhibit surrounding DSB formation
over long distances (up to 60 kb), keeping constant the number of
DSBs per chromosomal region. Together, these results demonstrate
that the targeting of Spo11 to new chromosomal locations leads to
both local stimulation and genome-wide redistribution of recombination
initiation and that some chromosomal regions are inherently cold
regardless of the presence of Spo11.},
doi = {10.1128/MCB.02063-06},
institution = {Institut Curie, Recombinaison et Instabilité Génétique, Centre
de Recherche, UMR7147 CNRS-Institut Curie-Université P. et M. Curie,
26 rue d'Ulm, 75248 Paris Cedex 05, France. valerie.borde@curie.fr},
owner = {jp},
pii = {MCB.02063-06},
pmid = {17189430},
timestamp = {2008.12.05},
url = {http://dx.doi.org/10.1128/MCB.02063-06}
}
@article{Robinson2000IMGT/HLAa,
author = {J. Robinson and A. Malik and P. Parham and J. G. Bodmer and S. G.
Marsh},
title = {{IMGT/HLA} database--a sequence database for the human major histocompatibility
complex.},
journal = {Tissue Antigens},
year = {2000},
volume = {55},
pages = {280--287},
number = {3},
month = {Mar},
abstract = {The IMGT/HLA Database is a specialist database for sequences of the
human major histocompatibility (MHC) system. It includes all the
HLA sequences officially recognised and named by the WHO Nomenclature
Committee for Factors of the HLA System. The database provides users
with online tools and facilities for the retrieval and analysis of
these sequences. These include allele reports, alignment tools and
a detailed database of all source cells. The online IMGT/HLA submission
tool allows the submission of both new and confirmatory allele sequences
directly to the WHO Nomenclature Committee for Factors of the HLA
System. The latest version (release 1.4.1, November 1999) contains
1,015 HLA alleles from over 2,270 component sequences derived from
the EMBL/GenBank/DDBJ databases. From its release in December 1998
until December 1999 the IMGT/HLA website received approximately 100,000
hits. The database currently focuses on the human major histocompatibility
complex but will be used as a model system to provide specialist
databases for the MHC sequences of other species.},
keywords = {Base Sequence; Databases, Factual; Humans; Major Histocompatibility
Complex; Molecular Sequence Data},
owner = {laurent},
pmid = {10777106},
timestamp = {2007.01.30}
}
@article{Roche2002virtual,
author = {Roche, O. and Trube, G. and Zuegge, J. and Pflimlin, P. and Alanine,
A. and Schneider, G.},
title = {{A} virtual screening method for prediction of the {HERG} potassium
channel liability of compound libraries.},
journal = {ChemBioChem},
year = {2002},
volume = {3},
pages = {455--459},
number = {5},
month = {May},
abstract = {A computer-based method has been developed for prediction of the hERG
(human ether-Ã -go-go related gene) K(+)-channel affinity of low
molecular weight compounds. hERG channel blockage is a major concern
in drug design, as such blocking agents can cause sudden cardiac
death. Various techniques were applied to finding appropriate molecular
descriptors for modeling structure-activity relationships: substructure
analysis, self-organizing maps (SOM), principal component analysis
(PCA), partial least squares fitting (PLS), and supervised neural
networks. The most accurate prediction system was based on an artificial
neural network. In a validation study, 93 \% of the nonblocking agents
and 71 \% of the hERG channel blockers were correctly classified.
This virtual screening method can be used for general compound-library
shaping and combinatorial library design.},
keywords = {chemoinformatics herg},
pmid = {12007180},
timestamp = {2006.10.05}
}
@book{Rockafellar1997Convex,
title = {Convex Analysis},
publisher = {Princeton Univ. Press},
year = {1997},
author = {Rockafellar, R.T.}
}
@article{Rodriguez-Paredes2011Cancer,
author = {Manuel Rodríguez-Paredes and Manel Esteller},
title = {Cancer epigenetics reaches mainstream oncology.},
journal = {Nat Med},
year = {2011},
volume = {17},
pages = {330--339},
number = {3},
month = {Mar},
abstract = {Epigenetics is one of the most promising and expanding fields in the
current biomedical research landscape. Since the inception of epigenetics
in the 1940s, the discoveries regarding its implications in normal
and disease biology have not stopped, compiling a vast amount of
knowledge in the past decade. The field has moved from just one recognized
marker, DNA methylation, to a variety of others, including a wide
spectrum of histone modifications. From the methodological standpoint,
the successful initial single gene candidate approaches have been
complemented by the current comprehensive epigenomic approaches that
allow the interrogation of genomes to search for translational applications
in an unbiased manner. Most important, the discovery of mutations
in the epigenetic machinery and the approval of the first epigenetic
drugs for the treatment of subtypes of leukemias and lymphomas has
been an eye-opener for many biomedical scientists and clinicians.
Herein, we will summarize the progress in the field of cancer epigenetics
research that has reached mainstream oncology in the development
of new biomarkers of the disease and new pharmacological strategies.},
doi = {10.1038/nm.2305},
institution = {Cancer Epigenetics and Biology Program, Bellvitge Biomedical Research
Institute, L'Hospitalet, and Department of Physiological Sciences
II, School of Medicine, University of Barcelona, Barcelona, Spain.},
keywords = {Amino Acid Sequence; DNA Methylation; Epigenesis, Genetic; Humans;
Molecular Sequence Data; Neoplasms, genetics/therapy; Tumor Markers,
Biological},
language = {eng},
medline-pst = {ppublish},
owner = {philippe},
pii = {nm.2305},
pmid = {21386836},
timestamp = {2011.06.04},
url = {http://dx.doi.org/10.1038/nm.2305}
}
@article{Roepstorff1984Proposal,
author = {P. Roepstorff and J. Fohlman},
title = {Proposal for a common nomenclature for sequence ions in mass spectra
of peptides.},
journal = {Biomed Mass Spectrom},
year = {1984},
volume = {11},
pages = {601},
number = {11},
month = {Nov},
doi = {10.1002/bms.1200111109},
keywords = {Mass Spectrometry; Peptides; Terminology as Topic},
owner = {phupe},
pmid = {6525415},
timestamp = {2010.09.03},
url = {http://dx.doi.org/10.1002/bms.1200111109}
}
@article{Rogers1994Application,
author = {D. Rogers and A. J. Hopfinger},
title = {Application of Genetic Function Approximation to Quantitative Structure-Activity
Relationships and Quantitative Structure-Property Relationships},
journal = {J Chem Inf Comput Sci},
year = {1994},
volume = {34},
pages = {854-866},
owner = {mahe},
timestamp = {2006.09.06}
}
@article{Rognan2007Chemogenomic,
author = {Rognan, D.},
title = {Chemogenomic approaches to rational drug design},
journal = {Br. J. Pharmacol.},
year = {2007},
volume = {152},
pages = {38--52},
month = {May},
abstract = {Paradigms in drug design and discovery are changing at a significant
pace. Concomitant to the sequencing of over 180 several genomes,
the high-throughput miniaturization of chemical synthesis and biological
evaluation of a multiple compounds on gene/protein expression and
function opens the way to global drug-discovery approaches, no more
focused on a single target but on an entire family of related proteins
or on a full metabolic pathway. Chemogenomics is this emerging research
field aimed at systematically studying the biological effect of a
wide array of small molecular-weight ligands on a wide array of macromolecular
targets. Since the quantity of existing data (compounds, targets
and assays) and of produced information (gene/protein expression
levels and binding constants) are too large for manual manipulation,
information technologies play a crucial role in planning, analysing
and predicting chemogenomic data. The present review will focus on
predictive in silico chemogenomic approaches to foster rational drug
design and derive information from the simultaneous biological evaluation
of multiple compounds on multiple targets. State-of-the-art methods
for navigating in either ligand or target space will be presented
and concrete drug design applications will be mentioned.British Journal
of Pharmacology advance online publication, 29 May 2007; doi:10.1038/sj.bjp.0707307.},
doi = {10.1038/sj.bjp.0707307},
owner = {laurent},
pii = {0707307},
pmid = {17533416},
timestamp = {2007.07.30},
url = {http://dx.doi.org/10.1038/sj.bjp.0707307}
}
@article{Rohlf2005J,
author = {Rohlf, F. James},
title = {J. Felsenstein, Inferring Phylogenies, Sinauer Assoc., 2004, pp.
xx + 664.},
journal = {J. Classif.},
year = {2005},
volume = {22},
pages = {139--142},
number = {1},
address = {Secaucus, NJ, USA},
doi = {http://dx.doi.org/10.1007/s00357-005-0009-4},
issn = {0176-4268},
publisher = {Springer-Verlag New York, Inc.}
}
@article{Rolland2005G-protein-coupled,
author = {Rolland, C. and Gozalbes, R. and Nicola{\"i}, A. and Paugam, M.-F.
and Coussy, L. and Barbosa, F. and Horvath, D. and Revah, F.},
title = {G-protein-coupled receptor affinity prediction based on the use of
a profiling dataset: QSAR design, synthesis, and experimental validation.},
journal = {J. Med. Chem.},
year = {2005},
volume = {48},
pages = {6563--6574},
number = {21},
month = {Oct},
abstract = {A QSAR model accounting for "average" G-protein-coupled receptor (GPCR)
binding was built from a large set of experimental standardized binding
data (1939 compounds systematically tested over 40 different GPCRs)
and applied to the design of a library of "GPCR-predicted" compounds.
Three hundred and sixty of these compounds were randomly selected
and tested in 21 GPCR binding assays. Positives were defined by their
ability to inhibit by more than 70\% the binding of reference compounds
at 10 microM. A 5.5-fold enrichment in positives was observed when
comparing the "GPCR-predicted" compounds with 600 randomly selected
compounds predicted as "non-GPCR" from a general collection. The
model was efficient in predicting strongest binders, since enrichment
was greater for higher cutoffs. Significant enrichment was also observed
for peptidic GPCRs and receptors not included to develop the QSAR
model, suggesting the usefulness of the model to design ligands binding
with newly identified GPCRs, including orphan ones.},
doi = {10.1021/jm0500673},
keywords = {chemogenomics},
owner = {laurent},
pmid = {16220973},
timestamp = {2008.01.16},
url = {http://dx.doi.org/10.1021/jm0500673}
}
@article{Rosasco2004loss,
author = {Rosasco, L. and Vito, E.D. and Caponnetto, A. and Piana, M. and Verri,
A.},
title = {Are loss functions all the same?},
journal = {Neural Computation},
year = {2004},
volume = {16},
pages = {1063--1076},
number = {5},
publisher = {MIT Press}
}
@article{Roschke2003Karyotypic,
author = {Anna V Roschke and Giovanni Tonon and Kristen S Gehlhaus and Nicolas
McTyre and Kimberly J Bussey and Samir Lababidi and Dominic A Scudiero
and John N Weinstein and Ilan R Kirsch},
title = {Karyotypic complexity of the NCI-60 drug-screening panel.},
journal = {Cancer Res},
year = {2003},
volume = {63},
pages = {8634--8647},
number = {24},
month = {Dec},
abstract = {We used spectral karyotyping to provide a detailed analysis of karyotypic
aberrations in the diverse group of cancer cell lines established
by the National Cancer Institute for the purpose of anticancer drug
discovery. Along with the karyotypic description of these cell lines
we defined and studied karyotypic complexity and heterogeneity (metaphase-to-metaphase
variations) based on three separate components of genomic anatomy:
(a) ploidy; (b) numerical changes; and (c) structural rearrangements.
A wide variation in these parameters was evident in these cell lines,
and different association patterns between them were revealed. Analysis
of the breakpoints and other specific features of chromosomal changes
across the entire set of cell lines or within particular lineages
pointed to a striking lability of centromeric regions that distinguishes
the epithelial tumor cell lines. We have also found that balanced
translocations are as frequent in absolute number within the cell
lines derived from solid as from hematopoietic tumors. Important
similarities were noticed between karyotypic changes in cancer cell
lines and that seen in primary tumors. This dataset offers insights
into the causes and consequences of the destabilizing events and
chromosomal instability that may occur during tumor development and
progression. It also provides a foundation for investigating associations
between structural genome anatomy and cancer molecular markers and
targets, gene expression, gene dosage, and resistance or sensitivity
to tens of thousands of molecular compounds.},
institution = {Genetics Branch, Center for Cancer Research, National Cancer Institute,
Bethesda, Maryland 20889-5105, USA.},
keywords = {Cell Line, Tumor; Chromosome Aberrations; DNA Repair, genetics; Drug
Screening Assays, Antitumor; Humans; Neoplasms, genetics/pathology;
Ploidies; Retinoblastoma Protein, genetics; Spectral Karyotyping;
Translocation, Genetic; Tumor Suppressor Protein p53, genetics},
language = {eng},
medline-pst = {ppublish},
owner = {philippe},
pmid = {14695175},
timestamp = {2010.08.05}
}
@article{Rose2005Correlation,
author = {Rose, J. R. and Turkett, W. H., Jr. and Oroian, I. C. and Laegreid,
W. W. and Keele, J.},
title = {Correlation of amino acid preference and mammalian viral genome type},
journal = {Bioinformatics},
year = {2005},
abstract = {Motivation: {I}n the event of an outbreak of a disease caused by an
initially unknown pathogen, the ability to characterize anonymous
sequences prior to isolation and culturing of the pathogen will be
helpful. {W}e show that it is possible to classify viral sequences
by genome type (ds{DNA}, ss{DNA}, ss{RNA} positive strand, ss{RNA}
negative strand, retroid) using amino acid distribution.{R}esults:
{I}n this paper we describe the results of analysis of amino acid
preference in mammalian viruses. {T}he study was carried out at the
genome level as well as two shorter sequence levels: short (300 amino
acids) and medium length (660 amino acids). {T}he analysis indicates
a correlation between the viral genome types ds{DNA}, ss{DNA}, ss{RNA}
positive strand, ss{RNA} negative strand, and retroid and amino acid
preference. {W}e investigated three different models of amino acid
preference. {T}he simplest amino acid preference model, 1-{AAP},
is a normalized description of the frequency of amino acids in genomes
of a viral genome type. {A} slightly more complex model is the ordered
pair amino acid preference model (2-{AAP}), which characterizes genomes
of different viral genome types by the frequency of ordered pairs
of amino acids. {T}he most complex and accurate model is the ordered
triple amino acid preference model (3-{AAP}), which is based on ordered
triples of amino acids. {T}he results demonstrate that mammalian
viral genome types differ in their amino acid preference.{A}vailability:
{T}he tools used to format and analyze data and supplementary material
are available at http://www.cse.sc.edu/~rose/amino{P}reference/index.html.},
doi = {10.1093/bioinformatics/bti174.},
pdf = {../local/Rose2005Correlation.pdf},
file = {Rose2005Correlation.pdf:local/Rose2005Correlation.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://bioinformatics.oupjournals.org/cgi/content/abstract/bti174v1}
}
@article{Rosenfeld1995Flexible,
author = {Rosenfeld, R. and Zheng, Q. and Vajda, S. and DeLisi, C.},
title = {{F}lexible docking of peptides to class {I} major-histocompatibility-complex
receptors.},
journal = {Genet. Anal.},
year = {1995},
volume = {12},
pages = {1--21},
number = {1},
month = {Mar},
abstract = {We present a new method for docking flexible peptides to class I Major-Histocompatibility-Complex
(MHC) receptors. Docking is performed in two steps: (a) The charged
terminal peptide residues are located by randomly distributing multiple
copies of each in volumes of approximately 150 A at either end of
the binding groove, and then minimizing the system energy using a
modified multiple-copy search algorithm. This is followed by (b)
construction of the intervening chain using the multiple-copy bond-scaling-relaxation
loop closure algorithm. In both steps, the copies tend to cluster
and the size of the resulting clusters is proportional to the basin
of attraction of the corresponding energy well. We show that native
MHC-bound peptides have broad minima and, consequently, that misfolded,
low-energy peptide conformations can be eliminated by restricting
consideration to groups of peptides which cluster into broad minima.
The accuracy of the method is assessed by comparing the predictions
with crystallographic data for three different MHC peptide systems,
at various degrees of stringency: (a) the extent to which we can
determine side chain function (anchor vs. T-cell epitopes); (b) the
extent to which we can determine the peptide-receptor orientation;
and (c) the accuracy with which we can predict atomic coordinates.
We find the method correct on (a) for 19 of the 22 non-Gly positions;
the failures appearing to be a consequence of omitting solvation.
Predictions related to (b) are also very encouraging, with the overall
orientation of the predicted peptides being very similar to the crystal
conformation, when measured by the hydrogen bonding pattern between
the two. The degree of success in predicting atomic coordinates varied
considerably, however, from 1.4 A for the HLA-A2 peptide to 2.7 A
for the Kb peptide. The inaccuracy of the latter appears to reflect
an incomplete target function, most likely the ommission of solvation.
The calculations thus define the current limits of accuracy in docking
flexible peptides to Class I receptors and identify the methodological
improvements that must be made for the next advance in accuracy.},
keywords = {immunoinformatics},
pmid = {7648466},
timestamp = {2007.01.25}
}
@article{Rosipal2001Kernel,
author = {Rosipal, R. and Trejo, L. J.},
title = {Kernel Partial Least Squares Regression in Reproducing Kernel Hilbert
Space},
journal = {J. Mach. Learn. Res.},
year = {2001},
volume = {2},
pages = {97--123},
owner = {vert},
timestamp = {2007.08.02}
}
@article{Ross2004Multiplexed,
author = {Philip L Ross and Yulin N Huang and Jason N Marchese and Brian Williamson
and Kenneth Parker and Stephen Hattan and Nikita Khainovski and Sasi
Pillai and Subhakar Dey and Scott Daniels and Subhasish Purkayastha
and Peter Juhasz and Stephen Martin and Michael Bartlet-Jones and
Feng He and Allan Jacobson and Darryl J Pappin},
title = {Multiplexed protein quantitation in Saccharomyces cerevisiae using
amine-reactive isobaric tagging reagents.},
journal = {Mol Cell Proteomics},
year = {2004},
volume = {3},
pages = {1154--1169},
number = {12},
month = {Dec},
abstract = {We describe here a multiplexed protein quantitation strategy that
provides relative and absolute measurements of proteins in complex
mixtures. At the core of this methodology is a multiplexed set of
isobaric reagents that yield amine-derivatized peptides. The derivatized
peptides are indistinguishable in MS, but exhibit intense low-mass
MS/MS signature ions that support quantitation. In this study, we
have examined the global protein expression of a wild-type yeast
strain and the isogenic upf1Delta and xrn1Delta mutant strains that
are defective in the nonsense-mediated mRNA decay and the general
5' to 3' decay pathways, respectively. We also demonstrate the use
of 4-fold multiplexing to enable relative protein measurements simultaneously
with determination of absolute levels of a target protein using synthetic
isobaric peptide standards. We find that inactivation of Upf1p and
Xrn1p causes common as well as unique effects on protein expression.},
doi = {10.1074/mcp.M400129-MCP200},
institution = {Applied Biosystems, Framingham, MA 01701, USA.},
keywords = {Cations; Chromatography, Ion Exchange; Chromatography, Liquid; Down-Regulation;
Exoribonucleases; Fungal Proteins; Indicators and Reagents; Ions;
Mass Spectrometry; Models, Chemical; Peptides; Phenotype; Proteomics;
RNA Helicases; RNA, Messenger; Saccharomyces cerevisiae; Saccharomyces
cerevisiae Proteins; Succinimides},
owner = {phupe},
pii = {M400129-MCP200},
pmid = {15385600},
timestamp = {2010.08.19},
url = {http://dx.doi.org/10.1074/mcp.M400129-MCP200}
}
@article{Rosset2007Piecewise,
author = {S. Rosset and J. Zhu},
title = {Piecewise Linear Regularized Solution Paths},
journal = {Annals of {S}tatistics},
year = {2007},
volume = {35},
pages = {1012--1030},
number = {3}
}
@article{Roth1998Finding,
author = {Roth, F. P. and Hughes, J. D. and Estep, P. W. and Church, G. M.},
title = {Finding {DNA} regulatory motifs within unaligned noncoding sequences
clustered by whole-genome mRNA quantitation.},
journal = {Nat. Biotechnol.},
year = {1998},
volume = {16},
pages = {939--945},
number = {10},
month = {October},
abstract = {Whole-genome mRNA quantitation can be used to identify the genes that
are most responsive to environmental or genotypic change. By searching
for mutually similar DNA elements among the upstream non-coding DNA
sequences of these genes, we can identify candidate regulatory motifs
and corresponding candidate sets of coregulated genes. We have tested
this strategy by applying it to three extensively studied regulatory
systems in the yeast Saccharomyces cerevisiae: galactose response,
heat shock, and mating type. Galactose-response data yielded the
known binding site of Gal4, and six of nine genes known to be induced
by galactose. Heat shock data yielded the cell-cycle activation motif,
which is known to mediate cell-cycle dependent activation, and a
set of genes coding for all four nucleosomal proteins. Mating type
alpha and a data yielded all of the four relevant DNA motifs and
most of the known a- and alpha-specific genes.},
address = {Harvard University Graduate Biophysics Program and Harvard Medical
School Department of Genetics, Boston, MA 02115, USA.},
doi = {10.1038/nbt1098-939},
issn = {1087-0156},
keywords = {bioinformatics, genome-wide, tfs},
url = {http://dx.doi.org/10.1038/nbt1098-939}
}
@article{Roth2004generalized,
author = {Volker Roth},
title = {The generalized {LASSO}.},
journal = {I{EEE} {T}rans {N}eural {N}etw},
year = {2004},
volume = {15},
pages = {16-28},
number = {1},
month = {Jan},
abstract = {In the last few years, the support vector machine ({SVM}) method has
motivated new interest in kernel regression techniques. {A}lthough
the {SVM} has been shown to exhibit excellent generalization properties
in many experiments, it suffers from several drawbacks, both of a
theoretical and a technical nature: the absence of probabilistic
outputs, the restriction to {M}ercer kernels, and the steep growth
of the number of support vectors with increasing size of the training
set. {I}n this paper, we present a different class of kernel regressors
that effectively overcome the above problems. {W}e call this approach
generalized {LASSO} regression. {I}t has a clear probabilistic interpretation,
can handle learning sets that are corrupted by outliers, produces
extremely sparse solutions, and is capable of dealing with large-scale
problems. {F}or regression functionals which can be modeled as iteratively
reweighted least-squares ({IRLS}) problems, we present a highly efficient
algorithm with guaranteed global convergence. {T}his defies a unique
framework for sparse regression models in the very rich class of
{IRLS} models, including various types of robust regression models
and logistic regression. {P}erformance studies for many standard
benchmark datasets effectively demonstrate the advantages of this
model over related approaches.},
doi = {10.1109/TNN.2003.809398},
pdf = {../local/Roth2004generalized.pdf},
file = {Roth2004generalized.pdf:local/Roth2004generalized.pdf:PDF},
keywords = {Algorithms, Bayes Theorem, Models, Neural Networks (Computer), Non-U.S.
Gov't, Research Design, Research Support, Theoretical, 15387244},
url = {http://dx.doi.org/10.1109/TNN.2003.809398}
}
@techreport{Roth02Thegeneralized,
author = {Volker Roth},
title = {The Generalized LASSO: a wrapper approach to gene selection for microarray
data},
institution = {Proceedings 14th International Conference on Automated Deduction
(CADE-14), 252--255},
year = {2002}
}
@inproceedings{Roth2002Thegeneralized,
author = {Volker Roth},
title = {The Generalized LASSO: a wrapper approach to gene selection for microarray
data},
booktitle = {Proc. CADE-14, 252--255},
year = {2002}
}
@inproceedings{Roth2008The,
author = {Roth, V. and Fischer, B.},
title = {The Group-Lasso for generalized linear models: uniqueness of solutions
and efficient algorithms},
booktitle = {ICML '08: Proceedings of the 25th international conference on Machine
learning},
year = {2008},
pages = {848-855},
bibsource = {DBLP, http://dblp.uni-trier.de},
ee = {http://doi.acm.org/10.1145/1390156.1390263},
pdf = {../local/Roth2008The.pdf},
file = {Roth2008The.pdf:Roth2008The.pdf:PDF},
keywords = {lasso}
}
@article{Roth2004Bayesian,
author = {Volker Roth and Tilman Lange},
title = {{B}ayesian class discovery in microarray datasets.},
journal = {IEEE Trans Biomed Eng},
year = {2004},
volume = {51},
pages = {707--718},
number = {5},
month = {May},
abstract = {A novel approach to class discovery in gene expression datasets is
presented. In the context of clinical diagnosis, the central goal
of class discovery algorithms is to simultaneously find putative
(sub-)types of diseases and to identify informative subsets of genes
with disease-type specific expression profile. Contrary to many other
approaches in the literature, the method presented implements a wrapper
strategy for feature selection, in the sense that the features are
directly selected by optimizing the discriminative power of the used
partitioning algorithm. The usual combinatorial problems associated
with wrapper approaches are overcome by a Bayesian inference mechanism.
On the technical side, we present an efficient optimization algorithm
with guaranteed local convergence property. The only free parameter
of the optimization method is selected by a resampling-based stability
analysis. Experiments with Leukemia and Lymphoma datasets demonstrate
that our method is able to correctly infer partitions and corresponding
subsets of genes which both are relevant in a biological sense. Moreover,
the frequently observed problem of ambiguities caused by different
but equally high-scoring partitions is successfully overcome by the
model selection method proposed.},
keywords = {Algorithms, Automated, Bayes Theorem, Cluster Analysis, Comparative
Study, DNA, Databases, Gene Expression Profiling, Genetic, Genetic
Screening, Humans, Leukemia, Models, Non-U.S. Gov't, Nucleic Acid,
Oligonucleotide Array Sequence Analysis, Pattern Recognition, Reproducibility
of Results, Research Support, Sensitivity and Specificity, Sequence
Alignment, Sequence Analysis, Statistical, 15132496},
pmid = {15132496},
timestamp = {2006.07.27}
}
@article{Rothman2008Sparse,
author = {Rothman, A. J. and Bickel, P. J. and Levina, E. and Zhu, J.},
title = {Sparse permutation invariant covariance estimation},
journal = {Electron. J. Statist.},
year = {2008},
volume = {2},
pages = {494--515},
doi = {10.1214/08-EJS176},
pdf = {../local/Rothman2008Sparse.pdf},
file = {Rothman2008Sparse.pdf:Rothman2008Sparse.pdf:PDF},
owner = {jp},
timestamp = {2012.03.20},
url = {http://dx.doi.org/10.1214/08-EJS176}
}
@article{Roweis2000Nonlinear,
author = {Roweis, S. T. and Saul, L. K.},
title = {Nonlinear dimensionality reduction by locally linear embedding.},
journal = {Science},
year = {2000},
volume = {290},
pages = {2323-6},
number = {5500},
month = {Dec},
abstract = {Many areas of science depend on exploratory data analysis and visualization.
{T}he need to analyze large amounts of multivariate data raises the
fundamental problem of dimensionality reduction: how to discover
compact representations of high-dimensional data. {H}ere, we introduce
locally linear embedding ({LLE}), an unsupervised learning algorithm
that computes low-dimensional, neighborhood-preserving embeddings
of high-dimensional inputs. {U}nlike clustering methods for local
dimensionality reduction, {LLE} maps its inputs into a single global
coordinate system of lower dimensionality, and its optimizations
do not involve local minima. {B}y exploiting the local symmetries
of linear reconstructions, {LLE} is able to learn the global structure
of nonlinear manifolds, such as those generated by images of faces
or documents of text.},
doi = {10.1126/science.290.5500.2323},
pdf = {../local/Roweis2000Nonlinear.pdf},
file = {Roweis2000Nonlinear.pdf:local/Roweis2000Nonlinear.pdf:PDF},
keywords = {dimred},
pii = {290/5500/2323},
url = {http://dx.doi.org/10.1126/science.290.5500.2323}
}
@article{Rual2005Towards,
author = {Jean-François Rual and Kavitha Venkatesan and Tong Hao and Tomoko
Hirozane-Kishikawa and Amélie Dricot and Ning Li and Gabriel F Berriz
and Francis D Gibbons and Matija Dreze and Nono Ayivi-Guedehoussou
and Niels Klitgord and Christophe Simon and Mike Boxem and Stuart
Milstein and Jennifer Rosenberg and Debra S Goldberg and Lan V Zhang
and Sharyl L Wong and Giovanni Franklin and Siming Li and Joanna
S Albala and Janghoo Lim and Carlene Fraughton and Estelle Llamosas
and Sebiha Cevik and Camille Bex and Philippe Lamesch and Robert
S Sikorski and Jean Vandenhaute and Huda Y Zoghbi and Alex Smolyar
and Stephanie Bosak and Reynaldo Sequerra and Lynn Doucette-Stamm
and Michael E Cusick and David E Hill and Frederick P Roth and Marc
Vidal},
title = {Towards a proteome-scale map of the human protein-protein interaction
network.},
journal = {Nature},
year = {2005},
volume = {437},
pages = {1173--1178},
number = {7062},
month = {Oct},
abstract = {Systematic mapping of protein-protein interactions, or 'interactome'
mapping, was initiated in model organisms, starting with defined
biological processes and then expanding to the scale of the proteome.
Although far from complete, such maps have revealed global topological
and dynamic features of interactome networks that relate to known
biological properties, suggesting that a human interactome map will
provide insight into development and disease mechanisms at a systems
level. Here we describe an initial version of a proteome-scale map
of human binary protein-protein interactions. Using a stringent,
high-throughput yeast two-hybrid system, we tested pairwise interactions
among the products of approximately 8,100 currently available Gateway-cloned
open reading frames and detected approximately 2,800 interactions.
This data set, called CCSB-HI1, has a verification rate of approximately
78\% as revealed by an independent co-affinity purification assay,
and correlates significantly with other biological attributes. The
CCSB-HI1 data set increases by approximately 70\% the set of available
binary interactions within the tested space and reveals more than
300 new connections to over 100 disease-associated proteins. This
work represents an important step towards a systematic and comprehensive
human interactome project.},
doi = {10.1038/nature04209},
institution = {Center for Cancer Systems Biology and Department of Cancer Biology,
Dana-Farber Cancer Institute, Harvard Medical School, 44 Binney Street,
Boston, Massachusetts 02115, USA.},
keywords = {Cloning, Molecular; Humans; Open Reading Frames; Protein Binding;
Proteome; RNA; Saccharomyces cerevisiae; Two-Hybrid System Techniques},
owner = {phupe},
pii = {nature04209},
pmid = {16189514},
timestamp = {2010.09.01},
url = {http://dx.doi.org/10.1038/nature04209}
}
@article{Rudd2005Eclair,
author = {Rudd, S. and Tetko, I. V.},
title = {Eclair--a web service for unravelling species origin of sequences
sampled from mixed host interfaces.},
journal = {Nucleic {A}cids {R}es},
year = {2005},
volume = {33},
pages = {W724-7},
number = {Web Server issue},
month = {Jul},
abstract = {The identification of the genes that participate at the biological
interface of two species remains critical to our understanding of
the mechanisms of disease resistance, disease susceptibility and
symbiosis. {T}he sequencing of complementary {DNA} (c{DNA}) libraries
prepared from the biological interface between two organisms provides
an inexpensive way to identify the novel genes that may be expressed
as a cause or consequence of compatible or incompatible interactions.
{S}equence classification and annotation of species origin typically
use an orthology-based approach and require access to large portions
of either genome, or a close relative. {N}ovel species- or clade-specific
sequences may have no counterpart within existing databases and remain
ambiguous features. {H}ere we present a web-service, {E}clair, which
utilizes support vector machines for the classification of the origin
of expressed sequence tags stemming from mixed host c{DNA} libraries.
{I}n addition to providing an interface for the classification of
sequences, users are presented with the opportunity to train a model
to suit their preferred species pair. {E}clair is freely available
at http://eclair.btk.fi.},
doi = {10.1093/nar/gki434},
pdf = {../local/Rudd2005Eclair.pdf},
file = {Rudd2005Eclair.pdf:local/Rudd2005Eclair.pdf:PDF},
keywords = {biosvm},
pii = {33/suppl_2/W724},
url = {http://dx.doi.org/10.1093/nar/gki434}
}
@article{Rudin1992Nonlinear,
author = {Rudin, L. I. and Osher, S. and Fatemi, E.},
title = {Nonlinear total variation based noise removal algorithms},
journal = {Physica D},
year = {1992},
volume = {60},
pages = {259--268},
abstract = {A constrained optimization type of numerical algorithm for removing
noise from images is presented. The total variation of the image
is minimized subject to constraints involvingthe statistics of the
noise. The constraints are imposed using Lagrange multipliers. The
solution is obtained using the gradient-projection method. This amounts
to solving a time dependent partial differential equation on a manifold
determined by the constraints. As t---~0othe solution converges to
a steady state which is the denoised image. The numerical algorithm
is simple and relatively fast. The results appear to be state-of-the-art
for very noisy images. The method is noninvasive, yielding sharp
edges in the image. The technique could be interpreted as a first
step of moving each level set of the the level set divided by the
magnitude of the gradient of the the constraint set.},
pdf = {../local/Rudin1992Nonlinear.pdf},
file = {Rudin1992Nonlinear.pdf:Rudin1992Nonlinear.pdf:PDF},
keywords = {segmentation},
owner = {jp},
timestamp = {2010.05.31}
}
@article{Ruepp2005Assessment,
author = {Ruepp, S. and Boess, F. and Suter, L. and de Vera, M. C. and Steiner,
G. and Steele, T. and Weiser, T. and Albertini, S.},
title = {Assessment of hepatotoxic liabilities by transcript profiling.},
journal = {Toxicol {A}ppl {P}harmacol},
year = {2005},
month = {Jun},
abstract = {Male {W}istar rats were treated with various model compounds or the
appropriate vehicle controls in order to create a reference database
for toxicogenomics assessment of novel compounds. {H}epatotoxic compounds
in the database were either known hepatotoxicants or showed hepatotoxicity
during preclinical testing. {H}istopathology and clinical chemistry
data were used to anchor the transcript profiles to an established
endpoint (steatosis, cholestasis, direct acting, peroxisomal proliferation
or nontoxic/control). {T}hese reference data were analyzed using
a supervised learning method (support vector machines, {SVM}) to
generate classification rules. {T}his predictive model was subsequently
used to assess compounds with regard to a potential hepatotoxic liability.
{A} steatotic and a non-hepatotoxic 5{HT}(6) receptor antagonist
compound from the same series were successfully discriminated by
this toxicogenomics model. {A}dditionally, an example is shown where
a hepatotoxic liability was correctly recognized in the absence of
pathological findings. {I}n vitro experiments and a dog study confirmed
the correctness of the toxicogenomics alert. {A}nother interesting
observation was that transcript profiles indicate toxicologically
relevant changes at an earlier timepoint than routinely used methods.
{T}ogether, these results support the useful application of toxicogenomics
in raising alerts for adverse effects and generating mechanistic
hypotheses that can be followed up by confirmatory experiments.},
doi = {10.1016/j.taap.2005.05.008},
pdf = {../local/Ruepp2005Assessment.pdf},
file = {Ruepp2005Assessment.pdf:local/Ruepp2005Assessment.pdf:PDF},
keywords = {biosvm},
pii = {S0041-008X(05)00295-4},
url = {http://dx.doi.org/10.1016/j.taap.2005.05.008}
}
@article{Rumble2009SHRiMP,
author = {Rumble, S. M. and Lacroute, P. and Dalca, A. V. and Fiume, M. and
Sidow, A. and Brudno, M.},
title = {{SHRiMP}: accurate mapping of short color-space reads.},
journal = {PLoS Comput. Biol.},
year = {2009},
volume = {5},
pages = {e1000386},
number = {5},
month = {May},
abstract = {The development of Next Generation Sequencing technologies, capable
of sequencing hundreds of millions of short reads (25-70 bp each)
in a single run, is opening the door to population genomic studies
of non-model species. In this paper we present SHRiMP - the SHort
Read Mapping Package: a set of algorithms and methods to map short
reads to a genome, even in the presence of a large amount of polymorphism.
Our method is based upon a fast read mapping technique, separate
thorough alignment methods for regular letter-space as well as AB
SOLiD (color-space) reads, and a statistical model for false positive
hits. We use SHRiMP to map reads from a newly sequenced Ciona savignyi
individual to the reference genome. We demonstrate that SHRiMP can
accurately map reads to this highly polymorphic genome, while confirming
high heterozygosity of C. savignyi in this second individual. SHRiMP
is freely available at http://compbio.cs.toronto.edu/shrimp.},
doi = {10.1371/journal.pcbi.1000386},
institution = {Department of Computer Science, University of Toronto, Toronto, Ontario,
Canada.},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pmid = {19461883},
timestamp = {2011.10.27},
url = {http://dx.doi.org/10.1371/journal.pcbi.1000386}
}
@article{Rumelhart1986Learning,
author = {Rumelhart, D. E. and Hinton, G. E. and Williams, R. J.},
title = {Learning representations by back-propagating errors},
journal = {Nature},
year = {1986},
volume = {323},
pages = {533--536},
pdf = {../local/Rumelhart1986Learning.pdf},
file = {Rumelhart1986Learning.pdf:Rumelhart1986Learning.pdf:PDF},
owner = {jp},
timestamp = {2012.12.13}
}
@article{Rusk2008Primer,
author = {Nicole Rusk and Veronique Kiermer},
title = {Primer: Sequencing - the next generation},
journal = {Nat. Methods},
year = {2008},
volume = {5},
pages = {15},
keywords = {csbcbook, csbcbook-ch2}
}
@article{Russell1992Multiple,
author = {R. B. Russell and G. J. Barton},
title = {Multiple protein sequence alignment from tertiary structure comparison:
assignment of global and residue confidence levels.},
journal = {Proteins},
year = {1992},
volume = {14},
pages = {309--323},
number = {2},
month = {Oct},
abstract = {An algorithm is presented for the accurate and rapid generation of
multiple protein sequence alignments from tertiary structure comparisons.
A preliminary multiple sequence alignment is performed using sequence
information, which then determines an initial superposition of the
structures. A structure comparison algorithm is applied to all pairs
of proteins in the superimposed set and a similarity tree calculated.
Multiple sequence alignments are then generated by following the
tree from the branches to the root. At each branchpoint of the tree,
a structure-based sequence alignment and coordinate transformations
are output, with the multiple alignment of all structures output
at the root. The algorithm encoded in STAMP (STructural Alignment
of Multiple Proteins) is shown to give alignments in good agreement
with published structural accounts within the dehydrogenase fold
domains, globins, and serine proteinases. In order to reduce the
need for visual verification, two similarity indices are introduced
to determine the quality of each generated structural alignment.
Sc quantifies the global structural similarity between pairs or groups
of proteins, whereas Pij' provides a normalized measure of the confidence
in the alignment of each residue. STAMP alignments have the quality
of each alignment characterized by Sc and Pij' values and thus provide
a reproducible resource for studies of residue conservation within
structural motifs.},
doi = {10.1002/prot.340140216},
keywords = {Algorithms; Amino Acid Sequence; Animals; Confidence Intervals; Globins;
Humans; Molecular Sequence Data; Protein Structure, Tertiary; Sequence
Alignment; Sequence Homology, Amino Acid; Serine Endopeptidases;
Software},
owner = {laurent},
pmid = {1409577},
timestamp = {2008.01.15},
url = {http://dx.doi.org/10.1002/prot.340140216}
}
@incollection{Ratsch2004Accurate,
author = {R{\"a}tsch, G. and Sonnenburg, S.},
title = {Accurate splice site detection for {C}aenorhabditis elegans},
booktitle = {Kernel {M}ethods in {C}omputational {B}iology},
publisher = {MIT Press},
year = {2004},
editor = {Sch{\"o}lkopf, B. and Tsuda, K. and Vert, J.P.},
pages = {277-298},
abstract = {During the past three years, the support vector machine learning algorithm
has been extensively applied within the field of computational biology.
{T}he algorithm has been used to detect patterns within and among
biological sequences, to classify genes and patients based upon gene
expression profiles, and has recently been applied to several new
biological problems. {T}his chapter reviews the state of the art
with respect to {SVM} applications in computational biology.},
keywords = {biosvm},
owner = {vert}
}
@article{Raetsch2005RASE,
author = {G. R{\"a}tsch and S. Sonnenburg and B. Sch{\"o}lkopf},
title = {R{ASE}: recognition of alternatively spliced exons in {C}.elegans.},
journal = {Bioinformatics},
year = {2005},
volume = {21},
pages = {i369-i377},
number = {Suppl. 1},
month = {Jun},
abstract = {M{OTIVATION}: {E}ukaryotic pre-m{RNA}s are spliced to form mature
m{RNA}. {P}re-m{RNA} alternative splicing greatly increases the complexity
of gene expression. {E}stimates show that more than half of the human
genes and at least one-third of the genes of less complex organisms,
such as nematodes or flies, are alternatively spliced. {I}n this
work, we consider one major form of alternative splicing, namely
the exclusion of exons from the transcript. {I}t has been shown that
alternatively spliced exons have certain properties that distinguish
them from constitutively spliced exons. {A}lthough most recent computational
studies on alternative splicing apply only to exons which are conserved
among two species, our method only uses information that is available
to the splicing machinery, i.e. the {DNA} sequence itself. {W}e employ
advanced machine learning techniques in order to answer the following
two questions: (1) {I}s a certain exon alternatively spliced? (2)
{H}ow can we identify yet unidentified exons within known introns?
{RESULTS}: {W}e designed a support vector machine ({SVM}) kernel
well suited for the task of classifying sequences with motifs having
positional preferences. {I}n order to solve the task (1), we combine
the kernel with additional local sequence information, such as lengths
of the exon and the flanking introns. {T}he resulting {SVM}-based
classifier achieves a true positive rate of 48.5\% at a false positive
rate of 1\%. {B}y scanning over single {EST} confirmed exons we identified
215 potential alternatively spliced exons. {F}or 10 randomly selected
such exons we successfully performed biological verification experiments
and confirmed three novel alternatively spliced exons. {T}o answer
question (2), we additionally used {SVM}-based predictions to recognize
acceptor and donor splice sites. {C}ombined with the above mentioned
features we were able to identify 85.2\% of skipped exons within
known introns at a false positive rate of 1\%. {AVAILABILITY}: {D}atasets,
model selection results, our predictions and additional experimental
results are available at http://www.fml.tuebingen.mpg.de/~raetsch/{RASE}
{CONTACT}: {G}unnar.{R}aetsch@tuebingen.mpg.de {SUPPLEMENTARY} {INFORMATION}:
http://www.fml.tuebingen.mpg.de/raetsch/{RASE}.},
doi = {10.1093/bioinformatics/bti1053},
pdf = {../local/Raetsch2005RASE.pdf},
file = {Raetsch2005RASE.pdf:local/Raetsch2005RASE.pdf:PDF},
keywords = {biosvm},
pii = {21/suppl_1/i369},
url = {http://dx.doi.org/10.1093/bioinformatics/bti1053}
}
@article{Roegnvaldsson2004Why,
author = {Thorsteinn R{\"o}gnvaldsson and Liwen You},
title = {Why neural networks should not be used for {HIV}-1 protease cleavage
site prediction.},
journal = {Bioinformatics},
year = {2004},
volume = {20},
pages = {1702-9},
number = {11},
month = {Jul},
abstract = {S{UMMARY}: {S}everal papers have been published where nonlinear machine
learning algorithms, e.g. artificial neural networks, support vector
machines and decision trees, have been used to model the specificity
of the {HIV}-1 protease and extract specificity rules. {W}e show
that the dataset used in these studies is linearly separable and
that it is a misuse of nonlinear classifiers to apply them to this
problem. {T}he best solution on this dataset is achieved using a
linear classifier like the simple perceptron or the linear support
vector machine, and it is straightforward to extract rules from these
linear models. {W}e identify key residues in peptides that are efficiently
cleaved by the {HIV}-1 protease and list the most prominent rules,
relating them to experimental results for the {HIV}-1 protease. {MOTIVATION}:
{U}nderstanding {HIV}-1 protease specificity is important when designing
{HIV} inhibitors and several different machine learning algorithms
have been applied to the problem. {H}owever, little progress has
been made in understanding the specificity because nonlinear and
overly complex models have been used. {RESULTS}: {W}e show that the
problem is much easier than what has previously been reported and
that linear classifiers like the simple perceptron or linear support
vector machines are at least as good predictors as nonlinear algorithms.
{W}e also show how sets of specificity rules can be generated from
the resulting linear classifiers. {AVAILABILITY}: {T}he datasets
used are available at http://www.hh.se/staff/bioinf/},
doi = {10.1093/bioinformatics/bth144},
pdf = {../local/Roegnvaldsson2004Why.pdf},
file = {Roegnvaldsson2004Why.pdf:local/Roegnvaldsson2004Why.pdf:PDF},
keywords = {biosvm},
pii = {bth144},
url = {http://dx.doi.org/10.1093/bioinformatics/bth144}
}
@article{Rotzschke1992Peptide,
author = {R{\"o}tzschke, O. and Falk, K. and Stevanovi{\'c}, S. and Jung, G.
and Rammensee, H. C.},
title = {{P}eptide motifs of closely related {HLA} class {I} molecules encompass
substantial differences.},
journal = {Eur. J. Immunol.},
year = {1992},
volume = {22},
pages = {2453--2456},
number = {9},
month = {Sep},
abstract = {The peptides presented by major histocompatibility complex class I
molecules adhere to strict rules concerning peptide length and occupancy
by certain amino acid residues at anchor positions. Peptides presented
by HLA-A*0201 molecules, for example, are generally nonapeptides
requiring Leu or Met at position 2 and an aliphatic residue, predominantly
Val, at position 9. A closely related molecule, HLA-A*0205, differing
from the former at four amino acid residues, has a related but substantially
different peptide motif. A*0205-presented peptides are still nonapeptides,
and position 9 is still aliphatic, although it is preferentially
occupied by Leu instead of Val. Position 2 not only allows aliphatic
residues but also polar ones. Occupancy at position 6, considered
as an auxiliary anchor in A*0201, as well as non-anchor residues
at positions 3, 4, and 8 are relatively well conserved between the
two peptide motifs. Thus, although a number of the T cell epitopes
presented by the two HLA-A2 forms is expected to be identical, a
considerable number of epitopes should be different.},
keywords = {immunoinformatics},
pmid = {1516632},
timestamp = {2007.01.25}
}
@article{Roesch2005Chemotaxonomic,
author = {Petra Rösch and Michaela Harz and Michael Schmitt and Klaus-Dieter
Peschke and Olaf Ronneberger and Hans Burkhardt and Hans-Walter Motzkus
and Markus Lankers and Stefan Hofer and Hans Thiele and Jürgen Popp},
title = {Chemotaxonomic identification of single bacteria by micro-{R}aman
spectroscopy: application to clean-room-relevant biological contaminations.},
journal = {Appl {E}nviron {M}icrobiol},
year = {2005},
volume = {71},
pages = {1626-37},
number = {3},
month = {Mar},
abstract = {Microorganisms, such as bacteria, which might be present as contamination
inside an industrial food or pharmaceutical clean room process need
to be identified on short time scales in order to minimize possible
health hazards as well as production downtimes causing financial
deficits. {H}ere we describe the first results of single-particle
micro-{R}aman measurements in combination with a classification method,
the so-called support vector machine technique, allowing for a fast,
reliable, and nondestructive online identification method for single
bacteria.},
doi = {10.1128/AEM.71.3.1626-1637.2005},
pdf = {../local/Roesch2005Chemotaxonomic.pdf},
file = {Roesch2005Chemotaxonomic.pdf:local/Roesch2005Chemotaxonomic.pdf:PDF},
pii = {71/3/1626},
url = {http://dx.doi.org/10.1128/AEM.71.3.1626-1637.2005}
}
@article{Sachidanandam2001Map,
author = {R. Sachidanandam and D. Weissman and S. C. Schmidt and J. M. Kakol
and L. D. Stein and G. Marth and S. Sherry and J. C. Mullikin and
B. J. Mortimore and D. L. Willey and S. E. Hunt and C. G. Cole and
P. C. Coggill and C. M. Rice and Z. Ning and J. Rogers and D. R.
Bentley and P. Y. Kwok and E. R. Mardis and R. T. Yeh and B. Schultz
and L. Cook and R. Davenport and M. Dante and L. Fulton and L. Hillier
and R. H. Waterston and J. D. McPherson and B. Gilman and S. Schaffner
and W. J. Van Etten and D. Reich and J. Higgins and M. J. Daly and
B. Blumenstiel and J. Baldwin and N. Stange-Thomann and M. C. Zody
and L. Linton and E. S. Lander and D. Altshuler and International
SNP Map Working Group},
title = {A map of human genome sequence variation containing 1.42 million
single nucleotide polymorphisms.},
journal = {Nature},
year = {2001},
volume = {409},
pages = {928--933},
number = {6822},
month = {Feb},
abstract = {We describe a map of 1.42 million single nucleotide polymorphisms
(SNPs) distributed throughout the human genome, providing an average
density on available sequence of one SNP every 1.9 kilobases. These
SNPs were primarily discovered by two projects: The SNP Consortium
and the analysis of clone overlaps by the International Human Genome
Sequencing Consortium. The map integrates all publicly available
SNPs with described genes and other genomic features. We estimate
that 60,000 SNPs fall within exon (coding and untranslated regions),
and 85\% of exons are within 5 kb of the nearest SNP. Nucleotide
diversity varies greatly across the genome, in a manner broadly consistent
with a standard population genetic model of human history. This high-density
SNP map provides a public resource for defining haplotype variation
across the genome, and should help to identify biomedically important
genes for diagnosis and therapy.},
institution = {Cold Spring Harbor, New York 11724, USA.},
keywords = {Chromosome Mapping; Genetic Variation; Genetics, Medical; Genetics,
Population; Genome, Human; Humans; Nucleotides; Polymorphism, Single
Nucleotide},
language = {eng},
medline-pst = {ppublish},
owner = {philippe},
pmid = {11237013},
timestamp = {2010.08.01}
}
@article{Sadik2004Detection,
author = {Omowunmi Sadik and Walker H Land and Adam K Wanekaya and Michiko
Uematsu and Mark J Embrechts and Lut Wong and Dale Leibensperger
and Alex Volykin},
title = {Detection and classification of organophosphate nerve agent simulants
using support vector machines with multiarray sensors.},
journal = {J {C}hem {I}nf {C}omput {S}ci},
year = {2004},
volume = {44},
pages = {499-507},
number = {2},
abstract = {The need for rapid and accurate detection systems is expanding and
the utilization of cross-reactive sensor arrays to detect chemical
warfare agents in conjunction with novel computational techniques
may prove to be a potential solution to this challenge. {W}e have
investigated the detection, prediction, and classification of various
organophosphate ({OP}) nerve agent simulants using sensor arrays
with a novel learning scheme known as support vector machines ({SVM}s).
{T}he {OP}s tested include parathion, malathion, dichlorvos, trichlorfon,
paraoxon, and diazinon. {A} new data reduction software program was
written in {MATLAB} {V}. 6.1 to extract steady-state and kinetic
data from the sensor arrays. {T}he program also creates training
sets by mixing and randomly sorting any combination of data categories
into both positive and negative cases. {T}he resulting signals were
fed into {SVM} software for "pairwise" and "one" vs all classification.
{E}xperimental results for this new paradigm show a significant increase
in classification accuracy when compared to artificial neural networks
({ANN}s). {T}hree kernels, the {S}2000, the polynomial, and the {G}aussian
radial basis function ({RBF}), were tested and compared to the {ANN}.
{T}he following measures of performance were considered in the pairwise
classification: receiver operating curve ({ROC}) {A}z indices, specificities,
and positive predictive values ({PPV}s). {T}he {ROC} {A}z) values,
specifities, and {PPV}s increases ranged from 5\% to 25\%, 108\%
to 204\%, and 13\% to 54\%, respectively, in all {OP} pairs studied
when compared to the {ANN} baseline. {D}ichlorvos, trichlorfon, and
paraoxon were perfectly predicted. {P}ositive prediction for malathion
was 95\%.},
doi = {10.1021/ci034220i},
pdf = {../local/Sadik2004Detection.pdf},
file = {Sadik2004Detection.pdf:local/Sadik2004Detection.pdf:PDF},
keywords = {Algorithms, Ambergris, Combinatorial Chemistry Techniques, Models,
Molecular, Molecular Conformation, Odors, P.H.S., Perfume, Predictive
Value of Tests, Quantitative Structure-Activity Relationship, Research
Support, U.S. Gov't, 15032529},
url = {http://dx.doi.org/10.1021/ci034220i}
}
@article{Saeh2005Lead,
author = {Saeh, J. and Lyne, P. and Takasaki, B. and Cosgrove, D.},
title = {Lead hopping using {SVM} and 3{D} pharmacophore fingerprints.},
journal = {J {C}hem {I}nf {M}odel},
year = {2005},
volume = {45},
pages = {1122-1133},
number = {4},
month = {Jul},
abstract = {The combination of 3{D} pharmacophore fingerprints and the support
vector machine classification algorithm has been used to generate
robust models that are able to classify compounds as active or inactive
in a number of {G}-protein-coupled receptor assays. {T}he models
have been tested against progressively more challenging validation
sets where steps are taken to ensure that compounds in the validation
set are chemically and structurally distinct from the training set.
{I}n the most challenging example, we simulate a lead-hopping experiment
by excluding an entire class of compounds (defined by a core substructure)
from the training set. {T}he left-out active compounds comprised
approximately 40\% of the actives. {T}he model trained on the remaining
compounds is able to recall 75\% of the actives from the "new" lead
series while correctly classifying >99\% of the 5000 inactives included
in the validation set.},
doi = {10.1021/ci049732r},
pdf = {../local/Saeh2005Lead.pdf},
file = {Saeh2005Lead.pdf:local/Saeh2005Lead.pdf:PDF},
keywords = {biosvm chemoinformatics},
url = {http://dx.doi.org/10.1021/ci049732r}
}
@article{Saetrom2004Predicting,
author = {Saetrom, P.},
title = {Predicting the efficacy of short oligonucleotides in antisense and
{RNA}i experiments with boosted genetic programming},
journal = {Bioinformatics},
year = {2004},
volume = {20},
pages = {3055-3063},
number = {17},
abstract = {Motivation: {B}oth small interfering {RNA}s (si{RNA}s) and antisense
oligonucleotides can selectively block gene expression. {A}lthough
the two methods rely on different cellular mechanisms, these methods
share the common property that not all oligonucleotides (oligos)
are equally effective. {T}hat is, if m{RNA} target sites are picked
at random, many of the antisense or si{RNA} oligos will not be effective.
{A}lgorithms that can reliably predict the efficacy of candidate
oligos can greatly reduce the cost of knockdown experiments, but
previous attempts to predict the efficacy of antisense oligos have
had limited success. {M}achine learning has not previously been used
to predict si{RNA} efficacy. {R}esults: {W}e develop a genetic programming
based prediction system that shows promising results on both antisense
and si{RNA} efficacy prediction. {W}e train and evaluate our system
on a previously published database of antisense efficacies and our
own database of si{RNA} efficacies collected from the literature.
{T}he best models gave an overall correlation between predicted and
observed efficacy of 0.46 on both antisense and si{RNA} data. {A}s
a comparison, the best correlations of support vector machine classifiers
trained on the same data were 0.40 and 0.30, respectively. {A}vailability:
{T}he prediction system uses proprietary hardware and is available
for both commercial and strategic academic collaborations. {T}he
si{RNA} database is available upon request.},
doi = {10.1093/bioinformatics/bth364},
pdf = {../local/Saetrom2004Predicting.pdf},
file = {Saetrom2004Predicting.pdf:local/Saetrom2004Predicting.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://bioinformatics.oupjournals.org/cgi/content/abstract/20/17/3055}
}
@article{Saetrom2004comparison,
author = {Saetrom, P. and Snøve, O.},
title = {A comparison of si{RNA} efficacy predictors.},
journal = {Biochem. {B}iophys. {R}es. {C}ommun.},
year = {2004},
volume = {321},
pages = {247-53},
number = {1},
month = {Aug},
abstract = {Short interfering {RNA} (si{RNA}) efficacy prediction algorithms aim
to increase the probability of selecting target sites that are applicable
for gene silencing by {RNA} interference. {M}any algorithms have
been published recently, and they base their predictions on such
different features as duplex stability, sequence characteristics,
m{RNA} secondary structure, and target site uniqueness. {W}e compare
the performance of the algorithms on a collection of publicly available
si{RNA}s. {F}irst, we show that our regularized genetic programming
algorithm {GP}boost appears to have a higher and more stable performance
than other algorithms on the collected datasets. {S}econd, several
algorithms gave close to random classification on unseen data, and
only {GP}boost and three other algorithms have a reasonably high
and stable performance on all parts of the dataset. {T}hird, the
results indicate that the si{RNA}s' sequence is sufficient input
to si{RNA} efficacy algorithms, and that other features that have
been suggested to be important may be indirectly captured by the
sequence.},
doi = {10.1016/j.bbrc.2004.06.116},
keywords = {sirna},
pii = {S0006-291X(04)01394-4},
url = {http://dx.doi.org/10.1016/j.bbrc.2004.06.116}
}
@article{Saeys2004Feature,
author = {Saeys, Y. and Degroeve, S. and Aeyels, D. and Rouzé, P. and Van
de Peer, Y.},
title = {Feature selection for splice site prediction: {A} new method using
{EDA}-based feature ranking},
journal = {B{MC} {B}ioinformatics},
year = {2004},
volume = {5},
number = {64},
abstract = {Background {T}he identification of relevant biological features in
large and complex datasets is an important step towards gaining insight
in the processes underlying the data. {O}ther advantages of feature
selection include the ability of the classification system to attain
good or even better solutions using a restricted subset of features,
and a faster classification. {T}hus, robust methods for fast feature
selection are of key importance in extracting knowledge from complex
biological data. {R}esults {I}n this paper we present a novel method
for feature subset selection applied to splice site prediction, based
on estimation of distribution algorithms, a more general framework
of genetic algorithms. {F}rom the estimated distribution of the algorithm,
a feature ranking is derived. {A}fterwards this ranking is used to
iteratively discard features. {W}e apply this technique to the problem
of splice site prediction, and show how it can be used to gain insight
into the underlying biological process of splicing. {C}onclusion
{W}e show that this technique proves to be more robust than the traditional
use of estimation of distribution algorithms for feature selection:
instead of returning a single best subset of features (as they normally
do) this method provides a dynamical view of the feature selection
process, like the traditional sequential wrapper methods. {H}owever,
the method is faster than the traditional techniques, and scales
better to datasets described by a large number of features.},
doi = {10.1186/1471-2105-5-64},
pdf = {../local/Saeys2004Feature.pdf},
file = {Saeys2004Feature.pdf:local/Saeys2004Feature.pdf:PDF},
keywords = {biosvm},
owner = {vert}
}
@article{Saeys2003Fast,
author = {Saeys, Y. and Degroeve, S. and Aeyels, D. and Van de Peer, Y. and
Rouze, P.},
title = {Fast feature selection using a simple estimation of distribution
algorithm: a case study on splice site prediction},
journal = {Bioinformatics},
year = {2003},
volume = {19},
pages = {ii179-ii188},
number = {Suppl. 1},
abstract = {Motivation: {F}eature subset selection is an important preprocessing
step for classification. {I}n biology, where structures or processes
are described by a large number of features, the elimination of irrelevant
and redundant information in a reasonable amount of time has a number
of advantages. {I}t enables the classification system to achieve
good or even better solutions with a restricted subset of features,
allows for a faster classification, and it helps the human expert
focus on a relevant subset of features, hence providing useful biological
knowledge. {R}esults: {W}e present a heuristic method based on {E}stimation
of {D}istribution {A}lgorithms to select relevant subsets of features
for splice site prediction in {A}rabidopsis thaliana. {W}e show that
this method performs a fast detection of relevant feature subsets
using the technique of constrained feature subsets. {C}ompared to
the traditional greedy methods the gain in speed can be up to one
order of magnitude, with results being comparable or even better
than the greedy methods. {T}his makes it a very practical solution
for classification tasks that can be solved using a relatively small
amount of discriminative features (or feature dependencies), but
where the initial set of potential discriminative features is rather
large. {K}eywords: {M}achine {L}earning, {F}eature {S}ubset {S}election,
{E}stimation of {D}istribution {A}lgorithms, {S}plice {S}ite {P}rediction.
{C}ontact: yvsae@gengenp.rug.ac.be},
pdf = {../local/Saeys2003Fast.pdf},
file = {Saeys2003Fast.pdf:local/Saeys2003Fast.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://bioinformatics.oupjournals.org/cgi/content/abstract/19/suppl_2/ii179}
}
@article{Saeys2007review,
author = {Saeys, Y. and Inza, I. and Larra{\~n}aga, P.},
title = {A review of feature selection techniques in bioinformatics},
journal = {Bioinformatics},
year = {2007},
volume = {23},
pages = {2507--2517},
number = {19},
publisher = {Oxford Univ Press}
}
@inproceedings{Saigo2006linear,
author = {Saigo, H. and Kadowaki, T. and Tsuda, K.},
title = {A linear programming approach for molecular {QSAR} analysis},
booktitle = {International workshop on mining and learning with graphs (MLG)},
year = {2006},
editor = {G{\"a}rtner, T. and Garriga, G. C. and Meinl, T.},
pages = {85--96},
pdf = {../local/Saigo2006linear.pdf},
file = {Saigo2006linear.pdf:Saigo2006linear.pdf:PDF},
owner = {jp},
timestamp = {2009.09.27}
}
@article{Saigo2009gBoost,
author = {Saigo, H. and Nowozin, S. and Kadowaki, T. and Kudo, T. and Tsuda,
K.},
title = {{gBoost}: a mathematical programming approach to graph classification
and regression},
journal = {Mach. Learn.},
year = {2009},
volume = {75},
pages = {69--89},
number = {1},
doi = {10.1007/s10994-008-5089-z},
pdf = {../local/Saigo2009gBoost.pdf},
file = {Saigo2009gBoost.pdf:Saigo2009gBoost.pdf:PDF},
owner = {jp},
timestamp = {2009.09.27},
url = {http://dx.doi.org/10.1007/s10994-008-5089-z}
}
@article{Saigo2004Protein,
author = {Saigo, H. and Vert, J.-P. and Ueda, N. and Akutsu, T.},
title = {Protein homology detection using string alignment kernels},
journal = {Bioinformatics},
year = {2004},
volume = {20},
pages = {1682-1689},
number = {11},
abstract = {Motivation: {R}emote homology detection between protein sequences
is a central problem in computational biology. {D}iscriminative methods
involving support vector machines ({SVM}s) are currently the most
effective methods for the problem of superfamily recognition in the
{S}tructural {C}lassification {O}f {P}roteins ({SCOP}) database.
{T}he performance of {SVM}s depends critically on the kernel function
used to quantify the similarity between sequences. {R}esults: {W}e
propose new kernels for strings adapted to biological sequences,
which we call local alignment kernels. {T}hese kernels measure the
similarity between two sequences by summing up scores obtained from
local alignments with gaps of the sequences. {W}hen tested in combination
with {SVM} on their ability to recognize {SCOP} superfamilies on
a benchmark dataset, the new kernels outperform state-of-the-art
methods for remote homology detection. {A}vailability: {S}oftware
and data available upon request.},
pdf = {../local/Saigo2004Protein.pdf},
file = {Saigo2004Protein.pdf:local/Saigo2004Protein.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://bioinformatics.oupjournals.org/cgi/content/abstract/20/11/1682}
}
@article{Saito1985ETL9b,
author = {T. Saito and H. Yamada and K. Yamamoto},
title = {On the data base ETL9B of handprinted characters in JIS Chinese characters
and its analysis},
journal = {IEICE Trans},
year = {1985},
volume = {68}
}
@book{Saitoh1988Theory,
title = {Theory of reproducing {K}ernels and its applications},
publisher = {Longman Scientific \& Technical},
year = {1988},
author = {S. Saitoh},
address = {Harlow, UK},
subject = {kernel}
}
@article{Salgado2006RegulonDB,
author = {Salgado, H. and Gama-Castro, S. and Peralta-Gil, M. and D{\'i}az-Peredo,
E. and S{\'a}nchez-Solano, F. and Santos-Zavaleta, A. and Mart{\'i}nez-Flores,
I. and Jim{\'e}nez-Jacinto, V. and Bonavides-Mart{\'i}nez, C. and
Segura-Salazar, J. and Mart{\'i}nez-Antonio, A. and Collado-Vides,
J.},
title = {{RegulonDB} (version 5.0): {Escherichia coli K-12} transcriptional
regulatory network, operon organization, and growth conditions.},
journal = {Nucleic Acids Res.},
year = {2006},
volume = {34},
pages = {D394--D397},
number = {Database issue},
month = {Jan},
abstract = {RegulonDB is the internationally recognized reference database of
Escherichia coli K-12 offering curated knowledge of the regulatory
network and operon organization. It is currently the largest electronically-encoded
database of the regulatory network of any free-living organism. We
present here the recently launched RegulonDB version 5.0 radically
different in content, interface design and capabilities. Continuous
curation of original scientific literature provides the evidence
behind every single object and feature. This knowledge is complemented
with comprehensive computational predictions across the complete
genome. Literature-based and predicted data are clearly distinguished
in the database. Starting with this version, RegulonDB public releases
are synchronized with those of EcoCyc since our curation supports
both databases. The complex biology of regulation is simplified in
a navigation scheme based on three major streams: genes, operons
and regulons. Regulatory knowledge is directly available in every
navigation step. Displays combine graphic and textual information
and are organized allowing different levels of detail and biological
context. This knowledge is the backbone of an integrated system for
the graphic display of the network, graphic and tabular microarray
comparisons with curated and predicted objects, as well as predictions
across bacterial genomes, and predicted networks of functionally
related gene products. Access RegulonDB at http://regulondb.ccg.unam.mx.},
doi = {10.1093/nar/gkj156},
institution = {Program of Computational Genomics, Centro de Ciencias Genómicas,
Universidad Nacional Autónoma de México, A.P. 565-A. Cuernavaca,
Morelos 62100, Mexico.},
keywords = {Databases, Genetic; Escherichia coli K12; Gene Expression Regulation,
Bacterial; Genome, Bacterial; Internet; Operon; Regulon; Software;
Transcription, Genetic; User-Computer Interface},
owner = {fantine},
pii = {34/suppl_1/D394},
pmid = {16381895},
timestamp = {2008.02.04},
url = {http://dx.doi.org/10.1093/nar/gkj156}
}
@article{Salim2003Combination,
author = {N. Salim and J. Holliday and P. Willett},
title = {{C}ombination of fingerprint-based similarity coefficients using
data fusion.},
journal = {J Chem Inf Comput Sci},
year = {2003},
volume = {43},
pages = {435--442},
number = {2},
abstract = {Many different types of similarity coefficients have been described
in the literature. Since different coefficients take into account
different characteristics when assessing the degree of similarity
between molecules, it is reasonable to combine them to further optimize
the measures of similarity between molecules. This paper describes
experiments in which data fusion is used to combine several binary
similarity coefficients to get an overall estimate of similarity
for searching databases of bioactive molecules. The results show
that search performances can be improved by combining coefficients
with little extra computational cost. However, there is no single
combination which gives a consistently high performance for all search
types.},
doi = {10.1021/ci025596j},
keywords = {80 and over, Acid-Base Imbalance, Acute, Acute Disease, Adolescent,
Adult, African Americans, Aged, Anemia, Animals, Anti-HIV Agents,
Anti-Infective Agents, Antibiotics, Antibodies, Antineoplastic, Antineoplastic
Agents, Antineoplastic Combined Chemotherapy Protocols, Antitubercular
Agents, Aorta, Asparaginase, Autoimmune, B-Cell, Bangladesh, Bicarbonates,
Biological Markers, Blood Glucose, California, Camptothecin, Cellulitis,
Chorionic Gonadotropin, Chronic Disease, Ciprofloxacin, Clinical
Protocols, Colorectal Neoplasms, Combination, Comparative Study,
Daunorubicin, Decision Trees, Dexamethasone, Diabetes Mellitus, Dideoxynucleosides,
Directly Observed Therapy, Disease Transmission, Drug Administration
Schedule, Drug Resistance, Drug Therapy, English Abstract, Female,
Fluorouracil, Follow-Up Studies, Glucose Tolerance Test, Glucosephosphate
Dehydrogenase, Glyburide, HIV Infections, HIV-1, Health Planning,
Health Resources, Helminth, Hemolysis, Hemolytic, Hormonal, Hospital
Mortality, Human, Humans, Hypoglycemic Agents, Immunoglobulin M,
In Vitro, Incidence, Indinavir, Insulin, Intensive Care Units, Interstitial,
Lactates, Leucovorin, Leukemia, Male, Maternal Age, Middle Aged,
Motor Activity, Multidrug-Resistant, Mutation, Nephritis, Non-U.S.
Gov't, Organoplatinum Compounds, Pennsylvania, Phytotherapy, Plant
Extracts, Plant Leaves, Population Dynamics, Potassium Channels,
Prednisone, Pregnancy, Pregnancy Outcome, Prenatal, Prenatal Care,
Progesterone, Prognosis, Prospective Studies, Pulmonary, Rabbits,
Randomized Controlled Trials, Rats, Research Support, Retrospective
Studies, Risk Assessment, Scalp Dermatoses, Schistosomiasis japonica,
Severity of Illness Index, Spondylarthropathies, Streptozocin, Survival
Rate, Trauma Centers, Trauma Severity Indices, Tubal, Tuberculosis,
Type 2, Ultrasonography, Vertical, Vincristine, Viral, Viral Load,
Wistar, Wounds and Injuries, Ziziphus, beta Subunit, 12653506},
owner = {mahe},
pmid = {12653506},
timestamp = {2006.09.01},
url = {http://dx.doi.org/10.1021/ci025596j}
}
@article{Salomon2006Predicting,
author = {Salomon, J. and Flower, D. R.},
title = {{P}redicting {C}lass {II} {MHC}-{P}eptide binding: a kernel based
approach using similarity scores.},
journal = {BMC Bioinformatics},
year = {2006},
volume = {7},
pages = {501},
abstract = {BACKGROUND: Modelling the interaction between potentially antigenic
peptides and Major Histocompatibility Complex (MHC) molecules is
a key step in identifying potential T-cell epitopes. For Class II
MHC alleles, the binding groove is open at both ends, causing ambiguity
in the positional alignment between the groove and peptide, as well
as creating uncertainty as to what parts of the peptide interact
with the MHC. Moreover, the antigenic peptides have variable lengths,
making naive modelling methods difficult to apply. This paper introduces
a kernel method that can handle variable length peptides effectively
by quantifying similarities between peptide sequences and integrating
these into the kernel. RESULTS: The kernel approach presented here
shows increased prediction accuracy with a significantly higher number
of true positives and negatives on multiple MHC class II alleles,
when testing data sets from MHCPEP 1, MCHBN 2, and MHCBench 3. Evaluation
by cross validation, when segregating binders and non-binders, produced
an average of 0.824 AROC for the MHCBench data sets (up from 0.756),
and an average of 0.96 AROC for multiple alleles of the MHCPEP database.
CONCLUSION: The method improves performance over existing state-of-the-art
methods of MHC class II peptide binding predictions by using a custom,
knowledge-based representation of peptides. Similarity scores, in
contrast to a fixed-length, pocket-specific representation of amino
acids, provide a flexible and powerful way of modelling MHC binding,
and can easily be applied to other dynamic sequence problems.},
doi = {10.1186/1471-2105-7-501},
keywords = {Amino Acid, Binding Sites, Computational Biology, Databases, Epitope
Mapping, Genetic, HLA-A Antigens, HLA-DR Antigens, Histocompatibility
Antigens Class II, Humans, Peptides, Protein, Protein Binding, Protein
Conformation, ROC Curve, Reproducibility of Results, Sequence Alignment,
Sequence Analysis, Sequence Homology, 17105666},
pii = {1471-2105-7-501},
pmid = {17105666},
timestamp = {2007.01.25},
url = {http://dx.doi.org/10.1186/1471-2105-7-501}
}
@article{Sample2002Using,
author = {Pamela A Sample and Michael H Goldbaum and Kwokleung Chan and Catherine
Boden and Te-Won Lee and Christiana Vasile and Andreas G Boehm and
Terrence Sejnowski and Chris A Johnson and Robert N Weinreb},
title = {Using machine learning classifiers to identify glaucomatous change
earlier in standard visual fields.},
journal = {Invest {O}phthalmol {V}is {S}ci},
year = {2002},
volume = {43},
pages = {2660-5},
number = {8},
month = {Aug},
abstract = {P{URPOSE}: {T}o compare the ability of several machine learning classifiers
to predict development of abnormal fields at follow-up in ocular
hypertensive ({OHT}) eyes that had normal visual fields in baseline
examination. {METHODS}: {T}he visual fields of 114 eyes of 114 patients
with {OHT} with four or more visual field tests with standard automated
perimetry over three or more years and for whom stereophotographs
were available were assessed. {T}he mean (+/-{SD}) number of visual
field tests was 7.89 +/- 3.04. {T}he mean number of years covered
(+/-{SD}) was 5.92 +/- 2.34 (range, 2.81-11.77). {F}ields were classified
as normal or abnormal based on {S}tatpac-like methods ({H}umphrey
{I}nstruments, {D}ublin, {CA}) and by several machine learning classifiers.
{T}he machine learning classifiers were two types of support vector
machine ({SVM}), a mixture of {G}aussian ({M}o{G}) classifier, a
constrained {M}o{G}, and a mixture of generalized {G}aussian ({MGG}).
{S}pecificity was set to 96\% for all classifiers, using data from
94 normal eyes evaluated longitudinally. {S}pecificity cutoffs required
confirmation of abnormality. {RESULTS}: {T}hirty-two percent (36/114)
of the eyes converted to abnormal fields during follow-up based on
the {S}tatpac-like methods. {A}ll 36 were identified by at least
one machine classifier. {I}n nearly all cases, the machine learning
classifiers predicted the confirmed abnormality, on average, 3.92
+/- 0.55 years earlier than traditional {S}tatpac-like methods. {CONCLUSIONS}:
{M}achine learning classifiers can learn complex patterns and trends
in data and adapt to create a decision surface without the constraints
imposed by statistical classifiers. {T}his adaptation allowed the
machine learning classifiers to identify abnormality in visual field
converts much earlier than the traditional methods.}
}
@article{Sanchez-Carbayo2003Gene,
author = {Marta Sanchez-Carbayo and Nicholas D Socci and Juan Jose Lozano and
Wentian Li and Elizabeth Charytonowicz and Thomas J Belbin and Michael
B Prystowsky and Angel R Ortiz and Geoffrey Childs and Carlos Cordon-Cardo},
title = {Gene discovery in bladder cancer progression using c{DNA} microarrays.},
journal = {Am. {J}. {P}athol.},
year = {2003},
volume = {163},
pages = {505-16},
number = {2},
month = {Aug},
abstract = {To identify gene expression changes along progression of bladder cancer,
we compared the expression profiles of early-stage and advanced bladder
tumors using c{DNA} microarrays containing 17,842 known genes and
expressed sequence tags. {T}he application of bootstrapping techniques
to hierarchical clustering segregated early-stage and invasive transitional
carcinomas into two main clusters. {M}ultidimensional analysis confirmed
these clusters and more importantly, it separated carcinoma in situ
from papillary superficial lesions and subgroups within early-stage
and invasive tumors displaying different overall survival. {A}dditionally,
it recognized early-stage tumors showing gene profiles similar to
invasive disease. {D}ifferent techniques including standard t-test,
single-gene logistic regression, and support vector machine algorithms
were applied to identify relevant genes involved in bladder cancer
progression. {C}ytokeratin 20, neuropilin-2, p21, and p33{ING}1 were
selected among the top ranked molecular targets differentially expressed
and validated by immunohistochemistry using tissue microarrays (n
= 173). {T}heir expression patterns were significantly associated
with pathological stage, tumor grade, and altered retinoblastoma
({RB}) expression. {M}oreover, p33{ING}1 expression levels were significantly
associated with overall survival. {A}nalysis of the annotation of
the most significant genes revealed the relevance of critical genes
and pathways during bladder cancer progression, including the overexpression
of oncogenic genes such as {DEK} in superficial tumors or immune
response genes such as {C}d86 antigen in invasive disease. {G}ene
profiling successfully classified bladder tumors based on their progression
and clinical outcome. {T}he present study has identified molecular
biomarkers of potential clinical significance and critical molecular
targets associated with bladder cancer progression.},
pdf = {../local/Sanchez-Carbayo2003Gene.pdf},
file = {Sanchez-Carbayo2003Gene.pdf:local/Sanchez-Carbayo2003Gene.pdf:PDF},
keywords = {biosvm},
url = {http://ajp.amjpathol.org/cgi/content/abstract/163/2/505}
}
@article{Sanguinetti2006hERG,
author = {Sanguinetti, M.C. and Tristani-Firouzi, M.},
title = {h{ERG} potassium channels and cardiac arrhythmia.},
journal = {Nature},
year = {2006},
volume = {440},
pages = {463--469},
number = {7083},
month = {Mar},
abstract = {hERG potassium channels are essential for normal electrical activity
in the heart. Inherited mutations in the HERG gene cause long QT
syndrome, a disorder that predisposes individuals to life-threatening
arrhythmias. Arrhythmia can also be induced by a blockage of hERG
channels by a surprisingly diverse group of drugs. This side effect
is a common reason for drug failure in preclinical safety trials.
Insights gained from the crystal structures of other potassium channels
have helped our understanding of the block of hERG channels and the
mechanisms of gating.},
doi = {10.1038/nature04710},
pdf = {../local/Sanguinetti2006hERG.pdf},
file = {Sanguinetti2006hERG.pdf:local/Sanguinetti2006hERG.pdf:PDF},
keywords = {herg},
pii = {nature04710},
pmid = {16554806},
timestamp = {2006.10.05},
url = {http://dx.doi.org/10.1038/nature04710}
}
@article{Sanguinetti2005Predicting,
author = {Sanguinetti, M. C. and Mitcheson, J. S.},
title = {{P}redicting drug-h{ERG} channel interactions that cause acquired
long {QT} syndrome.},
journal = {Trends Pharmacol. Sci.},
year = {2005},
volume = {26},
pages = {119--124},
number = {3},
month = {Mar},
abstract = {Avoiding drug-induced cardiac arrhythmia is recognized as a major
hurdle in the successful development of new drugs. The most common
problem is acquired long QT syndrome caused by drugs that block human
ether-a-go-go-related-gene (hERG) K(+) channels, delay cardiac repolarization
and increase the risk of torsades de pointes arrhythmia (TdP). Not
all hERG channel blockers induce TdP because they can also modulate
other channels that counteract the hERG channel-mediated effect.
However, hERG channel blockade is an important indicator of potential
pro-arrhythmic liability. The molecular determinants of hERG channel
blockade have been defined using a site-directed mutagenesis approach.
Combined with pharmacophore models, knowledge of the drug-binding
site of hERG channels will facilitate in silico design efforts to
discover drugs that are devoid of this rare, but potentially lethal,
side-effect.},
doi = {10.1016/j.tips.2005.01.003},
keywords = {herg},
pii = {S0165-6147(05)00004-0},
pmid = {15749156},
timestamp = {2006.10.05},
url = {http://dx.doi.org/10.1016/j.tips.2005.01.003}
}
@inproceedings{Sanjiv2003Discriminative,
author = {Sanjiv, K. and Martial, H.},
title = {Discriminative {R}andom {F}ields: {A} {D}iscriminative {F}ramework
for {C}ontextual {I}nteraction in {C}lassification},
booktitle = {Proceedings of the {N}inth {IEEE} {I}nternational {C}onference on
{C}omputer {V}ision},
year = {2003},
pages = {1150},
publisher = {IEEE Computer Society},
abstract = {In this work we present {D}iscriminative {R}andom {F}ields({DRF}s),
a discriminative framework for the classification ofimage regions
by incorporating neighborhood interactionsin the labels as well as
the observed data. {T}he discriminativerandom fields offer several
advantages over the conventional{M}arkov {R}andom {F}ield ({MRF})
framework. {F}irst,the {DRF}s allow to relax the strong assumption
of conditionalindependence of the observed data generally used inthe
{MRF} framework for tractability. {T}his assumption is toorestrictive
for a large number of applications in vision. {S}econd,the {DRF}s
derive their classification power by exploitingthe probabilistic
discriminative models instead of thegenerative models used in the
{MRF} framework. {F}inally, allthe parameters in the {DRF} model
are estimated simultaneouslyfrom the training data unlike the {MRF}
frameworkwhere likelihood parameters are usually learned separatelyfrom
the field parameters. {W}e illustrate the advantages ofthe {DRF}s
over the {MRF} framework in an application ofman-made structure detection
in natural images taken fromthe {C}orel database.},
owner = {vert}
}
@article{Sarda2005pSLIP,
author = {Deepak Sarda and Gek Huey Chua and Kuo-Bin Li and Arun Krishnan},
title = {p{SLIP}: {SVM} based protein subcellular localization prediction
using multiple physicochemical properties.},
journal = {B{MC} {B}ioinformatics},
year = {2005},
volume = {6},
pages = {152},
number = {1},
month = {Jun},
abstract = {B{ACKGROUND}: {P}rotein subcellular localization is an important determinant
of protein function and hence, reliable methods for prediction of
localization are needed. {A} number of prediction algorithms have
been developed based on amino acid compositions or on the {N}-terminal
characteristics (signal peptides) of proteins. {H}owever, such approaches
lead to a loss of contextual information. {M}oreover, where information
about the physicochemical properties of amino acids has been used,
the methods employed to exploit that information are less than optimal
and could use the information more effectively. {RESULTS}: {I}n this
paper, we propose a new algorithm called p{SLIP} which uses {S}upport
{V}ector {M}achines ({SVM}s) in conjunction with multiple physicochemical
properties of amino acids to predict protein subcellular localization
in eukaryotes across six different locations, namely, chloroplast,
cytoplasmic, extracellular, mitochondrial, nuclear and plasma membrane.
{T}he algorithm was applied to the dataset provided by {P}ark and
{K}anehisa and we obtained prediction accuracies for the different
classes ranging from 87.7\%-97.0\% with an overall accuracy of 93.1\%.
{CONCLUSIONS}: {T}his study presents a physicochemical property based
protein localization prediction algorithm. {U}nlike other algorithms,
contextual information is preserved by dividing the protein sequences
into clusters. {T}he prediction accuracy shows an improvement over
other algorithms based on various types of amino acid composition
(single, pair and gapped pair). {W}e have also implemented a web
server to predict protein localization across the six classes (available
at http://pslip.bii.a-star.edu.sg).},
doi = {10.1186/1471-2105-6-152},
pdf = {../local/Sarda2005pSLIP.pdf},
file = {Sarda2005pSLIP.pdf:local/Sarda2005pSLIP.pdf:PDF},
keywords = {biosvm},
pii = {1471-2105-6-152},
url = {http://dx.doi.org/10.1186/1471-2105-6-152}
}
@article{Sassi2005automated,
author = {Alexander P Sassi and Frank Andel and Hans-Marcus L Bitter and Michael
P S Brown and Robert G Chapman and Jeraldine Espiritu and Alfred
C Greenquist and Isabelle Guyon and Mariana Horchi-Alegre and Kathy
L Stults and Ann Wainright and Jonathan C Heller and John T Stults},
title = {An automated, sheathless capillary electrophoresis-mass spectrometry
platform for discovery of biomarkers in human serum.},
journal = {Electrophoresis},
year = {2005},
volume = {26},
pages = {1500-12},
number = {7-8},
month = {Apr},
abstract = {A capillary electrophoresis-mass spectrometry ({CE}-{MS}) method has
been developed to perform routine, automated analysis of low-molecular-weight
peptides in human serum. {T}he method incorporates transient isotachophoresis
for in-line preconcentration and a sheathless electrospray interface.
{T}o evaluate the performance of the method and demonstrate the utility
of the approach, an experiment was designed in which peptides were
added to sera from individuals at each of two different concentrations,
artificially creating two groups of samples. {T}he {CE}-{MS} data
from the serum samples were divided into separate training and test
sets. {A} pattern-recognition/feature-selection algorithm based on
support vector machines was used to select the mass-to-charge (m/z)
values from the training set data that distinguished the two groups
of samples from each other. {T}he added peptides were identified
correctly as the distinguishing features, and pattern recognition
based on these peptides was used to assign each sample in the independent
test set to its respective group. {A} twofold difference in peptide
concentration could be detected with statistical significance (p-value
< 0.0001). {T}he accuracy of the assignment was 95\%, demonstrating
the utility of this technique for the discovery of patterns of biomarkers
in serum.},
doi = {10.1002/elps.200410127},
pdf = {../local/Sassi2005automated.pdf},
file = {Sassi2005automated.pdf:local/Sassi2005automated.pdf:PDF},
keywords = {80 and over, Adult, Aged, Algorithms, Amino Acids, Animals, Area Under
Curve, Artifacts, Automated, Birefringence, Brain Chemistry, Brain
Neoplasms, Comparative Study, Computer-Assisted, Cornea, Cross-Sectional
Studies, Decision Trees, Diagnosis, Diagnostic Imaging, Diagnostic
Techniques, Discriminant Analysis, Evolution, Face, Female, Genetic,
Glaucoma, Humans, Intraocular Pressure, Lasers, Least-Squares Analysis,
Magnetic Resonance Imaging, Magnetic Resonance Spectroscopy, Male,
Middle Aged, Models, Molecular, Nerve Fibers, Non-U.S. Gov't, Numerical
Analysis, Ophthalmological, Optic Nerve Diseases, Optical Coherence,
P.H.S., Pattern Recognition, Photic Stimulation, Prospective Studies,
Protein, ROC Curve, Regression Analysis, Research Support, Retinal
Ganglion Cells, Sensitivity and Specificity, Sequence Analysis, Statistics,
Tomography, U.S. Gov't, Visual Fields, beta-Lactamases, 15765480},
url = {http://dx.doi.org/10.1002/elps.200410127}
}
@article{Satzinger2008Theodor,
author = {Helga Satzinger},
title = {{T}heodor and {M}arcella {B}overi: chromosomes and cytoplasm in heredity
and development},
journal = {Nat. Rev. Genet.},
year = {2008},
volume = {9},
pages = {231-238},
keywords = {csbcbook}
}
@article{Saul2003Think,
author = {Saul, L. K. and Roweis, S. T.},
title = {Think {G}lobally, {F}it {L}ocally: {U}nsupervised {L}earning of {L}ow
{D}imensional {M}anifolds},
journal = {J. {M}ach. {L}earn. {R}es.},
year = {2003},
volume = {4},
pages = {119-155},
abstract = {The problem of dimensionality reduction arises in many fields of information
processing, including machine learning, data compression, scientific
visualization, pattern recognition, and neural computation. {H}ere
we describe locally linear embedding ({LLE}), an unsupervised learning
algorithm that computes low dimensional, neighborhood preserving
embeddings of high dimensional data. {T}he data, assumed to be sampled
from an underlying manifold, are mapped into a single global coordinate
system of lower dimensionality. {T}he mapping is derived from the
symmetries of locally linear reconstructions, and the actual computation
of the embedding reduces to a sparse eigenvalue problem. {N}otably,
the optimizations in {LLE}---though capable of generating highly
nonlinear embeddings---are simple to implement, and they do not involve
local minima. {I}n this paper, we describe the implementation of
the algorithm in detail and discuss several extensions that enhance
its performance. {W}e present results of the algorithm applied to
data sampled from known manifolds, as well as to collections of images
of faces, lips, and handwritten digits. {T}hese examples are used
to provide extensive illustrations of the algorithm's performance---both
successes and failures---and to relate the algorithm to previous
and ongoing work in nonlinear dimensionality reduction.},
pdf = {../local/saul03a.pdf:http\},
file = {saul03a.pdf:http\://www.jmlr.org/papers/volume4/saul03a/saul03a.pdf:PDF},
keywords = {dimred},
url = {http://www.jmlr.org/papers/v4/saul03a.html}
}
@inproceedings{Saupe20013D,
author = {D. Saupe and D. V. Vranic},
title = {3D Model Retrieval with Spherical Harmonics and Moments},
booktitle = {Proceedings of the 23rd DAGM-Symposium on Pattern Recognition},
year = {2001},
pages = {392--397},
address = {London, UK},
publisher = {Springer-Verlag},
isbn = {3-540-42596-9}
}
@article{Sawyers2008cancer,
author = {Sawyers, C. L.},
title = {The cancer biomarker problem.},
journal = {Nature},
year = {2008},
volume = {452},
pages = {548--552},
number = {7187},
month = {Apr},
abstract = {Genomic technologies offer the promise of a comprehensive understanding
of cancer. These technologies are being used to characterize tumours
at the molecular level, and several clinical successes have shown
that such information can guide the design of drugs targeted to a
relevant molecule. One of the main barriers to further progress is
identifying the biological indicators, or biomarkers, of cancer that
predict who will benefit from a particular targeted therapy.},
doi = {10.1038/nature06913},
pdf = {../local/Sawyers2008cancer.pdf},
file = {Sawyers2008cancer.pdf:Sawyers2008cancer.pdf:PDF},
institution = {Howard Hughes Medical Institute, Human Oncology and Pathogenesis
Program, Memorial Sloan-Kettering Cancer Center, 1275 York Avenue,
New York, New York 10065, USA.},
keywords = {csbcbook-ch3, csbcbook},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {nature06913},
pmid = {18385728},
timestamp = {2011.11.30},
url = {http://dx.doi.org/10.1038/nature06913}
}
@article{Saxena2003Comparison,
author = {A.K. Saxena and P. Prathipati},
title = {Comparison of MLR, PLS and GA-MLR in QSAR analysis},
journal = {SAR. QSAR. Environ. Res.},
year = {2003},
volume = {14},
pages = {433-445},
owner = {mahe},
timestamp = {2006.09.07}
}
@article{Scacheri2004Short,
author = {Scacheri, P. C. and Rozenblatt-Rosen, O. and Caplen, N. J. and Wolfsberg,
T. G. and Umayam, L. and Lee, J. C. and Hughes, C. M. and Shanmugam,
K. S. and Bhattacharjee, A. and Meyerson, M. and Collins, F. S.},
title = {Short interfering {RNA}s can induce unexpected and divergent changes
in the levels of untargeted proteins in mammalian cells.},
journal = {Proc. {N}atl. {A}cad. {S}ci. {USA}},
year = {2004},
volume = {101},
pages = {1892-7},
number = {7},
month = {Feb},
abstract = {R{NA} interference ({RNA}i) mediated by short interfering {RNA}s (si{RNA}s)
is a widely used method to analyze gene function. {T}o use {RNA}i
knockdown accurately to infer gene function, it is essential to determine
the specificity of si{RNA}-mediated {RNA}i. {W}e have assessed the
specificity of 10 different si{RNA}s corresponding to the {MEN}1
gene by examining the expression of two additional genes, {TP}53
(p53) and {CDKN}1{A} (p21), which are considered functionally unrelated
to menin but are sensitive markers of cell state. {MEN}1 {RNA} and
corresponding protein levels were all reduced after si{RNA} transfection
of {H}e{L}a cells, although the degree of inhibition mediated by
individual si{RNA}s varied. {U}nexpectedly, we observed dramatic
and significant changes in protein levels of p53 and p21 that were
unrelated to silencing of the target gene. {T}he modulations in p53
and p21 levels were not abolished on titration of the si{RNA}s, and
similar results were obtained in three other cell lines; in none
of the cell lines tested did we see an effect on the protein levels
of actin. {T}hese data suggest that si{RNA}s can induce nonspecific
effects on protein levels that are si{RNA} sequence dependent but
that these effects may be difficult to detect until genes central
to a pivotal cellular response, such as p53 and p21, are studied.
{W}e find no evidence that activation of the double-stranded {RNA}-triggered
{IFN}-associated antiviral pathways accounts for these effects, but
we speculate that partial complementary sequence matches to off-target
genes may result in a micro-{RNA}-like inhibition of translation.},
doi = {10.1073/pnas.0308698100},
keywords = {sirna},
pii = {0308698100},
url = {http://dx.doi.org/10.1073/pnas.0308698100}
}
@article{Schachtner2008Knowledge-based,
author = {R. Schachtner and D. Lutter and P. Knollmüller and A. M. Tomé and
F. J. Theis and G. Schmitz and M. Stetter and P. Gómez Vilda and
E. W. Lang},
title = {Knowledge-based gene expression classification via matrix factorization.},
journal = {Bioinformatics},
year = {2008},
volume = {24},
pages = {1688--1697},
number = {15},
month = {Aug},
abstract = {MOTIVATION: Modern machine learning methods based on matrix decomposition
techniques, like independent component analysis (ICA) or non-negative
matrix factorization (NMF), provide new and efficient analysis tools
which are currently explored to analyze gene expression profiles.
These exploratory feature extraction techniques yield expression
modes (ICA) or metagenes (NMF). These extracted features are considered
indicative of underlying regulatory processes. They can as well be
applied to the classification of gene expression datasets by grouping
samples into different categories for diagnostic purposes or group
genes into functional categories for further investigation of related
metabolic pathways and regulatory networks. RESULTS: In this study
we focus on unsupervised matrix factorization techniques and apply
ICA and sparse NMF to microarray datasets. The latter monitor the
gene expression levels of human peripheral blood cells during differentiation
from monocytes to macrophages. We show that these tools are able
to identify relevant signatures in the deduced component matrices
and extract informative sets of marker genes from these gene expression
profiles. The methods rely on the joint discriminative power of a
set of marker genes rather than on single marker genes. With these
sets of marker genes, corroborated by leave-one-out or random forest
cross-validation, the datasets could easily be classified into related
diagnostic categories. The latter correspond to either monocytes
versus macrophages or healthy vs Niemann Pick C disease patients.},
doi = {10.1093/bioinformatics/btn245},
institution = {CIML/Biophysics, University of Regensburg, D-93040 Regensburg, Germany.},
keywords = {Algorithms; Artificial Intelligence; Gene Expression Profiling; Oligonucleotide
Array Sequence Analysis; Pattern Recognition, Automated},
owner = {laurent},
pii = {btn245},
pmid = {18535085},
timestamp = {2008.10.26},
url = {http://dx.doi.org/10.1093/bioinformatics/btn245}
}
@article{Schaffter2011GeneNetWeaver,
author = {Schaffter, T. and Marbach, D. and Floreano, D.},
title = {GeneNetWeaver: in silico benchmark generation and performance profiling
of network inference methods},
journal = {Bioinformatics},
year = {2011},
volume = {27},
pages = {2263-2270},
number = {16},
abstract = {Motivation: Over the last decade, numerous methods have been developed
for inference of regulatory networks from gene expression data. However,
accurate and systematic evaluation of these methods is hampered by
the difficulty of constructing adequate benchmarks and the lack of
tools for a differentiated analysis of network predictions on such
benchmarks.Results: Here, we describe a novel and comprehensive method
for in silico benchmark generation and performance profiling of network
inference methods available to the community as an open-source software
called GeneNetWeaver (GNW). In addition to the generation of detailed
dynamical models of gene regulatory networks to be used as benchmarks,
GNW provides a network motif analysis that reveals systematic prediction
errors, thereby indicating potential ways of improving inference
methods. The accuracy of network inference methods is evaluated using
standard metrics such as precision-recall and receiver operating
characteristic curves. We show how GNW can be used to assess the
performance and identify the strengths and weaknesses of six inference
methods. Furthermore, we used GNW to provide the international Dialogue
for Reverse Engineering Assessments and Methods (DREAM) competition
with three network inference challenges (DREAM3, DREAM4 and DREAM5).Availability:
GNW is available at http://gnw.sourceforge.net along with its Java
source code, user manual and supporting data.Supplementary information:
Supplementary data are available at Bioinformatics online.Contact:
dario.floreano@epfl.ch},
doi = {10.1093/bioinformatics/btr373},
eprint = {http://bioinformatics.oxfordjournals.org/content/27/16/2263.full.pdf+html},
url = {http://bioinformatics.oxfordjournals.org/content/27/16/2263.abstract}
}
@article{Schalon2008Simple,
author = {C. Schalon and J-S. Surgand and E. Kellenberger and D. Rognan},
title = {A simple and fuzzy method to align and compare druggable ligand-binding
sites.},
journal = {Proteins},
year = {2008},
volume = {71},
pages = {1755--1778},
number = {4},
month = {Jun},
abstract = {A novel method to measure distances between druggable protein cavities
is presented. Starting from user-defined ligand binding sites, eight
topological and physicochemical properties are projected from cavity-lining
protein residues to an 80 triangle-discretised sphere placed at the
centre of the binding site, thus defining a cavity fingerprint. Representing
binding site properties onto a discretised sphere presents many advantages:
(i) a normalised distance between binding sites of different sizes
may be easily derived by summing up the normalised differences between
the 8 computed descriptors; (ii) a structural alignment of two proteins
is simply done by systematically rotating/translating one mobile
sphere around one immobile reference; (iii) a certain degree of fuzziness
in the comparison is reached by projecting global amino acid properties
(e.g., charge, size, functional groups count, distance to the site
centre) independently of local rotameric/tautomeric states of cavity-lining
residues. The method was implemented in a new program (SiteAlign)
and tested in a number of various scenarios: measuring the distance
between 376 related active site pairs, computing the cross-similarity
of members of a protein family, predicting the targets of ligands
with various promiscuity levels. The proposed method is robust enough
to detect local similarity among active sites of different sizes,
to discriminate between protein subfamilies and to recover the known
targets of promiscuous ligands by virtual screening.},
doi = {10.1002/prot.21858},
institution = {Bioinformatics of the Drug, Institut Gilbert Laustriat, CNRS UMR
7175-LC1, 74 route du Rhin, F-67400 Illkirch.},
keywords = {Algorithms; Amino Acid Sequence; Binding Sites, drug effects; Drug
Design; Hydrogen Bonding; Ligands; Protein Binding; Sequence Alignment;
Structure-Activity Relationship},
owner = {bricehoffmann},
pmid = {18175308},
timestamp = {2009.02.13},
url = {http://dx.doi.org/10.1002/prot.21858}
}
@inproceedings{Schellewald2001Evaluation,
author = {Schellewald, C. and Roth, S. and Schn\"{o}rr, C.},
title = {Evaluation of Convex Optimization Techniques for the Weighted Graph-Matching
Problem in Computer Vision},
booktitle = {Proceedings of the 23rd DAGM-Symposium on Pattern Recognition},
year = {2001},
pages = {361--368},
address = {London, UK},
publisher = {Springer-Verlag},
isbn = {3-540-42596-9}
}
@inproceedings{Schellewald2005Probabilistic,
author = {Schellewald, C. and Schnorr, C.},
title = {Probabilistic Subgraph Matching Based on Convex Relaxation},
booktitle = {EMMCVPR05},
year = {2005},
pages = {171-186},
bibsource = {http://www.visionbib.com/bibliography/match558.html#TT46972}
}
@article{Schena1995Quantitative,
author = {M. Schena and D. Shalon and R. W. Davis and P. O. Brown},
title = {Quantitative monitoring of gene expression patterns with a complementary
DNA microarray.},
journal = {Science},
year = {1995},
volume = {270},
pages = {467--470},
number = {5235},
month = {Oct},
abstract = {A high-capacity system was developed to monitor the expression of
many genes in parallel. Microarrays prepared by high-speed robotic
printing of complementary DNAs on glass were used for quantitative
expression measurements of the corresponding genes. Because of the
small format and high density of the arrays, hybridization volumes
of 2 microliters could be used that enabled detection of rare transcripts
in probe mixtures derived from 2 micrograms of total cellular messenger
RNA. Differential expression measurements of 45 Arabidopsis genes
were made by means of simultaneous, two-color fluorescence hybridization.},
doi = {10.1126/science.270.5235.467},
pdf = {../local/Schena1995Quantitative.pdf},
file = {Schena1995Quantitative.pdf:Schena1995Quantitative.pdf:PDF},
institution = {Department of Biochemistry, Beckman Center, Stanford University Medical
Center, CA 94305, USA.},
keywords = {microarray},
owner = {jp},
pmid = {7569999},
timestamp = {2009.02.08},
url = {http://dx.doi.org/10.1126/science.270.5235.467}
}
@article{Schmidt1976Fast,
author = {Schmidt, D. C. and Druffel, L. E.},
title = {A Fast Backtracking Algorithm to Test Directed Graphs for Isomorphism
Using Distance Matrices},
journal = {J. ACM},
year = {1976},
volume = {23},
pages = {433--445},
number = {3},
address = {New York, NY, USA},
doi = {http://doi.acm.org/10.1145/321958.321963},
issn = {0004-5411},
publisher = {ACM}
}
@article{Schmitt2002New,
author = {Stefan Schmitt and Daniel Kuhn and Gerhard Klebe},
title = {A new method to detect related function among proteins independent
of sequence and fold homology.},
journal = {J. Mol. Biol.},
year = {2002},
volume = {323},
pages = {387--406},
number = {2},
month = {Oct},
abstract = {A new method has been developed to detect functional relationships
among proteins independent of a given sequence or fold homology.
It is based on the idea that protein function is intimately related
to the recognition and subsequent response to the binding of a substrate
or an endogenous ligand in a well-characterized binding pocket. Thus,
recognition of similar ligands, supposedly linked to similar function,
requires conserved recognition features exposed in terms of common
physicochemical interaction properties via the functional groups
of the residues flanking a particular binding cavity. Following a
technique commonly used in the comparison of small molecule ligands,
generic pseudocenters coding for possible interaction properties
were assigned for a large sample set of cavities extracted from the
entire PDB and stored in the database Cavbase. Using a particular
query cavity a series of related cavities of decreasing similarity
is detected based on a clique detection algorithm. The detected similarity
is ranked according to property-based surface patches shared in common
by the different clique solutions. The approach either retrieves
protein cavities accommodating the same (e.g. co-factors) or closely
related ligands or it extracts proteins exhibiting similar function
in terms of a related catalytic mechanism. Finally the new method
has strong potential to suggest alternative molecular skeletons in
de novo design. The retrieval of molecular building blocks accommodated
in a particular sub-pocket that shares similarity with the pocket
in a protein studied by drug design can inspire the discovery of
novel ligands.},
institution = {Inst. of Pharmaceutical Chemistry, Univ. of Marburg, Marbacher Weg
6, D-35032, Marburg, Germany.},
keywords = {Algorithms; Binding Sites; Databases, Protein; Models, Molecular;
Molecular Structure; Protein Binding; Protein Folding; Protein Structure,
Tertiary; Proteins, chemistry/metabolism; Reproducibility of Results},
owner = {bricehoffmann},
pii = {S0022283602008112},
pmid = {12381328},
timestamp = {2009.02.13}
}
@article{Schneider2004Advances,
author = {Gisbert Schneider and Uli Fechner},
title = {Advances in the prediction of protein targeting signals.},
journal = {Proteomics},
year = {2004},
volume = {4},
pages = {1571-80},
number = {6},
month = {Jun},
doi = {10.1002/pmic.200300786},
pdf = {../local/Schneider2004Advances.pdf},
file = {Schneider2004Advances.pdf:local/Schneider2004Advances.pdf:PDF},
url = {http://dx.doi.org/10.1002/pmic.200300786}
}
@article{Schneider1998Artificial,
author = {G. Schneider and P. Wrede},
title = {{A}rtificial neural networks for computer-based molecular design.},
journal = {Prog Biophys Mol Biol},
year = {1998},
volume = {70},
pages = {175--222},
number = {3},
abstract = {The theory of artificial neural networks is briefly reviewed focusing
on supervised and unsupervised techniques which have great impact
on current chemical applications. An introduction to molecular descriptors
and representation schemes is given. In addition, worked examples
of recent advances in this field are highlighted and pioneering publications
are discussed. Applications of several types of artificial neural
networks to compound classification, modelling of structure-activity
relationships, biological target identification, and feature extraction
from biopolymers are presented and compared to other techniques.
Advantages and limitations of neural networks for computer-aided
molecular design and sequence analysis are discussed.},
keywords = {Algorithms, Amino Acid Sequence, Amino Acids, Animals, Artificial
Intelligence, Automated, Bacterial, Bacterial Proteins, Bicuculline,
Binding Sites, Biological, Biological Availability, Blood Proteins,
Blood-Brain Barrier, Cation Transport Proteins, Cats, Cell Membrane
Permeability, Chemical, Chemistry, Cluster Analysis, Combinatorial
Chemistry Techniques, Comparative Study, Computational Biology, Computer
Simulation, Computer Systems, Computer-Aided Design, Computer-Assisted,
Computing Methodologies, DNA-Binding Proteins, Databases, Dogs, Drug
Design, Electric Stimulation, Electromyography, Enzyme Inhibitors,
Ether-A-Go-Go Potassium Channels, Excitatory Amino Acid Antagonists,
Factual, False Positive Reactions, Forecasting, Forelimb, GABA Antagonists,
Gene Expression Profiling, Genome, Glutamic Acid, Humans, Hydrogen
Bonding, Image Enhancement, Image Interpretation, Image Processing,
Information Storage and Retrieval, Iontophoresis, Kynurenic Acid,
Least-Squares Analysis, Linear Models, Liver, Markov Chains, Metabolic
Clearance Rate, Metalloendopeptidases, Microelectrodes, Models, Molecular,
Molecular Conformation, Molecular Sequence Data, Molecular Structure,
Motor Cortex, Movement, Multivariate Analysis, Nerve Net, Neural
Networks (Computer), Neuropeptides, Non-U.S. Gov't, Nonlinear Dynamics,
Pattern Recognition, Pharmaceutical, Pharmaceutical Preparations,
Pharmacokinetics, Phylogeny, Potassium Channels, Predictive Value
of Tests, Protein Interaction Mapping, Protein Sorting Signals, Protein
Structure, Proteins, Rats, Reproducibility of Results, Research Support,
Sensitivity and Specificity, Sequence Alignment, Sequence Analysis,
Shoulder, Signal Processing, Software, Statistical, Stereotaxic Techniques,
Structure-Activity Relationship, Terminology, Tertiary, Trans-Activators,
Voltage-Gated, Zinc, 9830312},
owner = {mahe},
pii = {S0079610798000261},
pmid = {9830312},
timestamp = {2006.09.06}
}
@article{Schoenberg1938Metric,
author = {Schoenberg, I. J.},
title = {Metric spaces and positive definite functions},
journal = {Trans. Am. Math. Soc.},
year = {1938},
volume = {44},
pages = {522--536},
number = {3},
pdf = {../local/Schoenberg1938Metric.pdf},
file = {Schoenberg1938Metric.pdf:Schoenberg1938Metric.pdf:PDF},
owner = {jp},
timestamp = {2011.05.15}
}
@article{Schones2008Genome,
author = {Dustin E. Schones and Keji Zhao},
title = {Genome-wide approaches to studying chromatin modifications},
journal = {Nat. Rev. Genet.},
year = {2008},
volume = {9},
pages = {179-191},
keywords = {csbcbook, csbcbook-ch2}
}
@book{Schrodinger1944Vie,
title = {Qu'est-ce que la vie?},
publisher = {Christian Bourgois Editeur, 1986},
year = {1944},
author = {Erwin Schrödinger},
pages = {242 p},
keywords = {csbcbook},
opteditor = {Christian Bourgois}
}
@article{Schubert2005Local,
author = {Schubert, S. and Grünweller, A. and Erdmann, V. A. and Kurreck,
J.},
title = {{L}ocal {RNA} target structure influences si{RNA} efficacy: systematic
analysis of intentionally designed binding regions.},
journal = {J. Mol. Biol.},
year = {2005},
volume = {348},
pages = {883--893},
number = {4},
month = {May},
abstract = {Contradictory reports in the literature have emphasised either the
sequence of small interfering RNAs (siRNA) or the structure of their
target molecules to be the major determinant of the efficiency of
RNA interference (RNAi) approaches. In the present study, we analyse
systematically the contributions of these parameters to siRNA activity
by using deliberately designed mRNA constructs. The siRNA target
sites were included in well-defined structural elements rendering
them either highly accessible or completely involved in stable base-pairing.
Furthermore, complementary sequence elements and various hairpins
with different stem lengths and designs were used as target sites.
Only one of the strands of the siRNA duplex was found to be capable
of silencing via its respective target site, indicating that thermodynamic
characteristics intrinsic to the siRNA strands are a basic determinant
of siRNA activity. A significant obstruction of gene silencing by
the same siRNA, however, was observed to be caused by structural
features of the substrate RNA. Bioinformatic analysis of the mRNA
structures suggests a direct correlation between the extent of gene-knockdown
and the local free energy in the target region. Our findings indicate
that, although a favourable siRNA sequence is a necessary prerequisite
for efficient RNAi, complex target structures may limit the applicability
even of carefully chosen siRNAs.},
doi = {10.1016/j.jmb.2005.03.011},
keywords = {sirna},
owner = {vert},
pii = {S0022-2836(05)00269-X},
pmid = {15843020},
timestamp = {2006.03.28},
url = {http://dx.doi.org/10.1016/j.jmb.2005.03.011}
}
@article{Schueler-Furman2000Structure-based,
author = {Schueler-Furman, O. and Altuvia, Y. and Sette, A. and Margalit, H.},
title = {{S}tructure-based prediction of binding peptides to {MHC} class {I}
molecules: application to a broad range of {MHC} alleles.},
journal = {Protein Sci.},
year = {2000},
volume = {9},
pages = {1838--1846},
number = {9},
month = {Sep},
abstract = {Specific binding of antigenic peptides to major histocompatibility
complex (MHC) class I molecules is a prerequisite for their recognition
by cytotoxic T-cells. Prediction of MHC-binding peptides must therefore
be incorporated in any predictive algorithm attempting to identify
immunodominant T-cell epitopes, based on the amino acid sequence
of the protein antigen. Development of predictive algorithms based
on experimental binding data requires experimental testing of a very
large number of peptides. A complementary approach relies on the
structural conservation observed in crystallographically solved peptide-MHC
complexes. By this approach, the peptide structure in the MHC groove
is used as a template upon which peptide candidates are threaded,
and their compatibility to bind is evaluated by statistical pairwise
potentials. Our original algorithm based on this approach used the
pairwise potential table of Miyazawa and Jernigan (Miyazawa S, Jernigan
RL, 1996, J Mol Biol 256:623-644) and succeeded to correctly identify
good binders only for MHC molecules with hydrophobic binding pockets,
probably because of the high emphasis of hydrophobic interactions
in this table. A recently developed pairwise potential table by Betancourt
and Thirumalai (Betancourt MR, Thirumalai D, 1999, Protein Sci 8:361-369)
that is based on the Miyazawa and Jernigan table describes the hydrophilic
interactions more appropriately. In this paper, we demonstrate how
the use of this table, together with a new definition of MHC contact
residues by which only residues that contribute exclusively to sequence
specific binding are included, allows the development of an improved
algorithm that can be applied to a wide range of MHC class I alleles.},
keywords = {immunoinformatics},
pmid = {11045629},
timestamp = {2007.01.25}
}
@article{Schuffenhauer2003Similarity,
author = {Schuffenhauer, A. and Floersheim, P. and Acklin, P. and Jacoby, E.},
title = {Similarity metrics for ligands reflecting the similarity of the target
proteins},
journal = {J. Chem. Inf. Comput. Sci.},
year = {2003},
volume = {43},
pages = {391--405},
number = {2},
abstract = {In this study we evaluate how far the scope of similarity searching
can be extended to identify not only ligands binding to the same
target as the reference ligand(s) but also ligands of other homologous
targets without initially known ligands. This "homology-based similarity
searching" requires molecular representations reflecting the ability
of a molecule to interact with target proteins. The Similog keys,
which are introduced here as a new molecular representation, were
designed to fulfill such requirements. They are based only on the
molecular constitution and are counts of atom triplets. Each triplet
is characterized by the graph distances and the types of its atoms.
The atom-typing scheme classifies each atom by its function as H-bond
donor or acceptor and by its electronegativity and bulkiness. In
this study the Similog keys are investigated in retrospective in
silico screening experiments and compared with other conformation
independent molecular representations. Studied were molecules of
the MDDR database for which the activity data was augmented by standardized
target classification information from public protein classification
databases. The MDDR molecule set was split randomly into two halves.
The first half formed the candidate set. Ligands of four targets
(dopamine D2 receptor, opioid delta-receptor, factor Xa serine protease,
and progesterone receptor) were taken from the second half to form
the respective reference sets. Different similarity calculation methods
are used to rank the molecules of the candidate set by their similarity
to each of the four reference sets. The accumulated counts of molecules
binding to the reference target and groups of targets with decreasing
homology to it were examined as a function of the similarity rank
for each reference set and similarity method. In summary, similarity
searching based on Unity 2D-fingerprints or Similog keys are found
to be equally effective in the identification of molecules binding
to the same target as the reference set. However, the application
of the Similog keys is more effective in comparison with the other
investigated methods in the identification of ligands binding to
any target belonging to the same family as the reference target.
We attribute this superiority to the fact that the Similog keys provide
a generalization of the chemical elements and that the keys are counted
instead of merely noting their presence or absence in a binary form.
The second most effective molecular representation are the occurrence
counts of the public ISIS key fragments, which like the Similog method,
incorporates key counting as well as a generalization of the chemical
elements. The results obtained suggest that ligands for a new target
can be identified by the following three-step procedure: 1. Select
at least one target with known ligands which is homologous to the
new target. 2. Combine the known ligands of the selected target(s)
to a reference set. 3. Search candidate ligands for the new targets
by their similarity to the reference set using the Similog method.
This clearly enlarges the scope of similarity searching from the
classical application for a single target to the identification of
candidate ligands for whole target families and is expected to be
of key utility for further systematic chemogenomics exploration of
previously well explored target families.},
doi = {10.1021/ci025569t},
keywords = {chemogenomics},
owner = {laurent},
pmid = {12653501},
timestamp = {2008.07.16},
url = {http://dx.doi.org/10.1021/ci025569t}
}
@article{Schuffenhauer2002ontology,
author = {Schuffenhauer, A. and Zimmermann, J. and Stoop, R. and van der Vyver,
J. J. and Lecchini, S. and Jacoby, E.},
title = {An ontology for pharmaceutical ligands and its application for in
silico screening and library design},
journal = {J. Chem. Inf. Comput. Sci.},
year = {2002},
volume = {42},
pages = {947--955},
number = {4},
abstract = {Annotation efforts in biosciences have focused in past years mainly
on the annotation of genomic sequences. Only very limited effort
has been put into annotation schemes for pharmaceutical ligands.
Here we propose annotation schemes for the ligands of four major
target classes, enzymes, G protein-coupled receptors (GPCRs), nuclear
receptors (NRs), and ligand-gated ion channels (LGICs), and outline
their usage for in silico screening and combinatorial library design.
The proposed schemes cover ligand functionality and hierarchical
levels of target classification. The classification schemes are based
on those established by the EC, GPCRDB, NuclearDB, and LGICDB. The
ligands of the MDL Drug Data Report (MDDR) database serve as a reference
data set of known pharmacologically active compounds. All ligands
were annotated according to the schemes when attribution was possible
based on the activity classification provided by the reference database.
The purpose of the ligand-target classification schemes is to allow
annotation-based searching of the ligand database. In addition, the
biological sequence information of the target is directly linkable
to the ligand, hereby allowing sequence similarity-based identification
of ligands of next homologous receptors. Ligands of specified levels
can easily be retrieved to serve as comprehensive reference sets
for cheminformatics-based similarity searches and for design of target
class focused compound libraries. Retrospective in silico screening
experiments within the MDDR01.1 database, searching for structures
binding to dopamine D2, all dopamine receptors and all amine-binding
class A GPCRs using known dopamine D2 binding compounds as a reference
set, have shown that such reference sets are in particular useful
for the identification of ligands binding to receptors closely related
to the reference system. The potential for ligand identification
drops with increasing phylogenetic distance. The analysis of the
focus of a tertiary amine based combinatorial library compared to
known amine binding class A GPCRs, peptide binding class A GPCRs,
and LGIC ligands constitutes a second application scenario which
illustrates how the focus of a combinatorial library can be treated
quantitatively. The provided annotation schemes, which bridge chem-
and bioinformatics by linking ligands to sequences, are expected
to be of key utility for further systematic chemogenomics exploration
of previously well explored target families.},
keywords = {chemogenomics},
owner = {laurent},
pii = {ci010385k},
pmid = {12132896},
timestamp = {2008.07.16}
}
@article{Schumacher2006Microarray,
author = {Schumacher, A. and Kapranov, P. and Kaminsky, Z. and Flanagan, J.
and Assadzadeh, A. and Yau, P. and Virtanen, C. and Winegarden, N.
and Cheng, J. and Gingeras, T. and Petronis, A.},
title = {{M}icroarray-based {D}{N}{A} methylation profiling: technology and
applications},
journal = {Nucleic Acids Res.},
year = {2006},
volume = {34},
pages = {528--542},
keywords = {csbcbook, csbcbook-ch2}
}
@article{Schuster2000general,
author = {Schuster, S. and Fell, D. A. and Dandekar, T.},
title = {A general definition of metabolic pathways useful for systematic
organization and analysis of complex metabolic networks.},
journal = {Nat Biotechnol},
year = {2000},
volume = {18},
pages = {326--332},
number = {3},
month = {Mar},
abstract = {A set of linear pathways often does not capture the full range of
behaviors of a metabolic network. The concept of 'elementary flux
modes' provides a mathematical tool to define and comprehensively
describe all metabolic routes that are both stoichiometrically and
thermodynamically feasible for a group of enzymes. We have used this
concept to analyze the interplay between the pentose phosphate pathway
(PPP) and glycolysis. The set of elementary modes for this system
involves conventional glycolysis, a futile cycle, all the modes of
PPP function described in biochemistry textbooks, and additional
modes that are a priori equally entitled to pathway status. Applications
include maximizing product yield in amino acid and antibiotic synthesis,
reconstruction and consistency checks of metabolism from genome data,
analysis of enzyme deficiencies, and drug target identification in
metabolic networks.},
doi = {10.1038/73786},
pdf = {../local/Schuster2000general.pdf},
file = {Schuster2000general.pdf:Schuster2000general.pdf:PDF},
institution = {Department of Bioinformatics, Max Delbrück Center for Molecular Medicine,
D-13092 Berlin-Buch, Germany.},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pmid = {10700151},
timestamp = {2011.11.30},
url = {http://dx.doi.org/10.1038/73786}
}
@article{Schuster1994elementary,
author = {Schuster, S. and Hilgetag, C.},
title = {On elementary flux modes in biochemical reaction systems at steady
state},
journal = {J. Biol. Syst.},
year = {1994},
volume = {2},
pages = {165--182},
number = {2},
doi = {10.1142/S0218339094000131},
pdf = {../local/Schuster1994elementary.pdf},
file = {Schuster1994elementary.pdf:Schuster1994elementary.pdf:PDF},
owner = {jp},
timestamp = {2011.11.30},
url = {http://dx.doi.org/10.1142/S0218339094000131}
}
@article{Schuster2002Reaction,
author = {Schuster, S. and Hilgetag, C. and Woods, J. H. and Fell, D. A.},
title = {Reaction routes in biochemical reaction systems: algebraic properties,
validated calculation procedure and example from nucleotide metabolism.},
journal = {J Math Biol},
year = {2002},
volume = {45},
pages = {153--181},
number = {2},
month = {Aug},
abstract = {Elementary flux modes (direct reaction routes) are minimal sets of
enzymes that can operate at steady state, with all irreversible reactions
used in the appropriate direction. They can be interpreted as component
pathways of a (bio)chemical reaction network. Here, two different
definitions of elementary modes are given and their equivalence is
proved. Several algebraic properties of elementary modes are then
presented and proved. This concerns, amongst other features, the
minimal number of enzymes of the network not used in an elementary
mode and the situations where irreversible reactions are replaced
by reversible ones. Based on these properties, a refined algorithm
is presented, and it is formally proved that this algorithm will
exclusively generate all the elementary flux modes of an arbitrary
network containing reversible or irreversible reactions or both.
The algorithm is illustrated by a biochemical example relevant in
nucleotide metabolism. The computer implementation in two different
programming languages is discussed.},
doi = {10.1007/s002850200143},
pdf = {../local/Schuster2002Reaction.pdf},
file = {Schuster2002Reaction.pdf:Schuster2002Reaction.pdf:PDF},
institution = {Department of Bioinformatics, Max Delbrück Centre for Molecular Medicine,
D-13092 Berlin-Buch, Germany. stschust@mdc-berlin.de},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pmid = {12181603},
timestamp = {2011.11.30},
url = {http://dx.doi.org/10.1007/s002850200143}
}
@article{Schuster2007Next,
author = {Schuster, S. C.},
title = {Next-generation sequencing transforms today's biology},
journal = {Nat. Methods},
year = {2007},
volume = {5},
pages = {16-18}
}
@article{Schwarz2003Asymmetry,
author = {Schwarz, D. S. and Hutvagner, G. and Du, T. and Xu, Z. and Aronin,
N. and Zamore, P. D.},
title = {Asymmetry in the assembly of the {RNA}i enzyme complex},
journal = {Cell},
year = {2003},
volume = {115},
pages = {199-208},
number = {2},
month = {Oct},
abstract = {A key step in {RNA} interference ({RNA}i) is assembly of the {RISC},
the protein-si{RNA} complex that mediates target {RNA} cleavage.
{H}ere, we show that the two strands of an si{RNA} duplex are not
equally eligible for assembly into {RISC}. {R}ather, both the absolute
and relative stabilities of the base pairs at the 5? ends of the
two si{RNA} strands determine the degree to which each strand participates
in the {RNA}i pathway. si{RNA} duplexes can be functionally asymmetric,
with only one of the two strands able to trigger {RNA}i. {A}symmetry
is the hallmark of a related class of small, single-stranded, noncoding
{RNA}s, micro{RNA}s (mi{RNA}s). {W}e suggest that single-stranded
mi{RNA}s are initially generated as si{RNA}-like duplexes whose structures
predestine one strand to enter the {RISC} and the other strand to
be destroyed. {T}hus, the common step of {RISC} assembly is an unexpected
source of asymmetry for both si{RNA} function and mi{RNA} biogenesis.},
doi = {10.1016/S0092-8674(03)00759-1},
pdf = {../local/Schwarz2003Asymmetry.pdf},
file = {Schwarz2003Asymmetry.pdf:local/Schwarz2003Asymmetry.pdf:PDF},
keywords = {sirna},
url = {http://dx.doi.org/10.1016/S0092-8674(03)00759-1}
}
@article{Schwarz1978Estimating,
author = {Schwarz, G.},
title = {Estimating the dimension of a model},
journal = {Annals of Statistics},
year = {1978},
volume = {6},
pages = {461--464}
}
@article{Schwender2004pilot,
author = {Holger Schwender and Manuela Zucknick and Katja Ickstadt and Hermann
M Bolt and G. E. N. I. C. A. network},
title = {A pilot study on the application of statistical classification procedures
to molecular epidemiological data.},
journal = {Toxicol {L}ett},
year = {2004},
volume = {151},
pages = {291-9},
number = {1},
month = {Jun},
abstract = {The development of new statistical methods for use in molecular epidemiology
comprises the building and application of appropriate classification
rules. {T}he aim of this study was to assess various classification
methods that can potentially handle genetic interactions. {A} data
set comprising genotypes at 25 single nucleotide polymorphic ({SNP})
loci from 518 breast cancer cases and 586 age-matched population-based
controls from the {GENICA} study was used to built a classification
rule with the discrimination methods {SVM} (support vector machine),
{CART} (classification and regression tree), {B}agging, {R}andom
{F}orest, {L}ogit{B}oost and k nearest neighbours (k{NN}). {A} blind
pilot analysis of the genotypic data set was a first approach to
obtain an impression of the statistical structure of the data. {F}urthermore,
this analysis was performed to explore classification methods that
may be applied to molecular-epidemiological evaluation. {T}he results
showed that all blindly applied classification methods had a slightly
smaller misclassification rate than a random classification. {T}he
findings, nevertheless, suggest that {SNP} data might be useful for
the classification of individuals into categories of high or low
risk of diseases.},
keywords = {biosvm}
}
@inproceedings{Scholkopf1995Extracting,
author = {B . Sch{\"o}lkopf and C. Burges and V. Vapnik},
title = {Extracting support data for a given task},
booktitle = {Proceedings of the First International Conference on Knowledge Discovery
\& Data Mining},
year = {1995},
editor = {M. Fayyad and R. Uthurusamy},
publisher = {AAAI Press},
owner = {mahe},
timestamp = {2006.09.13}
}
@inproceedings{Scholkopf1996Incorporating,
author = {Sch{\"o}lkopf, B. and Burges, C. and Vapnik, V.},
title = {Incorporating invariances in support vector learning machines},
booktitle = {ICANN 96: Proceedings of the 1996 International Conference on Artificial
Neural Networks},
year = {1996},
editor = {von der Malsburg, C. and von Seelen, W. and Vorbr\"{u}ggen, J. C.
and Sendhoff, B.},
pages = {47--52},
address = {London, UK},
publisher = {Springer-Verlag},
pdf = {../local/Scholkopf1996Incorporating.pdf},
file = {Scholkopf1996Incorporating.pdf:Scholkopf1996Incorporating.pdf:PDF},
owner = {jp},
timestamp = {2008.12.22}
}
@inproceedings{Scholkopf2001Generalized,
author = {B. Sch{\"o}lkopf and R. Herbrich and A. J. Smola},
title = {A Generalized Representer Theorem},
booktitle = {Proceedings of the 14th Annual Conference on Computational Learning
Theory},
year = {2001},
volume = {2011},
series = {Lecture Notes in Computer Science},
pages = {416--426},
address = {Berlin / Heidelberg},
publisher = {Springer},
doi = {http://dx.doi.org/10.1007/3-540-44581-1}
}
@article{Scholkopf2001Estimating,
author = {Sch{\"o}lkopf, B. and Platt, J. C. and Shawe-Taylor, J. and Smola,
A. J. and Williamson, R. C.},
title = {Estimating the support of a high-himensional distributions},
journal = {Neural Comput.},
year = {2001},
volume = {13},
pages = {1443--1471},
pdf = {../local/Scholkopf2001Estimating.pdf},
file = {Scholkopf2001Estimating.pdf:local/Scholkopf2001Estimating.pdf:PDF}
}
@incollection{Schoelkopf1999Kernel,
author = {Sch{\"o}lkopf, B. and Smola, A.J. and M{\"u}ller, K.-R.},
title = {Kernel principal component analysis},
booktitle = {Advances in {K}ernel {M}ethods - {S}upport {V}ector {L}earning},
publisher = {MIT Press},
year = {1999},
editor = {B. Sch{\"o}lkopf and C. Burges and A. Smola},
pages = {327--352},
pdf = {../local/scho99.pdf},
file = {scho99.pdf:local/scho99.pdf:PDF},
subject = {kernel}
}
@book{Scholkopf2002Learning,
title = {Learning with {K}ernels: {S}upport {V}ector {M}achines, {R}egularization,
{O}ptimization, and {B}eyond},
publisher = {MIT Press},
year = {2002},
author = {Sch{\"o}lkopf, B. and Smola, A. J.},
address = {Cambridge, MA},
subject = {kernel},
url = {http://www.learning-with-kernels.org}
}
@book{Schoelkopf2004Kernel,
title = {Kernel {M}ethods in {C}omputational {B}iology},
publisher = {MIT Press},
year = {2004},
author = {Sch{\"o}lkopf, B. and Tsuda, K. and Vert, J.-P.},
address = {The MIT Press, Cambridge, Massachussetts},
keywords = {biosvm},
owner = {vert}
}
@inproceedings{Schoelkopf2002Kernel,
author = {Sch{\"o}lkopf, B. and Weston, J. and Eskin, E. and Leslie, C. and
Noble, W.S.},
title = {A {K}ernel {A}pproach for {L}earning from {A}lmost {O}rthogonal {P}atterns},
booktitle = {Proceedings of {ECML} 2002},
year = {2002},
pdf = {../local/scho02.pdf},
file = {scho02.pdf:local/scho02.pdf:PDF},
subject = {biokernel},
url = {http://www.cs.columbia.edu/~cleslie/papers/domdiag.pdf}
}
@inproceedings{Scholkopf2000Support,
author = {Sch{\"o}lkopf, B. and Williamson, R. and Smola, A. and Shawe-Taylor,
J. and Platt, J.},
title = {Support {V}ector {M}ethod for {N}ovelty {D}etection},
booktitle = {Adv. {N}eural {I}nform. {P}rocess. {S}yst.},
year = {2000},
editor = {S.A. Solla and T.K. Leen and K.-R. M{\"u}ller},
volume = {12},
pages = {582--588},
publisher = {MIT Press},
pdf = {../local/scho99.pdf},
file = {scho99.pdf:local/scho99.pdf:PDF},
subject = {kernel},
url = {http://citeseer.nj.nec.com/400144.html}
}
@inproceedings{Scott2009Novelty,
author = {Scott, C. and Blanchard, G.},
title = {Novelty detection: Unlabeled data definitely help},
booktitle = {Proceedings of the Twelfth International Conference on Artificial
Intelligence and Statistics (AISTATS) 2009},
year = {2009},
editor = {van Dyk, D. and Welling, M.},
volume = {5},
pages = {464--471},
address = {Clearwater Beach, Florida},
publisher = {JMLR: W\&CP 5},
abstract = {In machine learning, one formulation of the novelty detection problem
is to build a detector based on a training sample consisting of only
nominal data. The standard (inductive) approach to this problem has
been to declare novelties where the nominal density is low, which
reduces the problem to density level set estimation. In this paper,
we consider the setting where an unlabeled and possibly contaminated
sample is also available at learning time. We argue that novelty
detection is naturally solved by a general reduction to a binary
classification problem. In particular, a detector with a desired
false positive rate can be achieved through a reduction to Neyman-Pearson
classification. Unlike the inductive approach, our approach yields
detectors that are optimal (e.g., statistically consistent) regardless
of the distribution on novelties. Therefore, in novelty detection,
unlabeled data have a substantial impact on the theoretical properties
of the decision rule.},
pdf = {../local/Scott2009Novelty.pdf},
file = {Scott2009Novelty.pdf:Scott2009Novelty.pdf:PDF},
keywords = {PUlearning},
owner = {jp},
timestamp = {2010.01.31},
url = {http://jmlr.csail.mit.edu/proceedings/papers/v5/scott09a.html}
}
@article{Scott2005Neyman,
author = {Scott, C. and Nowak, R.},
title = {{A Neyman-Pearson approach to statistical learning}},
journal = {IEEE Trans. Inf. Theory},
year = {2005},
volume = {51},
pages = {3806--3819},
number = {11},
abstract = {{The Neyman-Pearson (NP) approach to hypothesis testing is useful
in situations where different types of error have different consequences
or a priori probabilities are unknown. For any /spl alpha/>0, the
NP lemma specifies the most powerful test of size /spl alpha/, but
assumes the distributions for each hypothesis are known or (in some
cases) the likelihood ratio is monotonic in an unknown parameter.
This paper investigates an extension of NP theory to situations in
which one has no knowledge of the underlying distributions except
for a collection of independent and identically distributed (i.i.d.)
training examples from each hypothesis. Building on a "fundamental
lemma" of Cannon et al., we demonstrate that several concepts from
statistical learning theory have counterparts in the NP context.
Specifically, we consider constrained versions of empirical risk
minimization (NP-ERM) and structural risk minimization (NP-SRM),
and prove performance guarantees for both. General conditions are
given under which NP-SRM leads to strong universal consistency. We
also apply NP-SRM to (dyadic) decision trees to derive rates of convergence.
Finally, we present explicit algorithms to implement NP-SRM for histograms
and dyadic decision trees.}},
citeulike-article-id = {600676},
citeulike-linkout-0 = {http://dx.doi.org/10.1109/TIT.2005.856955},
citeulike-linkout-1 = {http://ieeexplore.ieee.org/xpls/abs\_all.jsp?arnumber=1522642},
doi = {10.1109/TIT.2005.856955},
keywords = {neyman, pearson},
owner = {fantinemordelet},
posted-at = {2008-11-02 10:16:52},
priority = {2},
timestamp = {2013.01.11},
url = {http://dx.doi.org/10.1109/TIT.2005.856955}
}
@article{Scott2005Identifying,
author = {Scott, M. S. and Perkins, T. and Bunnell, S. and Pepin, F. and Thomas,
D. Y. and Hallett, M.},
title = {Identifying regulatory subnetworks for a set of genes},
journal = {Mol. Cell. Proteomics},
year = {2005},
volume = {4},
pages = {683--692},
number = {5},
month = {May},
abstract = {High throughput genomic/proteomic strategies, such as microarray studies,
drug screens, and genetic screens, often produce a list of genes
that are believed to be important for one or more reasons. Unfortunately
it is often difficult to discern meaningful biological relationships
from such lists. This study presents a new bioinformatic approach
that can be used to identify regulatory subnetworks for lists of
significant genes or proteins. We demonstrate the utility of this
approach using an interaction network for yeast constructed from
BIND, TRANSFAC, SCPD, and chromatin immunoprecipitation (ChIP)-Chip
data bases and lists of genes from well known metabolic pathways
or differential expression experiments. The approach accurately rediscovers
known regulatory elements of the heat shock response as well as the
gluconeogenesis, galactose, glycolysis, and glucose fermentation
pathways in yeast. We also find evidence supporting a previous conjecture
that approximately half of the enzymes in a metabolic pathway are
transcriptionally co-regulated. Finally we demonstrate a previously
unknown connection between GAL80 and the diauxic shift in yeast.},
doi = {10.1074/mcp.M400110-MCP200},
pdf = {../local/Scott2005Identifying.pdf},
file = {Scott2005Identifying.pdf:Scott2005Identifying.pdf:PDF},
institution = {McGill Centre for Bioinformatics, 3775 University Street, Montreal
H3A 2B4, Canada.},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {M400110-MCP200},
pmid = {15722371},
timestamp = {2011.09.26},
url = {http://dx.doi.org/10.1074/mcp.M400110-MCP200}
}
@article{Scott99thegromos,
author = {W. R. P. Scott and I. G. Tironi and A. E. Mark and S. R. Billeter
and J. F. and A. E. Torda and T. Huber and P. Kruger},
title = {The Gromos biomolecular simulation program package},
journal = {J. Phys. Chem. A},
year = {1999},
volume = {103},
pages = {3596--3607}
}
@techreport{Scovel2004Fast,
author = {Scovel, C. and Steinwart, I.},
title = {Fast {R}ates for {S}upport {V}ector {M}achines},
institution = {Los Alamos National Laboratory},
year = {2004}
}
@inproceedings{Sebag1997Tractable,
author = {Sebag, M. and Rouveirol, C.},
title = {Tractable {I}nduction and {C}lassification in {F}irst-{O}rder {L}ogic
via {S}tochastic {M}atching.},
booktitle = {Proceedings of the 15th {I}nternational {J}oint {C}onference on {A}rtificial
{I}ntelligence},
year = {1997},
pages = {888-893},
publisher = {Morgan Kaufmann},
owner = {mahe},
timestamp = {2006.08.09}
}
@article{Sebat2007Major,
author = {Jonathan Sebat},
title = {Major changes in our DNA lead to major changes in our thinking.},
journal = {Nat. Genet.},
year = {2007},
volume = {39},
pages = {S3--S5},
number = {7 Suppl},
month = {Jul},
abstract = {Variability in the human genome has far exceeded expectations. In
the course of the past three years, we have learned that much of
our naturally occurring genetic variation consists of large-scale
differences in genome structure, including copy-number variants (CNVs)
and balanced rearrangements such as inversions. Recent studies have
begun to reveal that structural variants are an important contributor
to disease risk; however, structural variants as a class may not
conform well to expectations of current methods for gene mapping.
New approaches are needed to understand the contribution of structural
variants to disease.},
doi = {10.1038/ng2095},
institution = {Cold Spring Harbor Laboratory, One Bungtown Road, Cold Spring Harbor,
New York 11724, USA. sebat@cshl.edu},
keywords = {DNA; Gene Dosage; Gene Rearrangement; Genetic Diseases, I; Genetic
Variation; Genome, Human; Humans; nborn},
owner = {jp},
pii = {ng2095},
pmid = {17597778},
timestamp = {2009.02.08},
url = {http://dx.doi.org/10.1038/ng2095}
}
@article{Sebat2004Large-scale,
author = {Sebat, J. and Lakshmi, B. and Troge, J. and Alexander, J. and Young,
J. and Lundin, P. and Månér, S. and Massa, H. and Walker, M. and
Chi, M. and Navin, N. and Lucito, R. and Healy, J. and Hicks, J.
and Ye, K. and Reiner, A. and Gilliam, T. C. and Trask, B. and Patterson,
N. and Zetterberg, A. and Wigler, M.},
title = {Large-scale copy number polymorphism in the human genome},
journal = {Science},
year = {2004},
volume = {305},
pages = {525--528},
number = {5683},
month = {Jul},
abstract = {The extent to which large duplications and deletions contribute to
human genetic variation and diversity is unknown. Here, we show that
large-scale copy number polymorphisms (CNPs) (about 100 kilobases
and greater) contribute substantially to genomic variation between
normal humans. Representational oligonucleotide microarray analysis
of 20 individuals revealed a total of 221 copy number differences
representing 76 unique CNPs. On average, individuals differed by
11 CNPs, and the average length of a CNP interval was 465 kilobases.
We observed copy number variation of 70 different genes within CNP
intervals, including genes involved in neurological function, regulation
of cell growth, regulation of metabolism, and several genes known
to be associated with disease.},
doi = {10.1126/science.1098918},
pdf = {../local/Sebat2004Large-scale.pdf},
file = {Sebat2004Large-scale.pdf:Sebat2004Large-scale.pdf:PDF},
institution = {Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA.},
keywords = {cgh},
owner = {jp},
pii = {305/5683/525},
pmid = {15273396},
timestamp = {2009.02.08},
url = {http://dx.doi.org/10.1126/science.1098918}
}
@article{Seeger2004Gaussian,
author = {Matthias Seeger},
title = {Gaussian processes for machine learning.},
journal = {Int {J} {N}eural {S}yst},
year = {2004},
volume = {14},
pages = {69-106},
number = {2},
month = {Apr},
abstract = {Gaussian processes ({GP}s) are natural generalisations of multivariate
{G}aussian random variables to infinite (countably or continuous)
index sets. {GP}s have been applied in a large number of fields to
a diverse range of ends, and very many deep theoretical analyses
of various properties are available. {T}his paper gives an introduction
to {G}aussian processes on a fairly elementary level with special
emphasis on characteristics relevant in machine learning. {I}t draws
explicit connections to branches such as spline smoothing models
and support vector machines in which similar ideas have been investigated.
{G}aussian process models are routinely used to solve hard machine
learning problems. {T}hey are attractive because of their flexible
non-parametric nature and computational simplicity. {T}reated within
a {B}ayesian framework, very powerful statistical methods can be
implemented which offer valid estimates of uncertainties in our predictions
and generic model selection procedures cast as nonlinear optimization
problems. {T}heir main drawback of heavy computational scaling has
recently been alleviated by the introduction of generic sparse approximations.13,78,31
{T}he mathematical literature on {GP}s is large and often uses deep
concepts which are not required to fully understand most machine
learning applications. {I}n this tutorial paper, we aim to present
characteristics of {GP}s relevant to machine learning and to show
up precise connections to other "kernel machines" popular in the
community. {O}ur focus is on a simple presentation, but references
to more detailed sources are provided.},
keywords = {Algorithms, Amino Acids, Antibodies, Artificial Intelligence, Astrocytoma,
Automated, Bayes Theorem, Biological, Biopsy, Brain, Brain Mapping,
Brain Neoplasms, Calibration, Comparative Study, Computational Biology,
Computer-Assisted, Computing Methodologies, Cysteine, Cystine, Dysplastic
Nevus Syndrome, Electrodes, Electroencephalography, Entropy, Eosine
Yellowish-(YS), Evoked Potentials, Female, Gene Expression Profiling,
Hematoxylin, Horseradish Peroxidase, Humans, Image Interpretation,
Image Processing, Imagery (Psychotherapy), Imagination, Laterality,
Linear Models, Male, Melanoma, Models, Monoclonal, Movement, Neoplasms,
Neural Networks (Computer), Neuropeptides, Non-P.H.S., Non-U.S. Gov't,
Nonparametric, Normal Distribution, P.H.S., Pattern Recognition,
Perception, Principal Component Analysis, Protein, Protein Array
Analysis, Protein Interaction Mapping, Proteins, Regression Analysis,
Research Support, Sensitivity and Specificity, Sequence Alignment,
Sequence Ana, Sequence Analysis, Skin Neoplasms, Software, Statistical,
Statistics, Tumor Markers, U.S. Gov't, User-Computer Interface, World
Health Organization, lysis, 15112367},
pii = {S0129065704001899}
}
@inproceedings{Seeger2002Covariance,
author = {Seeger, M.},
title = {Covariance {K}ernels from {B}ayesian {G}enerative {M}odels},
booktitle = {Adv. {N}eural {I}nform. {P}rocess. {S}yst.},
year = {2002},
volume = {14},
pages = {905-912},
pdf = {../local/nips2001.pdf:http\://www.cs.berkeley.edu/~mseeger/papers/nips2001.pdf:PDF;nips2001.pdf:http\},
file = {nips2001.pdf:http\://www.cs.berkeley.edu/~mseeger/papers/nips2001.pdf:PDF;nips2001.pdf:http\://www.cs.berkeley.edu/~mseeger/papers/nips2001.pdf:PDF},
keywords = {biosvm},
owner = {vert}
}
@article{Segal2005From,
author = {Segal, E. and Friedman, N. and Kaminski, N. and Regev, A. and Koller,
D.},
title = {From signatures to models: understanding cancer using microarrays},
journal = {Nat {G}enet},
year = {2005},
volume = {37},
pages = {S38-45},
number = {6 Suppl},
abstract = {Genomics has the potential to revolutionize the diagnosis and management
of cancer by offering an unprecedented comprehensive view of the
molecular underpinnings of pathology. {C}omputational analysis is
essential to transform the masses of generated data into a mechanistic
understanding of disease. {H}ere we review current research aimed
at uncovering the modular organization and function of transcriptional
networks and responses in cancer. {W}e first describe how methods
that analyze biological processes in terms of higher-level modules
can identify robust signatures of disease mechanisms. {W}e then discuss
methods that aim to identify the regulatory mechanisms underlying
these modules and processes. {F}inally, we show how comparative analysis,
combining human data with model organisms, can lead to more robust
findings. {W}e conclude by discussing the challenges of generalizing
these methods from cells to tissues and the opportunities they offer
to improve cancer diagnosis and management.},
doi = {10.1038/ng1561},
pdf = {../local/Segal2005From.pdf},
file = {Segal2005From.pdf:Segal2005From.pdf:PDF},
keywords = {microarray},
url = {http://dx.doi.org/10.1038/ng1561}
}
@article{Segal2004module,
author = {Segal, E. and Friedman, N. and Koller, D. and Regev, A.},
title = {A module map showing conditional activity of expression modules in
cancer.},
journal = {Nat. {G}enet.},
year = {2004},
volume = {36},
pages = {1090--1098},
number = {10},
month = {Oct},
abstract = {D{NA} microarrays are widely used to study changes in gene expression
in tumors, but such studies are typically system-specific and do
not address the commonalities and variations between different types
of tumor. {H}ere we present an integrated analysis of 1,975 published
microarrays spanning 22 tumor types. {W}e describe expression profiles
in different tumors in terms of the behavior of modules, sets of
genes that act in concert to carry out a specific function. {U}sing
a simple unified analysis, we extract modules and characterize gene-expression
profiles in tumors as a combination of activated and deactivated
modules. {A}ctivation of some modules is specific to particular types
of tumor; for example, a growth-inhibitory module is specifically
repressed in acute lymphoblastic leukemias and may underlie the deregulated
proliferation in these cancers. {O}ther modules are shared across
a diverse set of clinical conditions, suggestive of common tumor
progression mechanisms. {F}or example, the bone osteoblastic module
spans a variety of tumor types and includes both secreted growth
factors and their receptors. {O}ur findings suggest that there is
a single mechanism for both primary tumor proliferation and metastasis
to bone. {O}ur analysis presents multiple research directions for
diagnostic, prognostic and therapeutic studies.},
doi = {10.1038/ng1434},
pdf = {../local/Segal2004module.pdf},
file = {Segal2004module.pdf:local/Segal2004module.pdf:PDF},
keywords = {biogm},
owner = {vert},
pii = {ng1434},
pmid = {15448693},
timestamp = {2006.01.18},
url = {http://dx.doi.org/10.1038/ng1434}
}
@article{Segal2004modulea,
author = {Segal, E. and Friedman, N. and Koller, D. and Regev, A.},
title = {A module map showing conditional activity of expression modules in
cancer},
journal = {Nat {G}enet},
year = {2004},
volume = {36},
pages = {1090-8},
number = {10},
abstract = {D{NA} microarrays are widely used to study changes in gene expression
in tumors, but such studies are typically system-specific and do
not address the commonalities and variations between different types
of tumor. {H}ere we present an integrated analysis of 1,975 published
microarrays spanning 22 tumor types. {W}e describe expression profiles
in different tumors in terms of the behavior of modules, sets of
genes that act in concert to carry out a specific function. {U}sing
a simple unified analysis, we extract modules and characterize gene-expression
profiles in tumors as a combination of activated and deactivated
modules. {A}ctivation of some modules is specific to particular types
of tumor; for example, a growth-inhibitory module is specifically
repressed in acute lymphoblastic leukemias and may underlie the deregulated
proliferation in these cancers. {O}ther modules are shared across
a diverse set of clinical conditions, suggestive of common tumor
progression mechanisms. {F}or example, the bone osteoblastic module
spans a variety of tumor types and includes both secreted growth
factors and their receptors. {O}ur findings suggest that there is
a single mechanism for both primary tumor proliferation and metastasis
to bone. {O}ur analysis presents multiple research directions for
diagnostic, prognostic and therapeutic studies.},
doi = {10.1038/ng1434},
pdf = {../local/Segal2004modulea.pdf},
file = {Segal2004modulea.pdf:Segal2004modulea.pdf:PDF},
url = {http://dx.doi.org/10.1038/ng1434}
}
@article{Segal2003Module,
author = {Segal, E. and Shapira, M. and Regev, A. and Pe'er, D. and Botstein,
D. and Koller, D. and Friedman, N.},
title = {Module networks: identifying regulatory modules and their condition-specific
regulators from gene expression data.},
journal = {Nat. {G}enet.},
year = {2003},
volume = {34},
pages = {166--176},
number = {2},
month = {Jun},
abstract = {Much of a cell's activity is organized as a network of interacting
modules: sets of genes coregulated to respond to different conditions.
{W}e present a probabilistic method for identifying regulatory modules
from gene expression data. {O}ur procedure identifies modules of
coregulated genes, their regulators and the conditions under which
regulation occurs, generating testable hypotheses in the form 'regulator
{X} regulates module {Y} under conditions {W}'. {W}e applied the
method to a {S}accharomyces cerevisiae expression data set, showing
its ability to identify functionally coherent modules and their correct
regulators. {W}e present microarray experiments supporting three
novel predictions, suggesting regulatory roles for previously uncharacterized
proteins.},
doi = {10.1038/ng1165},
pdf = {../local/Segal2003Module.pdf},
file = {Segal2003Module.pdf:Segal2003Module.pdf:PDF},
keywords = {biogm},
owner = {vert},
pii = {ng1165},
pmid = {12740579},
timestamp = {2006.01.18},
url = {http://dx.doi.org/10.1038/ng1165}
}
@article{Segal2003Regression,
author = {Segal, M. R. and Dahlquist, K. D. and Conklin, B. R.},
title = {Regression approaches for microarray data analysis.},
journal = {J. {C}omput. {B}iol.},
year = {2003},
volume = {10},
pages = {961-980},
number = {6},
abstract = {A variety of new procedures have been devised to handle the two-sample
comparison (e.g., tumor versus normal tissue) of gene expression
values as measured with microarrays. {S}uch new methods are required
in part because of some defining characteristics of microarray-based
studies: (i) the very large number of genes contributing expression
measures which far exceeds the number of samples (observations) available
and (ii) the fact that by virtue of pathway/network relationships,
the gene expression measures tend to be highly correlated. {T}hese
concerns are exacerbated in the regression setting, where the objective
is to relate gene expression, simultaneously for multiple genes,
to some external outcome or phenotype. {C}orrespondingly, several
methods have been recently proposed for addressing these issues.
{W}e briefly critique some of these methods prior to a detailed evaluation
of gene harvesting. {T}his reveals that gene harvesting, without
additional constraints, can yield artifactual solutions. {R}esults
obtained employing such constraints motivate the use of regularized
regression procedures such as the lasso, least angle regression,
and support vector machines. {M}odel selection and solution multiplicity
issues are also discussed. {T}he methods are evaluated using a microarray-based
study of cardiomyopathy in transgenic mice.},
doi = {10.1089/106652703322756177},
pdf = {../local/Segal2003Regression.pdf},
file = {Segal2003Regression.pdf:local/Segal2003Regression.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert}
}
@article{Segal2003Classificationa,
author = {Segal, N. H. and Pavlidis, P. and Antonescu, C. R. and Maki, R. G.
and Noble, W. S. and DeSantis, D. and Woodruff, J. M. and Lewis,
J. J. and Brennan, M. F. and Houghton, A. N. and Cordon-Cardo, C.},
title = {Classification and {S}ubtype {P}rediction of {A}dult {S}oft {T}issue
{S}arcoma by {F}unctional {G}enomics},
journal = {Am. {J}. {P}athol.},
year = {2003},
volume = {163},
pages = {691-700},
number = {2},
month = {Aug},
abstract = {Adult soft tissue sarcomas are a heterogeneous group of tumors, including
well-described subtypes by histological and genotypic criteria, and
pleomorphic tumors typically characterized by non-recurrent genetic
aberrations and karyotypic heterogeneity. {T}he latter pose a diagnostic
challenge, even to experienced pathologists. {W}e proposed that gene
expression profiling in soft tissue sarcoma would identify a genomic-based
classification scheme that is useful in diagnosis. {RNA} samples
from 51 pathologically confirmed cases, representing nine different
histological subtypes of adult soft tissue sarcoma, were examined
using the {A}ffymetrix {U}95{A} {G}ene{C}hip. {S}tatistical tests
were performed on experimental groups identified by cluster analysis,
to find discriminating genes that could subsequently be applied in
a support vector machine algorithm. {S}ynovial sarcomas, round-cell/myxoid
liposarcomas, clear-cell sarcomas and gastrointestinal stromal tumors
displayed remarkably distinct and homogenous gene expression profiles.
{P}leomorphic tumors were heterogeneous. {N}otably, a subset of malignant
fibrous histiocytomas, a controversialhistological subtype, was identified
as a distinct genomic group. {T}he support vector machine algorithm
supported a genomic basis for diagnosis, with both high sensitivity
and specificity. {I}n conclusion, we showed gene expression profiling
to be useful in classification and diagnosis, providing insights
into pathogenesis and pointing to potential new therapeutic targets
of soft tissue sarcoma.},
pdf = {../local/Segal2003Classificationa.pdf},
file = {Segal2003Classificationa.pdf:local/Segal2003Classificationa.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://ajp.amjpathol.org/cgi/content/abstract/163/2/691}
}
@article{Segal2003Classification,
author = {Segal, N. H. and Pavlidis, P. and Noble, W. S. and Antonescu, C.
R. and Viale, A. and Wesley, U. V. and Busam, K. and Gallardo, H.
and DeSantis, D. and Brennan, M. F. and Cordon-Cardo, C. and Wolchok,
J. D. and Houghton, A. N.},
title = {Classification of {C}lear-{C}ell {S}arcoma as a {S}ubtype of {M}elanoma
by {G}enomic {P}rofiling},
journal = {J. {C}lin. {O}ncol.},
year = {2003},
volume = {21},
pages = {1775-1781},
number = {9},
month = {May},
abstract = {Purpose: {T}o develop a genome-based classification scheme for clear-cell
sarcoma ({CCS}), also known as melanoma of soft parts ({MSP}), which
would have implications for diagnosis and treatment. {T}his tumor
displays characteristic features of soft tissue sarcoma ({STS}),
including deep soft tissue primary location and a characteristic
translocation, t(12;22)(q13;q12), involving {EWS} and {ATF}1 genes.
{CCS}/{MSP} also has typical melanoma features, including immunoreactivity
for {S}100 and {HMB}45, pigmentation, {MITF}-{M} expression, and
a propensity for regional lymph node metastases. {M}aterials and
{M}ethods: {RNA} samples from 21 cell lines and 60 pathologically
confirmed cases of {STS}, melanoma, and {CCS}/{MSP} were examined
using the {U}95{A} {G}ene{C}hip ({A}ffymetrix, {S}anta {C}lara, {CA}).
{H}ierarchical cluster analysis, principal component analysis, and
support vector machine ({SVM}) analysis exploited genomic correlations
within the data to classify {CCS}/{MSP}. {R}esults: {U}nsupervised
analyses demonstrated a clear distinction between {STS} and melanoma
and, furthermore, showed that {CCS}/{MSP} cluster with the melanomas
as a distinct group. {A} supervised {SVM} learning approach further
validated this finding and provided a user-independent approach to
diagnosis. {G}enes of interest that discriminate {CCS}/{MSP} included
those encoding melanocyte differentiation antigens, {MITF}, {SOX}10,
{ERBB}3, and {FGFR}1. {C}onclusion: {G}ene expression profiles support
the classification of {CCS}/{MSP} as a distinct genomic subtype of
melanoma. {A}nalysis of these gene profiles using the {SVM} may be
an important diagnostic tool. {G}enomic analysis identified potential
targets for the development of therapeutic strategies in the treatment
of this disease.},
doi = {10.1200/JCO.2003.10.108},
pdf = {../local/Segal2003Classification.pdf},
file = {Segal2003Classification.pdf:local/Segal2003Classification.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://dx.doi.org/10.1200/JCO.2003.10.108}
}
@article{Segre2002Analysis,
author = {Segr{\`e}, D. and Vitkup, D. and Church, G. M.},
title = {Analysis of optimality in natural and perturbed metabolic networks.},
journal = {Proc Natl Acad Sci U S A},
year = {2002},
volume = {99},
pages = {15112--15117},
number = {23},
month = {Nov},
abstract = {An important goal of whole-cell computational modeling is to integrate
detailed biochemical information with biological intuition to produce
testable predictions. Based on the premise that prokaryotes such
as Escherichia coli have maximized their growth performance along
evolution, flux balance analysis (FBA) predicts metabolic flux distributions
at steady state by using linear programming. Corroborating earlier
results, we show that recent intracellular flux data for wild-type
E. coli JM101 display excellent agreement with FBA predictions. Although
the assumption of optimality for a wild-type bacterium is justifiable,
the same argument may not be valid for genetically engineered knockouts
or other bacterial strains that were not exposed to long-term evolutionary
pressure. We address this point by introducing the method of minimization
of metabolic adjustment (MOMA), whereby we test the hypothesis that
knockout metabolic fluxes undergo a minimal redistribution with respect
to the flux configuration of the wild type. MOMA employs quadratic
programming to identify a point in flux space, which is closest to
the wild-type point, compatibly with the gene deletion constraint.
Comparing MOMA and FBA predictions to experimental flux data for
E. coli pyruvate kinase mutant PB25, we find that MOMA displays a
significantly higher correlation than FBA. Our method is further
supported by experimental data for E. coli knockout growth rates.
It can therefore be used for predicting the behavior of perturbed
metabolic networks, whose growth performance is in general suboptimal.
MOMA and its possible future extensions may be useful in understanding
the evolutionary optimization of metabolism.},
doi = {10.1073/pnas.232349399},
pdf = {../local/Segre2002Analysis.pdf},
file = {Segre2002Analysis.pdf:Segre2002Analysis.pdf:PDF},
institution = {Lipper Center for Computational Genetics and Department of Genetics,
Harvard Medical School, Boston, MA 02115, USA.},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {232349399},
pmid = {12415116},
timestamp = {2013.01.25},
url = {http://dx.doi.org/10.1073/pnas.232349399}
}
@article{Seike2005Proteomic,
author = {Seike, M. and Kondo, T. and Fujii, K. and Okano, T. and Yamada, T.
and Matsuno, Y. and Gemma, A. and Kudoh, S. and Hirohashi, S.},
title = {Proteomic signatures for histological types of lung cancer.},
journal = {Proteomics},
year = {2005},
month = {Jul},
abstract = {We performed proteomic studies on lung cancer cells to elucidate the
mechanisms that determine histological phenotype. {T}hirty lung cancer
cell lines with three different histological backgrounds (squamous
cell carcinoma, small cell lung carcinoma and adenocarcinoma) were
subjected to two-dimensional difference gel electrophoresis (2-{D}
{DIGE}) and grouped by multivariate analyses on the basis of their
protein expression profiles. 2-{D} {DIGE} achieves more accurate
quantification of protein expression by using highly sensitive fluorescence
dyes to label the cysteine residues of proteins prior to two-dimensional
polyacrylamide gel electrophoresis. {W}e found that hierarchical
clustering analysis and principal component analysis divided the
cell lines according to their original histology. {S}pot ranking
analysis using a support vector machine algorithm and unsupervised
classification methods identified 32 protein spots essential for
the classification. {T}he proteins corresponding to the spots were
identified by mass spectrometry. {N}ext, lung cancer cells isolated
from tumor tissue by laser microdissection were classified on the
basis of the expression pattern of these 32 protein spots. {B}ased
on the expression profile of the 32 spots, the isolated cancer cells
were categorized into three histological groups: the squamous cell
carcinoma group, the adenocarcinoma group, and a group of carcinomas
with other histological types. {I}n conclusion, our results demonstrate
the utility of quantitative proteomic analysis for molecular diagnosis
and classification of lung cancer cells.},
doi = {10.1002/pmic.200401166},
pdf = {../local/Seike2005Proteomic.pdf},
file = {Seike2005Proteomic.pdf:local/Seike2005Proteomic.pdf:PDF},
keywords = {biosvm proteomics},
url = {http://dx.doi.org/10.1002/pmic.200401166}
}
@article{Selinger2000RNA,
author = {Douglas W. Selinger and Kevin J. Cheung and Rui Mei and Erik M. Johansson
and Craig S. Richmond and Frederick R. Blattner and David J. Lockhart
and George M. Church},
title = {R{NA} expression analysis using a 30 base pair resolution {E}scherichia
coli genome array},
journal = {Nat. {B}iotechnol.},
year = {2000},
volume = {18},
pages = {1262--1268},
pdf = {../local/seli00.pdf},
file = {seli00.pdf:local/seli00.pdf:PDF},
subject = {microarray},
url = {http://www.nature.com/cgi-taf/DynaPage.taf?file=/nbt/journal/v18/n12/full/nbt1200_1262.html&filetype=PDF}
}
@article{Semizarov2003Specificity,
author = {Semizarov, D. and Frost, L. and Sarthy, A. and Kroeger, P. and Halbert,
D. N. and Fesik, S. W.},
title = {Specificity of short interfering {RNA} determined through gene expression
signatures.},
journal = {Proc. {N}atl. {A}cad. {S}ci. {USA}},
year = {2003},
volume = {100},
pages = {6347-52},
number = {11},
month = {May},
abstract = {Short interfering {RNA} (si{RNA}) is widely used for studying gene
function and holds great promise as a tool for validating drug targets
and treating disease. {A} critical assumption in these applications
is that the effect of si{RNA} on cells is specific, i.e., limited
to the specific knockdown of the target gene. {I}n this article,
we characterize the specificity of si{RNA} by applying gene expression
profiling. {S}everal si{RNA}s were designed against different regions
of the same target gene for three different targets. {T}heir effects
on cells were compared by using {DNA} microarrays to generate gene
expression signatures. {W}hen the si{RNA} design and transfection
conditions were optimized, the signatures for different si{RNA}s
against the same target were shown to correlate very closely, whereas
the signatures for different genes revealed no correlation. {T}hese
results indicate that si{RNA} is a highly specific tool for targeted
gene knockdown, establishing si{RNA}-mediated gene silencing as a
reliable approach for large-scale screening of gene function and
drug target validation.},
doi = {10.1073/pnas.1131959100},
keywords = {sirna},
pii = {1131959100},
url = {http://dx.doi.org/10.1073/pnas.1131959100}
}
@article{Sen2004Predicting,
author = {Sen, T.Z. and Kloczkowski, A. and Jernigan, R.L. and Yan, C. and
Honavar, V. and Ho, K.M. and Wang, C.Z. and Ihm, Y. and Cao, H. and
Gu, X. and Dobbs, D.},
title = {Predicting binding sites of hydrolase-inhibitor complexes by combining
several methods.},
journal = {B{MC} {B}ioinformatics},
year = {2004},
volume = {5},
number = {205},
abstract = {Background {P}rotein-protein interactions play a critical role in
protein function. {C}ompletion of many genomes is being followed
rapidly by major efforts to identify interacting protein pairs experimentally
in order to decipher the networks of interacting, coordinated-in-action
proteins. {I}dentification of protein-protein interaction sites and
detection of specific amino acids that contribute to the specificity
and the strength of protein interactions is an important problem
with broad applications ranging from rational drug design to the
analysis of metabolic and signal transduction networks. {R}esults
{I}n order to increase the power of predictive methods for protein-protein
interaction sites, we have developed a consensus methodology for
combining four different methods. {T}hese approaches include: data
mining using {S}upport {V}ector {M}achines, threading through protein
structures, prediction of conserved residues on the protein surface
by analysis of phylogenetic trees, and the {C}onservatism of {C}onservatism
method of {M}irny and {S}hakhnovich. {R}esults obtained on a dataset
of hydrolase-inhibitor complexes demonstrate that the combination
of all four methods yield improved predictions over the individual
methods. {C}onclusions {W}e developed a consensus method for predicting
protein-protein interface residues by combining sequence and structure-based
methods. {T}he success of our consensus approach suggests that similar
methodologies can be developed to improve prediction accuracies for
other bioinformatic problems.},
doi = {10.1186/1471-2105-5-205},
pdf = {../local/Sen2004Predicting.pdf},
file = {Sen2004Predicting.pdf:local/Sen2004Predicting.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert}
}
@article{Senawongse2005Predicting,
author = {Pasak Senawongse and Andrew R Dalby and Zheng Rong Yang},
title = {Predicting the phosphorylation sites using hidden markov models and
machine learning methods.},
journal = {J {C}hem {I}nf {M}odel},
year = {2005},
volume = {45},
pages = {1147-52},
number = {4},
abstract = {Accurately predicting phosphorylation sites in proteins is an important
issue in postgenomics, for which how to efficiently extract the most
predictive features from amino acid sequences for modeling is still
challenging. {A}lthough both the distributed encoding method and
the bio-basis function method work well, they still have some limits
in use. {T}he distributed encoding method is unable to code the biological
content in sequences efficiently, whereas the bio-basis function
method is a nonparametric method, which is often computationally
expensive. {A}s hidden {M}arkov models ({HMM}s) can be used to generate
one model for one cluster of aligned protein sequences, the aim in
this study is to use {HMM}s to extract features from amino acid sequences,
where sequence clusters are determined using available biological
knowledge. {I}n this novel method, {HMM}s are first constructed using
functional sequences only. {B}oth functional and nonfunctional training
sequences are then inputted into the trained {HMM}s to generate functional
and nonfunctional feature vectors. {F}rom this, a machine learning
algorithm is used to construct a classifier based on these feature
vectors. {I}t is found in this work that (1) this method provides
much better prediction accuracy than the use of {HMM}s only for prediction,
and (2) the support vector machines ({SVM}s) algorithm outperforms
decision trees and neural network algorithms when they are constructed
on the features extracted using the trained {HMM}s.},
doi = {10.1021/ci050047+},
pdf = {../local/Senawongse2005Predicting.pdf},
file = {Senawongse2005Predicting.pdf:local/Senawongse2005Predicting.pdf:PDF},
keywords = {biosvm},
url = {http://dx.doi.org/10.1021/ci050047+}
}
@article{Seol2001Skp1,
author = {J. H. Seol and A. Shevchenko and A. Shevchenko and R. J. Deshaies},
title = {Skp1 forms multiple protein complexes, including {RAVE}, a regulator
of {V}-{ATP}ase assembly.},
journal = {Nat {C}ell {B}iol},
year = {2001},
volume = {3},
pages = {384-91},
number = {4},
month = {Apr},
abstract = {S{CF} ubiquitin ligases are composed of {S}kp1, {C}dc53, {H}rt1 and
one member of a large family of substrate receptors known as {F}-box
proteins ({FBP}s). {H}ere we report the identification, using sequential
rounds of epitope tagging, affinity purification and mass spectrometry,
of 16 {S}kp1 and {C}dc53-associated proteins in budding yeast, including
all components of {SCF}, 9 {FBP}s, {Y}jr033 ({R}av1) and {Y}dr202
({R}av2). {R}av1, {R}av2 and {S}kp1 form a complex that we have named
'regulator of the ({H}+)-{ATP}ase of the vacuolar and endosomal membranes'
({RAVE}), which associates with the {V}1 domain of the vacuolar membrane
({H}+)-{ATP}ase ({V}-{ATP}ase). {V}-{ATP}ases are conserved throughout
eukaryotes, and have been implicated in tumour metastasis and multidrug
resistance, and here we show that {RAVE} promotes glucose-triggered
assembly of the {V}-{ATP}ase holoenzyme. {P}revious systematic genome-wide
two-hybrid screens yielded 17 proteins that interact with {S}kp1
and {C}dc53, only 3 of which overlap with those reported here. {T}hus,
our results provide a distinct view of the interactions that link
proteins into a comprehensive cellular network.},
doi = {10.1038/35070067},
pdf = {../local/Seol2001Skp1.pdf},
file = {Seol2001Skp1.pdf:local/Seol2001Skp1.pdf:PDF},
keywords = {Affinity, Affinity Labels, Amino Acid Sequence, Animals, Cell Cycle
Proteins, Cells, Chromatography, Cloning, Comparative Study, Cullin
Proteins, Cultured, Cytoplasm, DNA, DNA Damage, DNA Repair, Electrospray
Ionization, Fungal, Fungal Proteins, Gene Targeting, Genetic, Glucose,
Holoenzymes, Humans, Macromolecular Substances, Mass, Matrix-Assisted
Laser Desorption-Ionization, Mitosis, Molecular, Molecular Sequence
Data, Non-P.H.S., Non-U.S. Gov't, P.H.S., Phosphoric Monoester Hydrolases,
Protein Binding, Protein Interaction Mapping, Protein Kinases, Proteome,
Proteomics, Proton-Translocating ATPases, Recombinant Fusion Proteins,
Research Support, Ribonucleoproteins, Ribosomes, S-Phase Kinase-Associated
Proteins, Saccharomyces cerevisiae, Saccharomyces cerevisiae Proteins,
Sensitivity and Specificity, Sequence Alignment, Signal Transduction,
Species Specificity, Spectrometry, Spectrum Analysis, Transcription,
U.S. Gov't, Vacuolar Proton-Translocating ATPases, 11283612},
owner = {vert},
pii = {35070067},
url = {http://dx.doi.org/10.1038/35070067}
}
@article{Serra2003Development,
author = {Serra, J.R. and Thompson, E.D. and Jurs, P.C.},
title = {Development of binary classification of structural chromosome aberrations
for a diverse set of organic compounds from molecular structure},
journal = {Chem. {R}es. {T}oxicol.},
year = {2003},
volume = {16},
pages = {153-163},
number = {2},
abstract = {Classification models are generated to predict in vitro cytogenetic
results for a diverse set of 383 organic compounds. {B}oth k-nearest
neighbor and support vector machine models are developed. {T}hey
are based on calculated molecular structure descriptors. {E}ndpoints
used are the labels clastogenic or nonclastogenic according to an
in vitro chromosomal aberration assay with {C}hinese hamster lung
cells. {C}ompounds that were tested with both a 24 and 48 h exposure
are included. {E}ach compound is represented by calculated molecular
structure descriptors encoding the topological, electronic, geometrical,
or polar surface area aspects of the structure. {S}ubsets of informative
descriptors are identified with genetic algorithm feature selection
coupled to the appropriate classification algorithm. {T}he overall
classification success rate for a k-nearest neighbor classifier built
with just six topological descriptors is 81.2% for the training set
and 86.5% for an external prediction set. {T}he overall classification
success rate for a three-descriptor support vector machine model
is 99.7% for the training set, 92.1% for the cross-validation set,
and 83.8% for an external prediction set.},
doi = {10.1021/tx020077w},
pdf = {../local/Serra2003Development.pdf},
file = {Serra2003Development.pdf:local/Serra2003Development.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://dx.doi.org/10.1021/tx020077w}
}
@article{Sette2001HLA,
author = {Sette, A. and Chesnut, R. and Fikes, J.},
title = {{HLA} expression in cancer: implications for {T} cell-based immunotherapy},
journal = {Immunogenetics},
year = {2001},
volume = {53},
pages = {255--263},
number = {4},
abstract = {HLA class I expression is altered in a significant fraction of the
tumor types reviewed here, reflecting either immune pressure or,
simply, the accumulation of pathological changes and alterations.
However, in all tumor types analyzed, a majority of the tumors express
HLA class I. with a general tendency for the more severe alterations
to be found in later-stage and less differentiated tumors. These
results are encouraging for the development of specific immunotherapies,
especially considering that (1) the relatively low sensitivity of
immunohistochemical techniques might underestimate HLA expression
in tumors, (2) class I expression can be induced in tumor cells as
a result of local inflammation and lymphokine release, and (3) class
I-negative cells would be predicted to be sensitive to Iysis by natural
killer cells.},
keywords = {immunoinformatics},
pmid = {11491528},
timestamp = {2007.01.25}
}
@article{Sette1999Nine,
author = {A. Sette and J. Sidney},
title = {Nine major {HLA} class {I} supertypes account for the vast preponderance
of {HLA-A} and -{B} polymorphism.},
journal = {Immunogenetics},
year = {1999},
volume = {50},
pages = {201--212},
number = {3-4},
month = {Nov},
abstract = {Herein, we review the epitope approach to vaccine development, and
discuss how knowledge of HLA supertypes might be used as a tool in
the development of such vaccines. After reviewing the main structural
features of the A2-, A3-, B7-, and B44- supertype alleles, and biological
data demonstrating their immunological relevance, we analyze the
frequency at which these supertype alleles are expressed in various
ethnicities and discuss the relevance of those observations to vaccine
development. Next, the existence of five new supertypes (A1, A24,
B27, B58, and B62) is reported. As a result, it is possible to account
for the predominance of all known HLA class I with only nine main
functional binding specificities. The practical implications of this
finding, as well as its relevance to understanding the functional
implication of MHC polymorphism in humans, are discussed.},
keywords = {Alleles; Amino Acid Sequence; Animals; Epitopes; HLA-A Antigens; HLA-B
Antigens; Histocompatibility Antigens Class I; Humans; Molecular
Sequence Data; Polymorphism, Genetic},
owner = {laurent},
pii = {90500201.251},
pmid = {10602880},
timestamp = {2007.01.05}
}
@article{Sette1998HLA,
author = {A. Sette and J. Sidney},
title = {{HLA} supertypes and supermotifs: a functional perspective on {HLA}
polymorphism.},
journal = {Curr. Opin. Immunol.},
year = {1998},
volume = {10},
pages = {478--482},
number = {4},
month = {Aug},
abstract = {A large fraction of HLA class I, and possibly class II, molecules
can be classified into relatively few supertypes, characterized by
overlapping peptide-binding repertoires and consensus B- and F-pocket
structures. Cross-binding peptides are frequently recognized by specific
T cells in the course of natural disease processes and in the context
of multiple HLA molecules, validating the concept of HLA supertypes
at the functional level.},
keywords = {Animals; Communicable Diseases; Evolution, Molecular; HLA Antigens;
HLA-A2 Antigen; HLA-A3 Antigen; Humans; Neoplasms; Polymorphism,
Genetic},
owner = {laurent},
pii = {S0952-7915(98)80124-6},
pmid = {9722926},
timestamp = {2007.01.05}
}
@article{Sette1994relationship,
author = {A. Sette and A. Vitiello and B. Reherman and P. Fowler and R. Nayersina
and W. M. Kast and C. J. Melief and C. Oseroff and L. Yuan and J.
Ruppert and J. Sidney and M. F. del Guercio and S. Southwood and
R. T. Kubo and R. W. Chesnut and H. M. Grey and F. V. Chisari},
title = {The relationship between class I binding affinity and immunogenicity
of potential cytotoxic T cell epitopes.},
journal = {J. Immunol.},
year = {1994},
volume = {153},
pages = {5586--5592},
number = {12},
month = {Dec},
abstract = {The relationship between binding affinity for HLA class I molecules
and immunogenicity of discrete peptide epitopes has been analyzed
in two different experimental approaches. In the first approach,
the immunogenicity of potential epitopes ranging in MHC binding affinity
over a 10,000-fold range was analyzed in HLA-A*0201 transgenic mice.
In the second approach, the antigenicity of approximately 100 different
hepatitis B virus (HBV)-derived potential epitopes, all carrying
A*0201 binding motifs, was assessed by using PBL of acute hepatitis
patients. In both cases, it was found that an affinity threshold
of approximately 500 nM (preferably 50 nM or less) apparently determines
the capacity of a peptide epitope to elicit a CTL response. These
data correlate well with class I binding affinity measurements of
either naturally processed peptides or previously described T cell
epitopes. Taken together, these data have important implications
for the selection of epitopes for peptide-based vaccines, and also
formally demonstrate the crucial role of determinant selection in
the shaping of T cell responses. Because in most (but not all) cases,
high affinity peptides seem to be immunogenic, our data also suggest
that holes in the functional T cell repertoire, if they exist, may
be relatively rare.},
keywords = {Amino Acid Sequence; Animals; Cell Line; Cytotoxicity Tests, Immunologic;
Epitopes; HLA-A Antigens; Hepatitis B; Hepatitis B Antigens; Humans;
Mice; Mice, Transgenic; Molecular Sequence Data; Peptides; Protein
Binding; T-Lymphocytes, Cytotoxic},
owner = {laurent},
pmid = {7527444},
timestamp = {2007.07.12}
}
@article{Shabalina2006Computational,
author = {Shabalina, S. and Spiridonov, A. and Ogurtsov, A.},
title = {{C}omputational models with thermodynamic and composition features
improve si{RNA} design.},
journal = {BMC Bioinformatics},
year = {2006},
volume = {7},
pages = {65},
number = {1},
month = {Feb},
abstract = {ABSTRACT: BACKGROUND: Small interfering RNAs (siRNAs) have become
an important tool in cell and molecular biology. Reliable design
of siRNA molecules is essential for the needs of large functional
genomics projects. RESULTS: To improve the design of efficient siRNA
molecules, we performed a comparative, thermodynamic and correlation
analysis on a heterogeneous set of 653 siRNAs collected from the
literature. We used this training set to select siRNA features and
optimize computational models. We identified 18 parameters that correlate
significantly with silencing efficiency. Some of these parameters
characterize only the siRNA sequence, while others involve the whole
mRNA. Most importantly, we derived an siRNA position-dependent consensus,
and optimized the free-energy difference of the 5' and 3' terminal
dinucleotides of the siRNA antisense strand. The position-dependent
consensus is based on correlation and t-test analyses of the training
set, and accounts for both significantly preferred and avoided nucleotides
in all sequence positions. On the training set, the two parameters'
correlation with silencing efficiency was 0.5 and 0.36, respectively.
Among other features, a dinucleotide content index and the frequency
of potential targets for siRNA in the mRNA added predictive power
to our model (R = 0.55). We showed that our model is effective for
predicting the efficiency of siRNAs at different concentrations.
We optimized a neural network model on our training set using three
parameters characterizing the siRNA sequence, and predicted efficiencies
for the test siRNA dataset recently published by Novartis. On this
validation set, the correlation coefficient between predicted and
observed efficiency was 0.75. Using the same model, we performed
a transcriptome-wide analysis of optimal siRNA targets for 22,600
human mRNAs. CONCLUSIONS: We demonstrated that the properties of
the siRNAs themselves are essential for efficient RNA interference.
The 5' ends of antisense strands of efficient siRNAs are U-rich and
possess a content similarity to the pyrimidine-rich oligonucleotides
interacting with the polypurine RNA tracks that are recognized by
RNase H. The advantage of our method over similar methods is the
small number of parameters. As a result, our method requires a much
smaller training set to produce consistent results. Other mRNA features,
though expensive to compute, can slightly improve our model.},
doi = {10.1186/1471-2105-7-65},
pdf = {../local/Shabalina2006Computational.pdf},
file = {Shabalina2006Computational.pdf:local/Shabalina2006Computational.pdf:PDF},
keywords = {sirna},
owner = {vert},
pii = {1471-2105-7-65},
pmid = {16472402},
timestamp = {2006.03.28},
url = {http://dx.doi.org/10.1186/1471-2105-7-65}
}
@article{Shacham2004PREDICT,
author = {Shacham, S. and Marantz, Y. and Bar-Haim, S. and Kalid, O. and Warshaviak,
D. and Avisar, N. and Inbal, B. and Heifetz, A. and Fichman, M. and
Topf, M. and Naor, Z. and Noiman, S. and Becker, O. M.},
title = {{PREDICT} modeling and in-silico screening for {G}-protein coupled
receptors.},
journal = {Proteins},
year = {2004},
volume = {57},
pages = {51--86},
number = {1},
month = {Oct},
abstract = {G-protein coupled receptors (GPCRs) are a major group of drug targets
for which only one x-ray structure is known (the nondrugable rhodopsin),
limiting the application of structure-based drug discovery to GPCRs.
In this paper we present the details of PREDICT, a new algorithmic
approach for modeling the 3D structure of GPCRs without relying on
homology to rhodopsin. PREDICT, which focuses on the transmembrane
domain of GPCRs, starts from the primary sequence of the receptor,
simultaneously optimizing multiple 'decoy' conformations of the protein
in order to find its most stable structure, culminating in a virtual
receptor-ligand complex. In this paper we present a comprehensive
analysis of three PREDICT models for the dopamine D2, neurokinin
NK1, and neuropeptide Y Y1 receptors. A shorter discussion of the
CCR3 receptor model is also included. All models were found to be
in good agreement with a large body of experimental data. The quality
of the PREDICT models, at least for drug discovery purposes, was
evaluated by their successful utilization in in-silico screening.
Virtual screening using all three PREDICT models yielded enrichment
factors 9-fold to 44-fold better than random screening. Namely, the
PREDICT models can be used to identify active small-molecule ligands
embedded in large compound libraries with an efficiency comparable
to that obtained using crystal structures for non-GPCR targets.},
doi = {10.1002/prot.20195},
keywords = {chemogenomics},
owner = {laurent},
pmid = {15326594},
timestamp = {2008.03.27},
url = {http://dx.doi.org/10.1002/prot.20195}
}
@article{Shadforth2005Protein,
author = {Ian Shadforth and Daniel Crowther and Conrad Bessant},
title = {Protein and peptide identification algorithms using MS for use in
high-throughput, automated pipelines.},
journal = {Proteomics},
year = {2005},
volume = {5},
pages = {4082--4095},
number = {16},
month = {Nov},
abstract = {Current proteomics experiments can generate vast quantities of data
very quickly, but this has not been matched by data analysis capabilities.
Although there have been a number of recent reviews covering various
aspects of peptide and protein identification methods using MS, comparisons
of which methods are either the most appropriate for, or the most
effective at, their proposed tasks are not readily available. As
the need for high-throughput, automated peptide and protein identification
systems increases, the creators of such pipelines need to be able
to choose algorithms that are going to perform well both in terms
of accuracy and computational efficiency. This article therefore
provides a review of the currently available core algorithms for
PMF, database searching using MS/MS, sequence tag searches and de
novo sequencing. We also assess the relative performances of a number
of these algorithms. As there is limited reporting of such information
in the literature, we conclude that there is a need for the adoption
of a system of standardised reporting on the performance of new peptide
and protein identification algorithms, based upon freely available
datasets. We go on to present our initial suggestions for the format
and content of these datasets.},
doi = {10.1002/pmic.200402091},
institution = {Cranfield Centre for Bioinformatics and IT, Cranfield University,
Silsoe, UK.},
keywords = {Algorithms; Alternative Splicing; Databases, Protein; Peptides; Polymorphism,
Genetic; Proteins; Proteomics; Sequence Analysis; Software; Spectrometry,
Mass, Matrix-Assisted Laser Desorption-Ionization},
owner = {phupe},
pmid = {16196103},
timestamp = {2010.08.19},
url = {http://dx.doi.org/10.1002/pmic.200402091}
}
@article{Shah2008SVM-HUSTLE,
author = {Shah, A. R. and Oehmen, C. S. and Webb-Robertson, B.-J.},
title = {{SVM-HUSTLE}--an iterative semi-supervised machine learning approach
for pairwise protein remote homology detection.},
journal = {Bioinformatics},
year = {2008},
volume = {24},
pages = {783--790},
number = {6},
month = {Mar},
abstract = {MOTIVATION: As the amount of biological sequence data continues to
grow exponentially we face the increasing challenge of assigning
function to this enormous molecular 'parts list'. The most popular
approaches to this challenge make use of the simplifying assumption
that similar functional molecules, or proteins, sometimes have similar
composition, or sequence. However, these algorithms often fail to
identify remote homologs (proteins with similar function but dissimilar
sequence) which often are a significant fraction of the total homolog
collection for a given sequence. We introduce a Support Vector Machine
(SVM)-based tool to detect homology using semi-supervised iterative
learning (SVM-HUSTLE) that identifies significantly more remote homologs
than current state-of-the-art sequence or cluster-based methods.
As opposed to building profiles or position specific scoring matrices,
SVM-HUSTLE builds an SVM classifier for a query sequence by training
on a collection of representative high-confidence training sets,
recruits additional sequences and assigns a statistical measure of
homology between a pair of sequences. SVM-HUSTLE combines principles
of semi-supervised learning theory with statistical sampling to create
many concurrent classifiers to iteratively detect and refine, on-the-fly,
patterns indicating homology. RESULTS: When compared against existing
methods for identifying protein homologs (BLAST, PSI-BLAST, COMPASS,
PROF_SIM, RANKPROP and their variants) on two different benchmark
datasets SVM-HUSTLE significantly outperforms each of the above methods
using the most stringent ROC(1) statistic with P-values less than
1e-20. SVM-HUSTLE also yields results comparable to HHSearch but
at a substantially reduced computational cost since we do not require
the construction of HMMs. AVAILABILITY: The software executable to
run SVM-HUSTLE can be downloaded from http://www.sysbio.org/sysbio/networkbio/svm_hustle},
doi = {10.1093/bioinformatics/btn028},
pdf = {../local/Shah2008SVM-HUSTLE.pdf},
file = {Shah2008SVM-HUSTLE.pdf:Shah2008SVM-HUSTLE.pdf:PDF},
institution = {Scientific Data Management and Computational Biology and Bioinformatics,
Pacific Northwest National Laboratory, Richland, WA, USA.},
keywords = {PUlearning},
owner = {mordelet},
pii = {btn028},
pmid = {18245127},
timestamp = {2010.01.26},
url = {http://dx.doi.org/10.1093/bioinformatics/btn028}
}
@article{Shah2007Modeling,
author = {Shah, S.P. and Lam, W.L. and Ng, R.T. and Murphy, K.P.},
title = {Modeling recurrent {DNA} copy number alterations in array {CGH} data},
journal = {Bioinformatics},
year = {2007},
volume = {23},
pages = {i450-i458},
number = {13},
pdf = {../local/Shah2007Modeling.pdf},
file = {Shah2007Modeling.pdf:Shah2007Modeling.pdf:PDF},
owner = {jp},
publisher = {Oxford Univ Press},
timestamp = {2010.01.11}
}
@article{Shah2004Fingerprint,
author = {Shesha Shah and P. S. Sastry},
title = {Fingerprint classification using a feedback-based line detector.},
journal = {I{EEE} {T}rans {S}yst {M}an {C}ybern {B} {C}ybern},
year = {2004},
volume = {34},
pages = {85-94},
number = {1},
month = {Feb},
abstract = {We present a fingerprint classification algorithm in this paper. {T}his
algorithm classifies a fingerprint image into one of the five classes:
{A}rch, {L}eft loop, {R}ight loop, {W}horl, and {T}ented arch. {W}e
use a new low-dimensional feature vector obtained from the output
of a novel oriented line detector presented here. {O}ur line detector
is a co-operative dynamical system that gives oriented lines and
preserves multiple orientations at points where differently oriented
lines meet. {O}ur feature extraction process is based on characterizing
the distribution of orientations around the fingerprint. {W}e discuss
three different classifiers: support vector machines, nearest-neighbor
classifier, and neural network classifier. {W}e present results obtained
on a {N}ational {I}nstitute of {S}tandards and {T}echnology ({NIST})
fingerprint database and compare with other published results on
{NIST} databases. {A}ll our classifiers perform equally well, and
this suggests that our novel line detection and feature extraction
process indeed captures all the crucial information needed for classification
in this problem.}
}
@article{Shah2009Mutational,
author = {Shah, S. P. and Morin, R. D. and Khattra, J. and Prentice, L. and
Pugh, T. and Burleigh, A. and Delaney, A. and Gelmon, K. and Guliany,
R. and Senz, J. and Steidl, C. and Holt, R.A . and Jones, S. and
Sun, M. and Leung, G. and Moore, R. and Severson, T. and Taylor,
G. A. and Teschendorff, A. E. and Tse, K. and Turashvili, G. and
Varhol, R. and Warren, R. L. and Watson, P. and Zhao, Y. and Caldas,
C. and Huntsman, D. and Hirst, M. and Marra, M. A. and Aparicio,
A.},
title = {Mutational evolution in a lobular breast tumour profiled at single
nucleotide resolution},
journal = {Nature},
year = {2009},
volume = {461},
pages = {809--813},
number = {7265},
month = {Oct},
abstract = {Recent advances in next generation sequencing have made it possible
to precisely characterize all somatic coding mutations that occur
during the development and progression of individual cancers. Here
we used these approaches to sequence the genomes (>43-fold coverage)
and transcriptomes of an oestrogen-receptor-alpha-positive metastatic
lobular breast cancer at depth. We found 32 somatic non-synonymous
coding mutations present in the metastasis, and measured the frequency
of these somatic mutations in DNA from the primary tumour of the
same patient, which arose 9 years earlier. Five of the 32 mutations
(in ABCB11, HAUS3, SLC24A4, SNX4 and PALB2) were prevalent in the
DNA of the primary tumour removed at diagnosis 9 years earlier, six
(in KIF1C, USP28, MYH8, MORC1, KIAA1468 and RNASEH2A) were present
at lower frequencies (1-13\%), 19 were not detected in the primary
tumour, and two were undetermined. The combined analysis of genome
and transcriptome data revealed two new RNA-editing events that recode
the amino acid sequence of SRP9 and COG3. Taken together, our data
show that single nucleotide mutational heterogeneity can be a property
of low or intermediate grade primary breast cancers and that significant
evolution can occur with disease progression.},
doi = {10.1038/nature08489},
pdf = {../local/Shah2009Mutational.pdf},
file = {Shah2009Mutational.pdf:Shah2009Mutational.pdf:PDF},
institution = {Molecular Oncology, BC Cancer Agency, 675 West 10th Avenue, Vancouver
V5Z 1L3, Canada.},
keywords = {ngs},
owner = {jp},
pii = {nature08489},
pmid = {19812674},
timestamp = {2009.10.12},
url = {http://dx.doi.org/10.1038/nature08489}
}
@article{Shann2008Genome,
author = {Shann, Y.J. and Cheng, C. and Chiao, C.H. and Chen, D.T. and Li,
P.H. and Hsu, M.T.},
title = {Genome-Wide Mapping and Characterization of Hypomethylated Sites
in Human Tissues and Breast Cancer Cell Lines},
journal = {Genome Res.},
year = {2008},
volume = {18},
pages = {791-801},
keywords = {csbcbook}
}
@article{Shannon2003Cytoscape,
author = {Shannon, P. and Markiel, A. and Ozier, O. and Baliga, N. S. and Wang,
J. T. and Ramage, D. and Amin, N. and Schwikowski, B. and Ideker,
T.},
title = {{C}ytoscape: a software environment for integrated models of biomolecular
interaction networks.},
journal = {Genome Res.},
year = {2003},
volume = {13},
pages = {2498--2504},
number = {11},
month = {Nov},
abstract = {Cytoscape is an open source software project for integrating biomolecular
interaction networks with high-throughput expression data and other
molecular states into a unified conceptual framework. Although applicable
to any system of molecular components and interactions, Cytoscape
is most powerful when used in conjunction with large databases of
protein-protein, protein-DNA, and genetic interactions that are increasingly
available for humans and model organisms. Cytoscape's software Core
provides basic functionality to layout and query the network; to
visually integrate the network with expression profiles, phenotypes,
and other molecular states; and to link the network to databases
of functional annotations. The Core is extensible through a straightforward
plug-in architecture, allowing rapid development of additional computational
analyses and features. Several case studies of Cytoscape plug-ins
are surveyed, including a search for interaction pathways correlating
with changes in gene expression, a study of protein complexes involved
in cellular recovery to DNA damage, inference of a combined physical/functional
interaction network for Halobacterium, and an interface to detailed
stochastic/kinetic gene regulatory models.},
doi = {10.1101/gr.1239303},
pii = {13/11/2498},
pmid = {14597658},
timestamp = {2008.02.11},
url = {http://dx.doi.org/10.1101/gr.1239303}
}
@article{Shannon2003Analyzing,
author = {William Shannon and Robert Culverhouse and Jill Duncan},
title = {Analyzing microarray data using cluster analysis.},
journal = {Pharmacogenomics},
year = {2003},
volume = {4},
pages = {41-52},
number = {1},
month = {Jan},
abstract = {As pharmacogenetics researchers gather more detailed and complex data
on gene polymorphisms that effect drug metabolizing enzymes, drug
target receptors and drug transporters, they will need access to
advanced statistical tools to mine that data. {T}hese tools include
approaches from classical biostatistics, such as logistic regression
or linear discriminant analysis, and supervised learning methods
from computer science, such as support vector machines and artificial
neural networks. {I}n this review, we present an overview of another
class of models, cluster analysis, which will likely be less familiar
to pharmacogenetics researchers. {C}luster analysis is used to analyze
data that is not a priori known to contain any specific subgroups.
{T}he goal is to use the data itself to identify meaningful or informative
subgroups. {S}pecifically, we will focus on demonstrating the use
of distance-based methods of hierarchical clustering to analyze gene
expression data.},
keywords = {Algorithms, Automated, Base Pair Mismatch, Base Pairing, Base Sequence,
Biosensing Techniques, Cluster Analysis, Comparative Study, Computer-Assisted,
DNA, Gene Expression Profiling, Gene Expression Regulation, Genes,
Hemolysins, Humans, Markov Chains, Messenger, Molecular Probe Techniques,
Molecular Sequence Data, Nanotechnology, Neoplastic, Neural Networks
(Computer), Non-U.S. Gov't, Nucleic Acid Conformation, Oligonucleotide
Array Sequence Analysis, Pattern Recognition, Quality Control, RNA,
Research Support, Signal Processing, Stomach Neoplasms, 12517285}
}
@article{Shapiro1992Feature,
author = {L. S. Shapiro and M. Brady},
title = {Feature-based correspondence: an eigenvector approach},
journal = {Image Vision Comput.},
year = {1992},
volume = {10},
pages = {283-288},
number = {5},
bibsource = {DBLP, http://dblp.uni-trier.de},
ee = {http://dx.doi.org/10.1016/0262-8856(92)90043-3}
}
@article{Sharan2005motif-based,
author = {R. Sharan and E. W Myers},
title = {A motif-based framework for recognizing sequence families.},
journal = {Bioinformatics},
year = {2005},
volume = {21 Suppl 1},
pages = {i387-i393},
month = {Jun},
abstract = {M{OTIVATION}: {M}any signals in biological sequences are based on
the presence or absence of base signals and their spatial combinations.
{O}ne of the best known examples of this is the signal identifying
a core promoter-the site at which the basal transcription machinery
starts the transcription of a gene. {O}ur goal is a fully automatic
pattern recognition system for a family of sequences, which simultaneously
discovers the base signals, their spatial relationships and a classifier
based upon them. {RESULTS}: {I}n this paper we present a general
method for characterizing a set of sequences by their recurrent motifs.
{O}ur approach relies on novel probabilistic models for {DNA} binding
sites and modules of binding sites, on algorithms to study them from
the data and on a support vector machine that uses the models studied
to classify a set of sequences. {W}e demonstrate the applicability
of our approach to diverse instances, ranging from families of promoter
sequences to a dataset of intronic sequences flanking alternatively
spliced exons. {O}n a core promoter dataset our results are comparable
with the state-of-the-art {M}c{P}romoter. {O}n a dataset of alternatively
spliced exons we outperform a previous approach. {W}e also achieve
high success rates in recognizing cell cycle regulated genes. {T}hese
results demonstrate that a fully automatic pattern recognition algorithm
can meet or exceed the performance of hand-crafted approaches. {AVAILABILITY}:
{T}he software and datasets are available from the authors upon request.
{CONTACT}: roded@tau.ac.il.},
doi = {10.1093/bioinformatics/bti1002},
pdf = {../local/Sharan2005motif-based.pdf},
file = {Sharan2005motif-based.pdf:local/Sharan2005motif-based.pdf:PDF},
keywords = {biosvm},
pii = {21/suppl_1/i387},
url = {http://dx.doi.org/10.1093/bioinformatics/bti1002}
}
@article{Sharan2005Conserved,
author = {Sharan, R. and Suthram, S. and Kelley, R.M. and Kuhn, T. and McCuine,
S. and Uetz, P. and Sittler, T. and Karp, R.M. and Ideker, T.},
title = {Conserved patterns of protein interaction in multiple species.},
journal = {Proc. Natl. Acad. Sci. USA},
year = {2005},
volume = {102},
pages = {1974--1979},
number = {6},
month = {Feb},
abstract = {To elucidate cellular machinery on a global scale, we performed a
multiple comparison of the recently available protein-protein interaction
networks of Caenorhabditis elegans, Drosophila melanogaster, and
Saccharomyces cerevisiae. This comparison integrated protein interaction
and sequence information to reveal 71 network regions that were conserved
across all three species and many exclusive to the metazoans. We
used this conservation, and found statistically significant support
for 4,645 previously undescribed protein functions and 2,609 previously
undescribed protein interactions. We tested 60 interaction predictions
for yeast by two-hybrid analysis, confirming approximately half of
these. Significantly, many of the predicted functions and interactions
would not have been identified from sequence similarity alone, demonstrating
that network comparisons provide essential biological information
beyond what is gleaned from the genome.},
doi = {10.1073/pnas.0409522102},
pdf = {../local/Sharan2005Conserved.pdf},
file = {Sharan2005Conserved.pdf:local/Sharan2005Conserved.pdf:PDF},
institution = {Computer Science Division, University of California, Berkeley, CA
94704, USA.},
owner = {jp},
pii = {0409522102},
pmid = {15687504},
timestamp = {2008.10.02},
url = {http://dx.doi.org/10.1073/pnas.0409522102}
}
@book{Shawe-Taylor2004Kernel,
title = {Kernel {M}ethods for {P}attern {A}nalysis},
publisher = {Cambridge University Press},
year = {2004},
author = {Shawe-Taylor, J. and Cristianini, N.},
address = {New York, NY, USA},
owner = {vert}
}
@article{Shawe-Taylor2002On,
author = {J. Shawe-Taylor and N. Cristianini},
title = {On the {G}eneralization of {S}oft {M}argin {A}lgorithms},
journal = {I{EEE} {T}ransactions on {I}nformation {T}heory},
year = {2002},
volume = {48},
pages = {2721-2735},
number = {10},
month = {October},
doi = {10.1109/TIT.2002.802647},
pdf = {../local/Shawe-Taylor2002On.pdf},
file = {Shawe-Taylor2002On.pdf:local/Shawe-Taylor2002On.pdf:PDF},
url = {http://dx.doi.org/10.1109/TIT.2002.802647}
}
@inproceedings{She2003Frequent-subsequence-based,
author = {She, R. and Chen, F. and Wang, K. and Ester, M. and Gardy, J.L. and
Brinkman, F.S.L.},
title = {Frequent-subsequence-based prediction of outer membrane proteins},
booktitle = {K{DD} '03: {P}roceedings of the ninth {ACM} {SIGKDD} international
conference on {K}nowledge discovery and data mining},
year = {2003},
pages = {436-445},
publisher = {ACM Press},
abstract = {A number of medically important disease-causing bacteria (collectively
called {G}ram-negative bacteria) are noted for the extra "outer"
membrane that surrounds their cell. {P}roteins resident in this membrane
(outer membrane proteins, or {OMP}s) are of primary research interest
for antibiotic and vaccine drug design as they are on the surface
of the bacteria and so are the most accessible targets to develop
new drugs against. {W}ith the development of genome sequencing technology
and bioinformatics, biologists can now deduce all the proteins that
are likely produced in a given bacteria and have attempted to classify
where proteins are located in a bacterial cell. {H}owever such protein
localization programs are currently least accurate when predicting
{OMP}s, and so there is a current need for the development of a better
{OMP} classifier. {D}ata mining research suggests that the use of
frequent patterns has good performance in aiding the development
of accurate and efficient classification algorithms. {I}n this paper,
we present two methods to identify {OMP}s based on frequent subsequences
and test them on all {G}ram-negative bacterial proteins whose localizations
have been determined by biological experiments. {O}ne classifier
follows an association rule approach, while the other is based on
support vector machines ({SVM}s). {W}e compare the proposed methods
with the state-of-the-art methods in the biological domain. {T}he
results demonstrate that our methods are better both in terms of
accurately identifying {OMP}s and providing biological insights that
increase our understanding of the structures and functions of these
important proteins.},
doi = {10.1145/956750.956800},
pdf = {../local/She2003Frequent-subsequence-based.pdf},
file = {She2003Frequent-subsequence-based.pdf:local/She2003Frequent-subsequence-based.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert}
}
@article{Sheinerman2003Sequence,
author = {Felix B Sheinerman and Bissan Al-Lazikani and Barry Honig},
title = {Sequence, structure and energetic determinants of phosphopeptide
selectivity of {SH2} domains.},
journal = {J. Mol. Biol.},
year = {2003},
volume = {334},
pages = {823--841},
number = {4},
month = {Dec},
abstract = {Here, we present an approach for the prediction of binding preferences
of members of a large protein family for which structural information
for a number of family members bound to a substrate is available.
The approach involves a number of steps. First, an accurate multiple
alignment of sequences of all members of a protein family is constructed
on the basis of a multiple structural superposition of family members
with known structure. Second, the methods of continuum electrostatics
are used to characterize the energetic contribution of each residue
in a protein to the binding of its substrate. Residues that make
a significant contribution are mapped onto the protein sequence and
are used to define a "binding site signature" for the complex being
considered. Third, sequences whose structures have not been determined
are checked to see if they have binding-site signatures similar to
one of the known complexes. Predictions of binding affinity to a
given substrate are based on similarities in binding-site signature.
An important component of the approach is the introduction of a context-specific
substitution matrix suitable for comparison of binding-site residues.The
methods are applied to the prediction of phosphopeptide selectivity
of SH2 domains. To this end, the energetic roles of all protein residues
in 17 different complexes of SH2 domains with their cognate targets
are analyzed. The total number of residues that make significant
contributions to binding is found to vary from nine to 19 in different
complexes. These energetically important residues are found to contribute
to binding through a variety of mechanisms, involving both electrostatic
and hydrophobic interactions. Binding-site signatures are found to
involve residues in different positions in SH2 sequences, some of
them as far as 9A away from a bound peptide. Surprisingly, similarities
in the signatures of different domains do not correlate with whole-domain
sequence identities unless the latter is greater than 50\%.An extensive
comparison with the optimal binding motifs determined by peptide
library experiments, as well as other experimental data indicate
that the similarity in binding preferences of different SH2 domains
can be deduced on the basis of their binding-site signatures. The
analysis provides a rationale for the empirically derived classification
of SH2 domains described by Songyang & Cantley, in that proteins
in the same group are found to have similar residues at positions
important for binding. Confident predictions of binding preference
can be made for about 85\% of SH2 domain sequences found in SWISSPROT.
The approach described in this work is quite general and can, in
principle, be used to analyze binding preferences of members of large
protein families for which structural information for a number of
family members is available. It also offers a strategy for predicting
cross-reactivity of compounds designed to bind to a particular target,
for example in structure-based drug design.},
keywords = {Amino Acid Sequence; Binding Sites; Molecular Sequence Data; Peptide
Library; Phosphopeptides; Protein Binding; Sequence Alignment; Substrate
Specificity; src Homology Domains},
owner = {laurent},
pii = {S0022283603012373},
pmid = {14636606},
timestamp = {2007.01.03}
}
@article{Sheinerman2005High,
author = {Felix B Sheinerman and Elie Giraud and Abdelazize Laoui},
title = {High affinity targets of protein kinase inhibitors have similar residues
at the positions energetically important for binding.},
journal = {J. Mol. Biol.},
year = {2005},
volume = {352},
pages = {1134--1156},
number = {5},
month = {Oct},
abstract = {Inhibition of protein kinase activity is a focus of intense drug discovery
efforts in several therapeutic areas. Major challenges facing the
field include understanding of the factors determining the selectivity
of kinase inhibitors and the development of compounds with the desired
selectivity profile. Here, we report the analysis of sequence variability
among high and low affinity targets of eight different small molecule
kinase inhibitors (BIRB796, Tarceva, NU6102, Gleevec, SB203580, balanol,
H89, PP1). It is observed that all high affinity targets of each
inhibitor are found among a relatively small number of kinases, which
have similar residues at the specific positions important for binding.
The findings are highly statistically significant, and allow one
to exclude the majority of kinases in a genome from a list of likely
targets for an inhibitor. The findings have implications for the
design of novel inhibitors with a desired selectivity profile (e.g.
targeted at multiple kinases), the discovery of new targets for kinase
inhibitor drugs, comparative analysis of different in vivo models,
and the design of "a-la-carte" chemical libraries tailored for individual
kinases.},
doi = {10.1016/j.jmb.2005.07.074},
keywords = {Amino Acid Sequence; Amino Acids; Binding Sites; Electrostatics; Humans;
Ligands; Molecular Sequence Data; Piperazines; Protein Binding; Protein
Kinase Inhibitors; Protein Kinases; Pyrazoles; Pyrimidines; Sequence
Alignment; Thermodynamics},
owner = {laurent},
pii = {S0022-2836(05)00900-9},
pmid = {16139843},
timestamp = {2007.01.03},
url = {http://dx.doi.org/10.1016/j.jmb.2005.07.074}
}
@article{Shen2005[Detection,
author = {Li Shen and Jie Yang and Yue Zhou},
title = {Detection of {PVC}s with support vector machine},
journal = {Sheng {W}u {Y}i {X}ue {G}ong {C}heng {X}ue {Z}a {Z}hi},
year = {2005},
volume = {22},
pages = {78-81},
number = {1},
month = {Feb},
abstract = {The classifiction of heart beats is the foundation for automated arrhythmia
monitoring devices. {S}upport vector machnies ({SVM}s) have meant
a great advance in solving classification or pattern recognition.
{T}his study describes {SVM} for the identification of premature
ventricular contractions ({PVC}s) in surface {ECG}s. {F}eatures for
the classification task are extracted by analyzing the heart rate,
morphology and wavelet energy of the heart beats from a single lead.
{T}he performance of different {SVM}s is evaluated on the {MIT}-{BIH}
arrhythmia database following the association for the advancement
of medical instrumentation ({AAMI}) recommendations.},
keywords = {80 and over, Adult, Aged, Algorithms, Amino Acids, Animals, Area Under
Curve, Artifacts, Automated, Birefringence, Brain Chemistry, Brain
Neoplasms, Comparative Study, Computer-Assisted, Cornea, Cross-Sectional
Studies, Decision Trees, Diagnosis, Diagnostic Imaging, Diagnostic
Techniques, Discriminant Analysis, Evolution, Face, Female, Genetic,
Glaucoma, Humans, Intraocular Pressure, Lasers, Least-Squares Analysis,
Magnetic Resonance Imaging, Magnetic Resonance Spectroscopy, Male,
Middle Aged, Models, Molecular, Nerve Fibers, Non-U.S. Gov't, Numerical
Analysis, Ophthalmological, Optic Nerve Diseases, Optical Coherence,
P.H.S., Pattern Recognition, Photic Stimulation, Prospective Studies,
Protein, ROC Curve, Regression Analysis, Research Support, Retinal
Ganglion Cells, Sensitivity and Specificity, Sequence Analysis, Statistics,
Tomography, U.S. Gov't, Visual Fields, beta-Lactamases, 15762121}
}
@article{Shen2008Pathway,
author = {Shen, R. and Chinnaiyan, A. M. and Ghosh, D.},
title = {Pathway analysis reveals functional convergence of gene expression
profiles in breast cancer},
journal = {BMC Medical Genomics},
year = {2008},
volume = {1},
pages = {28},
number = {1},
doi = {10.1186/1755-8794-1-28},
pdf = {../local/Shen2008Pathway.pdf},
file = {Shen2008Pathway.pdf:Shen2008Pathway.pdf:PDF},
owner = {jp},
timestamp = {2011.01.13},
url = {http://dx.doi.org/10.1186/1755-8794-1-28}
}
@inproceedings{Sherashidze2009Efficient,
author = {Sherashidze, N. and Vishwanathan, S.V.N. and Petri, T.H. and Mehlhorn,
K. and Borgwardt, K.M.},
title = {Efficient Graphlet Kernels for Large Graph Comparison},
booktitle = {12th International Conference on Artificial Intelligence and Statistics
(AISTATS)},
year = {2009},
pages = {488--495},
address = {Clearwater Beach, Florida USA},
publisher = {Society for Artificial Intelligence and Statistics},
pdf = {../local/Sherashidze2009Efficient.pdf},
file = {Sherashidze2009Efficient.pdf:Sherashidze2009Efficient.pdf:PDF},
owner = {jp},
timestamp = {2009.09.27}
}
@article{Sherlock2001Stanford,
author = {G. Sherlock and T. Hernandez-Boussard and A. Kasarskis and G. Binkley
and J.C. Matese and S.S. Dwight and M. Kaloper and S. Weng and H.
Jin and C.A. Ball and M.B. Eisen and P.T. Spellman},
title = {The {S}tanford {M}icroarray {D}atabase},
journal = {Nucleic {A}cids {R}es.},
year = {2001},
volume = {29},
pages = {152--155},
number = {1},
month = {Jan},
pdf = {../local/sher01.pdf},
file = {sher01.pdf:local/sher01.pdf:PDF},
subject = {microarray},
url = {http://genome-www5.Stanford.EDU/MicroArray/SMD/SMD.pdf}
}
@article{Sherr2004Principles,
author = {Charles J. Sherr},
title = {Principles of Tumor Suppression},
journal = {Cell},
year = {2004},
volume = {116},
pages = {235-246},
keywords = {csbcbook}
}
@article{Sherry2001dbSNP,
author = {S. T. Sherry and M. H. Ward and M. Kholodov and J. Baker and L. Phan
and E. M. Smigielski and K. Sirotkin},
title = {dbSNP: the NCBI database of genetic variation.},
journal = {Nucleic Acids Res},
year = {2001},
volume = {29},
pages = {308--311},
number = {1},
month = {Jan},
abstract = {In response to a need for a general catalog of genome variation to
address the large-scale sampling designs required by association
studies, gene mapping and evolutionary biology, the National Center
for Biotechnology Information (NCBI) has established the dbSNP database
[S.T.Sherry, M.Ward and K. Sirotkin (1999) Genome Res., 9, 677-679].
Submissions to dbSNP will be integrated with other sources of information
at NCBI such as GenBank, PubMed, LocusLink and the Human Genome Project
data. The complete contents of dbSNP are available to the public
at website: http://www.ncbi.nlm.nih.gov/SNP. The complete contents
of dbSNP can also be downloaded in multiple formats via anonymous
FTP at ftp://ncbi.nlm.nih.gov/snp/.},
institution = {National Center for Biotechnology Information, National Library of
Medicine, National Institutes of Health, Bethesda, MD, 20894, USA.
sherry@ncbi.nlm.nih.gov},
keywords = {Animals; Biotechnology; Databases, Factual; Genetic Variation; Humans;
Information Services; Internet; National Institutes of Health (U.S.);
National Library of Medicine (U.S.); Polymorphism, Single Nucleotide,
genetics; United States},
language = {eng},
medline-pst = {ppublish},
owner = {philippe},
pmid = {11125122},
timestamp = {2010.08.01}
}
@article{Shevade2003simple,
author = {Shevade, S.K. and Keerthi, S.S.},
title = {A simple and efficient algorithm for gene selection using sparse
logistic regression},
journal = {Bioinformatics},
year = {2003},
volume = {19},
pages = {2246--2253},
number = {17},
publisher = {Oxford Univ Press}
}
@article{Shi2005Sensitivity,
author = {D. Shi and D. S. Yeung and J. Gao},
title = {Sensitivity analysis applied to the construction of radial basis
function networks.},
journal = {Neural {N}etw},
year = {2005},
month = {Jun},
abstract = {Conventionally, a radial basis function ({RBF}) network is constructed
by obtaining cluster centers of basis function by maximum likelihood
learning. {T}his paper proposes a novel learning algorithm for the
construction of radial basis function using sensitivity analysis.
{I}n training, the number of hidden neurons and the centers of their
radial basis functions are determined by the maximization of the
output's sensitivity to the training data. {I}n classification, the
minimal number of such hidden neurons with the maximal sensitivity
will be the most generalizable to unknown data. {O}ur experimental
results show that our proposed sensitivity-based {RBF} classifier
outperforms the conventional {RBF}s and is as accurate as support
vector machine ({SVM}). {H}ence, sensitivity analysis is expected
to be a new alternative way to the construction of {RBF} networks.},
doi = {10.1016/j.neunet.2005.02.006},
pii = {S0893-6080(05)00054-7},
url = {http://dx.doi.org/10.1016/j.neunet.2005.02.006}
}
@article{Shi2005Building,
author = {Lei Shi and Fabien Campagne},
title = {Building a protein name dictionary from full text: a machine learning
term extraction approach.},
journal = {B{MC} {B}ioinformatics},
year = {2005},
volume = {6},
pages = {88},
number = {1},
month = {Apr},
abstract = {B{ACKGROUND}: {T}he majority of information in the biological literature
resides in full text articles, instead of abstracts. {Y}et, abstracts
remain the focus of many publicly available literature data mining
tools. {M}ost literature mining tools rely on pre-existing lexicons
of biological names, often extracted from curated gene or protein
databases. {T}his is a limitation, because such databases have low
coverage of the many name variants which are used to refer to biological
entities in the literature. {RESULTS}: {W}e present an approach to
recognize named entities in full text. {T}he approach collects high
frequency terms in an article, and uses support vector machines ({SVM})
to identify biological entity names. {I}t is also computationally
efficient and robust to noise commonly found in full text material.
{W}e use the method to create a protein name dictionary from a set
of 80,528 full text articles. {O}nly 8.3\% of the names in this dictionary
match {S}wiss{P}rot description lines. {W}e assess the quality of
the dictionary by studying its protein name recognition performance
in full text. {CONCLUSION}: {T}his dictionary term lookup method
compares favourably to other published methods, supporting the significance
of our direct extraction approach. {T}he method is strong in recognizing
name variants not found in {S}wiss{P}rot.},
doi = {10.1186/1471-2105-6-88},
pdf = {../local/Shi2005Building.pdf},
file = {Shi2005Building.pdf:local/Shi2005Building.pdf:PDF},
keywords = {biosvm},
pii = {1471-2105-6-88},
url = {http://dx.doi.org/10.1186/1471-2105-6-88}
}
@article{Shi2010MicroArray,
author = {Leming Shi and Gregory Campbell and Wendell D Jones and Fabien Campagne
and Zhining Wen and Stephen J Walker and Zhenqiang Su and Tzu-Ming
Chu and Federico M Goodsaid and Lajos Pusztai and John D Shaughnessy
and André Oberthuer and Russell S Thomas and Richard S Paules and
Mark Fielden and Bart Barlogie and Weijie Chen and Pan Du and Matthias
Fischer and Cesare Furlanello and Brandon D Gallas and Xijin Ge and
Dalila B Megherbi and W. Fraser Symmans and May D Wang and John Zhang
and Hans Bitter and Benedikt Brors and Pierre R Bushel and Max Bylesjo
and Minjun Chen and Jie Cheng and Jing Cheng and Jeff Chou and Timothy
S Davison and Mauro Delorenzi and Youping Deng and Viswanath Devanarayan
and David J Dix and Joaquin Dopazo and Kevin C Dorff and Fathi Elloumi
and Jianqing Fan and Shicai Fan and Xiaohui Fan and Hong Fang and
Nina Gonzaludo and Kenneth R Hess and Huixiao Hong and Jun Huan and
Rafael A Irizarry and Richard Judson and Dilafruz Juraeva and Samir
Lababidi and Christophe G Lambert and Li Li and Yanen Li and Zhen
Li and Simon M Lin and Guozhen Liu and Edward K Lobenhofer and Jun
Luo and Wen Luo and Matthew N McCall and Yuri Nikolsky and Gene A
Pennello and Roger G Perkins and Reena Philip and Vlad Popovici and
Nathan D Price and Feng Qian and Andreas Scherer and Tieliu Shi and
Weiwei Shi and Jaeyun Sung and Danielle Thierry-Mieg and Jean Thierry-Mieg
and Venkata Thodima and Johan Trygg and Lakshmi Vishnuvajjala and
Sue Jane Wang and Jianping Wu and Yichao Wu and Qian Xie and Waleed
A Yousef and Liang Zhang and Xuegong Zhang and Sheng Zhong and Yiming
Zhou and Sheng Zhu and Dhivya Arasappan and Wenjun Bao and Anne Bergstrom
Lucas and Frank Berthold and Richard J Brennan and Andreas Buness
and Jennifer G Catalano and Chang Chang and Rong Chen and Yiyu Cheng
and Jian Cui and Wendy Czika and Francesca Demichelis and Xutao Deng
and Damir Dosymbekov and Roland Eils and Yang Feng and Jennifer Fostel
and Stephanie Fulmer-Smentek and James C Fuscoe and Laurent Gatto
and Weigong Ge and Darlene R Goldstein and Li Guo and Donald N Halbert
and Jing Han and Stephen C Harris and Christos Hatzis and Damir Herman
and Jianping Huang and Roderick V Jensen and Rui Jiang and Charles
D Johnson and Giuseppe Jurman and Yvonne Kahlert and Sadik A Khuder
and Matthias Kohl and Jianying Li and Li Li and Menglong Li and Quan-Zhen
Li and Shao Li and Zhiguang Li and Jie Liu and Ying Liu and Zhichao
Liu and Lu Meng and Manuel Madera and Francisco Martinez-Murillo
and Ignacio Medina and Joseph Meehan and Kelci Miclaus and Richard
A Moffitt and David Montaner and Piali Mukherjee and George J Mulligan
and Padraic Neville and Tatiana Nikolskaya and Baitang Ning and Grier
P Page and Joel Parker and R. Mitchell Parry and Xuejun Peng and
Ron L Peterson and John H Phan and Brian Quanz and Yi Ren and Samantha
Riccadonna and Alan H Roter and Frank W Samuelson and Martin M Schumacher
and Joseph D Shambaugh and Qiang Shi and Richard Shippy and Shengzhu
Si and Aaron Smalter and Christos Sotiriou and Mat Soukup and Frank
Staedtler and Guido Steiner and Todd H Stokes and Qinglan Sun and
Pei-Yi Tan and Rong Tang and Zivana Tezak and Brett Thorn and Marina
Tsyganova and Yaron Turpaz and Silvia C Vega and Roberto Visintainer
and Juergen von Frese and Charles Wang and Eric Wang and Junwei Wang
and Wei Wang and Frank Westermann and James C Willey and Matthew
Woods and Shujian Wu and Nianqing Xiao and Joshua Xu and Lei Xu and
Lun Yang and Xiao Zeng and Jialu Zhang and Li Zhang and Min Zhang
and Chen Zhao and Raj K Puri and Uwe Scherf and Weida Tong and Russell
D Wolfinger and M. A. Q. C. Consortium},
title = {The MicroArray Quality Control (MAQC)-II study of common practices
for the development and validation of microarray-based predictive
models.},
journal = {Nat Biotechnol},
year = {2010},
volume = {28},
pages = {827--838},
number = {8},
month = {Aug},
abstract = {Gene expression data from microarrays are being applied to predict
preclinical and clinical endpoints, but the reliability of these
predictions has not been established. In the MAQC-II project, 36
independent teams analyzed six microarray data sets to generate predictive
models for classifying a sample with respect to one of 13 endpoints
indicative of lung or liver toxicity in rodents, or of breast cancer,
multiple myeloma or neuroblastoma in humans. In total, >30,000 models
were built using many combinations of analytical methods. The teams
generated predictive models without knowing the biological meaning
of some of the endpoints and, to mimic clinical reality, tested the
models on data that had not been used for training. We found that
model performance depended largely on the endpoint and team proficiency
and that different approaches generated models of similar performance.
The conclusions and recommendations from MAQC-II should be useful
for regulatory agencies, study committees and independent investigators
that evaluate methods for global gene expression analysis.},
institution = {National Center for Toxicological Research, US Food and Drug Administration,
Jefferson, Arkansas, USA.},
keywords = {Animals; Breast Neoplasms, diagnosis/genetics; Disease Models, Animal;
Female; Gene Expression Profiling, methods/standards; Guidelines
as Topic; Humans; Liver Diseases, etiology/genetics/pathology; Lung
Diseases, etiology/genetics/pathology; Multiple Myeloma, diagnosis/genetics;
Neoplasms, diagnosis/genetics/mortality; Neuroblastoma, diagnosis/genetics;
Oligonucleotide Array Sequence Analysis, methods/standards; Predictive
Value of Tests; Quality Control; Rats; Survival Analysis},
language = {eng},
medline-pst = {ppublish},
owner = {phupe},
pmid = {20676074},
timestamp = {2011.04.08}
}
@article{Shi2010Functional,
author = {Shi, W. and Bessarabova, M. and Dosymbekov, D. and Dezso, Z. and
Nikolskaya, T. and Dudoladova, M. and Serebryiskaya, T. and Bugrim,
A. and Gyuryanov, A. and Brennan, R. J. and Shah, R. and Dopazo,
J. and Chen, M. and Deng, Y. and Shi, T. and Jurman, G. and Furnlanelle,
C. and Thomas, R. S. and Corton, J. C. and Tong, W. and Shi, L. and
Nikolsky, Y.},
title = {Functional analysis of multiple genomic signatures demonstrates that
classification algorithms choose phenotype-related genes},
journal = {Pharmacogenomics J.},
year = {2010},
volume = {10},
pages = {310--323},
number = {4},
doi = {10.1038/tpj.2010.35},
pdf = {../local/Shi2010Functional.pdf},
file = {Shi2010Functional.pdf:Shi2010Functional.pdf:PDF},
owner = {jp},
timestamp = {2011.01.13},
url = {http://dx.doi.org/10.1038/tpj.2010.35}
}
@article{Shields1993Universal,
author = {Shields, P.C. },
title = {Universal redundancy rates do not exist},
journal = {I{EEE} {T}rans. {I}nform. {T}heory},
year = {1993},
volume = {39},
pages = {520-524},
number = {2},
month = {Mar},
abstract = {The expected redundancy per symbol of an n-block prefix code {C}n
on a source ? measures how far the code is from being optimal for
that source. {T}he existence of sequences of codes with expected
redundancy per symbol of {O}((log n)/n) for `nice' classes of sources,
such as {M}arkov sources of a given order, is well known. {I}t is
shown that some restriction on the class of processes is necessary
in order to obtain such redundancy bounds, for there is no universal
redundancy rate for any sequence of prefix codes on the class of
all ergodic sources },
pdf = {../local/Shields1993Universal.pdf},
file = {Shields1993Universal.pdf:local/Shields1993Universal.pdf:PDF},
keywords = {information-theory},
owner = {vert}
}
@article{Shilton2005Incremental,
author = {Alistair Shilton and M. Palaniswami and Daniel Ralph and Ah Chung
Tsoi},
title = {Incremental training of support vector machines.},
journal = {I{EEE} {T}rans {N}eural {N}etw},
year = {2005},
volume = {16},
pages = {114-31},
number = {1},
month = {Jan},
abstract = {We propose a new algorithm for the incremental training of support
vector machines ({SVM}s) that is suitable for problems of sequentially
arriving data and fast constraint parameter variation. {O}ur method
involves using a "warm-start" algorithm for the training of {SVM}s,
which allows us to take advantage of the natural incremental properties
of the standard active set approach to linearly constrained optimization
problems. {I}ncremental training involves quickly retraining a support
vector machine after adding a small number of additional training
vectors to the training set of an existing (trained) support vector
machine. {S}imilarly, the problem of fast constraint parameter variation
involves quickly retraining an existing support vector machine using
the same training set but different constraint parameters. {I}n both
cases, we demonstrate the computational superiority of incremental
training over the usual batch retraining method.}
}
@article{Shimodaira2000Improving,
author = {Shimodaira, H.},
title = {Improving predictive inference under covariate shift by weighting
the log-likelihood function},
journal = {Journal of Statistical Planning and Inference},
year = {2000},
volume = {90},
pages = {227--244},
number = {2},
month = {October},
abstract = {A class of predictive densities is derived by weighting the observed
samples in maximizing the log-likelihood function. This approach
is effective in cases such as sample surveys or design of experiments,
where the observed covariate follows a different distribution than
that in the whole population. Under misspecification of the parametric
model, the optimal choice of the weight function is asymptotically
shown to be the ratio of the density function of the covariate in
the population to that in the observations. This is the pseudo-maximum
likelihood estimation of sample surveys. The optimality is defined
by the expected Kullback\&\#x2013;Leibler loss, and the optimal weight
is obtained by considering the importance sampling identity. Under
correct specification of the model, however, the ordinary maximum
likelihood estimate (i.e. the uniform weight) is shown to be optimal
asymptotically. For moderate sample size, the situation is in between
the two extreme cases, and the weight function is selected by minimizing
a variant of the information criterion derived as an estimate of
the expected loss. The method is also applied to a weighted version
of the Bayesian predictive density. Numerical examples as well as
Monte-Carlo simulations are shown for polynomial regression. A connection
with the robust parametric estimation is discussed.},
doi = {10.1016/S0378-3758(00)00115-4},
issn = {03783758},
keywords = {domain-adaptation},
url = {http://dx.doi.org/10.1016/S0378-3758(00)00115-4}
}
@inproceedings{Shimodaira2001Dynamic,
author = {Shimodaira, H. and Noma, K.-I. and Nakai, M. and Sagayama, S.},
title = {Dynamic time-alignment kernel in support vector machine},
booktitle = {Adv. Neural. Inform. Process Syst.},
year = {2001},
pages = {921-928},
timestamp = {2006.07.12}
}
@inproceedings{Shin2011Partitionable,
author = {Shin, K.},
title = {Partitionable Kernels for Mapping Kernels},
booktitle = {Proceedings of the 11th IEEE International Conference on Data Mining,
ICDM 2011, Vancouver, BC, Canada, December 11-14, 2011.},
year = {2011},
editor = {Cook, D. J. and Pei, J. and Wang, W. and Za\"{\i}ane, O. R. and Wu,
X.},
pages = {645--654},
doi = {10.1109/ICDM.2011.115},
pdf = {../local/Shin2011Partitionable.pdf},
file = {Shin2011Partitionable.pdf:Shin2011Partitionable.pdf:PDF},
owner = {jp},
timestamp = {2012.10.22},
url = {http://dx.doi.org/10.1109/ICDM.2011.115}
}
@article{Shipp2002Diffuse,
author = {Shipp, M. A. and Ross, K. N. and Tamayo, P. and Weng, A. P. and Kutok,
J. L. and Aguiar, R. C. T. and Gaasenbeek, M. and Angelo, M. and
Reich, M. and Pinkus, G. A. and Ray, T. S. and Koval, M. A. and Last,
K. W. and Norton, A. and Lister, T. A. and Mesirov, J. and Neuberg,
D. S. and Lander, E. S. and Aster, J. C. and Golub, T. R.},
title = {Diffuse large {B}-cell lymphoma outcome prediction by gene-expression
profiling and supervised machine learning},
journal = {Nat. {M}ed.},
year = {2002},
volume = {8},
pages = {68-74},
number = {1},
abstract = {Diffuse large {B}-cell lymphoma ({DLBCL}), the most common lymphoid
malignancy in adults, is curable in less than 50% of patients. {P}rognostic
models based on pre-treatment characteristics, such as the {I}nternational
{P}rognostic {I}ndex ({IPI}), are currently used to predict outcome
in {DLBCL}. {H}owever, clinical outcome models identify neither the
molecular basis of clinical heterogeneity, nor specific therapeutic
targets. {W}e analyzed the expression of 6,817 genes in diagnostic
tumor specimens from {DLBCL} patients who received cyclophosphamide,
adriamycin, vincristine and prednisone ({CHOP})-based chemotherapy,
and applied a supervised learning prediction method to identify cured
versus fatal or refractory disease. {T}he algorithm classified two
categories of patients with very different five-year overall survival
rates (70% versus 12%). {T}he model also effectively delineated patients
within specific {IPI} risk categories who were likely to be cured
or to die of their disease. {G}enes implicated in {DLBCL} outcome
included some that regulate responses to {B}-cell?receptor signaling,
critical serine/threonine phosphorylation pathways and apoptosis.
{O}ur data indicate that supervised learning classification techniques
can predict outcome in {DLBCL} and identify rational targets for
intervention.},
doi = {10.1038/nm0102-68},
pdf = {../local/Shipp2002Diffuse.pdf},
file = {Shipp2002Diffuse.pdf:local/Shipp2002Diffuse.pdf:PDF},
keywords = {biosvm},
owner = {vert}
}
@article{Shivakumar2009Structural,
author = {Pavithra Shivakumar and Michael Krauthammer},
title = {Structural similarity assessment for drug sensitivity prediction
in cancer.},
journal = {BMC Bioinformatics},
year = {2009},
volume = {10 Suppl 9},
pages = {S17},
abstract = {BACKGROUND: The ability to predict drug sensitivity in cancer is one
of the exciting promises of pharmacogenomic research. Several groups
have demonstrated the ability to predict drug sensitivity by integrating
chemo-sensitivity data and associated gene expression measurements
from large anti-cancer drug screens such as NCI-60. The general approach
is based on comparing gene expression measurements from sensitive
and resistant cancer cell lines and deriving drug sensitivity profiles
consisting of lists of genes whose expression is predictive of response
to a drug. Importantly, it has been shown that such profiles are
generic and can be applied to cancer cell lines that are not part
of the anti-cancer screen. However, one limitation is that the profiles
can not be generated for untested drugs (i.e., drugs that are not
part of an anti-cancer drug screen). In this work, we propose using
an existing drug sensitivity profile for drug A as a substitute for
an untested drug B given high structural similarities between drugs
A and B. RESULTS: We first show that structural similarity between
pairs of compounds in the NCI-60 dataset highly correlates with the
similarity between their activities across the cancer cell lines.
This result shows that structurally similar drugs can be expected
to have a similar effect on cancer cell lines. We next set out to
test our hypothesis that we can use existing drug sensitivity profiles
as substitute profiles for untested drugs. In a cross-validation
experiment, we found that the use of substitute profiles is possible
without a significant loss of prediction accuracy if the substitute
profile was generated from a compound with high structural similarity
to the untested compound. CONCLUSION: Anti-cancer drug screens are
a valuable resource for generating omics-based drug sensitivity profiles.
We show that it is possible to extend the usefulness of existing
screens to untested drugs by deriving substitute sensitivity profiles
from structurally similar drugs part of the screen.},
doi = {10.1186/1471-2105-10-S9-S17},
pdf = {../local/Shivakumar2009Structural.pdf},
file = {Shivakumar2009Structural.pdf:Shivakumar2009Structural.pdf:PDF},
institution = {Department of Pathology, Yale University School of Medicine, New
Haven, CT, USA. pavithra.shivakumar@yale.edu},
keywords = {chemogenomics},
language = {eng},
medline-pst = {epublish},
owner = {jp},
pii = {1471-2105-10-S9-S17},
pmid = {19761571},
timestamp = {2009.10.30},
url = {http://dx.doi.org/10.1186/1471-2105-10-S9-S17}
}
@article{Shock2007Whole-genome,
author = {Shock, J. L. and Fischer, K. F. and DeRisi, J. L.},
title = {Whole-genome analysis of mRNA decay in Plasmodium falciparum reveals
a global lengthening of mRNA half-life during the intra-erythrocytic
development cycle.},
journal = {Genome Biol.},
year = {2007},
volume = {8},
pages = {R134},
number = {7},
abstract = {BACKGROUND: The rate of mRNA decay is an essential element of post-transcriptional
regulation in all organisms. Previously, studies in several organisms
found that the specific half-life of each mRNA is precisely related
to its physiologic role, and plays an important role in determining
levels of gene expression. RESULTS: We used a genome-wide approach
to characterize mRNA decay in Plasmodium falciparum. We found that,
globally, rates of mRNA decay increase dramatically during the asexual
intra-erythrocytic developmental cycle. During the ring stage of
the cycle, the average mRNA half-life was 9.5 min, but this was extended
to an average of 65 min during the late schizont stage of development.
Thus, a major determinant of mRNA decay rate appears to be linked
to the stage of intra-erythrocytic development. Furthermore, we found
specific variations in decay patterns superimposed upon the dominant
trend of progressive half-life lengthening. These variations in decay
pattern were frequently enriched for genes with specific cellular
functions or processes. CONCLUSION: Elucidation of Plasmodium mRNA
decay rates provides a key element for deciphering mechanisms of
genetic control in this parasite, by complementing and extending
previous mRNA abundance studies. Our results indicate that progressive
stage-dependent decreases in mRNA decay rate function are a major
determinant of mRNA accumulation during the schizont stage of intra-erythrocytic
development. This type of genome-wide change in mRNA decay rate has
not been observed in any other organism to date, and indicates that
post-transcriptional regulation may be the dominant mechanism of
gene regulation in P. falciparum.},
doi = {10.1186/gb-2007-8-7-r134},
institution = {Department of Biochemistry and Biophysics, University of California
San Francisco, 1700 4th Street, San Francisco, California 94158-2330,
USA.},
keywords = {plasmodium},
owner = {jp},
pii = {gb-2007-8-7-r134},
pmid = {17612404},
timestamp = {2009.01.21},
url = {http://dx.doi.org/10.1186/gb-2007-8-7-r134}
}
@article{Shoeb2004Patient-specific,
author = {Ali Shoeb and Herman Edwards and Jack Connolly and Blaise Bourgeois
and S. Ted Treves and John Guttag},
title = {Patient-specific seizure onset detection.},
journal = {Epilepsy {B}ehav},
year = {2004},
volume = {5},
pages = {483-98},
number = {4},
month = {Aug},
abstract = {This article presents an automated, patient-specific method for the
detection of epileptic seizure onset from noninvasive electroencephalography.
{W}e adopt a patient-specific approach to exploit the consistency
of an individual patient's seizure and nonseizure electroencephalograms.
{O}ur method uses a wavelet decomposition to construct a feature
vector that captures the morphology and spatial distribution of an
electroencephalographic epoch, and then determines whether that vector
is representative of a patient's seizure or nonseizure electroencephalogram
using the support vector machine classification algorithm. {O}ur
completely automated method was tested on noninvasive electroencephalograms
from 36 pediatric subjects suffering from a variety of seizure types.
{I}t detected 131 of 139 seizure events within 8.0+/-3.2 seconds
of electrographic onset, and declared 15 false detections in 60 hours
of clinical electroencephalography. {O}ur patient-specific method
can be used to initiate delay-sensitive clinical procedures following
seizure onset, for example, the injection of a functional imaging
radiotracer.},
doi = {10.1016/j.yebeh.2004.05.005},
pdf = {../local/Shoeb2004Patient-specific.pdf},
file = {Shoeb2004Patient-specific.pdf:local/Shoeb2004Patient-specific.pdf:PDF},
keywords = {Algorithms, Comparative Study, Computational Biology, Computer-Assisted,
Databases, Diagnosis, Drug Resistance, Electroencephalography, Epilepsy,
Forecasting, Genetic, Genotype, HIV Protease Inhibitors, HIV-1, Humans,
Information Management, Information Storage and Retrieval, Kinetics,
Linear Models, Microbial Sensitivity Tests, Models, Monitoring, Non-U.S.
Gov't, P.H.S., Periodicals, Physiologic, Point Mutation, Pyrimidinones,
Reaction Time, Research Support, Reverse Transcriptase Inhibitors,
Signal Processing, Theoretical, Time Factors, U.S. Gov't, Viral,
15256184},
pii = {S1525505004001593},
url = {http://dx.doi.org/10.1016/j.yebeh.2004.05.005}
}
@article{Shoval2010Cell,
author = {Oren Shoval and Uri Alon},
title = {SnapShot: network motifs.},
journal = {Cell},
year = {2010},
volume = {143},
pages = {326--3e1},
number = {2},
month = {Oct},
doi = {10.1016/j.cell.2010.09.050},
institution = {Department of Molecular Cell Biology, Weizmann Institute of Science,
Rehovot 76100, Israel.},
keywords = {Animals; Feedback; Gene Regulatory Networks; Humans; Signal Transduction},
language = {eng},
medline-pst = {ppublish},
owner = {Andrei Zinovyev},
pii = {S0092-8674(10)01136-0},
pmid = {20946989},
timestamp = {2011.04.08},
url = {http://dx.doi.org/10.1016/j.cell.2010.09.050}
}
@article{Shulman2004Uniform,
author = {Shulman, N. and Feder, M.},
title = {The {U}niform {D}istribution as a {U}niversal {P}rior},
journal = {I{EEE} {T}rans. {I}nform. {T}heory},
year = {2004},
volume = {50},
pages = {1356 - 1362 },
number = {6},
month = {Jun},
abstract = {In this correspondence, we discuss the properties of the uniform prior
as a universal prior, i.e., a prior that induces a mutual information
that is simultaneously close to the capacity for all channels. {W}e
determine bounds on the amount of the mutual information loss in
using the uniform prior instead of the capacity-achieving prior.
{S}pecifically, for the class of binary input channels with any output
alphabet, we show that the${Z}$-channel has the minimal mutual information
with uniform prior, out of all channels with a given capacity. {F}rom
this, we conclude that the degradation of the mutual information
with respect to the capacity is at most 0.011 bit, and as was shown
previously, at most 6%. {A} related result is that the capacity-achieving
prior, for any channel, is not far from uniform. {S}ome of these
results are extended to channels with nonbinary input.},
pdf = {../local/Shulman2004Uniform.pdf},
file = {Shulman2004Uniform.pdf:local/Shulman2004Uniform.pdf:PDF},
keywords = {information-theory},
owner = {vert}
}
@article{Shulman-Peleg2005SiteEngines,
author = {Alexandra Shulman-Peleg and Ruth Nussinov and Haim J Wolfson},
title = {SiteEngines: recognition and comparison of binding sites and protein-protein
interfaces.},
journal = {Nucleic Acids Res},
year = {2005},
volume = {33},
pages = {W337--W341},
number = {Web Server issue},
month = {Jul},
abstract = {Protein surface regions with similar physicochemical properties and
shapes may perform similar functions and bind similar binding partners.
Here we present two web servers and software packages for recognition
of the similarity of binding sites and interfaces. Both methods recognize
local geometrical and physicochemical similarity, which can be present
even in the absence of overall sequence or fold similarity. The first
method, SiteEngine (http:/bioinfo3d.cs.tau.ac.il/SiteEngine), receives
as an input two protein structures and searches the complete surface
of one protein for regions similar to the binding site of the other.
The second, Interface-to-Interface (I2I)-SiteEngine (http:/bioinfo3d.cs.tau.ac.il/I2I-SiteEngine),
compares protein-protein interfaces, which are regions of interaction
between two protein molecules. It receives as an input two structures
of protein-protein complexes, extracts the interfaces and finds the
three-dimensional transformation that maximizes the similarity between
two pairs of interacting binding sites. The output of both servers
consists of a superimposition in PDB file format and a list of physicochemical
properties shared by the compared entities. The methods are highly
efficient and the freely available software packages are suitable
for large-scale database searches of the entire PDB.},
doi = {10.1093/nar/gki482},
institution = {School of Computer Science, Raymond and Beverly Sackler Faculty of
Exact Sciences, Tel Aviv University, Tel Aviv 69978, Israel. shulmana@tau.ac.il},
keywords = {Amino Acids, chemistry; Binding Sites; Internet; Multiprotein Complexes,
chemistry/metabolism; Protein Conformation; Protein Interaction Mapping,
methods; Software; User-Computer Interface},
language = {eng},
medline-pst = {ppublish},
owner = {bricehoffmann},
pii = {33/suppl_2/W337},
pmid = {15980484},
timestamp = {2009.11.12},
url = {http://dx.doi.org/10.1093/nar/gki482}
}
@article{Shulman-Peleg2004Recognition,
author = {Alexandra Shulman-Peleg and Ruth Nussinov and Haim J Wolfson},
title = {Recognition of functional sites in protein structures.},
journal = {J Mol Biol},
year = {2004},
volume = {339},
pages = {607--633},
number = {3},
month = {Jun},
abstract = {Recognition of regions on the surface of one protein, that are similar
to a binding site of another is crucial for the prediction of molecular
interactions and for functional classifications. We first describe
a novel method, SiteEngine, that assumes no sequence or fold similarities
and is able to recognize proteins that have similar binding sites
and may perform similar functions. We achieve high efficiency and
speed by introducing a low-resolution surface representation via
chemically important surface points, by hashing triangles of physico-chemical
properties and by application of hierarchical scoring schemes for
a thorough exploration of global and local similarities. We proceed
to rigorously apply this method to functional site recognition in
three possible ways: first, we search a given functional site on
a large set of complete protein structures. Second, a potential functional
site on a protein of interest is compared with known binding sites,
to recognize similar features. Third, a complete protein structure
is searched for the presence of an a priori unknown functional site,
similar to known sites. Our method is robust and efficient enough
to allow computationally demanding applications such as the first
and the third. From the biological standpoint, the first application
may identify secondary binding sites of drugs that may lead to side-effects.
The third application finds new potential sites on the protein that
may provide targets for drug design. Each of the three applications
may aid in assigning a function and in classification of binding
patterns. We highlight the advantages and disadvantages of each type
of search, provide examples of large-scale searches of the entire
Protein Data Base and make functional predictions.},
doi = {10.1016/j.jmb.2004.04.012},
institution = {School of Computer Science, Tel Aviv University, Tel Aviv 69978,
Israel.},
keywords = {Algorithms; Catalytic Domain; Hydrogen Bonding; Models, Molecular;
Protein Conformation; Proteins, chemistry},
language = {eng},
medline-pst = {ppublish},
owner = {bricehoffmann},
pii = {S0022283604004139},
pmid = {15147845},
timestamp = {2009.11.12},
url = {http://dx.doi.org/10.1016/j.jmb.2004.04.012}
}
@article{Shulman2008MultiBind,
author = {Shulman-Peleg, A. and Shatsky, M. and Nussinov, R. and Wolfson, H.
J. J. },
title = {MultiBind and MAPPIS: webservers for multiple alignment of protein
3D-binding sites and their interactions.},
journal = {Nucleic Acids Res.},
year = {2008},
volume = {36},
pages = {260--264},
month = {May},
abstract = {Analysis of protein-ligand complexes and recognition of spatially
conserved physico-chemical properties is important for the prediction
of binding and function. Here, we present two webservers for multiple
alignment and recognition of binding patterns shared by a set of
protein structures. The first webserver, MultiBind (http://bioinfo3d.cs.tau.ac.il/MultiBind),
performs multiple alignment of protein binding sites. It recognizes
the common spatial chemical binding patterns even in the absence
of similarity of the sequences or the folds of the compared proteins.
The input to the MultiBind server is a set of protein-binding sites
defined by interactions with small molecules. The output is a detailed
list of the shared physico-chemical binding site properties. The
second webserver, MAPPIS (http://bioinfo3d.cs.tau.ac.il/MAPPIS),
aims to analyze protein-protein interactions. It performs multiple
alignment of protein-protein interfaces (PPIs), which are regions
of interaction between two protein molecules. MAPPIS recognizes the
spatially conserved physico-chemical interactions, which often involve
energetically important hot-spot residues that are crucial for protein-protein
associations. The input to the MAPPIS server is a set of protein-protein
complexes. The output is a detailed list of the shared interaction
properties of the interfaces.},
address = {School of Computer Science, Raymond and Beverly Sackler Faculty of
Exact Sciences, Tel Aviv University, Tel Aviv 69978, Israel, Physical
Biosciences Division, Berkeley National Lab, California, CA, USA,
Sackler Inst. of Molecular Medicine, Sackler Faculty of Medicine,
Tel Aviv University, Tel Aviv, Israel and Basic Research Program,
SAIC-Frederick, Inc., Laboratory of Experimental and Computational
Biology, NCI-Frederick, Bldg 469, Rm 151, Frederick, MD 21702, USA.},
citeulike-article-id = {2882716},
doi = {http://dx.doi.org/10.1093/nar/gkn185},
issn = {1362-4962},
keywords = {binding-site},
posted-at = {2008-06-11 14:24:40},
priority = {2},
url = {http://dx.doi.org/10.1093/nar/gkn185}
}
@article{Sidney1996Definition,
author = {J. Sidney and H. M. Grey and S. Southwood and E. Celis and P. A.
Wentworth and M. F. del Guercio and R. T. Kubo and R. W. Chesnut
and A. Sette},
title = {Definition of an {HLA-A3}-like supermotif demonstrates the overlapping
peptide-binding repertoires of common {HLA} molecules.},
journal = {Hum Immunol},
year = {1996},
volume = {45},
pages = {79--93},
number = {2},
month = {Feb},
abstract = {An HLA-A3-like supertype (minimally comprised of products from the
HLA class I alleles A3, A11, A31, A*3301, and A*6801) has been defined
on the basis of (a) structural similarities in the antigen-binding
groove, (b) shared main anchor peptide-binding motifs, (c) the identification
of peptides cross-reacting with most or all of these molecules, and
(d) the definition of an A3-like supermotif that efficiently predicts
highly cross-reactive peptides. Detailed secondary anchor maps for
A3, A11, A31, A*3301, and A*6801 are also described. The biologic
relevance of the A3-like supertype is indicated by the fact that
high frequencies of the A3-like supertype alleles are conserved in
all major ethnic groups. Because A3-like supertype alleles are found
in most major HLA evolutionary lineages, possibly a reflection of
common ancestry, the A3-like supermotif might in fact represent a
primeval human HLA class I peptide-binding specificity. It is also
possible that these phenomena might be related to optimal exploitation
of the peptide specificity by human TAP molecules. The grouping of
HLA alleles into supertypes on the basis of their overlapping peptide-binding
repertoires represents an alternative to serologic or phylogenetic
classification.},
keywords = {Alleles; Amino Acid Sequence; Cell Line, Transformed; Cross Reactions;
HLA Antigens; HLA-A3 Antigen; HLA-B Antigens; Haplotypes; Humans;
Molecular Sequence Data; Peptide Fragments; Protein Binding; Structure-Activity
Relationship},
owner = {laurent},
pii = {0198-8859(95)00173-5},
pmid = {8882405},
timestamp = {2007.01.05}
}
@article{Sidney1995Several,
author = {J. Sidney and M. F. del Guercio and S. Southwood and V. H. Engelhard
and E. Appella and H. G. Rammensee and K. Falk and O. R\"otzschke
and M. Takiguchi and R. T. Kubo},
title = {Several {HLA} alleles share overlapping peptide specificities.},
journal = {J. Immunol.},
year = {1995},
volume = {154},
pages = {247--259},
number = {1},
month = {Jan},
abstract = {Herein we describe the establishment of assays to measure peptide
binding to purified HLA-B*0701, -B*0801, -B*2705, -B*3501-03, -B*5401,
-Cw*0401, -Cw*0602, and -Cw*0702 molecules. The binding of known
peptide epitopes or naturally processed peptides correlates well
with HLA restriction or origin, underscoring the immunologic relevance
of these assays. Analysis of the sequences of various HLA class I
alleles suggested that alleles with peptide motifs characterized
by proline in position 2 and aromatic or hydrophobic residues at
their C-terminus shared key consensus residues at positions 9, 63,
66, 67, and 70 (B pocket) and residue 116 (F pocket). Prediction
of the peptide-binding specificity of HLA-B*5401, on the basis of
this consensus B and F pocket structure, verified this hypothesis
and suggested that a relatively large family of HLA-B alleles (which
we have defined as the HLA-B7-like supertype) may significantly overlap
in peptide binding specificity. Availability of quantitative binding
assays allowed verification that, indeed, many (25\%) of the peptide
ligands carrying proline in position 2 and hydrophobic/aromatic residues
at the C-terminus (the B7-like supermotif) were capable of binding
at least three of five HLA-B7-like supertype alleles. Identification
of epitopes carrying the B7-like supermotif and binding to a family
of alleles represented in over 40\% of individuals from all major
ethnic groups may be of considerable use in the design of peptide
vaccines.},
keywords = {Alleles; Amino Acid Sequence; Cell Line, Transformed; Consensus Sequence;
Epitopes; Genes, MHC Class I; HLA-B Antigens; HLA-C Antigens; Humans;
Molecular Sequence Data; Peptide Fragments; Protein Binding; Protein
Structure, Tertiary; Structure-Activity Relationship; Substrate Specificity},
owner = {laurent},
pmid = {7527812},
timestamp = {2007.01.05}
}
@article{Siepen2003Beta,
author = {Siepen, J. A. and Radford, S. E. and Westhead, D. R.},
title = {Beta {E}dge strands in protein structure prediction and aggregation},
journal = {Protein {S}ci.},
year = {2003},
volume = {12},
pages = {2348-2359},
number = {10},
abstract = {It is well established that recognition between exposed edges of {beta}-sheets
is an important mode of protein-protein interaction and can have
pathological consequences; for instance, it has been linked to the
aggregation of proteins into a fibrillar structure, which is associated
with a number of predominantly neurodegenerative disorders. {A} number
of protective mechanisms have evolved in the edge strands of {beta}-sheets,
preventing the aggregation and insolubility of most natural {beta}-sheet
proteins. {S}uch mechanisms are unfavorable in the interior of a
{beta}-sheet. {T}he problem of distinguishing edge strands from central
strands based on sequence information alone is important in predicting
residues and mutations likely to be involved in aggregation, and
is also a first step in predicting folding topology. {H}ere we report
support vector machine ({SVM}) and decision tree methods developed
to classify edge strands from central strands in a representative
set of protein domains. {I}nterestingly, rules generated by the decision
tree method are in close agreement with our knowledge of protein
structure and are potentially useful in a number of different biological
applications. {W}hen trained on strands from proteins of known structure,
using structure-based ({D}ictionary of {S}econdary {S}tructure in
{P}roteins) strand assignments, both methods achieved mean cross-validated,
prediction accuracies of ~78%. {T}hese accuracies were reduced when
strand assignments from secondary structure prediction were used.
{F}urther investigation of this effect revealed that it could be
explained by a significant reduction in the accuracy of standard
secondary structure prediction methods for edge strands, in comparison
with central strands.},
doi = {10.1110/ps.03234503},
pdf = {../local/Siepen2003beta.pdf},
file = {Siepen2003beta.pdf:local/Siepen2003beta.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://www.proteinscience.org/cgi/content/abstract/12/10/2348}
}
@book{Lee1995Mouse,
title = {Mouse Genetics: Concepts and Applications},
publisher = {Oxford University Press},
year = {1995},
author = {Silver, L.M.},
owner = {fantine},
timestamp = {2010.10.20},
url = {http://www.informatics.jax.org/silver/}
}
@article{Silverman1982On,
author = {Silverman, B. W.},
title = {On the {E}stimation of a {P}robability {D}ensity {F}unction by the
{M}aximum {P}enalized {L}ikelihood {M}ethod},
journal = {Ann. {S}tat.},
year = {1982},
volume = {10},
pages = {795-810},
pdf = {../local/Silverman1982On.pdf},
file = {Silverman1982On.pdf:local/Silverman1982On.pdf:PDF},
url = {http://links.jstor.org/sici?sici=0090-5364%28198209%2910%3A3%3C795%3AOTEOAP%3E2.0.CO%3B2-S}
}
@inproceedings{Simard1992Tangent,
author = {Simard, P. and Victorri, B. and LeCun, Y. and Denker, J. S.},
title = {Tangent Prop - A Formalism for Specifying Selected Invariances in
an Adaptive Network},
booktitle = {Adv. Neural. Inform. Process Syst. 4},
year = {1992},
editor = {Moody, J. E. and Hanson, S. J. and Lippmann, R.},
pages = {895--903},
publisher = {Morgan Kaufman},
pdf = {../local/Simard1992Tangent.pdf},
file = {Simard1992Tangent.pdf:Simard1992Tangent.pdf:PDF},
owner = {jp},
timestamp = {2008.12.22}
}
@article{Simon2008Lost,
author = {Simon, R.},
title = {Lost in Translation Problems and Pitfalls in Translating Laboratory
Observations to Clinical Utility},
journal = {European journal of cancer (Oxford, England: 1990)},
year = {2008},
volume = {44},
pages = {2707},
number = {18},
publisher = {NIH Public Access}
}
@article{Simon2003Pitfalls,
author = {Simon, R. and Radmacher, M.D. and Dobbin, K. and McShane, L.M.},
title = {Pitfalls in the use of DNA microarray data for diagnostic and prognostic
classification},
journal = {Journal of the National Cancer Institute},
year = {2003},
volume = {95},
pages = {14--18},
number = {1},
publisher = {Oxford University Press}
}
@article{Simonis2007evaluation,
author = {Marieke Simonis and Jurgen Kooren and Wouter de Laat},
title = {An evaluation of 3C-based methods to capture DNA interactions.},
journal = {Nat Methods},
year = {2007},
volume = {4},
pages = {895--901},
number = {11},
month = {Nov},
abstract = {The shape of the genome is thought to play an important part in the
coordination of transcription and other DNA-metabolic processes.
Chromosome conformation capture (3C) technology allows us to analyze
the folding of chromatin in the native cellular state at a resolution
beyond that provided by current microscopy techniques. It has been
used, for example, to demonstrate that regulatory DNA elements communicate
with distant target genes through direct physical interactions that
loop out the intervening chromatin fiber. Here we discuss the intricacies
of 3C and new 3C-based methods including the 4C, 5C and ChIP-loop
assay.},
doi = {10.1038/nmeth1114},
institution = {Department of Cell Biology and Genetics, Erasmus MC, PO Box 2040,
3000 CA, Rotterdam.},
keywords = {Animals; Chromatin Immunoprecipitation, methods; Chromatin, chemistry/metabolism;
DNA Ligases, chemistry/metabolism; DNA Restriction Enzymes, chemistry/metabolism;
DNA, chemistry/genetics/metabolism; Formaldehyde, chemistry; Genetic
Techniques; Humans; Reproducibility of Results},
language = {eng},
medline-pst = {ppublish},
owner = {philippe},
pii = {nmeth1114},
pmid = {17971780},
timestamp = {2010.08.11},
url = {http://dx.doi.org/10.1038/nmeth1114}
}
@article{Sinden2004proteomic,
author = {R. E. Sinden},
title = {A proteomic analysis of malaria biology: integration of old literature
and new technologies.},
journal = {Int. J. Parasitol.},
year = {2004},
volume = {34},
pages = {1441--1450},
number = {13-14},
month = {Dec},
abstract = {The genomic revolution has brought a new vitality into research on
Plasmodium, its insect and vertebrate hosts. At the cellular level
nowhere is the impact greater than in the analysis of protein expression
and the 'assembly' of the supramolecular machines that together comprise
the functional cell. The repetitive phases of invasion and replication
that typify the malaria life cycle, together with the unique phase
of sexual differentiation provide a powerful platform on which to
investigate the 'molecular machines' that underpin parasite strategy
and stage-specific functions. This approach is illustrated here in
an analysis of the ookinete of Plasmodium berghei. Such analyses
are useful only if conducted with a secure understanding of parasite
biology. The importance of carefully searching the older literature
to reach this understanding cannot be over-emphasised. When viewed
together, the old and new data can give rapid and penetrating insights
into what some might now term the 'Systems-Biology' of Plasmodium.},
doi = {10.1016/j.ijpara.2004.10.005},
pdf = {../local/Sinden2004proteomic.pdf},
file = {Sinden2004proteomic.pdf:local/Sinden2004proteomic.pdf:PDF},
keywords = {plasmodium},
pii = {S0020-7519(04)00210-3},
pmid = {15582521},
timestamp = {2006.04.13},
url = {http://dx.doi.org/10.1016/j.ijpara.2004.10.005}
}
@techreport{Sindhwani2004Manifold,
author = {Sindhwani, V. and Niyogi, P. and Belkin, M.},
title = {Manifold {R}egularization: {A} {G}eometric {F}ramework for {L}earning
from {E}xamples},
institution = {The University of Chicago},
year = {2004},
number = {TR-2004-06},
owner = {vert}
}
@article{Sindhwani2004Feature,
author = {Vikas Sindhwani and Subrata Rakshit and Dipti Deodhare and Deniz
Erdogmus and Jose C Principe and Partha Niyogi},
title = {Feature selection in {MLP}s and {SVM}s based on maximum output information.},
journal = {I{EEE} {T}rans {N}eural {N}etw},
year = {2004},
volume = {15},
pages = {937-48},
number = {4},
month = {Jul},
abstract = {This paper presents feature selection algorithms for multilayer perceptrons
({MLP}s) and multiclass support vector machines ({SVM}s), using mutual
information between class labels and classifier outputs, as an objective
function. {T}his objective function involves inexpensive computation
of information measures only on discrete variables; provides immunity
to prior class probabilities; and brackets the probability of error
of the classifier. {T}he maximum output information ({MOI}) algorithms
employ this function for feature subset selection by greedy elimination
and directed search. {T}he output of the {MOI} algorithms is a feature
subset of user-defined size and an associated trained classifier
({MLP}/{SVM}). {T}hese algorithms compare favorably with a number
of other methods in terms of performance on various artificial and
real-world data sets.}
}
@article{Singer2002Universal,
author = {Singer, A.C. and Kozat, S.S. and Feder, M. },
title = {Universal linear least squares prediction: upper and lower bounds},
journal = {I{EEE} {T}rans. {I}nform. {T}heory},
year = {2002},
volume = {48},
pages = {2354 - 2362},
number = {8},
month = {Aug},
doi = {10.1109/TIT.2002.800489},
pdf = {../local/Singer2002Universal.pdf},
file = {Singer2002Universal.pdf:local/Singer2002Universal.pdf:PDF},
owner = {vert},
url = {http://dx.doi.org/10.1109/TIT.2002.800489}
}
@article{Singh2008Global,
author = {Singh, R. and Xu, J. and Berger, B.},
title = {Global alignment of multiple protein interaction networks with application
to functional orthology detection.},
journal = {Proc. Natl. Acad. Sci. USA},
year = {2008},
volume = {105},
pages = {12763--12768},
number = {35},
month = {Sep},
abstract = {Protein-protein interactions (PPIs) and their networks play a central
role in all biological processes. Akin to the complete sequencing
of genomes and their comparative analysis, complete descriptions
of interactomes and their comparative analysis is fundamental to
a deeper understanding of biological processes. A first step in such
an analysis is to align two or more PPI networks. Here, we introduce
an algorithm, IsoRank, for global alignment of multiple PPI networks.
The guiding intuition here is that a protein in one PPI network is
a good match for a protein in another network if their respective
sequences and neighborhood topologies are a good match. We encode
this intuition as an eigenvalue problem in a manner analogous to
Google's PageRank method. Using IsoRank, we compute a global alignment
of the Saccharomyces cerevisiae, Drosophila melanogaster, Caenorhabditis
elegans, Mus musculus, and Homo sapiens PPI networks. We demonstrate
that incorporating PPI data in ortholog prediction results in improvements
over existing sequence-only approaches and over predictions from
local alignments of the yeast and fly networks. Previous methods
have been effective at identifying conserved, localized network patterns
across pairs of networks. This work takes the further step of performing
a global alignment of multiple PPI networks. It simultaneously uses
sequence similarity and network data and, unlike previous approaches,
explicitly models the tradeoff inherent in combining them. We expect
IsoRank-with its simultaneous handling of node similarity and network
similarity-to be applicable across many scientific domains.},
doi = {10.1073/pnas.0806627105},
pdf = {../local/Singh2008Global.pdf},
file = {Singh2008Global.pdf:local/Singh2008Global.pdf:PDF},
institution = {Computer Science and Artificial Intelligence Laboratory, Massachusetts
Institute of Technology, Cambridge, MA 02139, USA.},
owner = {jp},
pii = {0806627105},
pmid = {18725631},
timestamp = {2008.10.02},
url = {http://dx.doi.org/10.1073/pnas.0806627105}
}
@article{Singh2007Pairwise,
author = {R. Singh and J. Xu and B. Berger},
title = {Pairwise Global Alignment of Protein Interaction Networks By Matching
Neighborhood Topology},
journal = {The Proceedings of the 11th International Conference on Research
in Computational Molecular Biology (RECOMB)},
year = {2007},
owner = {michael},
timestamp = {2008.10.02}
}
@article{Sjoelander2004Phylogenomic,
author = {Sj{\"o}lander, K.},
title = {Phylogenomic inference of protein molecular function: advances and
challenges},
journal = {Bioinformatics},
year = {2004},
volume = {20},
pages = {170--179},
number = {2},
month = {Jan},
abstract = {MOTIVATION: Protein families evolve a multiplicity of functions through
gene duplication, speciation and other processes. As a number of
studies have shown, standard methods of protein function prediction
produce systematic errors on these data. Phylogenomic analysis--combining
phylogenetic tree construction, integration of experimental data
and differentiation of orthologs and paralogs--has been proposed
to address these errors and improve the accuracy of functional classification.
The explicit integration of structure prediction and analysis in
this framework, which we call structural phylogenomics, provides
additional insights into protein superfamily evolution. RESULTS:
Results of protein functional classification using phylogenomic analysis
show fewer expected false positives overall than when pairwise methods
of functional classification are employed. We present an overview
of the motivations and fundamental principles of phylogenomic analysis,
new methods developed for the key tasks, benchmark datasets for these
tasks (when available) and suggest procedures to increase accuracy.
We also discuss some of the methods used in the Celera Genomics high-throughput
phylogenomic classification of the human genome. AVAILABILITY: Software
tools from the Berkeley Phylogenomics Group are available at http://phylogenomics.berkeley.edu},
pdf = {../local/Sjoelander2004Phylogenomic.pdf},
file = {Sjoelander2004Phylogenomic.pdf:local/Sjoelander2004Phylogenomic.pdf:PDF},
institution = {Berkeley Phylogenomics Group, Department of Bioengineering, University
of California, 473 Evans Hall 1762, Berkeley, CA 94720-1762, USA.
kimmen@uclink.berkeley.edu},
owner = {jp},
pmid = {14734307},
timestamp = {2008.10.02},
url = {http://bioinformatics.oxfordjournals.org/cgi/content/abstract/20/2/170}
}
@article{Slanina2000Random,
author = {Slanina, F. and Kotrla, M.},
title = {Random networks created by biological evolution},
journal = {Phys. {R}ev. {E}},
year = {2000},
volume = {62},
pages = {6170-6177},
number = {5},
pdf = {../local/slan00.pdf},
file = {slan00.pdf:local/slan00.pdf:PDF},
subject = {bionet},
url = {http://ojps.aip.org/getabs/servlet/GetabsServlet?prog=normal&id=PLEEE8000062000005006170000001&idtype=cvips&gifs=yes}
}
@article{Slonim2002From,
author = {Slonim, D. K.},
title = {From patterns to pathways: gene expression data analysis comes of
age},
journal = {Nat. Genet.},
year = {2002},
volume = {32 Suppl},
pages = {502--508},
month = {Dec},
abstract = {Many different biological questions are routinely studied using transcriptional
profiling on microarrays. A wide range of approaches are available
for gleaning insights from the data obtained from such experiments.
The appropriate choice of data-analysis technique depends both on
the data and on the goals of the experiment. This review summarizes
some of the common themes in microarray data analysis, including
detection of differential expression, clustering, and predicting
sample characteristics. Several approaches to each problem, and their
relative merits, are discussed and key areas for additional research
highlighted.},
doi = {10.1038/ng1033},
pdf = {../local/Slonim2002From.pdf},
file = {Slonim2002From.pdf:Slonim2002From.pdf:PDF},
institution = {Department of Genomics, Wyeth Research, 35 Cambridge Park Drive,
Cambridge, Massachusetts 02140, USA. dslonim@wyeth.com},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {ng1033},
pmid = {12454645},
timestamp = {2011.10.03},
url = {http://dx.doi.org/10.1038/ng1033}
}
@article{Smale2003Estimating,
author = {Smale, S. and Zhou, D.},
title = {Estimating the approximation error in learning theory},
journal = {Analysis and {A}pplications},
year = {2003},
volume = {1},
number = {1},
pdf = {../local/Smale2003Estimating.pdf},
file = {Smale2003Estimating.pdf:local/Smale2003Estimating.pdf:PDF},
owner = {jeanphilippevert}
}
@article{Smalter2009Feature,
author = {Aaron Smalter and Jun Huan and Gerald Lushington},
title = {Feature Selection in the Tensor Product Feature Space},
journal = {Data Mining, IEEE International Conference on},
year = {2009},
volume = {0},
pages = {1004-1009},
address = {Los Alamitos, CA, USA},
doi = {http://doi.ieeecomputersociety.org/10.1109/ICDM.2009.101},
issn = {1550-4786},
publisher = {IEEE Computer Society}
}
@article{Smart2008Cascading,
author = {Smart, A. G. and Amaral, L. A. N. and Ottino, J. M.},
title = {Cascading failure and robustness in metabolic networks},
journal = {Proc. Natl. Acad. Sci. USA},
year = {2008},
volume = {105},
pages = {13223--13228},
number = {36},
month = {Sep},
abstract = {We investigate the relationship between structure and robustness in
the metabolic networks of Escherichia coli, Methanosarcina barkeri,
Staphylococcus aureus, and Saccharomyces cerevisiae, using a cascading
failure model based on a topological flux balance criterion. We find
that, compared to appropriate null models, the metabolic networks
are exceptionally robust. Furthermore, by decomposing each network
into rigid clusters and branched metabolites, we demonstrate that
the enhanced robustness is related to the organization of branched
metabolites, as rigid cluster formations in the metabolic networks
appear to be consistent with null model behavior. Finally, we show
that cascading in the metabolic networks can be described as a percolation
process.},
doi = {10.1073/pnas.0803571105},
pdf = {../local/Smart2008Cascading.pdf},
file = {Smart2008Cascading.pdf:Smart2008Cascading.pdf:PDF},
institution = {Department of Chemical and Biological Engineering, Northwestern University,
2145 Sheridan Road, Evanston, IL 60208, USA. a-smart@u.northwestern.edu},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {0803571105},
pmid = {18765805},
timestamp = {2011.11.29},
url = {http://dx.doi.org/10.1073/pnas.0803571105}
}
@inproceedings{Smeaton2006Evaluation,
author = {Smeaton, A. F. and Over, P. and Kraaij, W.},
title = {Evaluation campaigns and {TRECVid}},
booktitle = {{MIR} '06: {P}roceedings of the 8th {ACM} {I}nternational {W}orkshop
on {M}ultimedia {I}nformation {R}etrieval},
year = {2006},
pages = {321--330},
address = {New-York, NY, USA},
publisher = {ACM Press},
doi = {10.1145/1178677.1178722},
timestamp = {2008.07.29},
url = {http://dx.doi.org/10.1145/1178677.1178722}
}
@article{Smith2004Towards,
author = {P. A. Smith and M. J. Sorich and L. S C Low and R. A. McKinnon and
J. O. Miners},
title = {Towards integrated {ADME} prediction: past, present and future directions
for modelling metabolism by {UDP}-glucuronosyltransferases.},
journal = {J {M}ol {G}raph {M}odel},
year = {2004},
volume = {22},
pages = {507-17},
number = {6},
month = {Jul},
abstract = {Undesirable absorption, distribution, metabolism, excretion ({ADME})
properties are the cause of many drug development failures and this
has led to the need to identify such problems earlier in the development
process. {T}his review highlights computational (in silico) approaches
that have been used to identify the characteristics of ligands influencing
molecular recognition and/or metabolism by the drug-metabolising
enzyme {UDP}-gucuronosyltransferase ({UGT}). {C}urrent studies applying
pharmacophore elucidation, 2{D}-quantitative structure metabolism
relationships (2{D}-{QSMR}), 3{D}-quantitative structure metabolism
relationships (3{D}-{QSMR}), and non-linear pattern recognition techniques
such as artificial neural networks and support vector machines for
modelling metabolism by {UGT} are reported. {A}n assessment of the
utility of in silico approaches for the qualitative and quantitative
prediction of drug glucuronidation parameters highlights the benefit
of using multiple pharmacophores and also non-linear techniques for
classification. {S}ome of the challenges facing the development of
generalisable models for predicting metabolism by {UGT}, including
the need for screening of more diverse structures, are also outlined.},
doi = {10.1016/j.jmgm.2004.03.011},
pdf = {../local/Smith2004Towards.pdf},
file = {Smith2004Towards.pdf:local/Smith2004Towards.pdf:PDF},
keywords = {Algorithms, Animals, Antisense, Artificial Intelligence, Astrocytoma,
Automated, Autonomic Nervous System, Brain, Brain Neoplasms, Cell
Line, Cerebral Cortex, Child, Cluster Analysis, Cognition, Comparative
Study, Computational Biology, Computer Simulation, Computer-Assisted,
DNA Fingerprinting, Databases, Diagnosis, Discriminant Analysis,
Drug Design, Drug Evaluation, Electroencephalography, Emotions, Event-Related
Potentials, Evoked Potentials, Factual, Fluorescence, Fuzzy Logic,
Gene Silencing, Gene Targeting, Genetic, Glucuronosyltransferase,
Hand, Hela Cells, Humans, Imaging, Intracellular Space, Magnetic
Resonance Spectroscopy, Male, Meningeal Neoplasms, Meningioma, Microscopy,
Models, Molecular Structure, Monitoring, Motor, Neoplasm Metastasis,
Neoplasms, Neural Networks (Computer), Non-U.S. Gov't, Oligonucleotides,
P.H.S., P300, Pattern Recognition, Peptides, Pharmaceutical Preparations,
Physiologic, Preclinical, Predictive Value of Tests, Preschool, Prognosis,
Protein Interaction Mapping, Protein Structure, Proteins, Proteomics,
Quantitative Structure-Activity Relationship, Quaternary, RNA, RNA
Interference, Recognition (Psychology), Reproducibility of Results,
Research Support, Sensitivity and Specificity, Signal Processing,
Small Interfering, Software, Thionucleotides, Three-Dimensional,
Tumor, U.S. Gov't, User-Computer Interface, Word Processing, 15182810},
pii = {S1093326304000269},
url = {http://dx.doi.org/10.1016/j.jmgm.2004.03.011}
}
@article{Smith1981Identification,
author = {T. Smith and M. Waterman},
title = {Identification of common molecular subsequences.},
journal = {J. {M}ol. {B}iol.},
year = {1981},
volume = {147},
pages = {195-197}
}
@inproceedings{Smola2003Kernels,
author = {Smola, A. and Kondor, R.},
title = {Kernels and {R}egularization on {G}raphs.},
booktitle = {Proceedings of 16th {A}nnual {C}onference on {C}omputational {L}earning
{T}heory},
year = {2003},
editor = {Sch{\"o}lkopf, B. and Warmuth,M.K.},
pages = {144-158},
publisher = {Springer-Verlag},
citeseerurl = {http://citeseer.ist.psu.edu/smola03kernels.html},
owner = {mahe},
timestamp = {2006.08.09}
}
@article{Smola1998connection,
author = {A.J. Smola and B. Sch{\"o}lkopf and K.-R. M{\"u}ller},
title = {The connection between regularization operators and support vector
kernels},
journal = {Neural {N}etworks},
year = {1998},
volume = {11},
pages = {637--649},
number = {4},
doi = {10.1016/S0893-6080(98)00032-X},
pdf = {../local/Smola1998connection.pdf},
file = {Smola1998connection.pdf:local/Smola1998connection.pdf:PDF},
url = {http://dx.doi.org/10.1016/S0893-6080(98)00032-X}
}
@article{Smoot2011Cytoscape,
author = {Smoot, M.E. and Ono, K. and Ruscheinski, J. and Wang, P.L. and Ideker,
T.},
title = {Cytoscape 2.8: new features for data integration and network visualization},
journal = {Bioinformatics},
year = {2011},
volume = {27},
pages = {431--432},
number = {3},
publisher = {Oxford Univ Press}
}
@inbook{Smyth2005Bioinformatics,
chapter = {Limma: linear model for microarray data},
pages = {397--420},
title = {Bioinformatics and Computational Biology Solutions using {R} and
{B}ioconductor},
publisher = {Springer},
year = {2005},
editor = {Gentleman, R. and Carey, V. and Dudoit, S. and Irizarry, R. and Huber,
W.},
author = {Smyth, G. K.},
address = {New York},
pdf = {../local/Smyth2005Bioinformatics.pdf},
file = {Smyth2005Bioinformatics.pdf:local/Smyth2005Bioinformatics.pdf:PDF},
timestamp = {2007.09.19}
}
@article{Smyth2004Linear,
author = {Smyth, G. K.},
title = {Linear models and empirical {B}ayes methods for assessing differential
expression in microarray experiments.},
journal = {Stat. Appl. Genet. Mol. Biol.},
year = {2004},
volume = {3},
pages = {Article3},
abstract = {The problem of identifying differentially expressed genes in designed
microarray experiments is considered. Lonnstedt and Speed (2002)
derived an expression for the posterior odds of differential expression
in a replicated two-color experiment using a simple hierarchical
parametric model. The purpose of this paper is to develop the hierarchical
model of Lonnstedt and Speed (2002) into a practical approach for
general microarray experiments with arbitrary numbers of treatments
and RNA samples. The model is reset in the context of general linear
models with arbitrary coefficients and contrasts of interest. The
approach applies equally well to both single channel and two color
microarray experiments. Consistent, closed form estimators are derived
for the hyperparameters in the model. The estimators proposed have
robust behavior even for small numbers of arrays and allow for incomplete
data arising from spot filtering or spot quality weights. The posterior
odds statistic is reformulated in terms of a moderated t-statistic
in which posterior residual standard deviations are used in place
of ordinary standard deviations. The empirical Bayes approach is
equivalent to shrinkage of the estimated sample variances towards
a pooled estimate, resulting in far more stable inference when the
number of arrays is small. The use of moderated t-statistics has
the advantage over the posterior odds that the number of hyperparameters
which need to estimated is reduced; in particular, knowledge of the
non-null prior for the fold changes are not required. The moderated
t-statistic is shown to follow a t-distribution with augmented degrees
of freedom. The moderated t inferential approach extends to accommodate
tests of composite null hypotheses through the use of moderated F-statistics.
The performance of the methods is demonstrated in a simulation study.
Results are presented for two publicly available data sets.},
doi = {10.2202/1544-6115.1027},
pdf = {../local/Smyth2004Linear.pdf},
file = {Smyth2004Linear.pdf:Smyth2004Linear.pdf:PDF},
institution = {Walter and Eliza Hall Institute. smyth@wehi.edu.au},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pmid = {16646809},
timestamp = {2012.01.15},
url = {http://dx.doi.org/10.2202/1544-6115.1027}
}
@article{Smyth2003Normalization,
author = {Smyth, G. K. and Speed, T. P.},
title = {Normalization of {cDNA} microarray data},
journal = {Methods},
year = {2003},
volume = {31},
pages = {265--273},
timestamp = {2007.09.19}
}
@article{Snijder2009Population,
author = {Berend Snijder and Raphael Sacher and Pauli Rämö and Eva-Maria Damm
and Prisca Liberali and Lucas Pelkmans},
title = {Population context determines cell-to-cell variability in endocytosis
and virus infection.},
journal = {Nature},
year = {2009},
volume = {461},
pages = {520--523},
number = {7263},
month = {Sep},
abstract = {Single-cell heterogeneity in cell populations arises from a combination
of intrinsic and extrinsic factors. This heterogeneity has been measured
for gene transcription, phosphorylation, cell morphology and drug
perturbations, and used to explain various aspects of cellular physiology.
In all cases, however, the causes of heterogeneity were not studied.
Here we analyse, for the first time, the heterogeneous patterns of
related cellular activities, namely virus infection, endocytosis
and membrane lipid composition in adherent human cells. We reveal
correlations with specific cellular states that are defined by the
population context of a cell, and we derive probabilistic models
that can explain and predict most cellular heterogeneity of these
activities, solely on the basis of each cell's population context.
We find that accounting for population-determined heterogeneity is
essential for interpreting differences between the activity levels
of cell populations. Finally, we reveal that synergy between two
molecular components, focal adhesion kinase and the sphingolipid
GM1, enhances the population-determined pattern of simian virus 40
(SV40) infection. Our findings provide an explanation for the origin
of heterogeneity patterns of cellular activities in adherent cell
populations.},
doi = {10.1038/nature08282},
pdf = {../local/Snijder2009Population.pdf},
file = {Snijder2009Population.pdf:Snijder2009Population.pdf:PDF},
institution = {Institute of Molecular Systems Biology, ETH Zurich (Swiss Federal
Institute of Technology), Wolfgang Pauli-Strasse 16, CH-8093 Zurich,
Switzerland.},
keywords = {highcontentscreening},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {nature08282},
pmid = {19710653},
timestamp = {2009.10.30},
url = {http://dx.doi.org/10.1038/nature08282}
}
@article{Snoeve2004Designing,
author = {Snøve, O. and Nedland, M. and Fjeldstad, S. H. and Humberset, H.
and Birkeland, O. R. and Gr{\"o}nfeld, T. and Saetrom, P.},
title = {Designing effective si{RNA}s with off-target control.},
journal = {Biochem. {B}iophys. {R}es. {C}ommun.},
year = {2004},
volume = {325},
pages = {769-73},
number = {3},
month = {Dec},
abstract = {Successful gene silencing by {RNA} interference requires a potent
and specific depletion of the target m{RNA}. {T}arget candidates
must be chosen so that their corresponding short interfering {RNA}s
are likely to be effective against that target and unlikely to accidentally
silence other transcripts due to sequence similarity. {W}e show that
both effective and unique targets exist in mouse, fruit fly, and
worm, and present a new design tool that enables users to make the
trade-off between efficacy and uniqueness. {T}he tool lists all targets
with partial sequence similarity to the primary target to highlight
candidates for negative controls.},
doi = {10.1016/j.bbrc.2004.10.097},
keywords = {sirna},
pii = {S0006-291X(04)02391-5},
url = {http://dx.doi.org/10.1016/j.bbrc.2004.10.097}
}
@article{Soares2009Identifying,
author = {Soares, H. D. and Chen, Y. and Sabbagh, M. and Roher, A. and Rohrer,
A. and Schrijvers, E. and Breteler, M.},
title = {Identifying early markers of Alzheimer's disease using quantitative
multiplex proteomic immunoassay panels},
journal = {Ann. N. Y. Acad. Sci.},
year = {2009},
volume = {1180},
pages = {56--67},
month = {Oct},
abstract = {Alzheimer's disease (AD) is a debilitating neurodegenerative disorder
with incidence expected to increase four-fold over the next decade.
Extensive research efforts are focused upon identifying new treatments,
and early diagnosis is considered key to successful intervention.
Although imaging and cerebrospinal fluid biomarkers have shown promise
in identifying patients in very early stages of the disease, more
noninvasive cost-effective tools have remained elusive. Recent studies
have reported that an 18-analyte multiplexed plasma panel can differentiate
AD from controls suggesting plasma-based screening tools for early
AD diagnosis exists. The current study tested the reproducibility
of a subset of the original 18-analyte panel using a bead-based multiplex
technology. Preliminary results suggest diagnostic accuracy using
the subset was 61\%. Multivariate analysis of an 89-analyte multivariate
panel yielded a diagnostic accuracy of 70\% suggesting a plasma-based
AD signature that may be a useful screening tool.},
doi = {10.1111/j.1749-6632.2009.05066.x},
pdf = {../local/Soares2009Identifying.pdf},
file = {Soares2009Identifying.pdf:Soares2009Identifying.pdf:PDF},
institution = {Pfizer Global Research and Development, Groton, Connecticut, USA.
holly.d.soares@pfizer.com},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {NYAS5066},
pmid = {19906261},
timestamp = {2011.03.14},
url = {http://dx.doi.org/10.1111/j.1749-6632.2009.05066.x}
}
@article{Sohler2004New,
author = {Sohler, F. and Hanisch, D. and Zimmer, R.},
title = {New methods for joint analysis of biological networks and expression
data},
journal = {Bioinformatics},
year = {2004},
volume = {20},
pages = {1517--1521},
number = {10},
month = {Jul},
abstract = {SUMMARY: Biological networks, such as protein interaction, regulatory
or metabolic networks, derived from public databases, biological
experiments or text mining can be useful for the analysis of high-throughput
experimental data. We present two algorithms embedded in the ToPNet
application that show promising performance in analyzing expression
data in the context of such networks. First, the Significant Area
Search algorithm detects subnetworks consisting of significantly
regulated genes. These subnetworks often provide hints on which biological
processes are affected in the measured conditions. Second, Pathway
Queries allow detection of networks including molecules that are
not necessarily significantly regulated, such as transcription factors
or signaling proteins. Moreover, using these queries, the user can
formulate biological hypotheses and check their validity with respect
to experimental data. All resulting networks and pathways can be
explored further using the interactive analysis tools provided by
ToPNet program.},
doi = {10.1093/bioinformatics/bth112},
pdf = {../local/Sohler2004New.pdf},
file = {Sohler2004New.pdf:Sohler2004New.pdf:PDF},
institution = {Institut für Informatik, Ludwig-Maximilians-Universität München,
Amalienstrasse 17, 80333 München, Germany. florian.sohler@ifi.lmu.edu},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {20/10/1517},
pmid = {15231545},
timestamp = {2011.09.26},
url = {http://dx.doi.org/10.1093/bioinformatics/bth112}
}
@article{Solinas1997Matrix,
author = {S. Solinas-Toldo and S. Lampel and S. Stilgenbauer and J. Nickolenko
and A. Benner and H. Dohner and T. Cremer and P. Lichter},
title = {Matrix-based comparative genomic hybridization: Biochips to screen
for genomic imbalances},
journal = {Genes Chromosomes Cancer},
year = {1997},
volume = {20},
pages = {399-407},
keywords = {csbcbook, csbcbook-ch2}
}
@techreport{Sole2001Model,
author = {Sol{\'e}, R. V. and Pastor-Satorras, R. and Smith, E. D. and Kepler,
T.},
title = {A {M}odel of {L}arge-{S}cale {P}roteome {E}volution},
institution = {Santa Fe Institute},
year = {2001},
note = {Working paper 01-08-041},
pdf = {../local/sole01.pdf},
file = {sole01.pdf:local/sole01.pdf:PDF},
subject = {bionetprot},
url = {http://www.santafe.edu/sfi/publications/Abstracts/01-08-041abs.html}
}
@article{Son2005Database,
author = {Son, C.G. and Bilke, S. and Davis, S. and Greer, B.T. and Wei, J.S.
and Whiteford, C.C. and Chen, Q-R. and Cenacchi, N. and Khan, J.},
title = {Database of m{RNA} gene expression profiles of multiple human organs},
journal = {Genome Res.},
year = {2005},
volume = {15},
pages = {443--450},
number = {3},
month = {Mar},
abstract = {Genome-wide expression profiling of normal tissue may facilitate our
understanding of the etiology of diseased organs and augment the
development of new targeted therapeutics. Here, we have developed
a high-density gene expression database of 18,927 unique genes for
158 normal human samples from 19 different organs of 30 different
individuals using DNA microarrays. We report four main findings.
First, despite very diverse sample parameters (e.g., age, ethnicity,
sex, and postmortem interval), the expression profiles belonging
to the same organs cluster together, demonstrating internal stability
of the database. Second, the gene expression profiles reflect major
organ-specific functions on the molecular level, indicating consistency
of our database with known biology. Third, we demonstrate that any
small (i.e., n approximately 100), randomly selected subset of genes
can approximately reproduce the hierarchical clustering of the full
data set, suggesting that the observed differential expression of
>90\% of the probed genes is of biological origin. Fourth, we demonstrate
a potential application of this database to cancer research by identifying
19 tumor-specific genes in neuroblastoma. The selected genes are
relatively underexpressed in all of the organs examined and belong
to therapeutically relevant pathways, making them potential novel
diagnostic markers and targets for therapy. We expect this database
will be of utility for developing rationally designed molecularly
targeted therapeutics in diseases such as cancer, as well as for
exploring the functions of genes.},
doi = {10.1101/gr.3124505},
institution = {Advanced Technology Center, Oncogenomics Section, Pediatric Oncology
Branch, National Cancer Institute, National Institutes of Health,
Gaithersburg, Maryland 20877, USA.},
keywords = {Cluster Analysis; Databases, Nucleic Acid; Gene Expression Profiling;
Humans; Oligonucleotide Array Sequence Analysis; Organ Specificity;
Principal Component Analysis; RNA, Messenger},
owner = {mordelet},
pii = {15/3/443},
pmid = {15741514},
timestamp = {2010.11.02},
url = {http://dx.doi.org/10.1101/gr.3124505}
}
@article{Soneson2010Integrative,
author = {Soneson, Charlotte and Lilljebjörn, Henrik and Fioretos, Thoas and
Fontes, Magnus},
title = {Integrative analysis of gene expression and copy number alterations
using canonical correlation analysis.},
journal = {BMC Bioinformatics},
year = {2010},
volume = {11},
pages = {191},
abstract = {With the rapid development of new genetic measurement methods, several
types of genetic alterations can be quantified in a high-throughput
manner. While the initial focus has been on investigating each data
set separately, there is an increasing interest in studying the correlation
structure between two or more data sets. Multivariate methods based
on Canonical Correlation Analysis (CCA) have been proposed for integrating
paired genetic data sets. The high dimensionality of microarray data
imposes computational difficulties, which have been addressed for
instance by studying the covariance structure of the data, or by
reducing the number of variables prior to applying the CCA. In this
work, we propose a new method for analyzing high-dimensional paired
genetic data sets, which mainly emphasizes the correlation structure
and still permits efficient application to very large data sets.
The method is implemented by translating a regularized CCA to its
dual form, where the computational complexity depends mainly on the
number of samples instead of the number of variables. The optimal
regularization parameters are chosen by cross-validation. We apply
the regularized dual CCA, as well as a classical CCA preceded by
a dimension-reducing Principal Components Analysis (PCA), to a paired
data set of gene expression changes and copy number alterations in
leukemia.Using the correlation-maximizing methods, regularized dual
CCA and PCA+CCA, we show that without pre-selection of known disease-relevant
genes, and without using information about clinical class membership,
an exploratory analysis singles out two patient groups, corresponding
to well-known leukemia subtypes. Furthermore, the variables showing
the highest relevance to the extracted features agree with previous
biological knowledge concerning copy number alterations and gene
expression changes in these subtypes. Finally, the correlation-maximizing
methods are shown to yield results which are more biologically interpretable
than those resulting from a covariance-maximizing method, and provide
different insight compared to when each variable set is studied separately
using PCA.We conclude that regularized dual CCA as well as PCA+CCA
are useful methods for exploratory analysis of paired genetic data
sets, and can be efficiently implemented also when the number of
variables is very large.},
doi = {10.1186/1471-2105-11-191},
institution = {Centre for Mathematical Sciences, Lund University, Box 118, SE-221
00 Lund, Sweden. lottas@maths.lth.se},
keywords = {Algorithms; Databases, Genetic; Gene Dosage; Gene Expression; Gene
Expression Profiling, methods; Genomics, methods; Humans; Leukemia,
classification/genetics; Principal Component Analysis},
language = {eng},
medline-pst = {epublish},
owner = {jp},
pii = {1471-2105-11-191},
pmid = {20398334},
timestamp = {2012.02.29},
url = {http://dx.doi.org/10.1186/1471-2105-11-191}
}
@article{Song2002Prediction,
author = {Minghu Song and Curt M Breneman and Jinbo Bi and N. Sukumar and Kristin
P Bennett and Steven Cramer and Nihal Tugcu},
title = {Prediction of protein retention times in anion-exchange chromatography
systems using support vector regression.},
journal = {J {C}hem {I}nf {C}omput {S}ci},
year = {2002},
volume = {42},
pages = {1347-57},
number = {6},
abstract = {Quantitative {S}tructure-{R}etention {R}elationship ({QSRR}) models
are developed for the prediction of protein retention times in anion-exchange
chromatography systems. {T}opological, subdivided surface area, and
{TAE} ({T}ransferable {A}tom {E}quivalent) electron-density-based
descriptors are computed directly for a set of proteins using molecular
connectivity patterns and crystal structure geometries. {A} novel
algorithm based on {S}upport {V}ector {M}achine ({SVM}) regression
has been employed to obtain predictive {QSRR} models using a two-step
computational strategy. {I}n the first step, a sparse linear {SVM}
was utilized as a feature selection procedure to remove irrelevant
or redundant information. {S}ubsequently, the selected features were
used to produce an ensemble of nonlinear {SVM} regression models
that were combined using bootstrap aggregation (bagging) techniques,
where various combinations of training and validation data sets were
selected from the pool of available data. {A} visualization scheme
(star plots) was used to display the relative importance of each
selected descriptor in the final set of "bagged" models. {O}nce these
predictive models have been validated, they can be used as an automated
prediction tool for virtual high-throughput screening ({VHTS}).},
keywords = {Acute, Algorithms, Animals, Anion Exchange Resins, Artificial Intelligence,
Automated, Base Pair Mismatch, Base Pairing, Base Sequence, Biological,
Biosensing Techniques, Carcinoma, Chemical, Chromatography, Classification,
Cluster Analysis, Comparative Study, Computational Biology, Computer-Assisted,
Cystadenoma, DNA, Decision Making, Diagnosis, Differential, Drug,
Drug Design, Electrostatics, Eukaryotic Cells, Feasibility Studies,
Female, Gene Expression, Gene Expression Profiling, Gene Expression
Regulation, Genes, Genetic, Genetic Markers, Hemolysins, Humans,
Internet, Ion Exchange, Leukemia, Ligands, Likelihood Functions,
Logistic Models, Lung Neoplasms, Lymphocytic, Lymphoma, Markov Chains,
Mathematics, Messenger, Models, Molecular, Molecular Probe Techniques,
Molecular Sequence Data, Nanotechnology, Neoplasm, Neoplasms, Neoplastic,
Neural Networks (Computer), Non-P.H.S., Non-Small-Cell Lung, Non-U.S.
Gov't, Nucleic Acid Conformation, Nucleic Acid Hybridization, Observer
Variation, Oligonucleotide Array Sequence Analysis, Ovarian Neoplasms,
P.H.S., Pattern Recognition, Probability, Protein Binding, Protein
Conformation, Proteins, Quality Control, Quantum Theory, RNA, RNA
Splicing, Receptors, Reference Values, Regression Analysis, Reproducibility
of Results, Research Support, Sensitivity and Specificity, Sequence
Analysis, Signal Processing, Software, Statistical, Stomach Neoplasms,
Thermodynamics, Transcription, Tumor Markers, U.S. Gov't, 12444731},
pii = {ci025580t}
}
@article{Song2006Development,
author = {Song, M. and Clark, M.},
title = {{D}evelopment and evaluation of an in silico model for h{ERG} binding.},
journal = {J. Chem. Inf. Model.},
year = {2006},
volume = {46},
pages = {392--400},
number = {1},
abstract = {It has been recognized that drug-induced QT prolongation is related
to blockage of the human ether-a-go-go-related gene (hERG) ion channel.
Therefore, it is prudent to evaluate the hERG binding of active compounds
in early stages of drug discovery. In silico approaches provide an
economic and quick method to screen for potential hERG liability.
A diverse set of 90 compounds with hERG IC(50) inhibition data was
collected from literature references. Fragment-based QSAR descriptors
and three different statistical methods, support vector regression,
partial least squares, and random forests, were employed to construct
QSAR models for hERG binding affinity. Important fragment descriptors
relevant to hERG binding affinity were identified through an efficient
feature selection method based on sparse linear support vector regression.
The support vector regression predictive model built upon selected
fragment descriptors outperforms the other two statistical methods
in this study, resulting in an r(2) of 0.912 and 0.848 for the training
and testing data sets, respectively. The support vector regression
model was applied to predict hERG binding affinities of 20 in-house
compounds belonging to three different series. The model predicted
the relative binding affinity well for two out of three compound
series. The hierarchical clustering and dendrogram results show that
the compound series with the best prediction has much higher structural
similarity and more neighbors of training compounds than the other
two compound series, demonstrating the predictive scope of the model.
The combination of a QSAR model and postprocessing analysis, such
as clustering and visualization, provides a way to assess the confidence
level of QSAR prediction results on the basis of similarity to the
training set.},
doi = {10.1021/ci050308f},
pdf = {../local/Song2006Development.pdf},
file = {Song2006Development.pdf:Song2006Development.pdf:PDF},
keywords = {chemoinformatics herg},
pmid = {16426073},
timestamp = {2006.10.06},
url = {http://dx.doi.org/10.1021/ci050308f}
}
@article{Song2004Comparison,
author = {Xiaowei Song and Arnold Mitnitski and Jafna Cox and Kenneth Rockwood},
title = {Comparison of machine learning techniques with classical statistical
models in predicting health outcomes.},
journal = {Medinfo},
year = {2004},
volume = {11},
pages = {736-40},
number = {Pt 1},
abstract = {Several machine learning techniques (multilayer and single layer perceptron,
logistic regression, least square linear separation and support vector
machines) are applied to calculate the risk of death from two biomedical
data sets, one from patient care records, and another from a population
survey. {E}ach dataset contained multiple sources of information:
history of related symptoms and other illnesses, physical examination
findings, laboratory tests, medications (patient records dataset),
health attitudes, and disabilities in activities of daily living
(survey dataset). {E}ach technique showed very good mortality prediction
in the acute patients data sample ({AUC} up to 0.89) and fair prediction
accuracy for six year mortality ({AUC} from 0.70 to 0.76) in individuals
from epidemiological database surveys. {T}he results suggest that
the nature of data is of primary importance rather than the learning
technique. {H}owever, the consistently superior performance of the
artificial neural network (multi-layer perceptron) indicates that
nonlinear relationships (which cannot be discerned by linear separation
techniques) can provide additional improvement in correctly predicting
health outcomes.},
keywords = {Aged, Air, Algorithms, Amino Acids, Animals, Area Under Curve, Artifacts,
Artificial Intelligence, Atrial, Automated, Canada, Carotid Stenosis,
Cerebrovascular Accident, Cerebrovascular Circulation, Comparative
Study, Computer-Assisted, Cysteine, Decision Trees, Dementia, Diagnosis,
Disulfides, Doppler, Embolism, Expert Systems, Extramural, Factor
Analysis, Female, Gene Expression, Gene Expression Profiling, Health
Status, Heart Septal Defects, Humans, Intracranial Embolism, Male,
Models, Molecular, Myocardial Infarction, N.I.H., Neoplasms, Neural
Networks (Computer), Non-U.S. Gov't, Oligonucleotide Array Sequence
Analysis, Oxidation-Reduction, P.H.S., Pattern Recognition, Prognosis,
Protein Binding, Protein Folding, Proteins, ROC Curve, Research Support,
Sensitivity and Specificity, Software, Statistical, Transcranial,
Treatment Outcome, U.S. Gov't, Ultrasonography, 15360910},
pii = {D040004933}
}
@article{Sonnenburg2006Large,
author = {Sonnenburg, S\"{o}ren and R\"{a}tsch, Gunnar and Sch\"{a}fer, Christin
and Sch\"{o}lkopf, Bernhard},
title = {Large Scale Multiple Kernel Learning},
journal = {J. Mach. Learn. Res.},
year = {2006},
volume = {7},
pages = {1531--1565},
address = {Cambridge, MA, USA},
issn = {1533-7928},
publisher = {MIT Press}
}
@inproceedings{Sonnenburg2002New,
author = {Sonnenburg, S. and R{\"a}tsch, G. and Jagota, A. and M{\"u}ller,
K.-R.},
title = {New methods for splice-site recognition},
booktitle = {Proc. {I}nternational conference on artificial {N}eural {N}etworks
? {ICANN}?02},
year = {2002},
editor = {JR. Dorronsoro},
number = {2415},
series = {LNCS},
pages = {329-336},
publisher = {Springer Berlin},
pdf = {../local/Sonnenburg2002New.pdf},
file = {Sonnenburg2002New.pdf:local/Sonnenburg2002New.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert}
}
@article{Sonnhammer1997Pfam,
author = {Sonnhammer, E. L. and Eddy, S. R. and Durbin, R.},
title = {Pfam: a comprehensive database of protein domain families based on
seed alignments.},
journal = {Proteins},
year = {1997},
volume = {28},
pages = {405--420},
number = {3},
month = {Jul},
abstract = {Databases of multiple sequence alignments are a valuable aid to protein
sequence classification and analysis. One of the main challenges
when constructing such a database is to simultaneously satisfy the
conflicting demands of completeness on the one hand and quality of
alignment and domain definitions on the other. The latter properties
are best dealt with by manual approaches, whereas completeness in
practice is only amenable to automatic methods. Herein we present
a database based on hidden Markov model profiles (HMMs), which combines
high quality and completeness. Our database, Pfam, consists of parts
A and B. Pfam-A is curated and contains well-characterized protein
domain families with high quality alignments, which are maintained
by using manually checked seed alignments and HMMs to find and align
all members. Pfam-B contains sequence families that were generated
automatically by applying the Domainer algorithm to cluster and align
the remaining protein sequences after removal of Pfam-A domains.
By using Pfam, a large number of previously unannotated proteins
from the Caenorhabditis elegans genome project were classified. We
have also identified many novel family memberships in known proteins,
including new kazal, Fibronectin type III, and response regulator
receiver domains. Pfam-A families have permanent accession numbers
and form a library of HMMs available for searching and automatic
annotation of new protein sequences.},
institution = {Sanger Centre, Wellcome Trust Genome Campus, Hinxton, Cambridge,
United Kingdom.},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {3.0.CO;2-L},
pmid = {9223186},
timestamp = {2010.02.21}
}
@article{Sorich2004Rapid,
author = {Michael J Sorich and Ross A McKinnon and John O Miners and David
A Winkler and Paul A Smith},
title = {Rapid prediction of chemical metabolism by human {UDP}-glucuronosyltransferase
isoforms using quantum chemical descriptors derived with the electronegativity
equalization method.},
journal = {J {M}ed {C}hem},
year = {2004},
volume = {47},
pages = {5311-7},
number = {21},
month = {Oct},
abstract = {This study aimed to evaluate in silico models based on quantum chemical
({QC}) descriptors derived using the electronegativity equalization
method ({EEM}) and to assess the use of {QC} properties to predict
chemical metabolism by human {UDP}-glucuronosyltransferase ({UGT})
isoforms. {V}arious {EEM}-derived {QC} molecular descriptors were
calculated for known {UGT} substrates and nonsubstrates. {C}lassification
models were developed using support vector machine and partial least
squares discriminant analysis. {I}n general, the most predictive
models were generated with the support vector machine. {C}ombining
{QC} and 2{D} descriptors (from previous work) using a consensus
approach resulted in a statistically significant improvement in predictivity
(to 84\%) over both the {QC} and 2{D} models and the other methods
of combining the descriptors. {EEM}-derived {QC} descriptors were
shown to be both highly predictive and computationally efficient.
{I}t is likely that {EEM}-derived {QC} properties will be generally
useful for predicting {ADMET} and physicochemical properties during
drug discovery.},
doi = {10.1021/jm0495529},
pdf = {../local/Sorich2004Rapid.pdf},
file = {Sorich2004Rapid.pdf:local/Sorich2004Rapid.pdf:PDF},
keywords = {biosvm},
url = {http://dx.doi.org/10.1021/jm0495529}
}
@article{Sorich2003Comparison,
author = {M. J. Sorich and J. O. Miners and R. A. McKinnon and D. A. Winkler
and F. R. Burden and P. A. Smith},
title = {Comparison of linear and nonlinear classification algorithms for
the prediction of drug and chemical metabolism by human {UDP}-glucuronosyltransferase
isoforms.},
journal = {J {C}hem {I}nf {C}omput {S}ci},
year = {2003},
volume = {43},
pages = {2019-24},
number = {6},
abstract = {Partial least squares discriminant analysis ({PLSDA}), {B}ayesian
regularized artificial neural network ({BRANN}), and support vector
machine ({SVM}) methodologies were compared by their ability to classify
substrates and nonsubstrates of 12 isoforms of human {UDP}-glucuronosyltransferase
({UGT}), an enzyme "superfamily" involved in the metabolism of drugs,
nondrug xenobiotics, and endogenous compounds. {S}imple two-dimensional
descriptors were used to capture chemical information. {F}or each
data set, 70\% of the data were used for training, and the remainder
were used to assess the generalization performance. {I}n general,
the {SVM} methodology was able to produce models with the best predictive
performance, followed by {BRANN} and then {PLSDA}. {H}owever, a small
number of data sets showed either equivalent or better predictability
using {PLSDA}, which may indicate relatively linear relationships
in these data sets. {A}ll {SVM} models showed predictive ability
(>60\% of test set predicted correctly) and five out of the 12 test
sets showed excellent prediction (>80\% prediction accuracy). {T}hese
models represent the first use of pattern recognition methods to
discriminate between substrates and nonsubstrates of human drug metabolizing
enzymes and the first thorough assessment of three classification
algorithms using multiple metabolic data sets.},
doi = {10.1021/ci034108k},
pdf = {../local/Sorich2003Comparison.pdf},
file = {Sorich2003Comparison.pdf:local/Sorich2003Comparison.pdf:PDF},
keywords = {biosvm},
url = {http://dx.doi.org/10.1021/ci034108k}
}
@article{Sotiriou2003Breast,
author = {Sotiriou, C. and Neo, S.-Y. and McShane, L. M. and Korn, E. L. and
Long, P. M. and Jazaeri, A. and Martiat, P. and Fox, S. B. and Harris,
A. L. and Liu, E. T.},
title = {Breast cancer classification and prognosis based on gene expression
profiles from a population-based study.},
journal = {Proc. Natl. Acad. Sci. U. S. A.},
year = {2003},
volume = {100},
pages = {10393--10398},
number = {18},
month = {Sep},
abstract = {Comprehensive gene expression patterns generated from cDNA microarrays
were correlated with detailed clinico-pathological characteristics
and clinical outcome in an unselected group of 99 node-negative and
node-positive breast cancer patients. Gene expression patterns were
found to be strongly associated with estrogen receptor (ER) status
and moderately associated with grade, but not associated with menopausal
status, nodal status, or tumor size. Hierarchical cluster analysis
segregated the tumors into two main groups based on their ER status,
which correlated well with basal and luminal characteristics. Cox
proportional hazards regression analysis identified 16 genes that
were significantly associated with relapse-free survival at a stringent
significance level of 0.001 to account for multiple comparisons.
Of 231 genes previously reported by others [van't Veer, L. J., et
al. (2002) Nature 415, 530-536] as being associated with survival,
93 probe elements overlapped with the set of 7,650 probe elements
represented on the arrays used in this study. Hierarchical cluster
analysis based on the set of 93 probe elements segregated our population
into two distinct subgroups with different relapse-free survival
(P < 0.03). The number of these 93 probe elements showing significant
univariate association with relapse-free survival (P < 0.05) in the
present study was 14, representing 11 unique genes. Genes involved
in cell cycle, DNA replication, and chromosomal stability were consistently
elevated in the various poor prognostic groups. In addition, glutathione
S-transferase M3 emerged as an important survival marker in both
studies. When taken together with other array studies, our results
highlight the consistent biological and clinical associations with
gene expression profiles.},
doi = {10.1073/pnas.1732912100},
pdf = {../local/Sotiriou2003Breast.pdf},
file = {Sotiriou2003Breast.pdf:Sotiriou2003Breast.pdf:PDF},
institution = {Division of Clinical Sciences, National Cancer Institute, Advanced
Technology Center, 8717 Grovemont Circle, Gaithersburg, MD 20877,
USA.},
keywords = {breastcancer},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {1732912100},
pmid = {12917485},
timestamp = {2010.07.02},
url = {http://dx.doi.org/10.1073/pnas.1732912100}
}
@article{Sotiriou2009Gene-Expression,
author = {Sotiriou, C. and Pusztai, L.},
title = {Gene-Expression Signatures in Breast Cancer},
journal = {N. Engl. J. Med.},
year = {2009},
volume = {360},
pages = {790--800},
number = {8},
doi = {10.1056/NEJMra0801289},
pdf = {../local/Sotiriou2009Gene-Expression.pdf},
file = {Sotiriou2009Gene-Expression.pdf:Sotiriou2009Gene-Expression.pdf:PDF},
owner = {jp},
timestamp = {2011.01.14},
url = {http://dx.doi.org/10.1056/NEJMra0801289}
}
@article{Sotiriou2006Gene,
author = {Christos Sotiriou and Pratyaksha Wirapati and Sherene Loi and Adrian
Harris and Steve Fox and Johanna Smeds and Hans Nordgren and Pierre
Farmer and Viviane Praz and Benjamin Haibe-Kains and Christine Desmedt
and Denis Larsimont and Fatima Cardoso and Hans Peterse and Dimitry
Nuyten and Marc Buyse and Marc J Van de Vijver and Jonas Bergh and
Martine Piccart and Mauro Delorenzi},
title = {Gene expression profiling in breast cancer: understanding the molecular
basis of histologic grade to improve prognosis.},
journal = {J Natl Cancer Inst},
year = {2006},
volume = {98},
pages = {262--272},
number = {4},
month = {Feb},
abstract = {Histologic grade in breast cancer provides clinically important prognostic
information. However, 30\%-60\% of tumors are classified as histologic
grade 2. This grade is associated with an intermediate risk of recurrence
and is thus not informative for clinical decision making. We examined
whether histologic grade was associated with gene expression profiles
of breast cancers and whether such profiles could be used to improve
histologic grading.We analyzed microarray data from 189 invasive
breast carcinomas and from three published gene expression datasets
from breast carcinomas. We identified differentially expressed genes
in a training set of 64 estrogen receptor (ER)-positive tumor samples
by comparing expression profiles between histologic grade 3 tumors
and histologic grade 1 tumors and used the expression of these genes
to define the gene expression grade index. Data from 597 independent
tumors were used to evaluate the association between relapse-free
survival and the gene expression grade index in a Kaplan-Meier analysis.
All statistical tests were two-sided.We identified 97 genes in our
training set that were associated with histologic grade; most of
these genes were involved in cell cycle regulation and proliferation.
In validation datasets, the gene expression grade index was strongly
associated with histologic grade 1 and 3 status; however, among histologic
grade 2 tumors, the index spanned the values for histologic grade
1-3 tumors. Among patients with histologic grade 2 tumors, a high
gene expression grade index was associated with a higher risk of
recurrence than a low gene expression grade index (hazard ratio =
3.61, 95\% confidence interval = 2.25 to 5.78; P < .001, log-rank
test).Gene expression grade index appeared to reclassify patients
with histologic grade 2 tumors into two groups with high versus low
risks of recurrence. This approach may improve the accuracy of tumor
grading and thus its prognostic value.},
doi = {10.1093/jnci/djj052},
institution = {Functional Genomics and Translational Research Unit, Université Libre
de Bruxelles, Brussels, Belgium. christos.sotiriou@bordet.be},
keywords = {Breast Neoplasms, chemistry/genetics/pathology; Cell Cycle; Cell Proliferation;
Disease-Free Survival; Female; Gene Expression Profiling; Gene Expression
Regulation, Neoplastic; Humans; Lymphatic Metastasis; Mathematical
Computing; Middle Aged; Multivariate Analysis; Oligonucleotide Array
Sequence Analysis; Prognosis; Proportional Hazards Models; Receptors,
Estrogen, analysis; Risk Factors},
language = {eng},
medline-pst = {ppublish},
owner = {phupe},
pii = {98/4/262},
pmid = {16478745},
timestamp = {2011.06.01},
url = {http://dx.doi.org/10.1093/jnci/djj052}
}
@article{Southern1999Molecular,
author = {Edwin Southern and Kalim Mir and Mikhail Shchepinov},
title = {Molecular interactions on microarrays},
journal = {Nat. Genet.},
year = {1999},
volume = {21},
pages = {5-9},
keywords = {csbcbook, csbcbook-ch2}
}
@article{Southern1975Detection,
author = {Southern, E. M.},
title = {Detection of specific sequences among {DNA} fragments separated by
gel electrophoresis},
journal = {J. {M}ol. {B}iol.},
year = {1975},
volume = {98},
pages = {503--517}
}
@article{Spellman1998Comprehensive,
author = {Spellman, P.T. and Sherlock, G. and Zhang, M.Q. and Iyer, V.R. and
Anders, K. and Eisen, M.B. and Brown, P.O. and Botstein, D. and Futcher,
B.},
title = {Comprehensive {I}dentification of {C}ell {C}ycle-regulated {G}enes
of the {Y}east {S}accharomyces cerevisiae by {M}icroarray {H}ybridization},
journal = {Mol. {B}iol. {C}ell},
year = {1998},
volume = {9},
pages = {3273--3297},
pdf = {../local/spel98.pdf},
file = {spel98.pdf:local/spel98.pdf:PDF},
subject = {microarray},
url = {http://www.molbiolcell.org/cgi/reprint/9/12/3273.pdf}
}
@article{Spencer2009Non-genetic,
author = {Sabrina L Spencer and Suzanne Gaudet and John G Albeck and John M
Burke and Peter K Sorger},
title = {Non-genetic origins of cell-to-cell variability in TRAIL-induced
apoptosis.},
journal = {Nature},
year = {2009},
volume = {459},
pages = {428--432},
number = {7245},
month = {May},
abstract = {In microorganisms, noise in gene expression gives rise to cell-to-cell
variability in protein concentrations. In mammalian cells, protein
levels also vary and individual cells differ widely in their responsiveness
to uniform physiological stimuli. In the case of apoptosis mediated
by TRAIL (tumour necrosis factor (TNF)-related apoptosis-inducing
ligand) it is common for some cells in a clonal population to die
while others survive-a striking divergence in cell fate. Among cells
that die, the time between TRAIL exposure and caspase activation
is highly variable. Here we image sister cells expressing reporters
of caspase activation and mitochondrial outer membrane permeabilization
after exposure to TRAIL. We show that naturally occurring differences
in the levels or states of proteins regulating receptor-mediated
apoptosis are the primary causes of cell-to-cell variability in the
timing and probability of death in human cell lines. Protein state
is transmitted from mother to daughter, giving rise to transient
heritability in fate, but protein synthesis promotes rapid divergence
so that sister cells soon become no more similar to each other than
pairs of cells chosen at random. Our results have implications for
understanding 'fractional killing' of tumour cells after exposure
to chemotherapy, and for variability in mammalian signal transduction
in general.},
doi = {10.1038/nature08012},
institution = {Center for Cell Decision Processes, Department of Systems Biology,
Harvard Medical School, Boston, Massachusetts 02115, USA.},
keywords = {highcontentscreening},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {nature08012},
pmid = {19363473},
timestamp = {2009.10.30},
url = {http://dx.doi.org/10.1038/nature08012}
}
@article{Speybroeck2002From,
author = {L Van Speybroeck},
title = {From Epigenesis to Epigenetics: The Case of {C. H. Waddington}},
journal = {Annals of the New York Academy of Sciences},
year = {2002},
volume = {981},
pages = {61-81},
keywords = {csbcbook}
}
@article{Spyrou2009BayesPeak,
author = {Christiana Spyrou and Rory Stark and Andy G Lynch and Simon Tavaré},
title = {BayesPeak: Bayesian analysis of ChIP-seq data.},
journal = {BMC Bioinformatics},
year = {2009},
volume = {10},
pages = {299},
abstract = {BACKGROUND: High-throughput sequencing technology has become popular
and widely used to study protein and DNA interactions. Chromatin
immunoprecipitation, followed by sequencing of the resulting samples,
produces large amounts of data that can be used to map genomic features
such as transcription factor binding sites and histone modifications.
METHODS: Our proposed statistical algorithm, BayesPeak, uses a fully
Bayesian hidden Markov model to detect enriched locations in the
genome. The structure accommodates the natural features of the Solexa/Illumina
sequencing data and allows for overdispersion in the abundance of
reads in different regions. Moreover, a control sample can be incorporated
in the analysis to account for experimental and sequence biases.
Markov chain Monte Carlo algorithms are applied to estimate the posterior
distributions of the model parameters, and posterior probabilities
are used to detect the sites of interest. CONCLUSION: We have presented
a flexible approach for identifying peaks from ChIP-seq reads, suitable
for use on both transcription factor binding and histone modification
data. Our method estimates probabilities of enrichment that can be
used in downstream analysis. The method is assessed using experimentally
verified data and is shown to provide high-confidence calls with
low false positive rates.},
doi = {10.1186/1471-2105-10-299},
pdf = {../local/Spyrou2009BayesPeak.pdf},
file = {Spyrou2009BayesPeak.pdf:Spyrou2009BayesPeak.pdf:PDF},
institution = {Statistical Laboratory, Centre for Mathematical Sciences, Wilberforce
Road, Cambridge, UK. C.Spyrou@statslab.cam.ac.uk},
keywords = {ngs},
language = {eng},
medline-pst = {epublish},
owner = {jp},
pii = {1471-2105-10-299},
pmid = {19772557},
timestamp = {2009.10.29},
url = {http://dx.doi.org/10.1186/1471-2105-10-299}
}
@inproceedings{Srebro2003Weighted,
author = {Srebro, N. and Jaakkola, T.},
title = {Weighted Low-Rank Approximations},
booktitle = {Proceedings of the Twentieth International Conference on Machine
Learning},
year = {2003},
editor = {Fawcett, T. and Mishra, N.},
pages = {720--727},
publisher = {AAAI Press},
timestamp = {2007.10.22}
}
@inproceedings{Srebro2005Maximum,
author = {N. Srebro and J. D. M. Rennie and T. S. Jaakkola},
title = {Maximum-Margin Matrix Factorization},
booktitle = {Adv. Neural. Inform. Process Syst. 17},
year = {2005},
editor = {L. K. Saul and Y. Weiss and L. Bottou},
pages = {1329-1336},
address = {Cambridge, MA},
publisher = {MIT Press},
timestamp = {2006.05.30}
}
@inproceedings{Srebro2005Rank,
author = {Nathan Srebro and Adi Shraibman},
title = {Rank, Trace-Norm and Max-Norm.},
booktitle = {COLT},
year = {2005},
pages = {545-560},
bibsource = {DBLP, http://dblp.uni-trier.de},
ee = {http://dx.doi.org/10.1007/11503415_37}
}
@article{Srebrow2006Connection,
author = {A. Srebrow and A. R. Kornblihtt},
title = {The connection between splicing and cancer},
journal = {J. Cell Sci.},
year = {2006},
volume = {119},
pages = {2635-2641},
keywords = {csbcbook}
}
@inproceedings{Sriphaew2009Cool,
author = {Sriphaew, K. and Takamura, H. and Okumura, M.},
title = {Cool Blog Classification from Positive and Unlabeled Examples},
booktitle = {Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge
Discovery and Data Mining},
year = {2009},
editor = {Theeramunkong, T. and Kijsirikul, B. and Cercone, C. and Ho, T-B.},
series = {PAKDD '09},
pages = {62--73},
address = {Berlin, Heidelberg},
publisher = {Springer-Verlag},
abstract = {We address the problem of cool blog classification using only positive
and unlabeled examples. We propose an algorithm, called PUB, that
exploits the information of unlabeled data together with the positive
examples to predict whether the unseen blogs are cool or not. The
algorithm uses the weighting technique to assign a weight to each
unlabeled example which is assumed to be negative in the training
set, and the bagging technique to obtain several weak classifiers,
each of which is learned on a small training set generated by randomly
sampling some positive examples and some unlabeled examples, which
are assumed to be negative. Each of the weak classifiers must achieve
admissible performance measure evaluated based on the whole labeled
positive examples or has the best performance measure within iteration
limit. The majority voting function on all weak classifiers is employed
to predict the class of a test instance. The experimental results
show that PUB can correctly predict the classes of unseen blogs where
this situation cannot be handled by the traditional learning from
positive and negative examples. The results also show that PUB outperforms
other algorithms for learning from positive and unlabeled examples
in the task of cool blog classification. },
acmid = {1533869},
date-added = {2010-07-14 19:22:27 +0200},
date-modified = {2010-07-14 19:22:27 +0200},
doi = {10.1007/978-3-642-01307-2_9},
isbn = {978-3-642-01306-5},
keywords = {Cool blog, PU-learning, bagging, weighting examples},
location = {Bangkok, Thailand},
m3 = {10.1007/978-3-642-01307-2{\_}9},
numpages = {12},
owner = {fantine},
timestamp = {2010.07.14},
ty = {CHAPTER},
url = {http://dx.doi.org/10.1007/978-3-642-01307-2_9}
}
@article{Srivastava1979Estimation,
author = {Srivastava, V. K. and Dwivedi, T. D.},
title = {Estimation of seemingly unrelated regression equations : A brief
survey},
journal = {Journal of Econometrics},
year = {1979},
volume = {10},
pages = {15-32},
number = {1},
month = {April},
url = {http://ideas.repec.org/a/eee/econom/v10y1979i1p15-32.html}
}
@incollection{Stadler1999"Spectral,
author = {Stadler, P. F.},
title = {Spectral landscape theory},
booktitle = { {E}volutionary {D}ynamics --- {E}xploring the {I}nterplay of {S}election,
{N}eutrality, {A}ccident and {F}unction},
publisher = {Oxford University Press},
year = {1999},
editor = {J.P. Crutchfield and P. Schuster},
address = {New York},
url = {http://citeseer.nj.nec.com/stadler99spectral.html}
}
@article{Stadler1996Landscapes,
author = {Stadler, P. F.},
title = {Landscapes and {T}heir {C}orrelation {F}unctions},
journal = {J. {M}ath. {C}hem.},
year = {1996},
volume = {20},
pages = {1--45},
pdf = {../local/stad96.pdf},
file = {stad96.pdf:local/stad96.pdf:PDF},
subject = {net},
url = {htt://citeseer.nj.nec.com/43461.html}
}
@article{Stahura2004Virtual,
author = {Florence L Stahura and Jürgen Bajorath},
title = {Virtual screening methods that complement {HTS}.},
journal = {Comb {C}hem {H}igh {T}hroughput {S}creen},
year = {2004},
volume = {7},
pages = {259-69},
number = {4},
month = {Jun},
abstract = {In this review, we discuss a number of computational methods that
have been developed or adapted for molecule classification and virtual
screening ({VS}) of compound databases. {I}n particular, we focus
on approaches that are complementary to high-throughput screening
({HTS}). {T}he discussion is limited to {VS} methods that operate
at the small molecular level, which is often called ligand-based
{VS} ({LBVS}), and does not take into account docking algorithms
or other structure-based screening tools. {W}e describe areas that
greatly benefit from combining virtual and biological screening and
discuss computational methods that are most suitable to contribute
to the integration of screening technologies. {R}elevant approaches
range from established methods such as clustering or similarity searching
to techniques that have only recently been introduced for {LBVS}
applications such as statistical methods or support vector machines.
{F}inally, we discuss a number of representative applications at
the interface between {VS} and {HTS}.},
keywords = {Algorithms, Animals, Antisense, Artificial Intelligence, Cell Line,
Cluster Analysis, Comparative Study, Computational Biology, Computer
Simulation, DNA Fingerprinting, Drug Evaluation, Fluorescence, Fuzzy
Logic, Gene Silencing, Gene Targeting, Genetic, Hela Cells, Humans,
Imaging, Intracellular Space, Microscopy, Models, Neoplasms, Neural
Networks (Computer), Non-U.S. Gov't, Oligonucleotides, P.H.S., Preclinical,
Prognosis, Proteomics, Quantitative Structure-Activity Relationship,
RNA, RNA Interference, Research Support, Sensitivity and Specificity,
Small Interfering, Thionucleotides, Three-Dimensional, Tumor, U.S.
Gov't, 15200375}
}
@article{Staiger2012Critical,
author = {Staiger, C. and Cadot, S. and Kooter, R. and Dittrich, M. and M{\"u}ller,
T. and Klau, G.W. and Wessels, L.F.A.},
title = {A Critical Evaluation of Network and Pathway-Based Classifiers for
Outcome Prediction in Breast Cancer},
journal = {PloS one},
year = {2012},
volume = {7},
pages = {e34796},
number = {4},
publisher = {Public Library of Science}
}
@inproceedings{Stapley2002Predicting,
author = {Stapley, B.J. and Kelley, L.A. and Sternberg, M.J.},
title = {Predicting the sub-cellular location of proteins from text using
support vector machines.},
booktitle = {Proceedings of the {P}acific {S}ymposium on {B}iocomputing 2002},
year = {2002},
editor = {Russ B. Altman and A. Keith Dunker and Lawrence Hunter and Kevin
Lauerdale and Teri E. Klein},
pages = {374-385},
publisher = {World Scientific},
abstract = {We present an automatic method to classify the sub-cellular location
of proteins based on the text of relevant medline abstracts. {F}or
each protein, a vector of terms is generated from medline abstracts
in which the protein/gene's name or synonym occurs. {A} {S}upport
{V}ector {M}achine ({SVM}) is used to automatically partition the
term space and to thus discriminate the textual features that define
sub-cellular location. {T}he method is benchmarked on a set of proteins
of known sub-cellular location from {S}. cerevisiae. {N}o prior knowledge
of the problem domain nor any natural language processing is used
at any stage. {T}he method out-performs support vector machines trained
on amino acid composition and has comparable performance to rule-based
text classifiers. {C}ombining text with protein amino-acid composition
improves recall for some sub-cellular locations. {W}e discuss the
generality of the method and its potential application to a variety
of biological classification problems.},
pdf = {../local/Stapley2002Predicting.pdf},
file = {Stapley2002Predicting.pdf:local/Stapley2002Predicting.pdf:PDF},
keywords = {biosvm},
subject = {biokernel},
url = {http://www.smi.stanford.edu/projects/helix/psb02/stapley.pdf}
}
@article{Stark2006BioGRID:,
author = {Stark, C. and Breitkreutz, B-J. and Reguly, T. and Boucher, L. and
Breitkreutz, A. and Tyers, M.},
title = {BioGRID: a general repository for interaction datasets.},
journal = {Nucleic Acids Res},
year = {2006},
volume = {34},
pages = {D535--D539},
number = {Database issue},
month = {Jan},
abstract = {Access to unified datasets of protein and genetic interactions is
critical for interrogation of gene/protein function and analysis
of global network properties. BioGRID is a freely accessible database
of physical and genetic interactions available at http://www.thebiogrid.org.
BioGRID release version 2.0 includes >116 000 interactions from Saccharomyces
cerevisiae, Caenorhabditis elegans, Drosophila melanogaster and Homo
sapiens. Over 30 000 interactions have recently been added from 5778
sources through exhaustive curation of the Saccharomyces cerevisiae
primary literature. An internally hyper-linked web interface allows
for rapid search and retrieval of interaction data. Full or user-defined
datasets are freely downloadable as tab-delimited text files and
PSI-MI XML. Pre-computed graphical layouts of interactions are available
in a variety of file formats. User-customized graphs with embedded
protein, gene and interaction attributes can be constructed with
a visualization system called Osprey that is dynamically linked to
the BioGRID.},
doi = {10.1093/nar/gkj109},
institution = {Ontario Cancer Institute, Princess Margaret Hospital, 610 University
Avenue, Toronto, Ontario, Canada M5G 2M9.},
owner = {fantine},
pii = {34/suppl_1/D535},
pmid = {16381927},
timestamp = {2010.10.21},
url = {http://dx.doi.org/10.1093/nar/gkj109}
}
@article{Starkuviene2007potential,
author = {Starkuviene, V. and Pepperkok, R.},
title = {The potential of high-content high-throughput microscopy in drug
discovery.},
journal = {Br. J. Pharmacol.},
year = {2007},
volume = {152},
pages = {62--71},
number = {1},
month = {Sep},
abstract = {Fluorescence microscopy is a powerful method to study protein function
in its natural habitat, the living cell. With the availability of
the green fluorescent protein and its spectral variants, almost any
gene of interest can be fluorescently labelled in living cells opening
the possibility to study protein localization, dynamics and interactions.
The emergence of automated cellular systems allows rapid visualization
of large groups of cells and phenotypic analysis in a quantitative
manner. Here, we discuss recent advances in high-content high-throughput
microscopy and its potential application to several steps of the
drug discovery process.},
doi = {10.1038/sj.bjp.0707346},
institution = {Cell Biology and Cell Biophysics Unit, European Molecular Biology
Laboratory (EMBL), Meyerhofstrasse 1, 69117 Heidelberg, Germany.
starkuvi@embl.de},
owner = {jp},
pii = {0707346},
pmid = {17603554},
timestamp = {2008.12.09},
url = {http://dx.doi.org/10.1038/sj.bjp.0707346}
}
@article{Statnikov2004Methods,
author = {Alexander Statnikov and Constantin F Aliferis and Ioannis Tsamardinos},
title = {Methods for multi-category cancer diagnosis from gene expression
data: a comprehensive evaluation to inform decision support system
development.},
journal = {Medinfo},
year = {2004},
volume = {11},
pages = {813-7},
number = {Pt 2},
abstract = {Cancer diagnosis is a major clinical applications area of gene expression
microarray technology. {W}e are seeking to develop a system for cancer
diagnostic model creation based on microarray data. {I}n order to
equip the system with the optimal combination of data modeling methods,
we performed a comprehensive evaluation of several major classification
algorithms, gene selection methods, and cross-validation designs
using 11 datasets spanning 74 diagnostic categories (41 cancer types
and 12 normal tissue types). {T}he {M}ulti-{C}ategory {S}upport {V}ector
{M}achine techniques by {C}rammer and {S}inger, {W}eston and {W}atkins,
and one-versus-rest were found to be the best methods and they outperform
other learning algorithms such as {K}-{N}earest {N}eighbors and {N}eural
{N}etworks often to a remarkable degree. {G}ene selection techniques
are shown to significantly improve classification performance. {T}hese
results guided the development of a software system that fully automates
cancer diagnostic model construction with quality on par with or
better than previously published results derived by expert human
analysts.},
keywords = {biosvm},
pii = {D040004907}
}
@article{Statnikov2005comprehensive,
author = {Statnikov, A. and Aliferis, C. F. and Tsamardinos, I. and Hardin,
D. and Levy, S.},
title = {A comprehensive evaluation of multicategory classification methods
for microarray gene expression cancer diagnosis},
journal = {Bioinformatics},
year = {2005},
note = {To appear},
abstract = {Motivation: {C}ancer diagnosis is one of the most important emerging
clinical applications of gene expression microarray technology. {W}e
are seeking to develop a computer system for powerful and reliable
cancer diagnostic model creation based on microarray data. {T}o keep
a realistic perspective on clinical applications we focus on multicategory
diagnosis. {I}n order to equip the system with the optimum combination
of classifier, gene selection and cross-validation methods, we performed
a systematic and comprehensive evaluation of several major algorithms
for multicategory classification, several gene selection methods,
multiple ensemble classifier methods, and two cross validation designs
using 11 datasets spanning 74 diagnostic categories and 41 cancer
types and 12 normal tissue types.{R}esults: {M}ulticategory {S}upport
{V}ector {M}achines ({MC}-{SVM}s) are the most effective classifiers
in performing accurate cancer diagnosis from gene expression data.
{T}he {MC}-{SVM} techniques by {C}rammer and {S}inger, {W}eston and
{W}atkins, and one-versus-rest were found to be the best methods
in this domain. {MC}-{SVM}s outperform other popular machine learning
algorithms such as {K}-{N}earest {N}eighbors, {B}ackpropagation and
{P}robabilistic {N}eural {N}etworks, often to a remarkable degree.
{G}ene selection techniques can significantly improve classification
performance of both {MC}-{SVM}s and other non-{SVM} learning algorithms.
{E}nsemble classifiers do not generally improve performance of the
best non-ensemble models. {T}hese results guided the construction
of a software system {GEMS} ({G}ene {E}xpression {M}odel {S}elector)
that automates high-quality model construction and enforces sound
optimization and performance estimation procedures. {T}his is the
first such system to be informed by a rigorous comparative analysis
of the available algorithms and datasets.{A}vailability: {T}he software
system {GEMS} is available for download from http://www.gems-system.org
for non-commercial use.},
pdf = {../local/Statnikov2005comprehensive.pdf},
file = {Statnikov2005comprehensive.pdf:local/Statnikov2005comprehensive.pdf:PDF},
keywords = {biosvm microarray},
owner = {jeanphilippevert},
url = {http://bioinformatics.oupjournals.org/cgi/content/abstract/bti033v1}
}
@article{Stein2010Case,
author = {Lincoln D Stein},
title = {The case for cloud computing in genome informatics.},
journal = {Genome Biol},
year = {2010},
volume = {11},
pages = {207},
number = {5},
doi = {10.1186/gb-2010-11-5-207},
language = {eng},
medline-pst = {ppublish},
owner = {philippe},
pii = {gb-2010-11-5-207},
pmid = {20441614},
timestamp = {2010.07.28},
url = {http://dx.doi.org/10.1186/gb-2010-11-5-207}
}
@article{Steiner2004Discriminating,
author = {Guido Steiner and Laura Suter and Franziska Boess and Rodolfo Gasser
and Maria Cristina de Vera and Silvio Albertini and Stefan Ruepp},
title = {Discriminating different classes of toxicants by transcript profiling.},
journal = {Environ. {H}ealth {P}erspect.},
year = {2004},
volume = {112},
pages = {1236-48},
number = {12},
month = {Aug},
abstract = {Male rats were treated with various model compounds or the appropriate
vehicle controls. {M}ost substances were either well-known hepatotoxicants
or showed hepatotoxicity during preclinical testing. {T}he aim of
the present study was to determine if biological samples from rats
treated with various compounds can be classified based on gene expression
profiles. {I}n addition to gene expression analysis using microarrays,
a complete serum chemistry profile and liver and kidney histopathology
were performed. {W}e analyzed hepatic gene expression profiles using
a supervised learning method (support vector machines; {SVM}s) to
generate classification rules and combined this with recursive feature
elimination to improve classification performance and to identify
a compact subset of probe sets with potential use as biomarkers.
{T}wo different {SVM} algorithms were tested, and the models obtained
were validated with a compound-based external cross-validation approach.
{O}ur predictive models were able to discriminate between hepatotoxic
and nonhepatotoxic compounds. {F}urthermore, they predicted the correct
class of hepatotoxicant in most cases. {W}e provide an example showing
that a predictive model built on transcript profiles from one rat
strain can successfully classify profiles from another rat strain.
{I}n addition, we demonstrate that the predictive models identify
nonresponders and are able to discriminate between gene changes related
to pharmacology and toxicity. {T}his work confirms the hypothesis
that compound classification based on gene expression data is feasible.},
pdf = {../local/Steiner2004Discriminating.pdf},
file = {Steiner2004Discriminating.pdf:local/Steiner2004Discriminating.pdf:PDF},
keywords = {biosvm},
url = {http://ehp.niehs.nih.gov/txg/docs/2004/7036/abstract.html}
}
@article{Steinwart2005Consistency,
author = {Steinwart, I.},
title = {Consistency of support vector machines and other regularized kernel
classifiers},
journal = {I{EEE} {T}rans. {I}nform. {T}heory},
year = {2005},
volume = {51},
pages = {128-142},
number = {1},
doi = {10.1109/TIT.2004.839514},
pdf = {../local/Steinwart2005Consistency.pdf},
file = {Steinwart2005Consistency.pdf:local/Steinwart2005Consistency.pdf:PDF},
owner = {jeanphilippevert},
url = {http://dx.doi.org/10.1109/TIT.2004.839514}
}
@article{Steinwart2003Sparseness,
author = {Steinwart, I.},
title = {Sparseness of {S}upport {V}ector {M}achines},
journal = {J. {M}ach. {L}earn. {R}es.},
year = {2003},
volume = {4},
pages = {1071-1105},
abstract = {Support vector machines ({SVM}s) construct decision functions that
are linear combinations of kernel evaluations on the training set.
{T}he samples with non-vanishing coefficients are called support
vectors. {I}n this work we establish lower (asymptotical) bounds
on the number of support vectors. {O}n our way we prove several results
which are of great importance for the understanding of {SVM}s. {I}n
particular, we describe to which "limit" {SVM} decision functions
tend, discuss the corresponding notion of convergence and provide
some results on the stability of {SVM}s using subdifferential calculus
in the associated reproducing kernel {H}ilbert space.},
pdf = {../local/Steinwart2003Sparseness.pdf},
file = {Steinwart2003Sparseness.pdf:local/Steinwart2003Sparseness.pdf:PDF}
}
@article{Steinwart2002Support,
author = {Steinwart, I.},
title = {Support {V}ector {M}achines are {U}niversally {C}onsistent},
journal = {J. {C}omplexity},
year = {2002},
volume = {18},
pages = {768--791},
abstract = {We show that support vector machines of the 1-norm soft margin type
are universally consistent provided that the regularization parameter
is chosen in a distinct manner and the kernel belongs to a specific
class?the so-called universal kernels?which has recently been considered
by the author. {I}n particular it is shown that the 1-norm soft margin
classifier with {G}aussian {RBF} kernel on a compact subset {X} of
d and regularization parameter cn=n??1 is universally consistent,
if n is the training set size and 0http://dx.doi.org/10.1006/jcom.2002.0642}
}
@article{Steinwart2001On,
author = {Steinwart, I.},
title = {On the influence of the kernel on the consistency of support vector
machines},
journal = {J. {M}ach. {L}earn. {R}es.},
year = {2001},
volume = {2},
pages = {67-93},
abstract = {In this article we study the generalization abilities of several classifiers
of support vector machine ({SVM}) type using a certain class of kernels
that we call universal. {I}t is shown that the soft margin algorithms
with universal kernels are consistent for a large class of classification
problems including some kind of noisy tasks provided that the regularization
parameter is chosen well. {I}n particular we derive a simple sufficient
condition for this parameter in the case of {G}aussian {RBF} kernels.
{O}n the one hand our considerations are based on an investigation
of an approximation property---the so-called universality---of the
used kernels that ensures that all continuous functions can be approximated
by certain kernel expressions. {T}his approximation property also
gives a new insight into the role of kernels in these and other algorithms.
{O}n the other hand the results are achieved by a precise study of
the underlying optimization problems of the classifiers. {F}urthermore,
we show consistency for the maximal margin classifier as well as
for the soft margin {SVM}'s in the presence of large margins. {I}n
this case it turns out that also constant regularization parameters
ensure consistency for the soft margin {SVM}'s. {F}inally we prove
that even for simple, noise free classification problems {SVM}'s
with polynomial kernels can behave arbitrarily badly.},
pdf = {../local/Steinwart2001On.pdf},
file = {Steinwart2001On.pdf:local/Steinwart2001On.pdf:PDF},
owner = {jeanphilippevert},
url = {http://jmlr.csail.mit.edu/papers/v2/steinwart01a.html}
}
@incollection{Steinwart2005Density,
author = {Steinwart, I. and Hush, D. and Scovel, C.},
title = {Density {L}evel {D}etection is {C}lassification},
booktitle = {Advances in {N}eural {I}nformation {P}rocessing {S}ystems 17},
publisher = {MIT Press},
year = {2005},
editor = {Lawrence K. Saul and Yair Weiss and {L\'{e}on} Bottou},
address = {Cambridge, MA},
pdf = {../local/Steinwart2005Density.pdf},
file = {Steinwart2005Density.pdf:local/Steinwart2005Density.pdf:PDF}
}
@article{Steinwart2005classification,
author = {Steinwart, I. and Hush, D. and Scovel, C.},
title = {A classification framework for anomaly detection},
journal = {J. Mach. Learn. Res.},
year = {2005},
volume = {6},
pages = {211-232},
pdf = {../local/Steinwart2005classification.pdf},
file = {Steinwart2005classification.pdf:Steinwart2005classification.pdf:PDF},
owner = {jp},
timestamp = {2009.01.26},
url = {http://jmlr.csail.mit.edu/papers/volume6/steinwart05a/steinwart05a.pdf}
}
@techreport{Steinwart2005explicit,
author = {Steinwart, I. and Hush, D. and Scovel, C.},
title = {An explicit description of the reproducing kernel {H}ilbert spaces
of {G}aussian {RBF} kernels},
institution = {Los Alamos National Laboratory},
year = {2004},
number = {LA-UR 04-8274}
}
@techreport{Steinwart2004Fast,
author = {I. Steinwart and C. Scovel},
title = {Fast {R}ates for {S}upport {V}ector {M}achines using {G}aussian {K}ernels},
institution = {Los Alamos National Laboratory},
year = {2004},
number = {LA-UR 04-8796}
}
@article{Stelling2002Metabolic,
author = {Stelling, J. and Klamt, S. and Bettenbrock, K. and Schuster, S. and
Gilles, E. D.},
title = {Metabolic network structure determines key aspects of functionality
and regulation.},
journal = {Nature},
year = {2002},
volume = {420},
pages = {190--193},
number = {6912},
month = {Nov},
abstract = {The relationship between structure, function and regulation in complex
cellular networks is a still largely open question. Systems biology
aims to explain this relationship by combining experimental and theoretical
approaches. Current theories have various strengths and shortcomings
in providing an integrated, predictive description of cellular networks.
Specifically, dynamic mathematical modelling of large-scale networks
meets difficulties because the necessary mechanistic detail and kinetic
parameters are rarely available. In contrast, structure-oriented
analyses only require network topology, which is well known in many
cases. Previous approaches of this type focus on network robustness
or metabolic phenotype, but do not give predictions on cellular regulation.
Here, we devise a theoretical method for simultaneously predicting
key aspects of network functionality, robustness and gene regulation
from network structure alone. This is achieved by determining and
analysing the non-decomposable pathways able to operate coherently
at steady state (elementary flux modes). We use the example of Escherichia
coli central metabolism to illustrate the method.},
doi = {10.1038/nature01166},
pdf = {../local/Stelling2002Metabolic.pdf},
file = {Stelling2002Metabolic.pdf:Stelling2002Metabolic.pdf:PDF},
institution = {Max Planck Institute for Dynamics of Complex Technical Systems, D-39106
Magdeburg, Germany. stelling@mpi-magdeburg.mpg.de},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {nature01166},
pmid = {12432396},
timestamp = {2013.01.25},
url = {http://dx.doi.org/10.1038/nature01166}
}
@article{Stelzl2005human,
author = {Ulrich Stelzl and Uwe Worm and Maciej Lalowski and Christian Haenig
and Felix H Brembeck and Heike Goehler and Martin Stroedicke and
Martina Zenkner and Anke Schoenherr and Susanne Koeppen and Jan Timm
and Sascha Mintzlaff and Claudia Abraham and Nicole Bock and Silvia
Kietzmann and Astrid Goedde and Engin Toksöz and Anja Droege and
Sylvia Krobitsch and Bernhard Korn and Walter Birchmeier and Hans
Lehrach and Erich E Wanker},
title = {A human protein-protein interaction network: a resource for annotating
the proteome.},
journal = {Cell},
year = {2005},
volume = {122},
pages = {957--968},
number = {6},
month = {Sep},
abstract = {Protein-protein interaction maps provide a valuable framework for
a better understanding of the functional organization of the proteome.
To detect interacting pairs of human proteins systematically, a protein
matrix of 4456 baits and 5632 preys was screened by automated yeast
two-hybrid (Y2H) interaction mating. We identified 3186 mostly novel
interactions among 1705 proteins, resulting in a large, highly connected
network. Independent pull-down and co-immunoprecipitation assays
validated the overall quality of the Y2H interactions. Using topological
and GO criteria, a scoring system was developed to define 911 high-confidence
interactions among 401 proteins. Furthermore, the network was searched
for interactions linking uncharacterized gene products and human
disease proteins to regulatory cellular pathways. Two novel Axin-1
interactions were validated experimentally, characterizing ANP32A
and CRMP1 as modulators of Wnt signaling. Systematic human protein
interaction screens can lead to a more comprehensive understanding
of protein function and cellular processes.},
doi = {10.1016/j.cell.2005.08.029},
institution = {Max Delbrueck Center for Molecular Medicine, 13092 Berlin-Buch, Germany.},
keywords = {Databases as Topic; Humans; Intracellular Signaling Peptides and Proteins;
Models, Molecular; Nerve Tissue Proteins; Protein Binding; Proteins;
Proteomics; Repressor Proteins; Two-Hybrid System Techniques},
owner = {phupe},
pii = {S0092-8674(05)00866-4},
pmid = {16169070},
timestamp = {2010.09.01},
url = {http://dx.doi.org/10.1016/j.cell.2005.08.029}
}
@phdthesis{Stempfel2009Robustesse,
author = {Stempfel, G.},
title = {Robustesse des s\'eparateurs lin\'eaires au bruit de classification},
school = {Universit`e de Provence},
year = {2009},
owner = {mordelet},
timestamp = {2010.12.08}
}
@incollection{Stempfel2009Learning,
author = {Stempfel, G. and Ralaivola, L.},
title = {Learning SVMs from Sloppily Labeled Data},
booktitle = {Artificial Neural Networks – ICANN 2009},
publisher = {Springer Berlin / Heidelberg},
year = {2009},
editor = {Alippi, Cesare and Polycarpou, Marios and Panayiotou, Christos and
Ellinas, Georgios},
volume = {5768},
series = {Lecture Notes in Computer Science},
pages = {884-893},
affiliation = {Aix-Marseille Université Laboratoire d’Informatique Fondamentale de
Marseille},
owner = {mordelet},
timestamp = {2010.12.08},
url = {http://dx.doi.org/10.1007/978-3-642-04274-4_91}
}
@article{Stephens2011Massive,
author = {Philip J Stephens and Chris D Greenman and Beiyuan Fu and Fengtang
Yang and Graham R Bignell and Laura J Mudie and Erin D Pleasance
and King Wai Lau and David Beare and Lucy A Stebbings and Stuart
McLaren and Meng-Lay Lin and David J McBride and Ignacio Varela and
Serena Nik-Zainal and Catherine Leroy and Mingming Jia and Andrew
Menzies and Adam P Butler and Jon W Teague and Michael A Quail and
John Burton and Harold Swerdlow and Nigel P Carter and Laura A Morsberger
and Christine Iacobuzio-Donahue and George A Follows and Anthony
R Green and Adrienne M Flanagan and Michael R Stratton and P. Andrew
Futreal and Peter J Campbell},
title = {Massive genomic rearrangement acquired in a single catastrophic event
during cancer development.},
journal = {Cell},
year = {2011},
volume = {144},
pages = {27--40},
number = {1},
month = {Jan},
abstract = {Cancer is driven by somatically acquired point mutations and chromosomal
rearrangements, conventionally thought to accumulate gradually over
time. Using next-generation sequencing, we characterize a phenomenon,
which we term chromothripsis, whereby tens to hundreds of genomic
rearrangements occur in a one-off cellular crisis. Rearrangements
involving one or a few chromosomes crisscross back and forth across
involved regions, generating frequent oscillations between two copy
number states. These genomic hallmarks are highly improbable if rearrangements
accumulate over time and instead imply that nearly all occur during
a single cellular catastrophe. The stamp of chromothripsis can be
seen in at least 2\%-3\% of all cancers, across many subtypes, and
is present in ∼25\% of bone cancers. We find that one, or indeed
more than one, cancer-causing lesion can emerge out of the genomic
crisis. This phenomenon has important implications for the origins
of genomic remodeling and temporal emergence of cancer.},
doi = {10.1016/j.cell.2010.11.055},
institution = {Wellcome Trust Sanger Institute, Hinxton, Cambridge, UK.},
keywords = {Bone Neoplasms, genetics; Cell Line, Tumor; Chromosome Aberrations;
Chromosome Painting; Female; Gene Rearrangement; Humans; Leukemia,
Lymphocytic, Chronic, B-Cell, genetics; Middle Aged; Neoplasms, genetics/pathology},
language = {eng},
medline-pst = {ppublish},
owner = {phupe},
pii = {S0092-8674(10)01377-2},
pmid = {21215367},
timestamp = {2011.06.01},
url = {http://dx.doi.org/10.1016/j.cell.2010.11.055}
}
@article{Stoddart2010Nucleobase,
author = {David Stoddart and Andrew J Heron and Jochen Klingelhoefer and Ellina
Mikhailova and Giovanni Maglia and Hagan Bayley},
title = {Nucleobase recognition in ssDNA at the central constriction of the
alpha-hemolysin pore.},
journal = {Nano Lett},
year = {2010},
volume = {10},
pages = {3633--3637},
number = {9},
month = {Sep},
abstract = {Nanopores are under investigation for single-molecule DNA sequencing.
The alpha-hemolysin (alphaHL) protein nanopore contains three recognition
points capable of nucleobase discrimination in individual immobilized
ssDNA molecules. We have modified the recognition point R(1) by extensive
mutagenesis of residue 113. Amino acids that provide an energy barrier
to ion flow (e.g., bulky or hydrophobic residues) strengthen base
identification, while amino acids that lower the barrier weaken it.
Amino acids with related side chains produce similar patterns of
nucleobase recognition providing a rationale for the redesign of
recognition points.},
doi = {10.1021/nl101955a},
institution = {Department of Chemistry, University of Oxford, Oxford OX1 3TA, United
Kingdom.},
keywords = {Amino Acid Substitution; Base Sequence; DNA, Single-Stranded, chemistry;
Hemolysin Proteins, chemistry; Models, Molecular; Mutagenesis},
language = {eng},
medline-pst = {ppublish},
owner = {phupe},
pmid = {20704324},
timestamp = {2011.06.01},
url = {http://dx.doi.org/10.1021/nl101955a}
}
@article{Stone1977Consistent,
author = {Stone, C.J.},
title = {Consistent nonparametric regression},
journal = {Ann. {S}tat.},
year = {1977},
volume = {8},
pages = {1348-1360},
pdf = {../local/Stone1977Consistent.pdf},
file = {Stone1977Consistent.pdf:local/Stone1977Consistent.pdf:PDF},
owner = {jeanphilippevert},
url = {http://links.jstor.org/sici?sici=0090-5364%28197707%295%3A4%3C595%3ACNR%3E2.0.CO%3B2-O}
}
@article{Strahl2000language,
author = {Strahl, B. D. and Allis, C. D.},
title = {The language of covalent histone modifications},
journal = {Nature},
year = {2000},
volume = {403},
pages = {41--45},
number = {6765},
month = {Jan},
abstract = {Histone proteins and the nucleosomes they form with DNA are the fundamental
building blocks of eukaryotic chromatin. A diverse array of post-translational
modifications that often occur on tail domains of these proteins
has been well documented. Although the function of these highly conserved
modifications has remained elusive, converging biochemical and genetic
evidence suggests functions in several chromatin-based processes.
We propose that distinct histone modifications, on one or more tails,
act sequentially or in combination to form a 'histone code' that
is, read by other proteins to bring about distinct downstream events.},
doi = {10.1038/47412},
institution = {Department of Biochemistry and Molecular Genetics, University of
Virginia Health Science Center, Charlottesville 22908, USA.},
keywords = {Acetylation; Amino Acid Sequence; Animals; Chromatin, physiology;
Histones, chemistry/metabolism/physiology; Humans; Lysine, physiology;
Microtubules, physiology; Models, Biological; Molecular Sequence
Data; Phosphorylation; Protein Processing, Post-Translational; Serine,
metabolism},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pmid = {10638745},
timestamp = {2010.11.23},
url = {http://dx.doi.org/10.1038/47412}
}
@article{Strahl2001Methylation,
author = {B. D. Strahl and S. D. Briggs and C. J. Brame and J. A. Caldwell
and S. S. Koh and H. Ma and R. G. Cook and J. Shabanowitz and D.
F. Hunt and M. R. Stallcup and C. D. Allis},
title = {Methylation of histone H4 at arginine 3 occurs in vivo and is mediated
by the nuclear receptor coactivator PRMT1.},
journal = {Curr Biol},
year = {2001},
volume = {11},
pages = {996--1000},
number = {12},
month = {Jun},
abstract = {Posttranslational modifications of histone amino termini play an important
role in modulating chromatin structure and function. Lysine methylation
of histones has been well documented, and recently this modification
has been linked to cellular processes involving gene transcription
and heterochromatin assembly. However, the existence of arginine
methylation on histones has remained unclear. Recent discoveries
of protein arginine methyltransferases, CARM1 and PRMT1, as transcriptional
coactivators for nuclear receptors suggest that histones may be physiological
targets of these enzymes as part of a poorly defined transcriptional
activation pathway. Here we show by using mass spectrometry that
histone H4, isolated from asynchronously growing human 293T cells,
is methylated at arginine 3 (Arg-3) in vivo. In support, a novel
antibody directed against histone H4 methylated at Arg-3 independently
demonstrates the in vivo occurrence of this modification and reveals
that H4 Arg-3 methylation is highly conserved throughout eukaryotes.
Finally, we show that PRMT1 is the major, if not exclusive, H4 Arg-3
methyltransfase in human 293T cells. These findings suggest a role
for arginine methylation of histones in the transcription process.},
institution = {Department of Biochemistry and Molecular Genetics, University of
Virginia Health Science Center, Charlottesville, VA 22908, USA.},
keywords = {Amino Acid Motifs; Animals; Arginine, metabolism; Cell Line; Genes,
Reporter; Histones, metabolism; Humans; Immunoblotting; Methylation;
Protein-Arginine N-Methyltransferases, metabolism; Recombinant Fusion
Proteins, genetics/metabolism},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {S0960-9822(01)00294-9},
pmid = {11448779},
timestamp = {2010.11.23}
}
@article{Strahl2002Set2,
author = {Brian D Strahl and Patrick A Grant and Scott D Briggs and Zu-Wen
Sun and James R Bone and Jennifer A Caldwell and Sahana Mollah and
Richard G Cook and Jeffrey Shabanowitz and Donald F Hunt and C. David
Allis},
title = {Set2 is a nucleosomal histone H3-selective methyltransferase that
mediates transcriptional repression.},
journal = {Mol Cell Biol},
year = {2002},
volume = {22},
pages = {1298--1306},
number = {5},
month = {Mar},
abstract = {Recent studies of histone methylation have yielded fundamental new
insights pertaining to the role of this modification in gene activation
as well as in gene silencing. While a number of methylation sites
are known to occur on histones, only limited information exists regarding
the relevant enzymes that mediate these methylation events. We thus
sought to identify native histone methyltransferase (HMT) activities
from Saccharomyces cerevisiae. Here, we describe the biochemical
purification and characterization of Set2, a novel HMT that is site-specific
for lysine 36 (Lys36) of the H3 tail. Using an antiserum directed
against Lys36 methylation in H3, we show that Set2, via its SET domain,
is responsible for methylation at this site in vivo. Tethering of
Set2 to a heterologous promoter reveals that Set2 represses transcription,
and part of this repression is mediated through the HMT activity
of the SET domain. These results suggest that Set2 and methylation
at H3 Lys36 play a role in the repression of gene transcription.},
institution = {Department of Biochemistry and Molecular Genetics,University of Virginia
Health System, University of Virginia, Charlottesville, Virginia
22908, USA.},
keywords = {Amino Acid Sequence; Gene Expression Regulation, Fungal; Histones,
metabolism; Methyltransferases, metabolism; Molecular Sequence Data;
Nucleosomes, enzymology; Saccharomyces cerevisiae Proteins, metabolism;
Saccharomyces cerevisiae, enzymology/genetics; Substrate Specificity;
Transcription, Genetic; Transcriptional Activation},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pmid = {11839797},
timestamp = {2010.11.23}
}
@article{Strahl1999Methylation,
author = {B. D. Strahl and R. Ohba and R. G. Cook and C. D. Allis},
title = {Methylation of histone H3 at lysine 4 is highly conserved and correlates
with transcriptionally active nuclei in Tetrahymena.},
journal = {Proc Natl Acad Sci U S A},
year = {1999},
volume = {96},
pages = {14967--14972},
number = {26},
month = {Dec},
abstract = {Studies into posttranslational modifications of histones, notably
acetylation, have yielded important insights into the dynamic nature
of chromatin structure and its fundamental role in gene expression.
The roles of other covalent histone modifications remain poorly understood.
To gain further insight into histone methylation, we investigated
its occurrence and pattern of site utilization in Tetrahymena, yeast,
and human HeLa cells. In Tetrahymena, transcriptionally active macronuclei,
but not transcriptionally inert micronuclei, contain a robust histone
methyltransferase activity that is highly selective for H3. Microsequence
analyses of H3 from Tetrahymena, yeast, and HeLa cells indicate that
lysine 4 is a highly conserved site of methylation, which to date,
is the major site detected in Tetrahymena and yeast. These data document
a nonrandom pattern of H3 methylation that does not overlap with
known acetylation sites in this histone. In as much as H3 methylation
at lysine 4 appears to be specific to macronuclei in Tetrahymena,
we suggest that this modification pattern plays a facilitatory role
in the transcription process in a manner that remains to be determined.
Consistent with this possibility, H3 methylation in yeast occurs
preferentially in a subpopulation of H3 that is preferentially acetylated.},
institution = {Department of Biochemistry, University of Virginia Health Science
Center, Charlottesville, VA 22908, USA.},
keywords = {Acetyltransferases, metabolism; Amino Acid Sequence; Animals; Cell
Nucleus, metabolism; Hela Cells; Histone Acetyltransferases; Histone-Lysine
N-Methyltransferase; Histones, metabolism; Humans; Lysine, analogs
/&/ derivatives/metabolism; Methylation; Methyltransferases, metabolism;
Molecular Sequence Data; Protein Methyltransferases; Protein Processing,
Post-Translational; Saccharomyces cerevisiae Proteins; Species Specificity;
Tetrahymena thermophila; Transcription, Genetic; Yeasts},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pmid = {10611321},
timestamp = {2010.11.23}
}
@article{Stransky2006Regional,
author = {Stransky, N. and Vallot, C. and Reyal, F. and Bernard-Pierrot, I.
and Diez de Medina, S. G. and Segraves, R. and de Rycke, Y. and Elvin,
P. and Cassidy, A. and Spraggon, C. and Graham, A. and Southgate,
J. and Asselain, B. and Allory, Y. and Abbou, C. C. and Albertson,
D. G. and Thiery, J.-P. and Chopin, D. K. and Pinkel, D. and Radvanyi,
F.},
title = {Regional copy number-independent deregulation of transcription in
cancer},
journal = {Nat. Genet.},
year = {2006},
volume = {38},
pages = {1386--1396},
number = {12},
month = {Dec},
abstract = {Genetic and epigenetic alterations have been identified that lead
to transcriptional deregulation in cancers. Genetic mechanisms may
affect single genes or regions containing several neighboring genes,
as has been shown for DNA copy number changes. It was recently reported
that epigenetic suppression of gene expression can also extend to
a whole region; this is known as long-range epigenetic silencing.
Various techniques are available for identifying regional genetic
alterations, but no large-scale analysis has yet been carried out
to obtain an overview of regional epigenetic alterations. We carried
out an exhaustive search for regions susceptible to such mechanisms
using a combination of transcriptome correlation map analysis and
array CGH data for a series of bladder carcinomas. We validated one
candidate region experimentally, demonstrating histone methylation
leading to the loss of expression of neighboring genes without DNA
methylation.},
doi = {10.1038/ng1923},
pdf = {../local/Stransky2006Regional.pdf},
file = {Stransky2006Regional.pdf:Stransky2006Regional.pdf:PDF},
institution = {UMR 144 Centre National de la Recherche Scientifique (CNRS)/Institut
Curie, 75248 Paris Cedex 05, France.},
keywords = {csbcbook},
owner = {jp},
pii = {ng1923},
pmid = {17099711},
timestamp = {2009.10.08},
url = {http://dx.doi.org/10.1038/ng1923}
}
@article{Stratton2008Emerging,
author = {Stratton, M.R. and Rahman, N.},
title = {{{T}he emerging landscape of breast cancer susceptibility}},
journal = {Nat. Genet.},
year = {2008},
volume = {40},
pages = {17--22},
keywords = {csbcbook}
}
@article{Strauss2004Objective,
author = {Daniel J Strauss and Wolfgang Delb and Peter K Plinkert},
title = {Objective detection of the central auditory processing disorder:
a new machine learning approach.},
journal = {I{EEE} {T}rans {B}iomed {E}ng},
year = {2004},
volume = {51},
pages = {1147-55},
number = {7},
month = {Jul},
abstract = {The objective detection of binaural interaction is of diagnostic interest
for the evaluation of the central auditory processing disorder ({CAPD}).
{T}he beta-wave of the binaural interaction component in auditory
brainstem responses has been suggested as an objective measure of
binaural interaction and has been shown to be of diagnostic value
in the {CAPD} diagnosis. {H}owever, a reliable and automated detection
of the beta-wave capable of clinical use still remains a challenge.
{W}e propose a new machine learning approach to the detection of
the {CAPD} that is based on adapted tight frame decompositions which
are tailored for support vector machines with radial kernels. {U}sing
shift-invariant scale and morphological features of the binaurally
evoked brainstem potentials, our approach provides at least comparable
results to the beta-wave detection in view of the discrimination
of subjects being at risk for {CAPD} and subjects being not at risk
for {CAPD}. {F}urthermore, as no information from the monaurally
evoked potentials is necessary, the measurement cost is reduced by
two-thirds compared to the computation of the binaural interaction
component. {W}e conclude that a machine learning approach in the
form of a hybrid tight frame-support vector classification is effective
in the objective detection of the {CAPD}.}
}
@article{Strobl2007Bias,
author = {Strobl, C. and Boulesteix, A.L. and Zeileis, A. and Hothorn, T.},
title = {Bias in random forest variable importance measures: Illustrations,
sources and a solution},
journal = {BMC bioinformatics},
year = {2007},
volume = {8},
pages = {25},
number = {1},
publisher = {BioMed Central Ltd}
}
@article{Strogatz2001Exploring,
author = {Strogatz, S. S.},
title = {Exploring complex networks},
journal = {Nature},
year = {2001},
volume = {410},
pages = {268--276},
pdf = {../local/stro01.pdf},
file = {stro01.pdf:local/stro01.pdf:PDF},
subject = {compnet},
url = {http://www.nature.com/cgi-taf/DynaPage.taf?file=/nature/journal/v410/n6825/full/410268a0_fs.html&content_filetype=pdf}
}
@article{Stucki2005JTB,
author = {Stucki, J. W. and Simon, H.-U.},
title = {Mathematical modeling of the regulation of caspase-3 activation and
degradation},
journal = {Journal of Theoretical Biology},
year = {2005},
volume = {234},
pages = {123--131},
number = {1},
abstract = {Caspases are thought to be important players in the execution process
of apoptosis. Inhibitors of apoptosis (IAPs) are able to block caspases
and therefore apoptosis. The fact that a subgroup of the IAP family
inhibits active caspases implies that not each caspase activation
necessarily leads to apoptosis. In such a scenario, however, processed
and enzymically active caspases should somehow be removed. Indeed,
IAP-caspase complexes covalently bind ubiquitin, resulting in degradation
by the 26S proteasome. Following release from mitochondria, IAP antagonists
(e.g. second mitochondrial activator of caspases (Smac)) inactivate
IAPs. Moreover, although pro-apoptotic factors such as irradiation
or anti-cancer drugs may release Smac from mitochondria in tumor
cells, high cytoplasmic survivin and ML-IAP levels might be able
to neutralize it and, consequently, IAPs would further be able to
bind activated caspases. Here, we propose a simple mathematical model,
describing the molecular interactions between Smac deactivators,
Smac, IAPs, and caspase-3, including the requirements for both induction
and prevention of apoptosis, respectively. In addition, we predict
a novel mechanism of caspase-3 degradation that might be particularly
relevant in long-living cells.},
doi = {DOI: 10.1016/j.jtbi.2004.11.011},
issn = {0022-5193},
keywords = {csbcbook},
url = {http://www.sciencedirect.com/science/article/B6WMD-4F9N72G-1/2/1dbb63d611f86dc936c8d6cb218685f0}
}
@article{Sturn2002Genesis:,
author = {Alexander Sturn and John Quackenbush and Zlatko Trajanoski},
title = {Genesis: cluster analysis of microarray data.},
journal = {Bioinformatics},
year = {2002},
volume = {18},
pages = {207-8},
number = {1},
month = {Jan},
abstract = {A versatile, platform independent and easy to use {J}ava suite for
large-scale gene expression analysis was developed. {G}enesis integrates
various tools for microarray data analysis such as filters, normalization
and visualization tools, distance measures as well as common clustering
algorithms including hierarchical clustering, self-organizing maps,
k-means, principal component analysis, and support vector machines.
{T}he results of the clustering are transparent across all implemented
methods and enable the analysis of the outcome of different algorithms
and parameters. {A}dditionally, mapping of gene expression data onto
chromosomal sequences was implemented to enhance promoter analysis
and investigation of transcriptional control mechanisms.},
keywords = {Algorithms, Artificial Intelligence, Cluster Analysis, Comparative
Study, Computational Biology, Databases, Gene Expression Profiling,
Genetic, Models, Molecular Structure, Neural Networks (Computer),
Non-U.S. Gov't, Oligonucleotide Array Sequence Analysis, Principal
Component Analysis, Programming Languages, Promoter Regions (Genetics),
Protein, Proteins, Research Support, Software, Statistical, Transcription,
11836235}
}
@article{Su2002Large-scale,
author = {Su, A.I. and Cooke, M.P. and Ching, K.A. and Hakak, Y. and Walker,
J.R. and Wiltshire, T. and Orth, A.P. and Vega, R.Q. and Sapinoso,
L.M. and Moqrich, A. and Patapoutian, A. and Hampton, G.M. and Schultz,
P.G. and Hogenesch, J.B.},
title = {Large-scale analysis of the human and mouse transcriptomes.},
journal = {Proc. Natl. Acad. Sci. U. S. A.},
year = {2002},
volume = {99},
pages = {4465--4470},
number = {7},
month = {Apr},
abstract = {High-throughput gene expression profiling has become an important
tool for investigating transcriptional activity in a variety of biological
samples. To date, the vast majority of these experiments have focused
on specific biological processes and perturbations. Here, we have
generated and analyzed gene expression from a set of samples spanning
a broad range of biological conditions. Specifically, we profiled
gene expression from 91 human and mouse samples across a diverse
array of tissues, organs, and cell lines. Because these samples predominantly
come from the normal physiological state in the human and mouse,
this dataset represents a preliminary, but substantial, description
of the normal mammalian transcriptome. We have used this dataset
to illustrate methods of mining these data, and to reveal insights
into molecular and physiological gene function, mechanisms of transcriptional
regulation, disease etiology, and comparative genomics. Finally,
to allow the scientific community to use this resource, we have built
a free and publicly accessible website (http://expression.gnf.org)
that integrates data visualization and curation of current gene annotations.},
doi = {10.1073/pnas.012025199},
institution = {Department of Chemistry, The Scripps Research Institute, La Jolla,
CA 92037, USA.},
keywords = {Animals; Collagen; Female; Gene Expression Profiling; Humans; Male;
Mice; Organ Specificity; Polymerase Chain Reaction; Receptors, Cell
Surface; Transcription, Genetic},
owner = {mordelet},
pii = {012025199},
pmid = {11904358},
timestamp = {2010.11.02},
url = {http://dx.doi.org/10.1073/pnas.012025199}
}
@article{Su2001Molecular,
author = {Su, A. I. and Welsh, J. B. and Sapinoso, L. M. and Kern, S. G. and
Dimitrov, P. and Lapp, H. and Schultz, P. G. and Powell, S. M. and
Moskaluk, C. A. and Frierson, H. F.Jr. and Hampton, G. M.},
title = {Molecular {C}lassification of {H}uman {C}arcinomas by {U}se of {G}ene
{E}xpression {S}ignatures},
journal = {Cancer {R}es.},
year = {2001},
volume = {61},
pages = {7388-7393},
number = {20},
abstract = {Classification of human tumors according to their primary anatomical
site of origin is fundamental for the optimal treatment of patients
with cancer. {H}ere we describe the use of large-scale {RNA} profiling
and supervised machine learning algorithms to construct a first-generation
molecular classification scheme for carcinomas of the prostate, breast,
lung, ovary, colorectum, kidney, liver, pancreas, bladder/ureter,
and gastroesophagus, which collectively account for [~]70% of all
cancer-related deaths in the {U}nited {S}tates. {T}he classification
scheme was based on identifying gene subsets whose expression typifies
each cancer class, and we quantified the extent to which these genes
are characteristic of a specific tumor type by accurately and confidently
predicting the anatomical site of tumor origin for 90% of 175 carcinomas,
including 9 of 12 metastatic lesions. {T}he predictor gene subsets
include those whose expression is typical of specific types of normal
epithelial differentiation, as well as other genes whose expression
is elevated in cancer. {T}his study demonstrates the feasibility
of predicting the tissue origin of a carcinoma in the context of
multiple cancer classes.},
pdf = {../local/Su2001Molecular.pdf.html},
file = {Su2001Molecular.pdf.html:local/Su2001Molecular.pdf.html:PDF},
keywords = {biosvm, breastcancer},
owner = {jeanphilippevert},
url = {http://cancerres.aacrjournals.org/cgi/content/abstract/61/20/7388}
}
@article{Su2003RankGene,
author = {Su, Yang and Murali, T.M. and Pavlovic, Vladimir and Schaffer, Michael
and Kasif, Simon},
title = {{{R}ank{G}ene}: identification of diagnostic genes based on expression
data},
journal = {Bioinformatics},
year = {2003},
volume = {19},
pages = {1578-1579},
number = {12},
abstract = {Summary: {R}ank{G}ene is a program for analyzing gene expression data
and computing diagnostic genes based on their predictive power in
distinguishing between different types of samples. {T}he program
integrates into one system a variety of popular ranking criteria,
ranging from the traditional t-statistic to one-dimensional support
vector machines. {T}his flexibility makes {R}ank{G}ene a useful tool
in gene expression analysis and feature selection. {A}vailability:
http://genomics10.bu.edu/yangsu/rankgene {C}ontact: murali@bu.edu},
pdf = {../local/Su2003RankGene.pdf},
file = {Su2003RankGene.pdf:local/Su2003RankGene.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://bioinformatics.oupjournals.org/cgi/content/abstract/19/12/1578}
}
@article{Subramanian2005Gene,
author = {Subramanian, A. and Tamayo, P. and Mootha, V. K. and Mukherjee, S.
and Ebert, B. L. and Gillette, M. A. and Paulovich, A. and Pomeroy,
S. L. and Golub, T. R. and Lander, E. S. and Mesirov, J. P.},
title = {Gene set enrichment analysis: a knowledge-based approach for interpreting
genome-wide expression profiles},
journal = {Proc. Natl. Acad. Sci. USA},
year = {2005},
volume = {102},
pages = {15545--15550},
number = {43},
month = {Oct},
abstract = {Although genomewide RNA expression analysis has become a routine tool
in biomedical research, extracting biological insight from such information
remains a major challenge. Here, we describe a powerful analytical
method called Gene Set Enrichment Analysis (GSEA) for interpreting
gene expression data. The method derives its power by focusing on
gene sets, that is, groups of genes that share common biological
function, chromosomal location, or regulation. We demonstrate how
GSEA yields insights into several cancer-related data sets, including
leukemia and lung cancer. Notably, where single-gene analysis finds
little similarity between two independent studies of patient survival
in lung cancer, GSEA reveals many biological pathways in common.
The GSEA method is embodied in a freely available software package,
together with an initial database of 1,325 biologically defined gene
sets.},
doi = {10.1073/pnas.0506580102},
pdf = {../local/Subramanian2005Gene.pdf},
file = {Subramanian2005Gene.pdf:Subramanian2005Gene.pdf:PDF},
institution = {Broad Institute of Massachusetts Institute of Technology and Harvard,
320 Charles Street, Cambridge, MA 02141, USA.},
owner = {jp},
pii = {0506580102},
pmid = {16199517},
timestamp = {2008.12.05},
url = {http://dx.doi.org/10.1073/pnas.0506580102}
}
@article{Sultan2002Binary,
author = {M. Sultan and D. A. Wigle and C. A. Cumbaa and M. Maziarz and J.
Glasgow and M. S. Tsao and I. Jurisica},
title = {Binary tree-structured vector quantization approach to clustering
and visualizing microarray data.},
journal = {Bioinformatics},
year = {2002},
volume = {18 Suppl 1},
pages = {S111-9},
abstract = {M{OTIVATION}: {W}ith the increasing number of gene expression databases,
the need for more powerful analysis and visualization tools is growing.
{M}any techniques have successfully been applied to unravel latent
similarities among genes and/or experiments. {M}ost of the current
systems for microarray data analysis use statistical methods, hierarchical
clustering, self-organizing maps, support vector machines, or k-means
clustering to organize genes or experiments into 'meaningful' groups.
{W}ithout prior explicit bias almost all of these clustering methods
applied to gene expression data not only produce different results,
but may also produce clusters with little or no biological relevance.
{O}f these methods, agglomerative hierarchical clustering has been
the most widely applied, although many limitations have been identified.
{RESULTS}: {S}tarting with a systematic comparison of the underlying
theories behind clustering approaches, we have devised a technique
that combines tree-structured vector quantization and partitive k-means
clustering ({BTSVQ}). {T}his hybrid technique has revealed clinically
relevant clusters in three large publicly available data sets. {I}n
contrast to existing systems, our approach is less sensitive to data
preprocessing and data normalization. {I}n addition, the clustering
results produced by the technique have strong similarities to those
of self-organizing maps ({SOM}s). {W}e discuss the advantages and
the mathematical reasoning behind our approach.}
}
@article{Sun2003Identifying,
author = {Sun, Y.F. and Fan, X.D. and Li, Y.D.},
title = {Identifying splicing sites in eukaryotic {RNA}: support vector machine
approach.},
journal = {Comput. {B}iol. {M}ed.},
year = {2003},
volume = {33},
pages = {17-29},
number = {1},
abstract = {We introduce a new method for splicing sites prediction based on the
theory of support vector machines ({SVM}). {T}he {SVM} represents
a new approach to supervised pattern classification and has been
successfully applied to a wide range of pattern recognition problems.
{I}n the process of splicing sites prediction, the statistical information
of {RNA} secondary structure in the vicinity of splice sites, e.g.
donor and acceptor sites, is introduced in order to compare recognition
ratio of true positive and true negative. {F}rom the results of comparison,
addition of structural information has brought no significant benefit
for the recognition of splice sites and had even lowered the rate
of recognition. {O}ur results suggest that, through three cross validation,
the {SVM} method can achieve a good performance for splice sites
identification.},
doi = {10.1016/S0010-4825(02)00057-4},
pdf = {../local/Sun2003Identifying.pdf},
file = {Sun2003Identifying.pdf:local/Sun2003Identifying.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://dx.doi.org/10.1016/S0010-4825(02)00057-4}
}
@article{Sun2004protein,
author = {Zhenghong Sun and Xiaoli Fu and Lu Zhang and Xiaoli Yang and Feizhou
Liu and Gengxi Hu},
title = {A protein chip system for parallel analysis of multi-tumor markers
and its application in cancer detection.},
journal = {Anticancer {R}es},
year = {2004},
volume = {24},
pages = {1159-65},
number = {2C},
abstract = {B{ACKGROUND}: {T}umor markers are routinely measured in clinical oncology.
{H}owever, their value in cancer detection has been controversial
largely because no single tumor marker is sensitive and specific
enough to meet strict diagnostic criteria. {O}ne strategy to overcome
the shortcomings of single tumor markers is to measure a combination
of tumor markers to increase sensitivity and look for distinct patterns
to increase specificity. {T}his study aimed to develop a system for
parallel detection of tumor markers as a tool for tumor detection
in both cancer patients and asymptomatic populations at high risk.
{MATERIALS} {AND} {METHODS}: {A} protein chip was fabricated with
twelve monoclonal antibodies against the following tumor markers
respectively: {CA}125, {CA}15-3, {CA}19-9, {CA}242, {CEA}, {AFP},
{PSA}, free-{PSA}, {HGH}, beta-{HCG}, {NSE} and ferritin. {T}umor
markers were captured after the protein chip was incubated with serum
samples. {A} secondary antibody conjugated with {HRP} was used to
detect the captured tumor markers using chemiluminescence technique.
{Q}uantification of the tumor markers was obtained after calibration
with standard curves. {RESULTS}: {T}he chip system showed an overall
sensitivity of 68.18\% after testing 1147 cancer patients, with high
sensitivities for liver, pancreas and ovarian tumors and low sensitivities
for gastrointestinal tumors, and a specificity of 97.1\% after testing
793 healthy individuals. {A}pplication of the chip system in physical
checkups of 15,867 individuals resulted in 16 cases that were subsequently
confirmed as having cancers. {A}nalysis of the detection results
with a {S}upport {V}ector {M}achine algorithm considerably increased
the specificity of the system as reflected in healthy individuals
and hepatitis/cirrhosis patients, but only modestly decreased the
sensitivity for cancer patients. {CONCLUSION}: {T}his protein chip
system is a potential tool for assisting cancer diagnosis and for
screening cancer in high-risk populations.},
keywords = {Antibodies, Artificial Intelligence, Biological, Calibration, Female,
Horseradish Peroxidase, Humans, Male, Monoclonal, Neoplasms, Protein
Array Analysis, Sensitivity and Specificity, Tumor Markers, 15154641}
}
@article{Surabhi2002RNA,
author = {Surabhi, R. M. and Gaynor, R. B.},
title = {{RNA} interference directed against viral and cellular targets inhibits
human immunodeficiency {V}irus {T}ype 1 replication.},
journal = {J. Virol.},
year = {2002},
volume = {76},
pages = {12963--12973},
number = {24},
month = {Dec},
abstract = {Human immunodeficiency virus type 1 (HIV-1) gene expression is regulated
by both cellular transcription factors and Tat. The ability of Tat
to stimulate transcriptional elongation is dependent on its binding
to TAR RNA in conjunction with cyclin T1 and CDK9. A variety of other
cellular factors that bind to the HIV-1 long terminal repeat, including
NF-kappaB, SP1, LBP, and LEF, are also important in the control of
HIV-1 gene expression. Although these factors have been demonstrated
to regulate HIV-1 gene expression by both genetic and biochemical
analysis, in most cases a direct in vivo demonstration of their role
on HIV-1 replication has not been established. Recently, the efficacy
of RNA interference in mammalian cells has been shown utilizing small
interfering RNAs (siRNAs) to result in the specific degradation of
host mRNAs and decreases the levels of their corresponding proteins.
In this study, we addressed whether siRNAs directed against either
HIV-1 tat or reverse transcriptase or the NF-kappaB p65 subunit could
specifically decrease the levels of these proteins and thus alter
HIV-1 replication. Our results demonstrate the specificity of siRNAs
for decreasing the expression of these viral and cellular proteins
and inhibiting HIV-1 replication. These studies suggest that RNA
interference is useful in exploring the biological role of cellular
and viral regulatory factors involved in the control of HIV-1 gene
expression.},
keywords = {sirna},
owner = {vert},
pmid = {12438622},
timestamp = {2006.03.28}
}
@article{Surgand2006chemogenomic,
author = {Jean-Sebastien Surgand and Jordi Rodrigo and Esther Kellenberger
and Didier Rognan},
title = {A chemogenomic analysis of the transmembrane binding cavity of human
G-protein-coupled receptors.},
journal = {Proteins},
year = {2006},
volume = {62},
pages = {509--538},
number = {2},
month = {Feb},
abstract = {The amino acid sequences of 369 human nonolfactory G-protein-coupled
receptors (GPCRs) have been aligned at the seven transmembrane domain
(TM) and used to extract the nature of 30 critical residues supposed--from
the X-ray structure of bovine rhodopsin bound to retinal--to line
the TM binding cavity of ground-state receptors. Interestingly, the
clustering of human GPCRs from these 30 residues mirrors the recently
described phylogenetic tree of full-sequence human GPCRs (Fredriksson
et al., Mol Pharmacol 2003;63:1256-1272) with few exceptions. A TM
cavity could be found for all investigated GPCRs with physicochemical
properties matching that of their cognate ligands. The current approach
allows a very fast comparison of most human GPCRs from the focused
perspective of the predicted TM cavity and permits to easily detect
key residues that drive ligand selectivity or promiscuity.},
doi = {10.1002/prot.20768},
keywords = {Amino Acid Sequence; Binding Sites; Genomics; Humans; Ligands; Models,
Molecular; Phylogeny; Receptors, G-Protein-Coupled},
owner = {laurent},
pmid = {16294340},
timestamp = {2008.03.27},
url = {http://dx.doi.org/10.1002/prot.20768}
}
@article{Sussenguth1963Graph,
author = {E. H. Sussenguth},
title = {A Graph-Theoretic Algorithm for Matching Chemical Structures},
journal = {J. Chem. Doc.},
year = {1963},
volume = {5},
pages = {36-43},
number = {1}
}
@article{Suter2008Two-hybrid,
author = {Bernhard Suter and Saranya Kittanakom and Igor Stagljar},
title = {Two-hybrid technologies in proteomics research.},
journal = {Curr Opin Biotechnol},
year = {2008},
volume = {19},
pages = {316--323},
number = {4},
month = {Aug},
abstract = {Given that protein-protein interactions (PPIs) regulate nearly every
living process; the exploration of global and pathway-specific protein
interaction networks is expected to have major implications in the
understanding of diseases and for drug discovery. Consequently, the
development and application of methodologies that address physical
associations among proteins is of major importance in today's proteomics
research. The most widely and successfully used methodology to assess
PPIs is the yeast two-hybrid system (YTH). Here we present an overview
on the current applications of YTH and variant technologies in yeast
and mammalian systems. Two-hybrid-based methods will not only continue
to have a dominant role in the assessment of protein interactomes
but will also become important in the development of novel compounds
that target protein interaction interfaces for therapeutic intervention.},
doi = {10.1016/j.copbio.2008.06.005},
institution = {Department of Biochemistry and Department of Molecular Genetics,
Terrence Donnelly Centre for Cellular and Biomolecular Research (DCCBR),
University of Toronto, 160 College Street, Toronto, ON M5S 3E1, Canada.},
keywords = {Animals; Drug Design; Mammals; Proteomics; Two-Hybrid System Techniques},
owner = {phupe},
pii = {S0958-1669(08)00075-X},
pmid = {18619540},
timestamp = {2010.08.31},
url = {http://dx.doi.org/10.1016/j.copbio.2008.06.005}
}
@article{Sutherland2009Transcription,
author = {Heidi Sutherland and Wendy A Bickmore},
title = {Transcription factories: gene expression in unions?},
journal = {Nat Rev Genet},
year = {2009},
volume = {10},
pages = {457--466},
number = {7},
month = {Jul},
abstract = {Transcription is a fundamental step in gene expression, yet it remains
poorly understood at a cellular level. Visualization of transcription
sites and active genes has led to the suggestion that transcription
occurs at discrete sites in the nucleus, termed transcription factories,
where multiple active RNA polymerases are concentrated and anchored
to a nuclear substructure. However, this concept is not universally
accepted. This Review discusses the experimental evidence in support
of the transcription factory model and the evidence that argues against
such a spatially structured view of transcription. The transcription
factory model has implications for the regulation of transcription
initiation and elongation, for the organization of genes in the genome,
for the co-regulation of genes and for genome instability.},
doi = {10.1038/nrg2592},
institution = {MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine,
Crewe Road, Edinburgh EH4 2XU, UK.},
keywords = {Animals; Cell Nucleus; DNA-Directed RNA Polymerases; Genome; Genomic
Instability; Humans; Models, Biological; Transcription, Genetic},
owner = {phupe},
pii = {nrg2592},
pmid = {19506577},
timestamp = {2010.08.27},
url = {http://dx.doi.org/10.1038/nrg2592}
}
@article{Sutherland2003Spline-fitting,
author = {Sutherland, J. J. and O'Brien, L. A. and Weaver, D. F.},
title = {Spline-fitting with a genetic algorithm: a method for developing
classification structure-activity relationships.},
journal = {J. Chem. Inf. Comput. Sci.},
year = {2003},
volume = {43},
pages = {1906--1915},
number = {6},
abstract = {Classification methods allow for the development of structure-activity
relationship models when the target property is categorical rather
than continuous. We describe a classification method which fits descriptor
splines to activities, with descriptors selected using a genetic
algorithm. This method, which we identify as SFGA, is compared to
the well-established techniques of recursive partitioning (RP) and
soft independent modeling by class analogy (SIMCA) using five series
of compounds: cyclooxygenase-2 (COX-2) inhibitors, benzodiazepine
receptor (BZR) ligands, estrogen receptor (ER) ligands, dihydrofolate
reductase (DHFR) inhibitors, and monoamine oxidase (MAO) inhibitors.
Only 1-D and 2-D descriptors were used. Approximately 40\% of compounds
in each series were assigned to a test set, "cherry-picked" from
the complete set such that they lie outside the training set as much
as possible. SFGA produced models that were more predictive for all
but the DHFR set, for which SIMCA was most predictive. RP gave the
least predictive models for all but the MAO set. A similar trend
was observed when using training and test sets to which compounds
were randomly assigned and when gradually eliminating compounds from
the (designed) training set. The stability of models was examined
for the random and reduced sets, where stability means that classification
statistics and the selected descriptors are similar for models derived
from different sets. Here, SIMCA produced the most stable models,
followed by SFGA and RP. We show that a consensus approach that combines
all three methods outperforms the single best model for all data
sets.},
doi = {10.1021/ci034143r},
pdf = {../local/Sutherland2003Spline-fitting.pdf},
file = {Sutherland2003Spline-fitting.pdf:Sutherland2003Spline-fitting.pdf:PDF},
institution = {Departments of Chemistry and Pathology, Queen's University, Kingston,
Ontario, Canada K7L 3N6.},
keywords = {chemoinformatics},
owner = {jp},
pmid = {14632439},
timestamp = {2009.03.12},
url = {http://dx.doi.org/10.1021/ci034143r}
}
@article{Suthram2005Plasmodium,
author = {Suthram, S. and Sittler, T. and Ideker, T.},
title = {The Plasmodium protein network diverges from those of other eukaryotes},
journal = {Nature},
year = {2005},
volume = {438},
pages = {108--112},
number = {7064},
month = {Nov},
abstract = {Plasmodium falciparum is the pathogen responsible for over 90\% of
human deaths from malaria. Therefore, it has been the focus of a
considerable research initiative, involving the complete DNA sequencing
of the genome, large-scale expression analyses, and protein characterization
of its life-cycle stages. The Plasmodium genome sequence is relatively
distant from those of most other eukaryotes, with more than 60\%
of the 5,334 encoded proteins lacking any notable sequence similarity
to other organisms. To systematically elucidate functional relationships
among these proteins, a large two-hybrid study has recently mapped
a network of 2,846 interactions involving 1,312 proteins within Plasmodium.
This network adds to a growing collection of available interaction
maps for a number of different organisms, and raises questions about
whether the divergence of Plasmodium at the sequence level is reflected
in the configuration of its protein network. Here we examine the
degree of conservation between the Plasmodium protein network and
those of model organisms. Although we find 29 highly connected protein
complexes specific to the network of the pathogen, we find very little
conservation with complexes observed in other organisms (three in
yeast, none in the others). Overall, the patterns of protein interaction
in Plasmodium, like its genome sequence, set it apart from other
species.},
doi = {10.1038/nature04135},
pdf = {../local/Suthram2005Plasmodium},
file = {Suthram2005Plasmodium:local/Suthram2005Plasmodium.pdf:PDF},
institution = {Bioinformatics Program, University of California, San Diego, California
92093, USA. ssuthram@ucsd.edu},
owner = {jp},
pii = {nature04135},
pmid = {16267557},
timestamp = {2008.10.02},
url = {http://dx.doi.org/10.1038/nature04135}
}
@article{Suykens2001Optimal,
author = {J. A. Suykens and J. Vandewalle and B. De Moor},
title = {Optimal control by least squares support vector machines.},
journal = {Neural {N}etw},
year = {2001},
volume = {14},
pages = {23-35},
number = {1},
month = {Jan},
abstract = {Support vector machines have been very successful in pattern recognition
and function estimation problems. {I}n this paper we introduce the
use of least squares support vector machines ({LS}-{SVM}'s) for the
optimal control of nonlinear systems. {L}inear and neural full static
state feedback controllers are considered. {T}he problem is formulated
in such a way that it incorporates the {N}-stage optimal control
problem as well as a least squares support vector machine approach
for mapping the state space into the action space. {T}he solution
is characterized by a set of nonlinear equations. {A}n alternative
formulation as a constrained nonlinear optimization problem in less
unknowns is given, together with a method for imposing local stability
in the {LS}-{SVM} control scheme. {T}he results are discussed for
support vector machines with radial basis function kernel. {A}dvantages
of {LS}-{SVM} control are that no number of hidden units has to be
determined for the controller and that no centers have to be specified
for the {G}aussian kernels when applying {M}ercer's condition. {T}he
curse of dimensionality is avoided in comparison with defining a
regular grid for the centers in classical radial basis function networks.
{T}his is at the expense of taking the trajectory of state variables
as additional unknowns in the optimization problem, while classical
neural network approaches typically lead to parametric optimization
problems. {I}n the {SVM} methodology the number of unknowns equals
the number of training data, while in the primal space the number
of unknowns can be infinite dimensional. {T}he method is illustrated
both on stabilization and tracking problems including examples on
swinging up an inverted pendulum with local stabilization at the
endpoint and a tracking problem for a ball and beam system.},
keywords = {Acute, Acute Disease, Adenocarcinoma, Algorithms, Amino Acid Sequence,
Artificial Intelligence, Automated, B-Lymphocytes, Bacterial Proteins,
Base Pair Mismatch, Base Sequence, Bayes Theorem, Binding Sites,
Biological, Bone Marrow Cells, Cell Compartmentation, Chemistry,
Child, Chromosome Aberrations, Comparative Study, Computational Biology,
Computer Simulation, Computer-Assisted, DNA, Data Interpretation,
Databases, Decision Trees, Diagnosis, Discriminant Analysis, Electric
Conductivity, Electrophysiology, Escherichia coli Proteins, Factual,
Feedback, Female, Fungal, Gastric Emptying, Gene Expression Profiling,
Gene Expression Regulation, Genes, Genetic, Genetic Markers, Hemolysins,
Humans, Ion Channels, Kinetics, Leukemia, Lipid Bilayers, Logistic
Models, Lymphocytic, Male, Markov Chains, Melanoma, Models, Molecular,
Myeloid, Neoplasm, Neoplastic, Neural Networks (Computer), Nevus,
Non-P.H.S., Non-U.S. Gov't, Nonlinear Dynamics, Normal Distribution,
Nucleic Acid Conformation, Organ Specificity, Organelles, P.H.S.,
Pattern Recognition, Physical, Pigmented, Predictive Value of Tests,
Promoter Regions (Genetics), Protein Folding, Protein Structure,
Proteins, Proteome, RNA, Reproducibility of Results, Research Support,
Saccharomyces cerevisiae, Secondary, Sensitivity and Specificity,
Sequence Alignment, Sex Characteristics, Skin Diseases, Skin Neoplasms,
Skin Pigmentation, Software, Statistical, Stomach Diseases, T-Lymphocytes,
Thermodynamics, Transcription, Transcription Factors, Tumor Markers,
U.S. Gov't, 11213211},
pii = {S0893608000000770}
}
@article{Swamidass2005Kernels,
author = {Swamidass, S. J. and Chen, J. and Bruand, J. and Phung, P. and Ralaivola,
L. and Baldi, P.},
title = {Kernels for small molecules and the prediction of mutagenicity, toxicity
and anti-cancer activity.},
journal = {Bioinformatics},
year = {2005},
volume = {21},
pages = {i359-i368},
number = {Suppl. 1},
month = {Jun},
abstract = {M{OTIVATION}: {S}mall molecules play a fundamental role in organic
chemistry and biology. {T}hey can be used to probe biological systems
and to discover new drugs and other useful compounds. {A}s increasing
numbers of large datasets of small molecules become available, it
is necessary to develop computational methods that can deal with
molecules of variable size and structure and predict their physical,
chemical and biological properties. {RESULTS}: {H}ere we develop
several new classes of kernels for small molecules using their 1{D},
2{D} and 3{D} representations. {I}n 1{D}, we consider string kernels
based on {SMILES} strings. {I}n 2{D}, we introduce several similarity
kernels based on conventional or generalized fingerprints. {G}eneralized
fingerprints are derived by counting in different ways subpaths contained
in the graph of bonds, using depth-first searches. {I}n 3{D}, we
consider similarity measures between histograms of pairwise distances
between atom classes. {T}hese kernels can be computed efficiently
and are applied to problems of classification and prediction of mutagenicity,
toxicity and anti-cancer activity on three publicly available datasets.
{T}he results derived using cross-validation methods are state-of-the-art.
{T}radeoffs between various kernels are briefly discussed. {AVAILABILITY}:
{D}atasets available from http://www.igb.uci.edu/servers/servers.html
{CONTACT}: pfbaldi@ics.uci.edu.},
doi = {10.1093/bioinformatics/bti1055},
pdf = {../local/Swamidass2005Kernels.pdf},
file = {Swamidass2005Kernels.pdf:Swamidass2005Kernels.pdf:PDF},
keywords = {biosvm},
pii = {21/suppl_1/i359},
url = {http://dx.doi.org/10.1093/bioinformatics/bti1055}
}
@article{Sylvester1878Chemistry,
author = {J. J. Sylvester},
title = {Chemistry and Algebra},
journal = {Nature},
year = {1878},
volume = {17},
number = {432}
}
@incollection{Szafranski2008Hierarchical,
author = {Marie Szafranski and Yves Grandvalet and Pierre Morizet-Mahoudeaux},
title = {Hierarchical Penalization},
booktitle = {Advances in Neural Information Processing Systems 20},
publisher = {MIT Press},
year = {2008},
editor = {J.C. Platt and D. Koller and Y. Singer and S. Roweis},
pages = {1457--1464},
address = {Cambridge, MA}
}
@inproceedings{Szafranski2008Composite,
author = {Szafranski, M. and Grandvalet, Y. and Rakotomamonjy, A.},
title = {Composite Kernel Learning},
booktitle = {ICML '08: Proceedings of the 25th international conference on Machine
learning},
year = {2008},
address = {Helsinki Finlande},
month = {07},
international = {y},
teams = {DI},
url = {http://hal.archives-ouvertes.fr/hal-00316016/en/}
}
@article{Soerlie2006Gene,
author = {S{\o}rlie, T. and Perou, C. M. and Fan, C. and Geisler, S. and Aas,
T. and Nobel, A. and Anker, G. and Akslen, L. A. and Botstein, D.
and B{\o}rresen-Dale, A.-L. and L{\o}nning, P. E.},
title = {Gene expression profiles do not consistently predict the clinical
treatment response in locally advanced breast cancer},
journal = {Mol. Cancer Ther.},
year = {2006},
volume = {5},
pages = {2914--2918},
number = {11},
month = {Nov},
abstract = {Neoadjuvant treatment offers an opportunity to correlate molecular
variables to treatment response and to explore mechanisms of drug
resistance in vivo. Here, we present a statistical analysis of large-scale
gene expression patterns and their relationship to response following
neoadjuvant chemotherapy in locally advanced breast cancers. We analyzed
cDNA expression data from 81 tumors from two patient series, one
treated with doxorubicin alone (51) and the other treated with 5-fluorouracil
and mitomycin (30), and both were previously studied for correlations
between TP53 status and response to therapy. We observed a low frequency
of progressive disease within the luminal A subtype from both series
(2 of 36 versus 13 of 45 patients; P = 0.0089) and a high frequency
of progressive disease among patients with luminal B type tumors
treated with doxorubicin (5 of 8 patients; P = 0.0078); however,
aside from these two observations, no other consistent associations
between response to chemotherapy and tumor subtype were observed.
These specific associations could possibly be explained by covariance
with TP53 mutation status, which also correlated with tumor subtype.
Using supervised analysis, we could not uncover a gene profile that
could reliably (>70\% accuracy and specificity) predict response
to either treatment regimen.},
doi = {10.1158/1535-7163.MCT-06-0126},
pdf = {../local/Soerlie2006Gene.pdf},
file = {Soerlie2006Gene.pdf:Soerlie2006Gene.pdf:PDF},
institution = {Department of Medicine, Section of Oncology, Haukeland University
Hospital, N-5021 Bergen, Norway.},
keywords = {csbcbook, csbcbook-ch3},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {5/11/2914},
pmid = {17121939},
timestamp = {2011.04.07},
url = {http://dx.doi.org/10.1158/1535-7163.MCT-06-0126}
}
@article{Soerlie2001Gene,
author = {S{\o}rlie, T. and Perou, C. M. and Tibshirani, R. and Aas, T. and
Geisler, S. and Johnsen, H. and Hastie, T. and Eisen, M. B. and van
de Rijn, M. and Jeffrey, S. S. and Thorsen, T. and Quist, H. and
Matese, J. C. and Brown, P. O. and Botstein, D. and Eystein L{\o}nning,
P. and B{\o}rresen-Dale, A. L.},
title = {Gene expression patterns of breast carcinomas distinguish tumor subclasses
with clinical implications},
journal = {Proc. Natl. Acad. Sci. USA},
year = {2001},
volume = {98},
pages = {10869--10874},
number = {19},
month = {Sep},
abstract = {The purpose of this study was to classify breast carcinomas based
on variations in gene expression patterns derived from cDNA microarrays
and to correlate tumor characteristics to clinical outcome. A total
of 85 cDNA microarray experiments representing 78 cancers, three
fibroadenomas, and four normal breast tissues were analyzed by hierarchical
clustering. As reported previously, the cancers could be classified
into a basal epithelial-like group, an ERBB2-overexpressing group
and a normal breast-like group based on variations in gene expression.
A novel finding was that the previously characterized luminal epithelial/estrogen
receptor-positive group could be divided into at least two subgroups,
each with a distinctive expression profile. These subtypes proved
to be reasonably robust by clustering using two different gene sets:
first, a set of 456 cDNA clones previously selected to reflect intrinsic
properties of the tumors and, second, a gene set that highly correlated
with patient outcome. Survival analyses on a subcohort of patients
with locally advanced breast cancer uniformly treated in a prospective
study showed significantly different outcomes for the patients belonging
to the various groups, including a poor prognosis for the basal-like
subtype and a significant difference in outcome for the two estrogen
receptor-positive groups.},
doi = {10.1073/pnas.191367098},
pdf = {../local/Soerlie2001Gene.pdf},
file = {Soerlie2001Gene.pdf:Soerlie2001Gene.pdf:PDF},
institution = {cs, The Norwegian Radium Hospital, Montebello, N-0310 Oslo, Norway.},
keywords = {breastcancer, csbcbook, csbcbook-ch2},
owner = {jp},
pii = {98/19/10869},
pmid = {11553815},
timestamp = {2008.11.15},
url = {http://dx.doi.org/10.1073/pnas.191367098}
}
@article{Sorlie2003Repeated,
author = {S{\o}rlie, T. and Tibshirani, R. and Parker, J. and Hastie, T. and
Marron, J.S. and Nobel, A. and Deng, S. and Johnsen, H. and Pesich,
R. and Geisler, S. and Demeter, J. and Perou, C.M. and Lønning, P.E.
and Brown, P.O. and Børresen-Dale, A.L. and Botstein, D.},
title = {Repeated observation of breast tumor subtypes in independent gene
expression data sets},
journal = {Proc. Natl. Acad. Sci. USA},
year = {2003},
volume = {100},
pages = {8418--8423},
number = {14},
month = {Jul},
abstract = {Characteristic patterns of gene expression measured by DNA microarrays
have been used to classify tumors into clinically relevant subgroups.
In this study, we have refined the previously defined subtypes of
breast tumors that could be distinguished by their distinct patterns
of gene expression. A total of 115 malignant breast tumors were analyzed
by hierarchical clustering based on patterns of expression of 534
"intrinsic" genes and shown to subdivide into one basal-like, one
ERBB2-overexpressing, two luminal-like, and one normal breast tissue-like
subgroup. The genes used for classification were selected based on
their similar expression levels between pairs of consecutive samples
taken from the same tumor separated by 15 weeks of neoadjuvant treatment.
Similar cluster analyses of two published, independent data sets
representing different patient cohorts from different laboratories,
uncovered some of the same breast cancer subtypes. In the one data
set that included information on time to development of distant metastasis,
subtypes were associated with significant differences in this clinical
feature. By including a group of tumors from BRCA1 carriers in the
analysis, we found that this genotype predisposes to the basal tumor
subtype. Our results strongly support the idea that many of these
breast tumor subtypes represent biologically distinct disease entities.},
doi = {10.1073/pnas.0932692100},
pdf = {../local/Sorlie2003Repeated.pdf},
file = {Sorlie2003Repeated.pdf:Sorlie2003Repeated.pdf:PDF},
keywords = {csbcbook, csbcbook-ch3},
url = {http://dx.doi.org/10.1073/pnas.0932692100}
}
@article{Toeroenen1999Analysis,
author = {T\"or\"onen, P. and Kolehmainen, M. and Wong, G. and Castr\'en, E.},
title = {Analysis of gene expression data using self-organizing maps.},
journal = {FEBS Lett.},
year = {1999},
volume = {451},
pages = {142--146},
number = {2},
month = {May},
abstract = {DNA microarray technologies together with rapidly increasing genomic
sequence information is leading to an explosion in available gene
expression data. Currently there is a great need for efficient methods
to analyze and visualize these massive data sets. A self-organizing
map (SOM) is an unsupervised neural network learning algorithm which
has been successfully used for the analysis and organization of large
data files. We have here applied the SOM algorithm to analyze published
data of yeast gene expression and show that SOM is an excellent tool
for the analysis and visualization of gene expression profiles.},
pdf = {../local/Toeroenen1999Analysis.pdf},
file = {Toeroenen1999Analysis.pdf:Toeroenen1999Analysis.pdf:PDF},
institution = {A.I. Virtanen Institute, University of Kuopio, Finland.},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {S0014-5793(99)00524-4},
pmid = {10371154},
timestamp = {2011.10.03}
}
@article{Taby2010Cancer,
author = {Rodolphe Taby and Jean-Pierre J Issa},
title = {Cancer epigenetics.},
journal = {CA Cancer J Clin},
year = {2010},
volume = {60},
pages = {376--392},
number = {6},
abstract = {Epigenetics refers to stable alterations in gene expression with no
underlying modifications in the genetic sequence and is best exemplified
by differentiation, in which multiple cell types diverge physiologically
despite a common genetic code. Interest in this area of science has
grown over the past decades, especially since it was found to play
a major role in physiologic phenomena such as embryogenesis, imprinting,
and X chromosome inactivation, and in disease states such as cancer.
The latter had been previously thought of as a disease with an exclusive
genetic etiology. However, recent data have demonstrated that the
complexity of human carcinogenesis cannot be accounted for by genetic
alterations alone, but also involves epigenetic changes in processes
such as DNA methylation, histone modifications, and microRNA expression.
In turn, these molecular alterations lead to permanent changes in
the expression of genes that regulate the neoplastic phenotype, such
as cellular growth and invasiveness. Targeting epigenetic modifiers
has been referred to as epigenetic therapy. The success of this approach
in hematopoietic malignancies validates the importance of epigenetic
alterations in cancer, not only at the therapeutic level but also
with regard to prevention, diagnosis, risk stratification, and prognosis.},
doi = {10.3322/caac.20085},
institution = {Department of Leukemia, The University of Texas M. D. Anderson Cancer
Center, Houston, TX 77030, USA.},
keywords = {Animals; Cell Cycle, genetics; Cell Transformation, Neoplastic, genetics;
DNA Methylation; Epigenesis, Genetic; Histones, genetics; Humans;
MicroRNAs, genetics; Neoplasm Invasiveness, genetics; Neoplasms,
classification/diagnosis/genetics/metabolism/prevention /&/ control/therapy;
Prognosis; Risk Assessment; Tumor Markers, Biological, genetics},
language = {eng},
medline-pst = {ppublish},
owner = {philippe},
pii = {caac.20085},
pmid = {20959400},
timestamp = {2011.06.04},
url = {http://dx.doi.org/10.3322/caac.20085}
}
@article{Takahashi2005Rigorous,
author = {Norikazu Takahashi and Tetsuo Nishi},
title = {Rigorous proof of termination of {SMO} algorithm for support vector
machines.},
journal = {I{EEE} {T}rans {N}eural {N}etw},
year = {2005},
volume = {16},
pages = {774-6},
number = {3},
month = {May},
abstract = {Sequential minimal optimization ({SMO}) algorithm is one of the simplest
decomposition methods for learning of support vector machines ({SVM}s).
{K}eerthi and {G}ilbert have recently studied the convergence property
of {SMO} algorithm and given a proof that {SMO} algorithm always
stops within a finite number of iterations. {I}n this letter, we
point out the incompleteness of their proof and give a more rigorous
proof.}
}
@article{Takahashi2003Proteomic,
author = {Nobuhiro Takahashi and Mitsuaki Yanagida and Sally Fujiyama and Toshiya
Hayano and Toshiaki Isobe},
title = {Proteomic snapshot analyses of preribosomal ribonucleoprotein complexes
formed at various stages of ribosome biogenesis in yeast and mammalian
cells.},
journal = {Mass {S}pectrom {R}ev},
year = {2003},
volume = {22},
pages = {287-317},
number = {5},
abstract = {Proteomic technologies powered by advancements in mass spectrometry
and bioinformatics and coupled with accumulated genome sequence data
allow a comprehensive study of cell function through large-scale
and systematic protein identifications of protein constituents of
the cell and tissues, as well as of multi-protein complexes that
carry out many cellular function in a higher-order network in the
cell. {O}ne of the most extensively analyzed cellular functions by
proteomics is the production of ribosome, the protein-synthesis machinery,
in the nucle(ol)us--the main site of ribosome biogenesis. {T}he use
of tagged proteins as affinity bait, coupled with mass spectrometric
identification, enabled us to isolate synthetic intermediates of
ribosomes that might represent snapshots of nascent ribosomes at
particular stages of ribosome biogenesis and to identify their constituents--some
of which showed dynamic changes for association with the intermediates
at various stages of ribosome biogenesis. {I}n this review, in conjunction
with the results from yeast cells, our proteomic approach to analyze
ribosome biogenesis in mammalian cells is described.},
doi = {10.1002/mas.10057},
pdf = {../local/Takahashi2003Proteomic.pdf},
file = {Takahashi2003Proteomic.pdf:local/Takahashi2003Proteomic.pdf:PDF},
keywords = {Affinity Labels, Animals, Comparative Study, Electrospray Ionization,
Genetic, Macromolecular Substances, Mass, Mitosis, Non-P.H.S., Non-U.S.
Gov't, P.H.S., Protein Interaction Mapping, Proteome, Proteomics,
Research Support, Ribonucleoproteins, Ribosomes, Saccharomyces cerevisiae,
Saccharomyces cerevisiae Proteins, Signal Transduction, Spectrometry,
Transcription, U.S. Gov't, 12949916},
owner = {vert},
url = {http://dx.doi.org/10.1002/mas.10057}
}
@article{Takaoka2003Development,
author = {Y. Takaoka and Y. Endo and S. Yamanobe and H. Kakinuma and T. Okubo
and Y. Shimazaki and T. Ota and S. Sumiya and K. Yoshikawa},
title = {Development of a method for evaluating drug-likeness and ease of
synthesis using a data set in which compounds are assigned scores
based on chemists' intuition.},
journal = {J {C}hem {I}nf {C}omput {S}ci},
year = {2003},
volume = {43},
pages = {1269-75},
number = {4},
abstract = {The concept of drug-likeness, an important characteristic for any
compound in a screening library, is nevertheless difficult to pin
down. {B}ased on our belief that this concept is implicit within
the collective experience of working chemists, we devised a data
set to capture an intuitive human understanding of both this characteristic
and ease of synthesis, a second key characteristic. {F}ive chemists
assigned a pair of scores to each of 3980 diverse compounds, with
the component scores of each pair corresponding to drug-likeness
and ease of synthesis, respectively. {U}sing this data set, we devised
binary classifiers with an artificial neural network and a support
vector machine. {T}hese models were found to efficiently eliminate
compounds that are not drug-like and/or hard-to-synthesize derivatives,
demonstrating the suitability of these models for use as compound
acquisition filters.},
doi = {10.1021/ci034043l},
pdf = {../local/Takaoka2003Development.pdf},
file = {Takaoka2003Development.pdf:local/Takaoka2003Development.pdf:PDF},
keywords = {biosvm},
url = {http://dx.doi.org/10.1021/ci034043l}
}
@article{Takemoto2012Analysis,
author = {Takemoto, K. and Tamura, T. and Cong, Y. and Ching, W.-K. and Vert,
J.-P. and Akutsu, T.},
title = {Analysis of the impact degree distribution inmetabolic networks using
branching process approximation},
journal = {Physica A},
year = {2012},
volume = {391},
pages = {379--387},
doi = {10.1016/j.physa.2011.08.011},
pdf = {../local/Takemoto2012Analysis.pdf},
file = {Takemoto2012Analysis.pdf:Takemoto2012Analysis.pdf:PDF},
owner = {jp},
timestamp = {2011.10.12},
url = {http://dx.doi.org/10.1016/j.physa.2011.08.011}
}
@article{Takeuchi2005Bio-medical,
author = {Koichi Takeuchi and Nigel Collier},
title = {Bio-medical entity extraction using support vector machines.},
journal = {Artif. {I}ntell. {M}ed.},
year = {2005},
volume = {33},
pages = {125-37},
number = {2},
month = {Feb},
abstract = {O{BJECTIVE}: {S}upport vector machines ({SVM}s) have achieved state-of-the-art
performance in several classification tasks. {I}n this article we
apply them to the identification and semantic annotation of scientific
and technical terminology in the domain of molecular biology. {T}his
illustrates the extensibility of the traditional named entity task
to special domains with large-scale terminologies such as those in
medicine and related disciplines. {METHODS} {AND} {MATERIALS}: {T}he
foundation for the model is a sample of text annotated by a domain
expert according to an ontology of concepts, properties and relations.
{T}he model then learns to annotate unseen terms in new texts and
contexts. {T}he results can be used for a variety of intelligent
language processing applications. {W}e illustrate {SVM}s capabilities
using a sample of 100 journal abstracts texts taken from the {human,
blood cell, transcription factor} domain of {MEDLINE}. {RESULTS}:
{A}pproximately 3400 terms are annotated and the model performs at
about 74\% {F}-score on cross-validation tests. {A} detailed analysis
based on empirical evidence shows the contribution of various feature
sets to performance. {CONCLUSION}: {O}ur experiments indicate a relationship
between feature window size and the amount of training data and that
a combination of surface words, orthographic features and head noun
features achieve the best performance among the feature sets tested.},
doi = {10.1016/j.artmed.2004.07.019},
pdf = {../local/Takeuchi2005Bio-medical.pdf},
file = {Takeuchi2005Bio-medical.pdf:local/Takeuchi2005Bio-medical.pdf:PDF},
keywords = {biosvm},
pii = {S0933-3657(04)00130-7},
url = {http://dx.doi.org/10.1016/j.artmed.2004.07.019}
}
@article{Talagrand1996Majorizing,
author = {Talagrand, M.},
title = {Majorizing measures: {T}he generic chaining},
journal = {Ann. {P}robab.},
year = {1996},
volume = {24},
pages = {1049--1103},
pdf = {../local/tala96b.pdf},
file = {tala96b.pdf:local/tala96b.pdf:PDF},
subject = {stat},
url = {http://www.math.ohio-state.edu/~talagran/preprints/majmeas.dvi}
}
@article{Talagrand1996New,
author = {Talagrand, M.},
title = {New concentration inequalities for product spaces},
journal = {Inventionnes {M}ath.},
year = {1996},
volume = {126},
pages = {505--563},
pdf = {../local/tala96.pdf},
file = {tala96.pdf:local/tala96.pdf:PDF},
subject = {stat},
url = {http://www.math.ohio-state.edu/~talagran/preprints/newcon.dvi}
}
@article{Talagrand1996Newa,
author = {Talagrand, M.},
title = {A {N}ew {L}ook at {I}ndependence},
journal = {Ann. {P}robab.},
year = {1996},
volume = {24},
pages = {1--34},
pdf = {../local/tala96c.pdf},
file = {tala96c.pdf:local/tala96c.pdf:PDF},
subject = {stat},
url = {http://www.math.ohio-state.edu/~talagran/preprints/newlook.dvi}
}
@article{Talagrand1995Concentration,
author = {Talagrand, M.},
title = {Concentration of measure and isoperimetric inequalities in product
spaces},
journal = {Publ. {M}ath. {I}.{H}.{E}.{S}.},
year = {1995},
volume = {81},
pages = {73--203},
pdf = {../local/tala95.pdf},
file = {tala95.pdf:local/tala95.pdf:PDF},
subject = {stat},
url = {http://www.math.ohio-state.edu/~talagran/preprints/ihes.dvi}
}
@article{Talih2005Structural,
author = {Talih, M. and Hengartner, N.},
title = {Structural learning with time-varying components: tracking the cross-section
of financial time series},
journal = {J. R. Stat. Soc. Ser. B},
year = {2005},
volume = {67},
pages = {321--341},
number = {3},
pdf = {../local/Talih2005Structural.pdf},
file = {Talih2005Structural.pdf:Talih2005Structural.pdf:PDF},
owner = {jp},
timestamp = {2011.04.13}
}
@article{Talukder2001closed-form,
author = {A. Talukder and D. Casasent},
title = {A closed-form neural network for discriminatory feature extraction
from high-dimensional data.},
journal = {Neural {N}etw},
year = {2001},
volume = {14},
pages = {1201-18},
number = {9},
month = {Nov},
abstract = {We consider a new neural network for data discrimination in pattern
recognition applications. {W}e refer to this as a maximum discriminating
feature ({MDF}) neural network. {I}ts weights are obtained in closed-form,
thereby overcoming problems associated with other nonlinear neural
networks. {I}t uses neuron activation functions that are dynamically
chosen based on the application. {I}t is theoretically shown to provide
nonlinear transforms of the input data that are more general than
those provided by other nonlinear multilayer perceptron neural network
and support-vector machine techniques for cases involving high-dimensional
(image) inputs where training data are limited and the classes are
not linearly separable. {W}e experimentally verify this on synthetic
examples.}
}
@article{Tamayo1999Interpreting,
author = {Tamayo, P. and Slonim, D. and Mesirov, J. and Zhu, Q. and Kitareewan,
S. and Dmitrovsky, E. and Lander, E. S. and Golub, T. R.},
title = {Interpreting patterns of gene expression with self-organizing maps:
methods and application to hematopoietic differentiation.},
journal = {Proc. Natl. Acad. Sci. U. S. A.},
year = {1999},
volume = {96},
pages = {2907--2912},
number = {6},
month = {Mar},
abstract = {Array technologies have made it straightforward to monitor simultaneously
the expression pattern of thousands of genes. The challenge now is
to interpret such massive data sets. The first step is to extract
the fundamental patterns of gene expression inherent in the data.
This paper describes the application of self-organizing maps, a type
of mathematical cluster analysis that is particularly well suited
for recognizing and classifying features in complex, multidimensional
data. The method has been implemented in a publicly available computer
package, GENECLUSTER, that performs the analytical calculations and
provides easy data visualization. To illustrate the value of such
analysis, the approach is applied to hematopoietic differentiation
in four well studied models (HL-60, U937, Jurkat, and NB4 cells).
Expression patterns of some 6,000 human genes were assayed, and an
online database was created. GENECLUSTER was used to organize the
genes into biologically relevant clusters that suggest novel hypotheses
about hematopoietic differentiation-for example, highlighting certain
genes and pathways involved in "differentiation therapy" used in
the treatment of acute promyelocytic leukemia.},
pdf = {../local/Tamayo1999Interpreting.pdf},
file = {Tamayo1999Interpreting.pdf:Tamayo1999Interpreting.pdf:PDF},
institution = {Whitehead Institute for Biomedical Research, 9 Cambridge Center,
Cambridge, MA 02142, USA.},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pmid = {10077610},
timestamp = {2011.10.04}
}
@article{Tang2005siRNA,
author = {Tang, G.},
title = {{siRNA} and {miRNA}: an insight into {RISC}s.},
journal = {Trends {B}iochem. {S}ci.},
year = {2005},
volume = {30},
pages = {106-14},
number = {2},
month = {Feb},
abstract = {Two classes of short {RNA} molecule, small interfering {RNA} (si{RNA})
and micro{RNA} (mi{RNA}), have been identified as sequence-specific
posttranscriptional regulators of gene expression. si{RNA} and mi{RNA}
are incorporated into related {RNA}-induced silencing complexes ({RISC}s),
termed si{RISC} and mi{RISC}, respectively. {T}he current model argues
that si{RISC} and mi{RISC} are functionally interchangeable and target
specific m{RNA}s for cleavage or translational repression, depending
on the extent of sequence complementarity between the small {RNA}
and its target. {E}merging evidence indicates, however, that si{RISC}
and mi{RISC} are distinct complexes that regulate m{RNA} stability
and translation. {T}he assembly of {RISC}s can be traced from the
biogenesis of the small {RNA} molecules and the recruitment of these
{RNA}s by the {RISC} loading complex ({RLC}) to the transition of
the {RLC} into the active {RISC}. {T}arget recognition by the {RISC}
can then take place through different interacting modes.},
doi = {10.1016/j.tibs.2004.12.007},
keywords = {sirna},
pii = {S0968-0004(04)00321-4},
url = {http://dx.doi.org/10.1016/j.tibs.2004.12.007}
}
@article{Tang2005Discovering,
author = {Thomas Tang and Jinbo Xu and Ming Li},
title = {Discovering sequence-structure motifs from protein segments and two
applications.},
journal = {Pac {S}ymp {B}iocomput},
year = {2005},
pages = {370-81},
abstract = {We present a novel method for clustering short protein segments having
strong sequence-structure correlations, and demonstrate that these
clusters contain useful structural information via two applications.
{W}hen applied to local tertiary structure prediction, we achieve
approximately 60\% accuracy with a novel dynamic programming algorithm.
{W}hen applied to secondary structure prediction based on {S}upport
{V}ector {M}achines, we obtain a approximately 2\% gain in {Q}3 performance
by incorporating cluster-derived data into training and classification.
{T}hese encouraging results illustrate the great potential of using
conserved local motifs to tackle protein structure predictions and
possibly other important problems in biology.},
keywords = {biosvm}
}
@article{Tang2005Granular,
author = {Yuchun Tang and Bo Jin and Yan-Qing Zhang},
title = {Granular support vector machines with association rules mining for
protein homology prediction.},
journal = {Artif. {I}ntell. {M}ed.},
year = {2005},
month = {Jul},
abstract = {O{BJECTIVE}:: {P}rotein homology prediction between protein sequences
is one of critical problems in computational biology. {S}uch a complex
classification problem is common in medical or biological information
processing applications. {H}ow to build a model with superior generalization
capability from training samples is an essential issue for mining
knowledge to accurately predict/classify unseen new samples and to
effectively support human experts to make correct decisions. {METHODOLOGY}::
{A} new learning model called granular support vector machines ({GSVM})
is proposed based on our previous work. {GSVM} systematically and
formally combines the principles from statistical learning theory
and granular computing theory and thus provides an interesting new
mechanism to address complex classification problems. {I}t works
by building a sequence of information granules and then building
support vector machines ({SVM}) in some of these information granules
on demand. {A} good granulation method to find suitable granules
is crucial for modeling a {GSVM} with good performance. {I}n this
paper, we also propose an association rules-based granulation method.
{F}or the granules induced by association rules with high enough
confidence and significant support, we leave them as they are because
of their high "purity" and significant effect on simplifying the
classification task. {F}or every other granule, a {SVM} is modeled
to discriminate the corresponding data. {I}n this way, a complex
classification problem is divided into multiple smaller problems
so that the learning task is simplified. {RESULTS} {AND} {CONCLUSIONS}::
{T}he proposed algorithm, here named {GSVM}-{AR}, is compared with
{SVM} by {KDDCUP}04 protein homology prediction data. {T}he experimental
results show that finding the splitting hyperplane is not a trivial
task (we should be careful to select the association rules to avoid
overfitting) and {GSVM}-{AR} does show significant improvement compared
to building one single {SVM} in the whole feature space. {A}nother
advantage is that the utility of {GSVM}-{AR} is very good because
it is easy to be implemented. {M}ore importantly and more interestingly,
{GSVM} provides a new mechanism to address complex classification
problems.},
doi = {10.1016/j.artmed.2005.02.003},
pdf = {../local/Tang2005Granular.pdf},
file = {Tang2005Granular.pdf:local/Tang2005Granular.pdf:PDF},
keywords = {, , 16024240},
pii = {S0933-3657(05)00054-0},
url = {http://dx.doi.org/10.1016/j.artmed.2005.02.003}
}
@article{Tartakovsky2006novel,
author = {Tartakovsky, A. G. and Rozovskii, B. L. and Blazek, R. B. and Hongjoong
Kim},
title = {A novel approach to detection of intrusions in computer networks
via adaptive sequential and batch-sequential change-point detection
methods},
journal = {IEEE T. Signal. Proces.},
year = {2006},
volume = {54},
pages = {3372--3382},
number = {9},
doi = {10.1109/TSP.2006.879308},
pdf = {../local/Tartakovsky2006novel.pdf},
file = {Tartakovsky2006novel.pdf:Tartakovsky2006novel.pdf:PDF},
owner = {jp},
timestamp = {2011.04.13},
url = {http://dx.doi.org/10.1109/TSP.2006.879308}
}
@article{Tarumi2003Remote,
author = {Toshiyasu Tarumi and Gary W Small and Roger J Combs and Robert T
Kroutil},
title = {Remote detection of heated ethanol plumes by airborne passive {F}ourier
transform infrared spectrometry.},
journal = {Appl {S}pectrosc},
year = {2003},
volume = {57},
pages = {1432-41},
number = {11},
month = {Nov},
abstract = {Methodology is developed for the automated detection of heated plumes
of ethanol vapor with airborne passive {F}ourier transform infrared
spectrometry. {P}ositioned in a fixed-wing aircraft in a downward-looking
mode, the spectrometer is used to detect ground sources of ethanol
vapor from an altitude of 2000-3000 ft. {C}hallenges to the use of
this approach for the routine detection of chemical plumes include
(1) the presence of a constantly changing background radiance as
the aircraft flies, (2) the cost and complexity of collecting the
data needed to train the classification algorithms used in implementing
the plume detection, and (3) the need for rapid interferogram scans
to minimize the ground area viewed per scan. {T}o address these challenges,
this work couples a novel ground-based data collection and training
protocol with the use of signal processing and pattern recognition
methods based on short sections of the interferogram data collected
by the spectrometer. {I}n the data collection, heated plumes of ethanol
vapor are released from a portable emission stack and viewed by the
spectrometer from ground level against a synthetic background designed
to simulate a terrestrial radiance source. {C}lassifiers trained
with these data are subsequently tested with airborne data collected
over a period of 2.5 years. {T}wo classifier architectures are compared
in this work: support vector machines ({SVM}) and piecewise linear
discriminant analysis ({PLDA}). {W}hen applied to the airborne test
data, the {SVM} classifiers perform best, failing to detect ethanol
in only 8\% of the cases in which it is present. {F}alse detections
occur at a rate of less than 0.5\%. {T}he classifier performs well
in spite of differences between the backgrounds associated with the
ground-based and airborne data collections and the instrumental drift
arising from the long time span of the data collection. {F}urther
improvements in classification performance are judged to require
increased sophistication in the ground-based data collection in order
to provide a better match to the infrared backgrounds observed from
the air.},
keywords = {Air Pollutants, Aircraft, Algorithms, Artificial Intelligence, Automated,
Comparative Study, Computer Simulation, Computer-Assisted, Computing
Methodologies, Environmental Monitoring, Ethanol, Fourier Transform
Infrared, Humans, Image Interpretation, Non-P.H.S., Non-U.S. Gov't,
Online Systems, Pattern Recognition, Photography, Reproducibility
of Results, Research Support, Sensitivity and Specificity, Signal
Processing, Spectroscopy, Subtraction Technique, U.S. Gov't, Video
Recording, Walking, 14658159}
}
@inproceedings{Taskar2004Max-Margin,
author = {Taskar, B. and Guestrin, C. and Koller, D.},
title = {Max-{M}argin {M}arkov {N}etworks},
booktitle = {Advances in {N}eural {I}nformation {P}rocessing {S}ystems 16},
year = {2004},
editor = {Sebastian Thrun and Lawrence Saul and Bernhard {Sch\"{o}lkopf}},
address = {Cambridge, MA},
publisher = {MIT Press},
pdf = {../local/Taskar2004Max-Margin.pdf},
file = {Taskar2004Max-Margin.pdf:local/Taskar2004Max-Margin.pdf:PDF},
keywords = {conditional-random-field},
owner = {vert}
}
@article{Taslim2009Comparative,
author = {Taslim, C. and Wu, J. and Yan, P. and Singer, G. and Parvin, J. and
Huan, T. and Lin, S. and Huang, K.},
title = {Comparative study on {ChIP}-seq data: normalization and binding pattern
characterization.},
journal = {Bioinformatics},
year = {2009},
volume = {25},
pages = {2334--2340},
number = {18},
month = {Sep},
abstract = {MOTIVATION: Antibody-based Chromatin Immunoprecipitation assay followed
by high-throughput sequencing technology (ChIP-seq) is a relatively
new method to study the binding patterns of specific protein molecules
over the entire genome. ChIP-seq technology allows scientist to get
more comprehensive results in shorter time. Here, we present a non-linear
normalization algorithm and a mixture modeling method for comparing
ChIP-seq data from multiple samples and characterizing genes based
on their RNA polymerase II (Pol II) binding patterns. RESULTS: We
apply a two-step non-linear normalization method based on locally
weighted regression (LOESS) approach to compare ChIP-seq data across
multiple samples and model the difference using an Exponential-Normal(K)
mixture model. Fitted model is used to identify genes associated
with differential binding sites based on local false discovery rate
(fdr). These genes are then standardized and hierarchically clustered
to characterize their Pol II binding patterns. As a case study, we
apply the analysis procedure comparing normal breast cancer (MCF7)
to tamoxifen-resistant (OHT) cell line. We find enriched regions
that are associated with cancer (P < 0.0001). Our findings also imply
that there may be a dysregulation of cell cycle and gene expression
control pathways in the tamoxifen-resistant cells. These results
show that the non-linear normalization method can be used to analyze
ChIP-seq data across multiple samples. AVAILABILITY: Data are available
at http://www.bmi.osu.edu/~khuang/Data/ChIP/RNAPII/.},
doi = {10.1093/bioinformatics/btp384},
institution = { Medical Genetics, Ohio State University, Columbus, OH 43210, USA.
taslim.2@osu.edu},
owner = {jp},
pii = {btp384},
pmid = {19561022},
timestamp = {2009.10.12},
url = {http://dx.doi.org/10.1093/bioinformatics/btp384}
}
@article{Tavazoie1999Systematic,
author = {Tavazoie, S. and Hughes, J. D. and Campbell, M. J. and Cho, R. J.
and Church, G. M.},
title = {Systematic determination of genetic network architecture},
journal = {Nat. Genet.},
year = {1999},
volume = {22},
pages = {281--285},
doi = {doi:10.1038/10343},
pdf = {../local/Tavazoie1999Systematic.pdf},
file = {Tavazoie1999Systematic.pdf:local/Tavazoie1999Systematic.pdf:PDF},
subject = {microarray},
url = {http://dx.doi.org/10.1038/10343}
}
@article{Tax2001Uniform,
author = {Tax, D. M. J. and Duin, R. P. W.},
title = {Uniform {O}bject {G}eneration for {O}ptimizing {O}ne-class {C}lassifiers},
journal = {J. {M}ach. {L}earn. {R}es.},
year = {2001},
volume = {2},
pages = {155-173},
pdf = {../local/Tax2001Uniform.pdf},
file = {Tax2001Uniform.pdf:local/Tax2001Uniform.pdf:PDF}
}
@article{Taylor2008Guidelines,
author = {Chris F Taylor and Pierre-Alain Binz and Ruedi Aebersold and Michel
Affolter and Robert Barkovich and Eric W Deutsch and David M Horn
and Andreas Hühmer and Martin Kussmann and Kathryn Lilley and Marcus
Macht and Matthias Mann and Dieter Müller and Thomas A Neubert and
Janice Nickson and Scott D Patterson and Roberto Raso and Kathryn
Resing and Sean L Seymour and Akira Tsugita and Ioannis Xenarios
and Rong Zeng and Randall K Julian},
title = {Guidelines for reporting the use of mass spectrometry in proteomics.},
journal = {Nat Biotechnol},
year = {2008},
volume = {26},
pages = {860--861},
number = {8},
month = {Aug},
doi = {10.1038/nbt0808-860},
keywords = {Databases, Protein; Guidelines as Topic; Mass Spectrometry; Proteomics},
owner = {phupe},
pii = {nbt0808-860},
pmid = {18688232},
timestamp = {2010.08.13},
url = {http://dx.doi.org/10.1038/nbt0808-860}
}
@article{Taylor2007minimum,
author = {Chris F Taylor and Norman W Paton and Kathryn S Lilley and Pierre-Alain
Binz and Randall K Julian and Andrew R Jones and Weimin Zhu and Rolf
Apweiler and Ruedi Aebersold and Eric W Deutsch and Michael J Dunn
and Albert J R Heck and Alexander Leitner and Marcus Macht and Matthias
Mann and Lennart Martens and Thomas A Neubert and Scott D Patterson
and Peipei Ping and Sean L Seymour and Puneet Souda and Akira Tsugita
and Joel Vandekerckhove and Thomas M Vondriska and Julian P Whitelegge
and Marc R Wilkins and Ioannnis Xenarios and John R Yates and Henning
Hermjakob},
title = {The minimum information about a proteomics experiment (MIAPE).},
journal = {Nat Biotechnol},
year = {2007},
volume = {25},
pages = {887--893},
number = {8},
month = {Aug},
abstract = {Both the generation and the analysis of proteomics data are now widespread,
and high-throughput approaches are commonplace. Protocols continue
to increase in complexity as methods and technologies evolve and
diversify. To encourage the standardized collection, integration,
storage and dissemination of proteomics data, the Human Proteome
Organization's Proteomics Standards Initiative develops guidance
modules for reporting the use of techniques such as gel electrophoresis
and mass spectrometry. This paper describes the processes and principles
underpinning the development of these modules; discusses the ramifications
for various interest groups such as experimentalists, funders, publishers
and the private sector; addresses the issue of overlap with other
reporting guidelines; and highlights the criticality of appropriate
tools and resources in enabling 'MIAPE-compliant' reporting.},
doi = {10.1038/nbt1329},
institution = {The HUPO Proteomics Standards Initiative, Wellcome Trust Genome Campus,
Hinxton, Cambridgeshire CB10 1SD, UK. chris.taylor@ebi.ac.uk},
keywords = {Databases, Protein; Gene Expression Profiling; Genome, Human; Guidelines
as Topic; Humans; Information Storage and Retrieval; Internationality;
Proteomics; Research},
owner = {phupe},
pii = {nbt1329},
pmid = {17687369},
timestamp = {2010.08.13},
url = {http://dx.doi.org/10.1038/nbt1329}
}
@article{Taylor2002Protein,
author = {Taylor, W. R.},
title = {Protein structure comparison using bipartite graph matching and its
application to protein structure classification.},
journal = {Mol. Cell. Proteomics},
year = {2002},
volume = {1},
pages = {334--339},
number = {4},
month = {April},
abstract = {A measure of protein structure similarity is calculated from the matching
of pairs of secondary structure elements between two proteins. The
interaction of each pair was estimated from their axial line segments
and combined with other geometric features to produce an optimal
discrimination between intrafamily and interfamily relationships.
The matching used a fast bipartite graph-matching algorithm that
avoids the computational complexity of searching for the full subgraph
isomorphism between the two sets of interactions. The main algorithm
used was the "stable marriage" algorithm, which works on the ranked
"preferences" of one interaction for another. The method takes 1/10
of a second for a typical comparison making it suitable as a fast
pre-filter for slower, more exhaustive approaches. An application
to protein structure classification is described.},
address = {Division of Mathematical Biology, National Institute for Medical
Research, The Ridgeway, Mill Hill, London NW7 1AA, United Kingdom.
wtaylor@nimr.mrc.ac.uk},
citeulike-article-id = {892571},
citeulike-linkout-0 = {http://view.ncbi.nlm.nih.gov/pubmed/12096115},
citeulike-linkout-1 = {http://www.hubmed.org/display.cgi?uids=12096115},
issn = {1535-9476},
keywords = {structure\_classification, structure\_comparison},
posted-at = {2006-10-11 11:21:15},
priority = {2},
url = {http://view.ncbi.nlm.nih.gov/pubmed/12096115}
}
@article{Teer2010Exome,
author = {Jamie K Teer and James C Mullikin},
title = {Exome sequencing: the sweet spot before whole genomes.},
journal = {Hum Mol Genet},
year = {2010},
month = {Aug},
abstract = {The development of massively parallel sequencing technologies, coupled
with new massively parallel DNA enrichment technologies (genomic
capture), has allowed the sequencing of targeted regions of the human
genome in rapidly increasing numbers of samples. Genomic capture
can target specific areas in the genome, including genes of interest
and linkage regions, but this limits the study to what is already
known. Exome capture allows an unbiased investigation of the complete
protein-coding regions in the genome. Researchers can use exome capture
to focus on a critical part of the human genome, allowing larger
numbers of samples than are currently practical with whole-genome
sequencing. In this review, we briefly describe some of the methodologies
currently used for genomic and exome capture and highlight recent
applications of this technology.},
doi = {10.1093/hmg/ddq333},
institution = {Genetic Disease Research Branch and.},
owner = {phupe},
pii = {ddq333},
pmid = {20705737},
timestamp = {2010.08.30},
url = {http://dx.doi.org/10.1093/hmg/ddq333}
}
@article{Tegner2003Reverse,
author = {Tegner, J. and Yeung, M. K. S. and Hasty, J. and Collins, J. J.},
title = {{R}everse engineering gene networks: integrating genetic perturbations
with dynamical modeling},
journal = {Proc. Natl. Acad. Sci. USA},
year = {2003},
volume = {100},
pages = {5944--5949},
number = {10},
month = {May},
abstract = {While the fundamental building blocks of biology are being tabulated
by the various genome projects, microarray technology is setting
the stage for the task of deducing the connectivity of large-scale
gene networks. We show how the perturbation of carefully chosen genes
in a microarray experiment can be used in conjunction with a reverse
engineering algorithm to reveal the architecture of an underlying
gene regulatory network. Our iterative scheme identifies the network
topology by analyzing the steady-state changes in gene expression
resulting from the systematic perturbation of a particular node in
the network. We highlight the validity of our reverse engineering
approach through the successful deduction of the topology of a linear
in numero gene network and a recently reported model for the segmentation
polarity network in Drosophila melanogaster. Our method may prove
useful in identifying and validating specific drug targets and in
deconvolving the effects of chemical compounds.},
doi = {10.1073/pnas.0933416100},
pii = {0933416100},
pmid = {12730377},
timestamp = {2008.02.04},
url = {http://dx.doi.org/10.1073/pnas.0933416100}
}
@article{Teixeira2001Recent,
author = {R. D. Teixeira and A. P. Braga and R. H. Takahashi and R. R. Saldanha},
title = {Recent advances in the {MOBJ} algorithm for training artificial neural
networks.},
journal = {Int {J} {N}eural {S}yst},
year = {2001},
volume = {11},
pages = {265-70},
number = {3},
month = {Jun},
abstract = {This paper presents a new scheme for training {MLP}s which employs
a relaxation method for multi-objective optimization. {T}he algorithm
works by obtaining a reduced set of solutions, from which the one
with the best generalization is selected. {T}his approach allows
balancing between the training error and norm of network weight vectors,
which are the two objective functions of the multi-objective optimization
problem. {T}he method is applied to classification and regression
problems and compared with {W}eight {D}ecay ({WD}), {S}upport {V}ector
{M}achines ({SVM}s) and standard {B}ackpropagation ({BP}). {I}t is
shown that the systematic procedure for training proposed results
on good generalization neural models, and outperforms traditional
methods.},
pii = {S0129065701000709}
}
@article{Tenenbaum2000global,
author = {Tenenbaum, J. B. and de Silva, V. and Langford, J. C.},
title = {A global geometric framework for nonlinear dimensionality reduction},
journal = {Science},
year = {2000},
volume = {290},
pages = {2319-23},
number = {5500},
month = {Dec},
abstract = {Scientists working with large volumes of high-dimensional data, such
as global climate patterns, stellar spectra, or human gene distributions,
regularly confront the problem of dimensionality reduction: finding
meaningful low-dimensional structures hidden in their high-dimensional
observations. {T}he human brain confronts the same problem in everyday
perception, extracting from its high-dimensional sensory inputs-30,000
auditory nerve fibers or 10(6) optic nerve fibers-a manageably small
number of perceptually relevant features. {H}ere we describe an approach
to solving dimensionality reduction problems that uses easily measured
local metric information to learn the underlying global geometry
of a data set. {U}nlike classical techniques such as principal component
analysis ({PCA}) and multidimensional scaling ({MDS}), our approach
is capable of discovering the nonlinear degrees of freedom that underlie
complex natural observations, such as human handwriting or images
of a face under different viewing conditions. {I}n contrast to previous
algorithms for nonlinear dimensionality reduction, ours efficiently
computes a globally optimal solution, and, for an important class
of data manifolds, is guaranteed to converge asymptotically to the
true structure.},
doi = {10.1126/science.290.5500.2319},
pdf = {../local/Tenenbaum2000global.pdf},
file = {Tenenbaum2000global.pdf:local/Tenenbaum2000global.pdf:PDF},
keywords = {dimred},
pii = {290/5500/2319},
url = {http://dx.doi.org/10.1126/science.290.5500.2319}
}
@article{Teramoto2005Prediction,
author = {Reiji Teramoto and Mikio Aoki and Toru Kimura and Masaharu Kanaoka},
title = {Prediction of si{RNA} functionality using generalized string kernel
and support vector machine.},
journal = {F{EBS} {L}ett.},
year = {2005},
volume = {579},
pages = {2878-82},
number = {13},
month = {May},
abstract = {Small interfering {RNA}s (si{RNA}s) are becoming widely used for sequence-specific
gene silencing in mammalian cells, but designing an effective si{RNA}
is still a challenging task. {I}n this study, we developed an algorithm
for predicting si{RNA} functionality by using generalized string
kernel ({GSK}) combined with support vector machine ({SVM}). {W}ith
{GSK}, si{RNA} sequences were represented as vectors in a multi-dimensional
feature space according to the numbers of subsequences in each si{RNA},
and subsequently classified with {SVM} into effective or ineffective
si{RNA}s. {W}e applied this algorithm to published si{RNA}s, and
could classify effective and ineffective si{RNA}s with 90.6\%, 86.2\%
accuracy, respectively.},
doi = {10.1016/j.febslet.2005.04.045},
pdf = {../local/Teramoto2005Prediction.pdf},
file = {Teramoto2005Prediction.pdf:local/Teramoto2005Prediction.pdf:PDF},
keywords = {sirna biosvm},
pii = {S0014-5793(05)00520-X},
url = {http://dx.doi.org/10.1016/j.febslet.2005.04.045}
}
@article{Terentiev2009Dynamic,
author = {A. A. Terentiev and N. T. Moldogazieva and K. V. Shaitan},
title = {Dynamic proteomics in modeling of the living cell. Protein-protein
interactions.},
journal = {Biochemistry (Mosc)},
year = {2009},
volume = {74},
pages = {1586--1607},
number = {13},
month = {Dec},
abstract = {This review is devoted to describing, summarizing, and analyzing of
dynamic proteomics data obtained over the last few years and concerning
the role of protein-protein interactions in modeling of the living
cell. Principles of modern high-throughput experimental methods for
investigation of protein-protein interactions are described. Systems
biology approaches based on integrative view on cellular processes
are used to analyze organization of protein interaction networks.
It is proposed that finding of some proteins in different protein
complexes can be explained by their multi-modular and polyfunctional
properties; the different protein modules can be located in the nodes
of protein interaction networks. Mathematical and computational approaches
to modeling of the living cell with emphasis on molecular dynamics
simulation are provided. The role of the network analysis in fundamental
medicine is also briefly reviewed.},
institution = {Russian State Medical University, ul. Ostrovityanova 1, Moscow, Russia.
aaterent@mtu-net.ru},
keywords = {Animals; Humans; Mass Spectrometry; Models, Theoretical; Molecular
Dynamics Simulation; Multiprotein Complexes; Protein Conformation;
Protein Interaction Mapping; Proteins; Proteomics; Systems Biology;
Two-Hybrid System Techniques},
owner = {phupe},
pii = {BCM74131586},
pmid = {20210711},
timestamp = {2010.08.31}
}
@article{Thies2004Optimal,
author = {Thorsten Thies and Frank Weber},
title = {Optimal reduced-set vectors for support vector machines with a quadratic
kernel.},
journal = {Neural {C}omput},
year = {2004},
volume = {16},
pages = {1769-77},
number = {9},
month = {Sep},
abstract = {To reduce computational cost, the discriminant function of a support
vector machine ({SVM}) should be represented using as few vectors
as possible. {T}his problem has been tackled in different ways. {I}n
this article,we develop an explicit solution in the case of a general
quadratic kernel k(x. x') = ({C} + {D} x{T} x')2. {F}or a given number
of vectors, this solution provides the best possible approximation
and can even recover the discriminant function if the number of used
vectors is large enough. {T}he key idea is to express the inhomogeneous
kernel as a homogeneous kernel ona space having one dimension more
than the original one and to follow the approach of {B}urges (1996).}
}
@article{Thimm2004Comparison,
author = {M. Thimm and A. Goede and S. Hougardy and R. Preissner},
title = {{C}omparison of 2{D} similarity and 3{D} superposition. {A}pplication
to searching a conformational drug database.},
journal = {J Chem Inf Comput Sci},
year = {2004},
volume = {44},
pages = {1816--1822},
number = {5},
abstract = {In a database of about 2000 approved drugs, represented by 10(5) structural
conformers, we have performed 2D comparisons (Tanimoto coefficients)
and 3D superpositions. For one class of drugs the correlation between
structural resemblance and similar action was analyzed in detail.
In general Tanimoto coefficients and 3D scores give similar results,
but we find that 2D similarity measures neglect important structural/funtional
features. Examples for both over- and underestimation of similarity
by 2D metrics are discussed. The required additional effort for 3D
superpositions is assessed by implementation of a fast algorithm
with a processing time below 0.01 s and a more sophisticated approach
(0.5 s per superposition). According to the improvement of similarity
detection compared to 2D screening and the pleasant rapidity on a
desktop PC, full-atom 3D superposition will be an upcoming method
of choice for library prioritization or similarity screening approaches.},
doi = {10.1021/ci049920h},
keywords = {Arabidopsis, Carbohydrates, Circadian Rhythm, Comparative Study, Database
Management Systems, Gene Expression Regulation, Genes, Genome, Messenger,
Molecular Conformation, Mutation, Nitrogen, Non-U.S. Gov't, Oligonucleotide
Array Sequence Analysis, Pharmaceutical Preparations, Plant, RNA,
Research Support, 15446841},
owner = {mahe},
pmid = {15446841},
timestamp = {2006.08.22},
url = {http://dx.doi.org/10.1021/ci049920h}
}
@article{Thissen2004Multivariate,
author = {Uwe Thissen and Bülent Ustün and Willem J Melssen and Lutgarde
M C Buydens},
title = {Multivariate calibration with least-squares support vector machines.},
journal = {Anal {C}hem},
year = {2004},
volume = {76},
pages = {3099-105},
number = {11},
month = {Jun},
abstract = {This paper proposes the use of least-squares support vector machines
({LS}-{SVM}s) as a relatively new nonlinear multivariate calibration
method, capable of dealing with ill-posed problems. {LS}-{SVM}s are
an extension of "traditional" {SVM}s that have been introduced recently
in the field of chemistry and chemometrics. {T}he advantages of {SVM}-based
methods over many other methods are that these lead to global models
that are often unique, and nonlinear regression can be performed
easily as an extension to linear regression. {A}n additional advantage
of {LS}-{SVM} (compared to {SVM}) is that model calculation and optimization
can be performed relatively fast. {A}s a test case to study the use
of {LS}-{SVM}, the well-known and important chemical problem is considered
in which spectra are affected by nonlinear interferences. {A}s one
specific example, a commonly used case is studied in which near-infrared
spectra are affected by temperature-induced spectral variation. {U}sing
this test case, model optimization, pruning, and model interpretation
of the {LS}-{SVM} have been demonstrated. {F}urthermore, excellent
performance of the {LS}-{SVM}, compared to other approaches, has
been presented on the specific example. {T}herefore, it can be concluded
that {LS}-{SVM}s can be seen as very promising techniques to solve
ill-posed problems. {F}urthermore, these have been shown to lead
to robust models in cases of spectral variations due to nonlinear
interferences.},
doi = {10.1021/ac035522m},
pdf = {../local/Thissen2004Multivariate.pdf},
file = {Thissen2004Multivariate.pdf:local/Thissen2004Multivariate.pdf:PDF},
url = {http://dx.doi.org/10.1021/ac035522m}
}
@article{ThomasDLF2001,
author = {Thomas, R. and Kaufman, M.},
title = {Multistationarity, the basis of cell differentiation and memory.
{II}. {L}ogical analysis of regulatory networks in terms of feedback
circuits},
journal = {Chaos},
year = {2001},
volume = {11},
pages = {180-195},
number = {1},
abstract = {Circuits and their involvement in complex dynamics are described in
differential terms in {P}art {I} of this work. {H}ere, we first explain
why it may be appropriate to use a logical description, either by
itself or in symbiosis with the differential description. {T}he major
problem of a logical description is to find an adequate way to involve
time. {T}he procedure we adopted differs radically from the classical
one by its fully asynchronous character. {I}n {S}ec. {II} we describe
our "naive" logical approach, and use it to illustrate the major
laws of circuitry (namely, the involvement of positive circuits in
multistationarity and of negative circuits in periodicity) and in
a biological example. {A}lready in the naive description, the major
steps of the logical description are to: (i) describe a model as
a set of logical equations, (ii) derive the state table from the
equations, (iii) derive the graph of the sequences of states from
the state table, and (iv) determine which of the possible pathways
will be actually followed in terms of time delays. {I}n the following
sections we consider multivalued variables where required, the introduction
of logical parameters and of logical values ascribed to the thresholds,
and the concept of characteristic state of a circuit. {T}his generalized
logical description provides an image whose qualitative fit with
the differential description is quite remarkable. {A} major interest
of the generalized logical description is that it implies a limited
and often quite small number of possible combinations of values of
the logical parameters. {T}he space of the logical parameters is
thus cut into a limited number of boxes, each of which is characterized
by a defined qualitative behavior of the system. {O}ur analysis tells
which constraints on the logical parameters must be fulfilled in
order for any circuit (or combination of circuits) to be functional.
{F}unctionality of a circuit will result in multistationarity (in
the case of a positive circuit) or in a cycle (in the case of a negative
circuit). {T}he last sections deal with "more about time delays"
and "reverse logic," an approach that aims to proceed rationally
from facts to models. (c) 2001 {A}merican {I}nstitute of {P}hysics.}
}
@article{Thomassen2007Comparison,
author = {Thomassen, M. and Tan, Q. and Eiriksdottir, F. and Bak, M. and Cold,
S. and Kruse, T.A.},
title = {Comparison of gene sets for expression profiling: prediction of metastasis
from low-malignant breast cancer},
journal = {Clinical Cancer Research},
year = {2007},
volume = {13},
pages = {5355--5360},
number = {18},
publisher = {AACR}
}
@article{Thompson2011properties,
author = {John F Thompson and Patrice M Milos},
title = {The properties and applications of single-molecule DNA sequencing.},
journal = {Genome Biol},
year = {2011},
volume = {12},
pages = {217},
number = {2},
month = {Feb},
abstract = {ABSTRACT: Single-molecule sequencing enables DNA or RNA to be sequenced
directly from biological samples, making it well-suited for diagnostic
and clinical applications. Here we review the properties and applications
of this rapidly evolving and promising technology.},
doi = {10.1186/gb-2011-12-2-217},
institution = {Helicos BioSciences Corporation, Building 200LL, One Kendall Square,
Cambridge, MA 02139, USA. jthompson@helicosbio.com.},
language = {eng},
medline-pst = {aheadofprint},
owner = {phupe},
pii = {gb-2011-12-2-217},
pmid = {21349208},
timestamp = {2011.06.01},
url = {http://dx.doi.org/10.1186/gb-2011-12-2-217}
}
@article{Thukral2005Prediction,
author = {Sushil K Thukral and Paul J Nordone and Rong Hu and Leah Sullivan
and Eric Galambos and Vincent D Fitzpatrick and Laura Healy and Michael
B Bass and Mary E Cosenza and Cynthia A Afshari},
title = {Prediction of nephrotoxicant action and identification of candidate
toxicity-related biomarkers.},
journal = {Toxicol {P}athol},
year = {2005},
volume = {33},
pages = {343-55},
number = {3},
abstract = {A vast majority of pharmacological compounds and their metabolites
are excreted via the urine, and within the complex structure of the
kidney,the proximal tubules are a main target site of nephrotoxic
compounds. {W}e used the model nephrotoxicants mercuric chloride,
2-bromoethylamine hydrobromide, hexachlorobutadiene, mitomycin, amphotericin,
and puromycin to elucidate time- and dose-dependent global gene expression
changes associated with proximal tubular toxicity. {M}ale {S}prague-{D}awley
rats were dosed via intraperitoneal injection once daily for mercuric
chloride and amphotericin (up to 7 doses), while a single dose was
given for all other compounds. {A}nimals were exposed to 2 different
doses of these compounds and kidney tissues were collected on day
1, 3, and 7 postdosing. {G}ene expression profiles were generated
from kidney {RNA} using 17{K} rat c{DNA} dual dye microarray and
analyzed in conjunction with histopathology. {A}nalysis of gene expression
profiles showed that the profiles clustered based on similarities
in the severity and type of pathology of individual animals. {F}urther,
the expression changes were indicative of tubular toxicity showing
hallmarks of tubular degeneration/regeneration and necrosis. {U}se
of gene expression data in predicting the type of nephrotoxicity
was then tested with a support vector machine ({SVM})-based approach.
{A} {SVM} prediction module was trained using 120 profiles of total
profiles divided into four classes based on the severity of pathology
and clustering. {A}lthough mitomycin {C} and amphotericin {B} treatments
did not cause toxicity, their expression profiles were included in
the {SVM} prediction module to increase the sample size. {U}sing
this classifier, the {SVM} predicted the type of pathology of 28
test profiles with 100\% selectivity and 82\% sensitivity. {T}hese
data indicate that valid predictions could be made based on gene
expression changes from a small set of expression profiles. {A} set
of potential biomarkers showing a time- and dose-response with respect
to the progression of proximal tubular toxicity were identified.
{T}hese include several transporters ({S}lc21a2, {S}lc15, {S}lc34a2),
{K}im 1, {IGF}bp-1, osteopontin, alpha-fibrinogen, and {G}stalpha.},
doi = {10.1080/01926230590927230},
keywords = {Algorithms, Animals, Antibiotics, Antineoplastic, Artificial Intelligence,
Butadienes, Chloroplasts, Comparative Study, Computer Simulation,
Computer-Assisted, Diagnosis, Disinfectants, Dose-Response Relationship,
Drug, Drug Toxicity, Electrodes, Electroencephalography, Ethylamines,
Expert Systems, Feedback, Fungicides, Gene Expression Profiling,
Genes, Genetic Markers, Humans, Implanted, Industrial, Information
Storage and Retrieval, Kidney, Kidney Tubules, MEDLINE, Male, Mercuric
Chloride, Microarray Analysis, Molecular Biology, Motor Cortex, Movement,
Natural Language Processing, Neural Networks (Computer), Non-P.H.S.,
Non-U.S. Gov't, Plant Proteins, Predictive Value of Tests, Proteins,
Proteome, Proximal, Puromycin Aminonucleoside, Rats, Reproducibility
of Results, Research Support, Sprague-Dawley, Subcellular Fractions,
Terminology, Therapy, Time Factors, Toxicogenetics, U.S. Gov't, User-Computer
Interface, 15805072},
pii = {X3U2206L2747H31G},
url = {http://dx.doi.org/10.1080/01926230590927230}
}
@article{Tian2005Discovering,
author = {Tian, L and Greenberg, S. A. and Kong, S. W. and Altschuler, J. and
Kohane, I. S. and Park, P. J.},
title = {Discovering statistically significant pathways in expression profiling
studies.},
journal = {Proc Natl Acad Sci U S A},
year = {2005},
volume = {102},
pages = {13544--13549},
number = {38},
month = {Sep},
abstract = {Accurate and rapid identification of perturbed pathways through the
analysis of genome-wide expression profiles facilitates the generation
of biological hypotheses. We propose a statistical framework for
determining whether a specified group of genes for a pathway has
a coordinated association with a phenotype of interest. Several issues
on proper hypothesis-testing procedures are clarified. In particular,
it is shown that the differences in the correlation structure of
each set of genes can lead to a biased comparison among gene sets
unless a normalization procedure is applied. We propose statistical
tests for two important but different aspects of association for
each group of genes. This approach has more statistical power than
currently available methods and can result in the discovery of statistically
significant pathways that are not detected by other methods. This
method is applied to data sets involving diabetes, inflammatory myopathies,
and Alzheimer's disease, using gene sets we compiled from various
public databases. In the case of inflammatory myopathies, we have
correctly identified the known cytotoxic T lymphocyte-mediated autoimmunity
in inclusion body myositis. Furthermore, we predicted the presence
of dendritic cells in inclusion body myositis and of an IFN-alpha/beta
response in dermatomyositis, neither of which was previously described.
These predictions have been subsequently corroborated by immunohistochemistry.},
doi = {10.1073/pnas.0506577102},
pdf = {../local/Tian2005Discovering.pdf},
file = {Tian2005Discovering.pdf:Tian2005Discovering.pdf:PDF},
institution = {Department of Preventive Medicine, Feinberg School of Medicine, Northwestern
University, 680 North Lake Shore Drive, Chicago, IL 60611, USA.},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {0506577102},
pmid = {16174746},
timestamp = {2011.08.07},
url = {http://dx.doi.org/10.1073/pnas.0506577102}
}
@article{Tian2004novel,
author = {Liang Tian and Afzel Noore},
title = {A novel approach for short-term load forecasting using support vector
machines.},
journal = {Int {J} {N}eural {S}yst},
year = {2004},
volume = {14},
pages = {329-35},
number = {5},
month = {Oct},
abstract = {A support vector machine ({SVM}) modeling approach for short-term
load forecasting is proposed. {T}he {SVM} learning scheme is applied
to the power load data, forcing the network to learn the inherent
internal temporal property of power load sequence. {W}e also study
the performance when other related input variables such as temperature
and humidity are considered. {T}he performance of our proposed {SVM}
modeling approach has been tested and compared with feed-forward
neural network and cosine radial basis function neural network approaches.
{N}umerical results show that the {SVM} approach yields better generalization
capability and lower prediction error compared to those neural network
approaches.},
pii = {S0129065704002078}
}
@article{Tibshirani1997lasso,
author = {Tibshirani, R.},
title = {The lasso method for variable selection in the {C}ox model.},
journal = {Stat. Med.},
year = {1997},
volume = {16},
pages = {385--395},
number = {4},
month = {Feb},
abstract = {I propose a new method for variable selection and shrinkage in Cox's
proportional hazards model. My proposal minimizes the log partial
likelihood subject to the sum of the absolute values of the parameters
being bounded by a constant. Because of the nature of this constraint,
it shrinks coefficients and produces some coefficients that are exactly
zero. As a result it reduces the estimation variance while providing
an interpretable final model. The method is a variation of the 'lasso'
proposal of Tibshirani, designed for the linear regression context.
Simulations indicate that the lasso can be more accurate than stepwise
selection in this setting.},
pdf = {../local/Tibshirani1997lasso.pdf},
file = {Tibshirani1997lasso.pdf:Tibshirani1997lasso.pdf:PDF},
institution = {Department of Preventive Medicine and Biostatistics, University of
Toronto, Ontario, Canada.},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {3.0.CO;2-3},
pmid = {9044528},
timestamp = {2010.01.10}
}
@article{Tibshirani1996Regression,
author = {Tibshirani, R.},
title = {Regression shrinkage and selection via the lasso},
journal = {J. R. Stat. Soc. Ser. B},
year = {1996},
volume = {58},
pages = {267-288},
number = {1}
}
@article{Tibshirani2005Sparsity,
author = {Tibshirani, R. and Saunders, M. and Rosset, S. and Zhu, J. and Knight,
K.},
title = {Sparsity and smoothness via the fused lasso},
journal = {J. R. Stat. Soc. Ser. B Stat. Methodol.},
year = {2005},
volume = {67},
pages = {91-108},
number = {1},
url = {http://ideas.repec.org/a/bla/jorssb/v67y2005i1p91-108.html}
}
@article{Tibshirani2001Estimating,
author = {Tibshirani, R. and Walther, G. and Hastie, T.},
title = {Estimating the number of clusters in a data set via the gap statistics},
journal = {J. R. Stat. Soc. Ser. B},
year = {2001},
volume = {63},
pages = {411--423},
owner = {jp},
timestamp = {2011.12.30}
}
@article{Tibshirani2008Spatial,
author = {Tibshirani, R. and Wang, P.},
title = {Spatial smoothing and hot spot detection for CGH data using the fused
lasso.},
journal = {Biostatistics (Oxford, England)},
year = {2008},
volume = {9},
pages = {18--29},
number = {1},
month = {January},
abstract = {We apply the "fused lasso" regression method of (TSRZ2004) to the
problem of "hot- spot detection", in particular, detection of regions
of gain or loss in comparative genomic hybridization (CGH) data.
The fused lasso criterion leads to a convex optimization problem,
and we provide a fast algorithm for its solution. Estimates of false-discovery
rate are also provided. Our studies show that the new method generally
outperforms competing methods for calling gains and losses in CGH
data.},
address = {Department of Health, Stanford University Stanford, CA 94305, USA.
tibs@stat.stanford.edu},
citeulike-article-id = {2744846},
citeulike-linkout-0 = {http://dx.doi.org/10.1093/biostatistics/kxm013},
citeulike-linkout-1 = {http://biostatistics.oxfordjournals.org/cgi/content/abstract/9/1/18},
citeulike-linkout-2 = {http://view.ncbi.nlm.nih.gov/pubmed/17513312},
citeulike-linkout-3 = {http://www.hubmed.org/display.cgi?uids=17513312},
doi = {10.1093/biostatistics/kxm013},
issn = {1465-4644},
keywords = {copy\_number},
posted-at = {2008-05-02 10:45:47},
priority = {4},
url = {http://dx.doi.org/10.1093/biostatistics/kxm013}
}
@article{Tiffin2005Integration,
author = {Tiffin, N. and Kelso, J. F. and Powell, A. R. and Pan, H. and Bajic,
V. B. and Hide, W. A.},
title = {Integration of text- and data-mining using ontologies successfully
selects disease gene candidates},
journal = {Nucleic Acids Res.},
year = {2005},
volume = {33},
pages = {1544--1552},
number = {5},
abstract = {Genome-wide techniques such as microarray analysis, Serial Analysis
of Gene Expression (SAGE), Massively Parallel Signature Sequencing
(MPSS), linkage analysis and association studies are used extensively
in the search for genes that cause diseases, and often identify many
hundreds of candidate disease genes. Selection of the most probable
of these candidate disease genes for further empirical analysis is
a significant challenge. Additionally, identifying the genes that
cause complex diseases is problematic due to low penetrance of multiple
contributing genes. Here, we describe a novel bioinformatic approach
that selects candidate disease genes according to their expression
profiles. We use the eVOC anatomical ontology to integrate text-mining
of biomedical literature and data-mining of available human gene
expression data. To demonstrate that our method is successful and
widely applicable, we apply it to a database of 417 candidate genes
containing 17 known disease genes. We successfully select the known
disease gene for 15 out of 17 diseases and reduce the candidate gene
set to 63.3\% (+/-18.8\%) of its original size. This approach facilitates
direct association between genomic data describing gene expression
and information from biomedical texts describing disease phenotype,
and successfully prioritizes candidate genes according to their expression
in disease-affected tissues.},
doi = {10.1093/nar/gki296},
pdf = {../local/Tiffin2005Integration.pdf},
file = {Tiffin2005Integration.pdf:Tiffin2005Integration.pdf:PDF},
institution = {South African National Bioinformatics Institute, University of the
Western Cape Belville 7535, South Africa. nicki@sanbi.ac.za},
owner = {jp},
pii = {33/5/1544},
pmid = {15767279},
timestamp = {2009.03.18},
url = {http://dx.doi.org/10.1093/nar/gki296}
}
@article{Tikhonov1963Solution,
author = {A.N. Tikhonov},
title = {Solution of incorrectly problems and the regularization method},
journal = {{S}oviet {M}athematics {D}oklady},
year = {1963},
volume = {4},
pages = {1035-1038}
}
@article{Tikhonov1943stability,
author = {A.N. Tikhonov},
title = {On the stability of inverse problems},
journal = {{D}oklady {A}kademii nauk {SSSR}},
year = {1943},
volume = {39},
pages = {195-198},
number = {5}
}
@book{Tikhonov1977Solutions,
title = {Solutions of ill-posed problems},
publisher = {W.H. Winston},
year = {1977},
author = {A.N. Tikhonov and V.Y. Arsenin},
address = {Washington, D.C.},
subject = {ml}
}
@inproceedings{Tillmann06Efficient,
author = {Tillmann, C.},
title = {{Efficient Dynamic Programming Search Algorithms For Phrase-Based
SMT}},
booktitle = {{Workshop On Computationally Hard Problems And Joint Inference In
Speech And Language Processing}},
year = {2006},
url = {http://www.aclweb.org/anthology-new/W/W06/W06-3602.pdf}
}
@article{Tillman03Word,
author = {Tillmann,, C. and Ney,, H.},
title = {Word reordering and a dynamic programming beam search algorithm for
statistical machine translation},
journal = {Comput. Linguist.},
year = {2003},
volume = {29},
pages = {97--133},
number = {1},
address = {Cambridge, MA, USA},
doi = {http://dx.doi.org/10.1162/089120103321337458},
issn = {0891-2017},
publisher = {MIT Press}
}
@article{Tjalkens1992universal,
author = {Tjalkens, T.J. and Willems, F.M.J.},
title = {A universal variable-to-fixed length source code based on {L}awrence's
algorithm},
journal = {I{EEE} {T}rans. {I}nform. {T}heory},
year = {1992},
volume = {38},
pages = {247-253},
number = {2},
month = {Mar},
abstract = {It is shown that the modified {L}awrence algorithm is universal over
the class of binary memoryless sources and that the rate converges
asymptotically optimally fast to the source entropy. {I}t is proven
that no codes exist that have a better asymptotic performance. {T}he
asymptotic bounds show that universal variable-to-fixed-length codes
can have a significantly lower redundancy than universal fixed-to-variable-length
codes with the same number of codewords},
pdf = {../local/Tjalkens1992universal.pdf},
file = {Tjalkens1992universal.pdf:local/Tjalkens1992universal.pdf:PDF},
keywords = {information-theory source-coding},
owner = {vert}
}
@inproceedings{Tjalkens1997Implementing,
author = {Tj.J. Tjalkens and F. M. J. Willems},
title = {Implementing the {C}ontext-{T}ree {W}eighting {M}ethod: {A}rithmetic
{C}oding,},
booktitle = {Int. {C}onf. on {C}ombinatorics, {I}nformation {T}heory and {S}tatistics},
year = {1997},
pages = {83},
address = {Portland, Maine, U.S.A},
month = {18-20},
pdf = {../local/tjal97.pdf},
file = {tjal97.pdf:local/tjal97.pdf:PDF},
subject = {it},
url = {http://ei1.ei.ele.tue.nl/~frans/maine.ps}
}
@article{Tjong2012Physical,
author = {Tjong, H. and Gong, K. and Chen, L. and Alber, F.},
title = {Physical tethering and volume exclusion determine higher-order genome
organization in budding yeast.},
journal = {Genome Res.},
year = {2012},
month = {May},
abstract = {In this paper we show that tethering of heterochromatic regions to
nuclear landmarks and random encounters of chromosomes in the confined
nuclear volume are sufficient to explain the higher-order organization
of the budding yeast genome. We have quantitatively characterized
the contact patterns and nuclear territories that emerge when chromosomes
are allowed to behave as constrained but otherwise randomly configured
flexible polymer chains in the nucleus. Remarkably, this constrained
random encounter model explains in a statistical manner the experimental
hallmarks of the S. cerevisiae genome organization, including (1)
the folding patterns of individual chromosomes; (2) the highly enriched
interactions between specific chromatin regions and chromosomes;
(3) the emergence, shape, and position of gene territories; (4) the
mean distances between pairs of telomeres; and (5) even the co-location
of functionally related gene loci, including early replication start
sites and tRNA genes. Therefore, most aspects of the yeast genome
organization can be explained without calling on biochemically mediated
chromatin interactions. Such interactions may modulate the pre-existing
propensity for co-localization but seem not to be the cause for the
observed higher-order organization. The fact that geometrical constraints
alone yield a highly organized genome structure, on which different
functional elements are specifically distributed, has strong implications
for the folding principles of the genome and the evolution of its
function.},
doi = {10.1101/gr.129437.111},
pdf = {../local/Tjong2012Physical.pdf},
file = {Tjong2012Physical.pdf:Tjong2012Physical.pdf:PDF},
institution = {Molecular and Computational Biology, Department of Biological Sciences,
University of Southern California, Los Angeles, California 90089,
USA;},
language = {eng},
medline-pst = {aheadofprint},
owner = {jp},
pii = {gr.129437.111},
pmid = {22619363},
timestamp = {2012.06.24},
url = {http://dx.doi.org/10.1101/gr.129437.111}
}
@article{Tobita2005discriminant,
author = {Tobita, M. and Nishikawa, T. and Nagashima, R.},
title = {A discriminant model constructed by the support vector machine method
for {HERG} potassium channel inhibitors.},
journal = {Bioorg. {M}ed. {C}hem. {L}ett.},
year = {2005},
volume = {15},
pages = {2886-90},
number = {11},
month = {Jun},
abstract = {H{ERG} attracts attention as a risk factor for arrhythmia, which might
trigger torsade de pointes. {A} highly accurate classifier of chemical
compounds for inhibition of the {HERG} potassium channel is constructed
using support vector machine. {F}or two test sets, our discriminant
models achieved 90\% and 95\% accuracy, respectively. {T}he classifier
is even applied for the prediction of cardio vascular adverse effects
to achieve about 70\% accuracy. {W}hile modest inhibitors are partly
characterized by properties linked to global structure of a molecule
including hydrophobicity and diameter, strong inhibitors are exclusively
characterized by properties linked to substructures of a molecule.},
doi = {10.1016/j.bmcl.2005.03.080},
pdf = {../local/Tobita2005discriminant.pdf},
file = {Tobita2005discriminant.pdf:local/Tobita2005discriminant.pdf:PDF},
keywords = {biosvm chemoinformatics herg},
pii = {S0960-894X(05)00403-8},
url = {http://dx.doi.org/10.1016/j.bmcl.2005.03.080}
}
@book{Todeschini2002Handbook,
title = {Handbook of Molecular Descriptors},
publisher = {Wiley-VCH},
year = {2002},
author = {Todeschini, R. and Consonni, V.},
address = {New York},
keywords = {chemoinformatics},
timestamp = {2007.09.03}
}
@article{Tomari2005Perspective,
author = {Tomari, Y. and Zamore, P. D.},
title = {Perspective: machines for {RNA}i.},
journal = {Genes {D}ev.},
year = {2005},
volume = {19},
pages = {517-29},
number = {5},
month = {Mar},
abstract = {R{NA} silencing pathways convert the sequence information in long
{RNA}, typically double-stranded {RNA}, into approximately 21-nt
{RNA} signaling molecules such as small interfering {RNA}s (si{RNA}s)
and micro{RNA}s (mi{RNA}s). si{RNA}s and mi{RNA}s provide specificity
to protein effector complexes that repress m{RNA} transcription or
translation, or catalyze m{RNA} destruction. {H}ere, we review our
current understanding of how small {RNA}s are produced, how they
are loaded into protein complexes, and how they repress gene expression.},
doi = {10.1101/gad.1284105},
keywords = {sirna},
pii = {19/5/517},
url = {http://dx.doi.org/10.1101/gad.1284105}
}
@article{Tomfohr2005Pathway,
author = {Tomfohr, J. and Lu, J. and Kepler, T. B.},
title = {Pathway level analysis of gene expression using singular value decomposition.},
journal = {BMC Bioinformatics},
year = {2005},
volume = {6},
pages = {225},
abstract = {A promising direction in the analysis of gene expression focuses on
the changes in expression of specific predefined sets of genes that
are known in advance to be related (e.g., genes coding for proteins
involved in cellular pathways or complexes). Such an analysis can
reveal features that are not easily visible from the variations in
the individual genes and can lead to a picture of expression that
is more biologically transparent and accessible to interpretation.
In this article, we present a new method of this kind that operates
by quantifying the level of 'activity' of each pathway in different
samples. The activity levels, which are derived from singular value
decompositions, form the basis for statistical comparisons and other
applications.We demonstrate our approach using expression data from
a study of type 2 diabetes and another of the influence of cigarette
smoke on gene expression in airway epithelia. A number of interesting
pathways are identified in comparisons between smokers and non-smokers
including ones related to nicotine metabolism, mucus production,
and glutathione metabolism. A comparison with results from the related
approach, 'gene-set enrichment analysis', is also provided.Our method
offers a flexible basis for identifying differentially expressed
pathways from gene expression data. The results of a pathway-based
analysis can be complementary to those obtained from one more focused
on individual genes. A web program PLAGE (Pathway Level Analysis
of Gene Expression) for performing the kinds of analyses described
here is accessible at http://dulci.biostat.edu/pathways.},
doi = {10.1186/1471-2105-6-225},
pdf = {../local/Tomfohr2005Pathway.pdf},
file = {Tomfohr2005Pathway.pdf:Tomfohr2005Pathway.pdf:PDF},
institution = {Department of Biostatistics and Bioinformatics, Center for Bioinformatics
and Computational Biology, Institute for Genome Sciences and Policy,
Duke University, Durham, North Carolina 27708, USA. tomfohr@duke.edu},
language = {eng},
medline-pst = {epublish},
owner = {jp},
pii = {1471-2105-6-225},
pmid = {16156896},
timestamp = {2011.08.07},
url = {http://dx.doi.org/10.1186/1471-2105-6-225}
}
@techreport{Tomioka2008Sparse,
author = {Tomioka, R. and Sugiyama, M.},
title = {Sparse learning with duality gap guarantee},
institution = {Department of Computer Science; Graduate School of Information Science
and Engineering, Tokyo Institute of Technology, 152-8552, Tokyo,
Japan},
year = {2008},
journal = {NIPS2008Workshop on Optimization for Machine Learning (OPT2008)}
}
@article{Tomioka2011Super,
author = {Tomioka, R. and Suzuki, T. and Sugiyama, M.},
title = {Super-Linear Convergence of Dual Augmented-Lagrangian Algorithm for
Sparsity Regularized Estimation},
journal = {J. Mach. Learn. Res.},
year = {2011},
volume = {12},
pages = {1537--1586},
pdf = {../local/Tomioka2011Super.pdf},
file = {Tomioka2011Super.pdf:Tomioka2011Super.pdf:PDF}
}
@article{Tomita1999Bioinformatics,
author = {Tomita, M. and Hashimoto, K. and Takahashi, K. and Shimizu, T. S.
and Matsuzaki, Y. and Miyoshi, F. and Saito, K. and Tanida, S. and
Yugi, K. and Venter, J. C. and Hutchison, C. A.},
title = {{E-CELL}: software environment for whole-cell simulation},
journal = {Bioinformatics},
year = {1999},
volume = {15},
pages = {72-84},
number = {1},
doi = {10.1093/bioinformatics/15.1.72},
eprint = {http://bioinformatics.oxfordjournals.org/cgi/reprint/15/1/72.pdf},
keywords = {csbcbook},
url = {http://bioinformatics.oxfordjournals.org/cgi/content/abstract/15/1/72}
}
@article{Tomizaki2010Protein,
author = {{Kin-ya} Tomizaki and Kenji Usui and Hisakazu Mihara},
title = {Protein-protein interactions and selection: array-based techniques
for screening disease-associated biomarkers in predictive/early diagnosis.},
journal = {FEBS J},
year = {2010},
volume = {277},
pages = {1996--2005},
number = {9},
month = {May},
abstract = {There has been considerable interest in recent years in the development
of miniaturized and parallelized array technology for protein-protein
interaction analysis and protein profiling, namely 'protein-detecting
microarrays'. Protein-detecting microarrays utilize a wide variety
of capture agents (antibodies, fusion proteins, DNA/RNA aptamers,
synthetic peptides, carbohydrates, and small molecules) immobilized
at high spatial density on a solid surface. Each capture agent binds
selectively to its target protein in a complex mixture, such as serum
or cell lysate samples. Captured proteins are subsequently detected
and quantified in a high-throughput fashion, with minimal sample
consumption. Protein-detecting microarrays were first described by
MacBeath and Schreiber in 2000, and the number of publications involving
this technology is rapidly increasing. Furthermore, the first multiplex
immunoassay systems have been cleared by the US Food and Drug Administration,
signaling recognition of the usefulness of miniaturized and parallelized
array technology for protein detection in predictive/early diagnosis.
Although genetic tests still predominate, with further development
protein-based diagnosis will become common in clinical use within
a few years.},
doi = {10.1111/j.1742-4658.2010.07626.x},
institution = {Innovative Materials and Processing Research Center and Department
of Materials Chemistry, Ryukoku University, Otsu, Japan.},
keywords = {Animals; Biological Markers, analysis/metabolism; Early Diagnosis;
Humans; Mass Screening, methods; Protein Array Analysis, methods;
Proteins, analysis/metabolism; Risk Factors},
language = {eng},
medline-pst = {ppublish},
owner = {philippe},
pii = {EJB7626},
pmid = {20412053},
timestamp = {2010.07.28},
url = {http://dx.doi.org/10.1111/j.1742-4658.2010.07626.x}
}
@article{Tomlins2007Integrative,
author = {Scott A Tomlins and Rohit Mehra and Daniel R Rhodes and Xuhong Cao
and Lei Wang and Saravana M Dhanasekaran and Shanker Kalyana-Sundaram
and John T Wei and Mark A Rubin and Kenneth J Pienta and Rajal B
Shah and Arul M Chinnaiyan},
title = {Integrative molecular concept modeling of prostate cancer progression.},
journal = {Nat. Genet.},
year = {2007},
volume = {39},
pages = {41--51},
number = {1},
month = {Jan},
abstract = {Despite efforts to profile prostate cancer, the genetic alterations
and biological processes that correlate with the observed histological
progression are unclear. Using laser-capture microdissection to isolate
101 cell populations, we have profiled prostate cancer progression
from benign epithelium to metastatic disease. By analyzing expression
signatures in the context of over 14,000 'molecular concepts', or
sets of biologically connected genes, we generated an integrative
model of progression. Molecular concepts that demarcate critical
transitions in progression include protein biosynthesis, E26 transformation-specific
(ETS) family transcriptional targets, androgen signaling and cell
proliferation. Of note, relative to low-grade prostate cancer (Gleason
pattern 3), high-grade cancer (Gleason pattern 4) shows an attenuated
androgen signaling signature, similar to metastatic prostate cancer,
which may reflect dedifferentiation and explain the clinical association
of grade with prognosis. Taken together, these data show that analyzing
gene expression signatures in the context of a compendium of molecular
concepts is useful in understanding cancer biology.},
doi = {10.1038/ng1935},
pdf = {../local/Tomlins2007Integrative.pdf},
file = {Tomlins2007Integrative.pdf:Tomlins2007Integrative.pdf:PDF},
institution = {Department of Pathology, University of Michigan Medical School, Ann
Arbor, Michigan 48109, USA.},
owner = {laurent},
pii = {ng1935},
pmid = {17173048},
timestamp = {2008.10.26},
url = {http://dx.doi.org/10.1038/ng1935}
}
@article{Tommaso2003Steady-state,
author = {Marina de Tommaso and Sebastiano Stramaglia and Jan Mathijs Schoffelen
and Marco Guido and Giuseppe Libro and Luciana Losito and Vittorio
Sciruicchio and Michele Sardaro and Mario Pellicoro and Franco Michele
Puca},
title = {Steady-state visual evoked potentials in the low frequency range
in migraine: a study of habituation and variability phenomena.},
journal = {Int {J} {P}sychophysiol},
year = {2003},
volume = {49},
pages = {165-74},
number = {2},
month = {Aug},
abstract = {Previous studies have revealed that migraine patients display an increased
photic driving to flash stimuli in the medium frequency range. {T}he
aim of this study was to perform a topographic analysis of steady-state
visual evoked potentials ({SVEP}s) in the low frequency range (3-9
{H}z), evaluating the temporal behaviour of the {F}1 amplitude by
investigating habituation and variability phenomena. {T}he main component
of {SVEP}s, the {F}1, demonstrated an increased amplitude in several
channels at 3 {H}z. {B}ehaviour of {F}1 amplitude was rather variable
over time, and the wavelet-transform standard deviation was increased
in migraine patients at a low stimulus rate. {T}he discriminative
value of the {F}1 mean amplitude and variability index, tested by
both an artificial neural network classifier and a support vector
machine, were high according to both methods. {T}he increased photic
driving in migraine should be subtended by a more generic abnormality
of visual reactivity instead of a selective impairment of a visual
subsystem. {T}emporal behaviour of {SVEP}s is not influenced by a
clear tendency to habituation, but the {F}1 amplitude seemed to change
in a complex way, which is better described by variability phenomena.
{A}n increased variability in response to flicker stimuli in migraine
patients could be interpreted as an overactive regulation mechanism,
prone to instability and consequently to headache attacks, whether
spontaneous or triggered.},
pii = {S016787600300117X}
}
@article{Tompa2005Assessing,
author = {Martin Tompa and Nan Li and Timothy L Bailey and George M Church
and Bart De Moor and Eleazar Eskin and Alexander V Favorov and Martin
C Frith and Yutao Fu and W James Kent and Vsevolod J Makeev and Andrei
A Mironov and William Stafford Noble and Giulio Pavesi and Graziano
Pesole and Mireille Régnier and Nicolas Simonis and Saurabh Sinha
and Gert Thijs and Jacques Van Helden and Mathias Vandenbogaert and
Zhiping Weng and Christopher Workman and Chun Ye and Zhou Zhu},
title = {Assessing computational tools for the discovery of transcription
factor binding sites},
journal = {Nat. Biotechnol.},
year = {2005},
volume = {23},
pages = {137-144},
keywords = {csbcbook}
}
@article{Tong2007In,
author = {Joo Chuan Tong and Tin Wee Tan and Shoba Ranganathan},
title = {In silico grouping of peptide/HLA class I complexes using structural
interaction characteristics.},
journal = {Bioinformatics},
year = {2007},
volume = {23},
pages = {177--183},
number = {2},
month = {Jan},
abstract = {MOTIVATION: Classification of human leukocyte antigen (HLA) proteins
into supertypes underpins the development of epitope-based vaccines
with wide population coverage. Current methods for HLA supertype
definition, based on common structural features of HLA proteins and/or
their functional binding specificities, leave structural interaction
characteristics among different HLA supertypes with antigenic peptides
unexplored. METHODS: We describe the use of structural interaction
descriptors for the analysis of 68 peptide/HLA class I crystallographic
structures. Interaction parameters computed include the number of
intermolecular hydrogen bonds between each HLA protein and its corresponding
bound peptide, solvent accessibility, gap volume and gap index. RESULTS:
The structural interactions patterns of peptide/HLA class I complexes
investigated herein vary among individual alleles and may be grouped
in a supertype dependent manner. Using the proposed methodology,
eight HLA class I supertypes were defined based on existing experimental
crystallographic structures which largely overlaps (77\% consensus)
with the definitions by binding motifs. This mode of classification,
which considers conformational information of both peptide and HLA
proteins, provides an alternative to the characterization of supertypes
using either peptide or HLA protein information alone.},
doi = {10.1093/bioinformatics/btl563},
owner = {laurent},
pii = {btl563},
pmid = {17090577},
timestamp = {2007.01.28},
url = {http://dx.doi.org/10.1093/bioinformatics/btl563}
}
@article{Tong2006Prediction,
author = {Tong, J. C. and Zhang, G. L. and Tan, T. W. and August, J. T. and
Brusic, V. and Ranganathan, S.},
title = {{P}rediction of {HLA}-{DQ}3.2beta ligands: evidence of multiple registers
in class {II} binding peptides.},
journal = {Bioinformatics},
year = {2006},
volume = {22},
pages = {1232--1238},
number = {10},
month = {May},
doi = {10.1093/bioinformatics/btl071},
keywords = {immunoinformatics},
pii = {btl071},
pmid = {16510499},
timestamp = {2007.01.25},
url = {http://dx.doi.org/10.1093/bioinformatics/btl071}
}
@article{Topiol2009X-ray,
author = {Sid Topiol and Michael Sabio},
title = {X-ray structure breakthroughs in the {GPCR} transmembrane region.},
journal = {Biochem Pharmacol},
year = {2009},
volume = {78},
pages = {11--20},
number = {1},
month = {Jul},
abstract = {G-protein-coupled receptor (GPCR) proteins [Lundstrom KH, Chiu ML,
editors. G protein-coupled receptors in drug discovery. CRC Press;
2006] are the single largest drug target, representing 25-50\% of
marketed drugs [Overington JP, Al-Lazikani B, Hopkins AL. How many
drug targets are there? Nat Rev Drug Discov 2006;5(12):993-6; Parrill
AL. Crystal structures of a second G protein-coupled receptor: triumphs
and implications. ChemMedChem 2008;3:1021-3]. While there are six
subclasses of GPCR proteins, the hallmark of all GPCR proteins is
the transmembrane-spanning region. The general architecture of this
transmembrane (TM) region has been known for some time to contain
seven alpha-helices. From a drug discovery and design perspective,
structural information of the GPCRs has been sought as a tool for
structure-based drug design. The advances in the past decade of technologies
for structure-based design have proven to be useful in a number of
areas. Invoking these approaches for GPCR targets has remained challenging.
Until recently, the most closely related structures available for
GPCR modeling have been those of bovine rhodopsin. While a representative
of class A GPCRs, bovine rhodopsin is not a ligand-activated GPCR
and is fairly distant in sequence homology to other class A GPCRs.
Thus, there is a variable degree of uncertainty in the use of the
rhodopsin X-ray structure as a template for homology modeling of
other GPCR targets. Recent publications of X-ray structures of class
A GPCRs now offer the opportunity to better understand the molecular
mechanism of action at the atomic level, to deploy X-ray structures
directly for their use in structure-based design, and to provide
more promising templates for many other ligand-mediated GPCRs. We
summarize herein some of the recent findings in this area and provide
an initial perspective of the emerging opportunities, possible limitations,
and remaining questions. Other aspects of the recent X-ray structures
are described by Weis and Kobilka [Weis WI, Kobilka BK. Structural
insights into G-protein-coupled receptor activation. Curr Opin Struct
Biol 2008;18:734-40] and Mustafi and Palczewski [Mustafi D, Palczewski
K. Topology of class A G protein-coupled receptors: insights gained
from crystal structures of rhodopsins, adrenergic and adenosine receptors.
Mol Pharmacol 2009;75:1-12].},
doi = {10.1016/j.bcp.2009.02.012},
institution = {Department of Computational Chemistry, Lundbeck Research USA, Inc.,
215 College Road, Paramus, NJ 07652, USA.},
keywords = {Animals; Cell Membrane; Humans; Models, Molecular; Molecular Conformation;
Pindolol; Propanolamines; Protein Conformation; Receptor, Adenosine
A2A; Receptors, Adrenergic, beta-2; Receptors, G-Protein-Coupled;
Retinaldehyde; Rhodopsin; X-Ray Diffraction},
owner = {ljacob},
pii = {S0006-2952(09)00113-0},
pmid = {19447219},
timestamp = {2009.11.09},
url = {http://dx.doi.org/10.1016/j.bcp.2009.02.012}
}
@article{Tothill2005expression-based,
author = {Richard W Tothill and Adam Kowalczyk and Danny Rischin and Alex Bousioutas
and Izhak Haviv and Ryan K van Laar and Paul M Waring and John Zalcberg
and Robyn Ward and Andrew V Biankin and Robert L Sutherland and Susan
M Henshall and Kwun Fong and Jonathan R Pollack and David D L Bowtell
and Andrew J Holloway},
title = {An expression-based site of origin diagnostic method designed for
clinical application to cancer of unknown origin.},
journal = {Cancer {R}es.},
year = {2005},
volume = {65},
pages = {4031-40},
number = {10},
month = {May},
abstract = {Gene expression profiling offers a promising new technique for the
diagnosis and prognosis of cancer. {W}e have applied this technology
to build a clinically robust site of origin classifier with the ultimate
aim of applying it to determine the origin of cancer of unknown primary
({CUP}). {A} single c{DNA} microarray platform was used to profile
229 primary and metastatic tumors representing 14 tumor types and
multiple histologic subtypes. {T}his data set was subsequently used
for training and validation of a support vector machine ({SVM}) classifier,
demonstrating 89\% accuracy using a 13-class model. {F}urther, we
show the translation of a five-class classifier to a quantitative
{PCR}-based platform. {S}electing 79 optimal gene markers, we generated
a quantitative-{PCR} low-density array, allowing the assay of both
fresh-frozen and formalin-fixed paraffin-embedded ({FFPE}) tissue.
{D}ata generated using both quantitative {PCR} and microarray were
subsequently used to train and validate a cross-platform {SVM} model
with high prediction accuracy. {F}inally, we applied our {SVM} classifiers
to 13 cases of {CUP}. {W}e show that the microarray {SVM} classifier
was capable of making high confidence predictions in 11 of 13 cases.
{T}hese predictions were supported by comprehensive review of the
patients' clinical histories.},
doi = {10.1158/0008-5472.CAN-04-3617},
pdf = {../local/Tothill2005expression-based.pdf},
file = {Tothill2005expression-based.pdf:Tothill2005expression-based.pdf:PDF},
keywords = {biosvm microarray},
pii = {65/10/4031},
url = {http://dx.doi.org/10.1158/0008-5472.CAN-04-3617}
}
@article{Tournier2009JTB,
author = {Tournier, L. and Chaves, M.},
title = {Uncovering operational interactions in genetic networks using asynchronous
Boolean dynamics},
journal = {Journal of Theoretical Biology},
year = {2009},
volume = {260},
pages = {196--209},
number = {2},
abstract = {Biological networks of large dimensions, with their diagram of interactions,
are often well represented by a Boolean model with a family of logical
rules. The state space of a Boolean model is finite, and its asynchronous
dynamics are fully described by a transition graph in the state space.
In this context, a model reduction method will be developed for identifying
the active or operational interactions responsible for a given dynamic
behaviour. The first step in this procedure is the decomposition
of the asynchronous transition graph into its strongly connected
components, to obtain a reduced and hierarchically organized graph
of transitions. The second step consists of the identification of
a partial graph of interactions and a sub-family of logical rules
that remain operational in a given region of the state space. This
model reduction method and its usefulness are illustrated by an application
to a model of programmed cell death. The method identifies two mechanisms
used by the cell to respond to death-receptor stimulation and decide
between the survival and apoptotic pathways.},
doi = {10.1016/j.jtbi.2009.06.006},
issn = {0022-5193},
keywords = {csbcbook},
url = {http://www.sciencedirect.com/science/article/B6WMD-4WH8CGD-2/2/e9f844daaad4eef66eacf065c1416383}
}
@article{Tranchevent2010guide,
author = {Tranchevent, L.-C. and Capdevila, F. B. and Nitsch, D. and De Moor,
B. and De Causmaecker, P. and Moreau, Y.},
title = {A guide to web tools to prioritize candidate genes},
journal = {Brief. Bioinform.},
year = {2011},
volume = {12},
pages = {22--32},
number = {12},
abstract = {Finding the most promising genes among large lists of candidate genes
has been defined as the gene prioritization problem. It is a recurrent
problem in genetics in which genetic conditions are reported to be
associated with chromosomal regions. In the last decade, several
different computational approaches have been developed to tackle
this challenging task. In this study, we review 19 computational
solutions for human gene prioritization that are freely accessible
as web tools and illustrate their differences. We summarize the various
biological problems to which they have been successfully applied.
Ultimately, we describe several research directions that could increase
the quality and applicability of the tools. In addition we developed
a website (http://www.esat.kuleuven.be/gpp) containing detailed information
about these and other tools, which is regularly updated. This review
and the associated website constitute together a guide to help users
select a gene prioritization strategy that suits best their needs.},
doi = {10.1093/bib/bbq007},
eprint = {http://bib.oxfordjournals.org/content/early/2010/03/21/bib.bbq007.full.pdf+html},
owner = {mordelet},
timestamp = {2010.12.08},
url = {http://bib.oxfordjournals.org/content/early/2010/03/21/bib.bbq007.abstract}
}
@article{Trapnell2013Differential,
author = {Trapnell, C. and Hendrickson, D. G. and Sauvageau, M. and Goff, L.
and Rinn, J. L. and Pachter, L.},
title = {Differential analysis of gene regulation at transcript resolution
with {RNA-seq}.},
journal = {Nat Biotechnol},
year = {2013},
volume = {31},
pages = {46--53},
number = {1},
month = {Jan},
abstract = {Differential analysis of gene and transcript expression using high-throughput
RNA sequencing (RNA-seq) is complicated by several sources of measurement
variability and poses numerous statistical challenges. We present
Cuffdiff 2, an algorithm that estimates expression at transcript-level
resolution and controls for variability evident across replicate
libraries. Cuffdiff 2 robustly identifies differentially expressed
transcripts and genes and reveals differential splicing and promoter-preference
changes. We demonstrate the accuracy of our approach through differential
analysis of lung fibroblasts in response to loss of the developmental
transcription factor HOXA1, which we show is required for lung fibroblast
and HeLa cell cycle progression. Loss of HOXA1 results in significant
expression level changes in thousands of individual transcripts,
along with isoform switching events in key regulators of the cell
cycle. Cuffdiff 2 performs robust differential analysis in RNA-seq
experiments at transcript resolution, revealing a layer of regulation
not readily observable with other high-throughput technologies.},
doi = {10.1038/nbt.2450},
pdf = {../local/Trapnell2013Differential.pdf},
file = {Trapnell2013Differential.pdf:Trapnell2013Differential.pdf:PDF},
institution = {Department of Stem Cell and Regenerative Biology, Harvard University,
Cambridge, Massachusetts, USA.},
keywords = {ngs, rnaseq},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {nbt.2450},
pmid = {23222703},
timestamp = {2013.03.29},
url = {http://dx.doi.org/10.1038/nbt.2450}
}
@article{Trapnell2009TopHat,
author = {Trapnell, C. and Pachter, L. and Salzberg, S. L.},
title = {{TopHat}: discovering splice junctions with {RNA-Seq}.},
journal = {Bioinformatics},
year = {2009},
volume = {25},
pages = {1105--1111},
number = {9},
month = {May},
abstract = {A new protocol for sequencing the messenger RNA in a cell, known as
RNA-Seq, generates millions of short sequence fragments in a single
run. These fragments, or 'reads', can be used to measure levels of
gene expression and to identify novel splice variants of genes. However,
current software for aligning RNA-Seq data to a genome relies on
known splice junctions and cannot identify novel ones. TopHat is
an efficient read-mapping algorithm designed to align reads from
an RNA-Seq experiment to a reference genome without relying on known
splice sites.We mapped the RNA-Seq reads from a recent mammalian
RNA-Seq experiment and recovered more than 72\% of the splice junctions
reported by the annotation-based software from that study, along
with nearly 20,000 previously unreported junctions. The TopHat pipeline
is much faster than previous systems, mapping nearly 2.2 million
reads per CPU hour, which is sufficient to process an entire RNA-Seq
experiment in less than a day on a standard desktop computer. We
describe several challenges unique to ab initio splice site discovery
from RNA-Seq reads that will require further algorithm development.TopHat
is free, open-source software available from http://tophat.cbcb.umd.edu.Supplementary
data are available at Bioinformatics online.},
doi = {10.1093/bioinformatics/btp120},
pdf = {../local/Trapnell2009TopHat.pdf},
file = {Trapnell2009TopHat.pdf:Trapnell2009TopHat.pdf:PDF},
institution = {Center for Bioinformatics and Computational Biology, University of
Maryland, College Park, MD 20742, USA. cole@cs.umd.edu},
keywords = {ngs, rnaseq},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {btp120},
pmid = {19289445},
timestamp = {2013.03.29},
url = {http://dx.doi.org/10.1093/bioinformatics/btp120}
}
@article{Trapnell2012Differential,
author = {Trapnell, C. and Roberts, A. and Goff, L. and Pertea, G. and Kim,
D. and Kelley, D. R. and Pimentel, H. and Salzberg, S. L. and Rinn,
J. L. and Pachter, L.},
title = {Differential gene and transcript expression analysis of {RNA-seq}
experiments with {TopHat} and {Cufflinks}.},
journal = {Nat Protoc},
year = {2012},
volume = {7},
pages = {562--578},
number = {3},
month = {Mar},
abstract = {Recent advances in high-throughput cDNA sequencing (RNA-seq) can reveal
new genes and splice variants and quantify expression genome-wide
in a single assay. The volume and complexity of data from RNA-seq
experiments necessitate scalable, fast and mathematically principled
analysis software. TopHat and Cufflinks are free, open-source software
tools for gene discovery and comprehensive expression analysis of
high-throughput mRNA sequencing (RNA-seq) data. Together, they allow
biologists to identify new genes and new splice variants of known
ones, as well as compare gene and transcript expression under two
or more conditions. This protocol describes in detail how to use
TopHat and Cufflinks to perform such analyses. It also covers several
accessory tools and utilities that aid in managing data, including
CummeRbund, a tool for visualizing RNA-seq analysis results. Although
the procedure assumes basic informatics skills, these tools assume
little to no background with RNA-seq analysis and are meant for novices
and experts alike. The protocol begins with raw sequencing reads
and produces a transcriptome assembly, lists of differentially expressed
and regulated genes and transcripts, and publication-quality visualizations
of analysis results. The protocol's execution time depends on the
volume of transcriptome sequencing data and available computing resources
but takes less than 1 d of computer time for typical experiments
and ∼1 h of hands-on time.},
doi = {10.1038/nprot.2012.016},
pdf = {../local/Trapnell2012Differential.pdf},
file = {Trapnell2012Differential.pdf:Trapnell2012Differential.pdf:PDF},
institution = {Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA.
cole@broadinstitute.org},
keywords = {ngs, rnaseq},
owner = {laurent},
pii = {nprot.2012.016},
pmid = {22383036},
timestamp = {2012.04.11},
url = {http://dx.doi.org/10.1038/nprot.2012.016}
}
@article{Trapnell2010Transcript,
author = {Trapnell, C. and Williams, B. A. and Pertea, G. and Mortazavi, A.
and Kwan, G. and {van Baren}, M. J. and Salzberg, S. L. and Wold,
B. J. and Pachter, L.},
title = {Transcript assembly and quantification by RNA-Seq reveals unannotated
transcripts and isoform switching during cell differentiation.},
journal = {Nat Biotechnol},
year = {2010},
volume = {28},
pages = {511--515},
number = {5},
month = {May},
abstract = {High-throughput mRNA sequencing (RNA-Seq) promises simultaneous transcript
discovery and abundance estimation. However, this would require algorithms
that are not restricted by prior gene annotations and that account
for alternative transcription and splicing. Here we introduce such
algorithms in an open-source software program called Cufflinks. To
test Cufflinks, we sequenced and analyzed >430 million paired 75-bp
RNA-Seq reads from a mouse myoblast cell line over a differentiation
time series. We detected 13,692 known transcripts and 3,724 previously
unannotated ones, 62\% of which are supported by independent expression
data or by homologous genes in other species. Over the time series,
330 genes showed complete switches in the dominant transcription
start site (TSS) or splice isoform, and we observed more subtle shifts
in 1,304 other genes. These results suggest that Cufflinks can illuminate
the substantial regulatory flexibility and complexity in even this
well-studied model of muscle development and that it can improve
transcriptome-based genome annotation.},
doi = {10.1038/nbt.1621},
pdf = {../local/Trapnell2010Transcript.pdf},
file = {Trapnell2010Transcript.pdf:Trapnell2010Transcript.pdf:PDF},
institution = {Department of Computer Science, University of Maryland, College Park,
Maryland, USA.},
keywords = {ngs, rnaseq},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {nbt.1621},
pmid = {20436464},
timestamp = {2012.03.06},
url = {http://dx.doi.org/10.1038/nbt.1621}
}
@article{Trinh2009Elementary,
author = {Trinh, C. T. and Wlaschin, A. and Srienc, F.},
title = {Elementary mode analysis: a useful metabolic pathway analysis tool
for characterizing cellular metabolism.},
journal = {Appl Microbiol Biotechnol},
year = {2009},
volume = {81},
pages = {813--826},
number = {5},
month = {Jan},
abstract = {Elementary mode analysis is a useful metabolic pathway analysis tool
to identify the structure of a metabolic network that links the cellular
phenotype to the corresponding genotype. The analysis can decompose
the intricate metabolic network comprised of highly interconnected
reactions into uniquely organized pathways. These pathways consisting
of a minimal set of enzymes that can support steady state operation
of cellular metabolism represent independent cellular physiological
states. Such pathway definition provides a rigorous basis to systematically
characterize cellular phenotypes, metabolic network regulation, robustness,
and fragility that facilitate understanding of cell physiology and
implementation of metabolic engineering strategies. This mini-review
aims to overview the development and application of elementary mode
analysis as a metabolic pathway analysis tool in studying cell physiology
and as a basis of metabolic engineering.},
doi = {10.1007/s00253-008-1770-1},
pdf = {../local/Trinh2009Elementary.pdf},
file = {Trinh2009Elementary.pdf:Trinh2009Elementary.pdf:PDF},
institution = {Department of Chemical Engineering and Materials Science, University
of Minnesota, 151 Amundson Hall, 421 Washington Ave SE, Minneapolis,
MN 55455, USA.},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pmid = {19015845},
timestamp = {2011.11.30},
url = {http://dx.doi.org/10.1007/s00253-008-1770-1}
}
@article{Tropp2004Greed,
author = {Joel A. Tropp},
title = {Greed is good: Algorithmic results for sparse approximation},
journal = {IEEE Trans. Inform. Theory},
year = {2004},
volume = {50},
pages = {2231--2242}
}
@article{Tropp2006Algorithms,
author = {Tropp, Joel A. and Gilbert, Anna C. and Strauss, Martin J.},
title = {Algorithms for simultaneous sparse approximation: part I: Greedy
pursuit},
journal = {Signal Process.},
year = {2006},
volume = {86},
pages = {572--588},
number = {3},
address = {Amsterdam, The Netherlands, The Netherlands},
doi = {http://dx.doi.org/10.1016/j.sigpro.2005.05.030},
issn = {0165-1684},
publisher = {Elsevier North-Holland, Inc.}
}
@article{Troyanskaya2001Missing,
author = {Troyanskaya, O. and Cantor, M. and Sherlock, G. and Brown, P. and
Hastie, T. and Tibshirani, R. and Botstein, D. and Altman, R. B.},
title = {Missing value estimation methods for {DNA} microarrays},
journal = {Bioinformatics},
year = {2001},
volume = {17},
pages = {520--525},
pdf = {../local/Troyanskaya2001Missing.pdf},
file = {Troyanskaya2001Missing.pdf:local/Troyanskaya2001Missing.pdf:PDF},
url = {http://bioinformatics.oxfordjournals.org/cgi/content/abstract/17/6/520}
}
@article{Troyanskaya2003Bayesian,
author = {Troyanskaya, O. G. and Dolinski, K. and Owen, A. B. and Altman, R.
B. and Botstein, D.},
title = {A Bayesian framework for combining heterogeneous data sources for
gene function prediction (in {\it Saccharomyces cerevisiae})},
journal = {Proc. Natl. Acad. Sci. USA},
year = {2003},
volume = {100},
pages = {8348--8353},
number = {14},
abstract = {Genomic sequencing is no longer a novelty, but gene function annotation
remains a key challenge in modern biology. A variety of functional
genomics experimental techniques are available, from classic methods
such as affinity precipitation to advanced high-throughput techniques
such as gene expression microarrays. In the future, more disparate
methods will be developed, further increasing the need for integrated
computational analysis of data generated by these studies. We address
this problem with magic (Multisource Association of Genes by Integration
of Clusters), a general framework that uses formal Bayesian reasoning
to integrate heterogeneous types of high-throughput biological data
(such as large-scale two-hybrid screens and multiple microarray analyses)
for accurate gene function prediction. The system formally incorporates
expert knowledge about relative accuracies of data sources to combine
them within a normative framework. magic provides a belief level
with its output that allows the user to vary the stringency of predictions.
We applied magic to Saccharomyces cerevisiae genetic and physical
interactions, microarray, and transcription factor binding sites
data and assessed the biological relevance of gene groupings using
Gene Ontology annotations produced by the Saccaromyces Genome Database.
We found that by creating functional groupings based on heterogeneous
data types, magic improved accuracy of the groupings compared with
microarray analysis alone. We describe several of the biological
gene groupings identified.},
doi = {10.1073/pnas.0832373100},
owner = {jp},
timestamp = {2008.12.09},
url = {http://dx.doi.org/10.1073/pnas.0832373100}
}
@article{Truss2005HuSiDa,
author = {Truss, M. and Swat, M. and Kielbasa, S. M. and Sch{\"a}fer, R. and
Herzel, H. and Hagemeier, C.},
title = {Hu{S}i{D}a--the human si{RNA} database: an open-access database for
published functional si{RNA} sequences and technical details of efficient
transfer into recipient cells.},
journal = {Nucleic {A}cids {R}es.},
year = {2005},
volume = {33},
pages = {D108-11},
number = {Database issue},
month = {Jan},
abstract = {Small interfering {RNA}s (si{RNA}s) have become a standard tool in
functional genomics. {O}nce incorporated into the {RNA}-induced silencing
complex ({RISC}), si{RNA}s mediate the specific recognition of corresponding
target m{RNA}s and their cleavage. {H}owever, only a small fraction
of randomly chosen si{RNA} sequences is able to induce efficient
gene silencing. {I}n common laboratory practice, successful {RNA}
interference experiments typically require both, the labour and cost-intensive
identification of an active si{RNA} sequence and the optimization
of target cell line-specific procedures for optimal si{RNA} delivery.
{T}o optimize the design and performance of si{RNA} experiments,
we have established the human si{RNA} database ({H}u{S}i{D}a). {T}he
database provides sequences of published functional si{RNA} molecules
targeting human genes and important technical details of the corresponding
gene silencing experiments, including the mode of si{RNA} generation,
recipient cell lines, transfection reagents and procedures and direct
links to published references ({P}ub{M}ed). {T}he database can be
accessed at http://www.human-si{RNA}-database.net. {W}e used the
si{RNA} sequence information stored in the database for scrutinizing
published sequence selection parameters for efficient gene silencing.},
doi = {10.1093/nar/gki131},
keywords = {sirna},
pii = {33/suppl_1/D108},
url = {http://dx.doi.org/10.1093/nar/gki131}
}
@article{Tsai2004Gene,
author = {Tsai, C.A. and Chen, C.H. and Lee, T.C. and Ho, I.C. and Yang, U.C.
and Chen, J.J.},
title = {Gene selection for sample classifications in microarray experiments.},
journal = {D{NA} {C}ell {B}iol.},
year = {2004},
volume = {23},
pages = {607-614},
number = {10},
abstract = {D{NA} microarray technology provides useful tools for profiling global
gene expression patterns in different cell/tissue samples. {O}ne
major challenge is the large number of genes relative to the number
of samples. {T}he use of all genes can suppress or reduce the performance
of a classification rule due to the noise of nondiscriminatory genes.
{S}election of an optimal subset from the original gene set becomes
an important prestep in sample classification. {I}n this study, we
propose a family-wise error ({FWE}) rate approach to selection of
discriminatory genes for two-sample or multiple-sample classification.
{T}he {FWE} approach controls the probability of the number of one
or more false positives at a prespecified level. {A} public colon
cancer data set is used to evaluate the performance of the proposed
approach for the two classification methods: k nearest neighbors
(k-{NN}) and support vector machine ({SVM}). {T}he selected gene
sets from the proposed procedure appears to perform better than or
comparable to several results reported in the literature using the
univariate analysis without performing multivariate search. {I}n
addition, we apply the {FWE} approach to a toxicogenomic data set
with nine treatments (a control and eight metals, {A}s, {C}d, {N}i,
{C}r, {S}b, {P}b, {C}u, and {A}s{V}) for a total of 55 samples for
a multisample classification. {T}wo gene sets are considered: the
gene set omega{F} formed by the {ANOVA} {F}-test, and a gene set
omega{T} formed by the union of one-versus-all t-tests. {T}he predicted
accuracies are evaluated using the internal and external crossvalidation.
{U}sing the {SVM} classification, the overall accuracies to predict
55 samples into one of the nine treatments are above 80% for internal
crossvalidation. {O}mega{F} has slightly higher accuracy rates than
omega{T}. {T}he overall predicted accuracies are above 70% for the
external crossvalidation; the two gene sets omega{T} and omega{F}
performed equally well.},
doi = {10.1089/1044549042476947},
pdf = {../local/Tsai2004Gene.pdf},
file = {Tsai2004Gene.pdf:local/Tsai2004Gene.pdf:PDF},
keywords = {biosvm microarray},
owner = {jeanphilippevert},
url = {http://dx.doi.org/10.1089/1044549042476947}
}
@article{Tsai1979Error,
author = {Tsai, W.H. and Fu, K.S.},
title = {Error-Correcting Isomorphisms of Attributed Relational Graphs for
Pattern Analysis},
journal = {SMC},
year = {1979},
volume = {9},
pages = {757-768},
number = {12},
month = {December},
bibsource = {http://www.visionbib.com/bibliography/match557.html#TT46385},
owner = {misha}
}
@inproceedings{Tsang2003Distance,
author = {Tsang, I. W. and Kwok, J. T.},
title = {Distance metric learning with kernels},
booktitle = {Proceedings of the International Conference on Artificial Neural
Networks},
year = {2003},
pages = {126--129},
timestamp = {2007.06.06}
}
@article{Tseng2009Coordinate,
author = {Tseng, P. and Sangwoon, Y.},
title = {A Coordinate Gradient Descent Method for Nonsmooth Separable Minimization},
journal = {Math. Program.},
year = {2009},
volume = {117},
pages = {387--423},
number = {1-2},
doi = {10.1007/s10107-007-0170-0},
pdf = {../local/Tseng2009Coordinate.pdf},
file = {Tseng2009Coordinate.pdf:Tseng2009Coordinate.pdf:PDF},
keywords = {lasso},
owner = {jp},
timestamp = {2010.06.25},
url = {http://dx.doi.org/10.1007/s10107-007-0170-0}
}
@article{Tsirigos2005sensitive,
author = {Tsirigos, A. and Rigoutsos, I.},
title = {A sensitive, support-vector-machine method for the detection of horizontal
gene transfers in viral, archaeal and bacterial genomes.},
journal = {Nucleic {A}cids {R}es.},
year = {2005},
volume = {33},
pages = {3699-707},
number = {12},
abstract = {In earlier work, we introduced and discussed a generalized computational
framework for identifying horizontal transfers. {T}his framework
relied on a gene's nucleotide composition, obviated the need for
knowledge of codon boundaries and database searches, and was shown
to perform very well across a wide range of archaeal and bacterial
genomes when compared with previously published approaches, such
as {C}odon {A}daptation {I}ndex and {C} + {G} content. {N}onetheless,
two considerations remained outstanding: we wanted to further increase
the sensitivity of detecting horizontal transfers and also to be
able to apply the method to increasingly smaller genomes. {I}n the
discussion that follows, we present such a method, {W}n-{SVM}, and
show that it exhibits a very significant improvement in sensitivity
compared with earlier approaches. {W}n-{SVM} uses a one-class support-vector
machine and can learn using rather small training sets. {T}his property
makes {W}n-{SVM} particularly suitable for studying small-size genomes,
similar to those of viruses, as well as the typically larger archaeal
and bacterial genomes. {W}e show experimentally that the new method
results in a superior performance across a wide range of organisms
and that it improves even upon our own earlier method by an average
of 10\% across all examined genomes. {A}s a small-genome case study,
we analyze the genome of the human cytomegalovirus and demonstrate
that {W}n-{SVM} correctly identifies regions that are known to be
conserved and prototypical of all beta-herpesvirinae, regions that
are known to have been acquired horizontally from the human host
and, finally, regions that had not up to now been suspected to be
horizontally transferred. {A}typical region predictions for many
eukaryotic viruses, including the alpha-, beta- and gamma-herpesvirinae,
and 123 archaeal and bacterial genomes, have been made available
online at http://cbcsrv.watson.ibm.com/{HGT}_{SVM}/.},
doi = {10.1093/nar/gki660},
pdf = {../local/Tsirigos2005sensitive.pdf},
file = {Tsirigos2005sensitive.pdf:local/Tsirigos2005sensitive.pdf:PDF},
keywords = {biosvm},
pii = {33/12/3699},
url = {http://dx.doi.org/10.1093/nar/gki660}
}
@inproceedings{Tsochantaridis2004Support,
author = {Tsochantaridis, I. and Hofmann, T. and Joachims, T. and Altun, Y.},
title = {Support vector machine learning for interdependent and structured
output spaces},
booktitle = {Twenty-first international conference on {M}achine learning},
year = {2004},
publisher = {ACM Press},
abstract = {Learning general functional dependencies is one of the main goals
in machine learning. {R}ecent progress in kernel-based methods has
focused on designing flexible and powerful input representations.
{T}his paper addresses the complementary issue of problems involving
complex outputs such as multiple dependent output variables and structured
output spaces. {W}e propose to generalize multiclass {S}upport {V}ector
{M}achine learning in a formulation that involves features extracted
jointly from inputs and outputs. {T}he resulting optimization problem
is solved efficiently by a cutting plane algorithm that exploits
the sparseness and structural decomposition of the problem. {W}e
demonstrate the versatility and effectiveness of our method on problemsranging
from supervised grammar learning and named-entity recognition, totaxonomic
text classification and sequence alignment.},
doi = {http://doi.acm.org/10.1145/1015330.1015341},
pdf = {../local/Tsochantaridis2004Support.pdf},
file = {Tsochantaridis2004Support.pdf:local/Tsochantaridis2004Support.pdf:PDF},
isbn = {1-58113-828-5},
keywords = {structured-output},
location = {Banff, Alberta, Canada}
}
@article{Tsochantaridis2005Large,
author = {Tsochantaridis, I. and Joachims, T. and Hofmann, T. and Altun, Y.},
title = {Large margin methods for structured and interdependent output variables},
journal = {J. Mach. Learn. Res.},
year = {2005},
volume = {6},
pages = {1453-1484},
abstract = {Learning general functional dependencies between arbitrary input and
output spaces is one of the key challenges in computational intelligence.
While recent progress in machine learning has mainly focused on designing
flexible and powerful input representations, this paper addresses
the complementary issue of designing classification algorithms that
can deal with more complex outputs, such as trees, sequences, or
sets. More generally, we consider problems involving multiple dependent
output variables, structured output spaces, and classification problems
with class attributes. In order to accomplish this, we propose to
appropriately generalize the well-known notion of a separation margin
and derive a corresponding maximum-margin formulation. While this
leads to a quadratic program with a potentially prohibitive, i.e.
exponential, number of constraints, we present a cutting plane algorithm
that solves the optimization problem in polynomial time for a large
class of problems. The proposed method has important applications
in areas such as computational biology, natural language processing,
information retrieval/extraction, and optical character recognition.
Experiments from various domains involving different types of output
spaces emphasize the breadth and generality of our approach.},
pdf = {../local/Tsochantaridis2005Large.pdf},
file = {Tsochantaridis2005Large.pdf:local/Tsochantaridis2005Large.pdf:PDF},
timestamp = {2006.09.29},
url = {http://jmlr.csail.mit.edu/papers/v6/tsochantaridis05a.html}
}
@inproceedings{Tsuda2007Entire,
author = {Tsuda, K.},
title = {Entire regularization path for graph data},
booktitle = {ICML '07: Proceedings of the 24th international conference on Machine
learning},
year = {2007},
pages = {919--926},
address = {New York, NY, USA},
publisher = {ACM},
doi = {10.1145/1273496.1273612},
pdf = {../local/Tsuda2007Entire.pdf},
file = {Tsuda2007Entire.pdf:Tsuda2007Entire.pdf:PDF},
owner = {jp},
timestamp = {2009.09.29},
url = {http://doi.acm.org/10.1145/1273496.1273612}
}
@article{Tsuda2003em,
author = {Tsuda, K. and Akaho, S. and Asai, K.},
title = {The em {A}lgorithm for {K}ernel {M}atrix {C}ompletion with {A}uxiliary
{D}ata},
journal = {J. {M}ach. {L}earn. {R}es.},
year = {2003},
volume = {4},
pages = {67-81},
abstract = {In biological data, it is often the case that observed data are available
only for a subset of samples. {W}hen a kernel matrix is derived from
such data, we have to leave the entries for unavailable samples as
missing. {I}n this paper, the missing entries are completed by exploiting
an auxiliary kernel matrix derived from another information source.
{T}he parametric model of kernel matrices is created as a set of
spectral variants of the auxiliary kernel matrix, and the missing
entries are estimated by fitting this model to the existing entries.
{F}or model fitting, we adopt the em algorithm (distinguished from
the {EM} algorithm of {D}empster et al., 1977) based on the information
geometry of positive definite matrices. {W}e will report promising
results on bacteria clustering experiments using two marker sequences:
16{S} and gyr{B}.},
pdf = {../local/Tsuda2003em.pdf},
file = {Tsuda2003em.pdf:local/Tsuda2003em.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://www.jmlr.org/papers/v4/tsuda03a.html}
}
@article{Tsuda2002new,
author = {K. Tsuda and M. Kawanabe and G. R{\"a}tsch and S. Sonnenburg and
K.-R. M{\"u}ller},
title = {A new discriminative kernel from probabilistic models},
journal = {Neural {C}omputation},
year = {2002},
volume = {14},
pages = {2397--2414},
number = {10},
doi = {10.1162/08997660260293274},
pdf = {../local/Tsuda2002new.pdf},
file = {Tsuda2002new.pdf:local/Tsuda2002new.pdf:PDF},
keywords = {biosvm},
url = {http://dx.doi.org/10.1162/08997660260293274}
}
@article{Tsuda2002Marginalized,
author = {K. Tsuda and T. Kin and K. Asai},
title = {Marginalized {K}ernels for {B}iological {S}equences},
journal = {Bioinformatics},
year = {2002},
volume = {18},
pages = {S268--S275},
abstract = {Motivation: {K}ernel methods such as support vector machines require
a kernel function between objects to be defined a priori. {S}everal
works have been done to derive kernels from probability distributions,
e.g., the {F}isher kernel. {H}owever, a general methodology to design
a kernel is not fully developed. {R}esults: {W}e propose a reasonable
way of designing a kernel when objects are generated from latent
variable models (e.g., {HMM}). {F}irst of all, a joint kernel is
designed for complete data which include both visible and hidden
variables. {T}hen a marginalized kernel for visible data is obtained
by taking the expectation with respect to hidden variables. {W}e
will show that the {F}isher kernel is a special case of marginalized
kernels, which gives another viewpoint to the {F}isher kernel theory.
{A}lthough our approach can be applied to any object, we particularly
derive several marginalized kernels useful for biological sequences
(e.g., {DNA} and proteins). {T}he effectiveness of marginalized kernels
is illustrated in the task of classifying bacterial gyrase subunit
{B} (gyr{B}) amino acid sequences.},
comment = {Introduces the idea of marginalized kernel. Show that the Fisher kernel
is a particular case of it, and modify it. Application to bacterial
gyrB classification.},
pdf = {../local/Tsuda2002Marginalized.pdf},
file = {Tsuda2002Marginalized.pdf:local/Tsuda2002Marginalized.pdf:PDF},
keywords = {biosvm}
}
@article{Tsuda2004Learning,
author = {Tsuda, K. and Noble, W.S.},
title = {Learning kernels from biological networks by maximizing entropy},
journal = {Bioinformatics},
year = {2004},
volume = {20},
pages = {i326--i333},
abstract = {Motivation: {T}he diffusion kernel is a general method for computing
pairwise distances among all nodes in a graph, based on the sum of
weighted paths between each pair of nodes. {T}his technique has been
used successfully, in conjunction with kernel-based learning methods,
to draw inferences from several types of biological networks. {R}esults:
{W}e show that computing the diffusion kernel is equivalent to maximizing
the von {N}eumann entropy, subject to a global constraint on the
sum of the {E}uclidean distances between nodes. {T}his global constraint
allows for high variance in the pairwise distances. {A}ccordingly,
we propose an alternative, locally constrained diffusion kernel,
and we demonstrate that the resulting kernel allows for more accurate
support vector machine prediction of protein functional classifications
from metabolic and protein?protein interaction networks. {A}vailability:
{S}upplementary results and data are available at noble.gs.washington.edu/proj/maxent},
comment = {Problem = multiclass classification of tumor cells from gene expression.
Show that the one-versus-all approach of combining SVM yields the
minimum number of classification errors on their Affymetrix data
with 14 tumor types. In addition to not taking variability estimates
of repeated measurements into account, this approach selects different
relevant features (genes) for each binary classifier.},
doi = {10.1093/bioinformatics/bth906},
pdf = {../local/Tsuda2004Learning.pdf},
file = {Tsuda2004Learning.pdf:local/Tsuda2004Learning.pdf:PDF},
keywords = {learning-kernel graph-kernel biosvm},
owner = {vert},
url = {http://dx.doi.org/10.1093/bioinformatics/bth906}
}
@article{Tsujinishi2003Fuzzy,
author = {Daisuke Tsujinishi and Shigeo Abe},
title = {Fuzzy least squares support vector machines for multiclass problems.},
journal = {Neural {N}etw},
year = {2003},
volume = {16},
pages = {785-92},
number = {5-6},
abstract = {In least squares support vector machines ({LS}-{SVM}s), the optimal
separating hyperplane is obtained by solving a set of linear equations
instead of solving a quadratic programming problem. {B}ut since {SVM}s
and {LS}-{SVM}s are formulated for two-class problems, unclassifiable
regions exist when they are extended to multiclass problems. {I}n
this paper, we discuss fuzzy {LS}-{SVM}s that resolve unclassifiable
regions for multiclass problems. {W}e define a membership function
in the direction perpendicular to the optimal separating hyperplane
that separates a pair of classes. {U}sing the minimum or average
operation for these membership functions, we define a membership
function for each class. {U}sing some benchmark data sets, we show
that recognition performance of fuzzy {LS}-{SVM}s with the minimum
operator is comparable to that of fuzzy {SVM}s, but fuzzy {LS}-{SVM}s
with the average operator showed inferior performance.},
pii = {S0893608003001102}
}
@book{Tsybakov2004Introduction,
title = {Introduction {\`a} l'estimation non-param{\'e}trique},
publisher = {Springer},
year = {2004},
author = {Tsybakov, A. B.},
owner = {vert}
}
@article{Tsybakov1997On,
author = {Tsybakov, A. B.},
title = {On {N}onparametric {E}stimation of {D}ensity {L}evel {S}ets},
journal = {Ann. {S}tat.},
year = {1997},
volume = {25},
pages = {948-969},
month = {June},
pdf = {../local/Tsybakov1997On.pdf},
file = {Tsybakov1997On.pdf:local/Tsybakov1997On.pdf:PDF},
url = {http://links.jstor.org/sici?sici=0090-5364%28199706%2925%3A3%3C948%3AONEODL%3E2.0.CO%3B2-D}
}
@article{Tu2004Image,
author = {Zhuowen Tu and Xiangrong Chen and Alan L. Yuille},
title = {Image Parsing: Unifying Segmentation, Detection, and Recognition},
journal = {Int. J. Comput. Vis.},
year = {2004},
citeseercitationcount = {0},
citeseerurl = {http://citeseer.ist.psu.edu/640923.html},
owner = {michael},
timestamp = {2009.11.10}
}
@article{Tubio2011Cancer:,
author = {Jose M C Tubio and Xavier Estivill},
title = {Cancer: When catastrophe strikes a cell.},
journal = {Nature},
year = {2011},
volume = {470},
pages = {476--477},
number = {7335},
month = {Feb},
doi = {10.1038/470476a},
keywords = {Apoptosis; Bone Neoplasms, genetics/pathology; Cell Survival; Cell
Transformation, Neoplastic, genetics; Chromosomes, Human, genetics/metabolism;
DNA Breaks; DNA Copy Number Variations, genetics; DNA Repair; Disease
Progression; Genes, Neoplasm, genetics; Humans; Leukemia, genetics;
Mutagenesis, genetics; Mutation, genetics; Neoplasms, genetics/pathology;
Recombination, Genetic, genetics},
language = {eng},
medline-pst = {ppublish},
owner = {phupe},
pii = {470476a},
pmid = {21350479},
timestamp = {2011.06.01},
url = {http://dx.doi.org/10.1038/470476a}
}
@article{Tucker2002Gene,
author = {Tucker, D. L. and Tucker, N. and Conway, T.},
title = {Gene expression profiling of the pH response in Escherichia coli.},
journal = {J Bacteriol.},
year = {2002},
volume = {184},
pages = {6551--6558},
number = {23},
month = {Dec},
abstract = {Escherichia coli MG1655 acid-inducible genes were identified by whole-genome
expression profiling. Cultures were grown to the mid-logarithmic
phase on acidified glucose minimal medium, conditions that induce
glutamate-dependent acid resistance (AR), while the other AR systems
are either repressed or not induced. A total of 28 genes were induced
in at least two of three experiments in which the gene expression
profiles of cells grown in acid (pH 5.5 or 4.5) were compared to
those of cells grown at pH 7.4. As expected, the genes encoding glutamate
decarboxylase, gadA and gadB, were significantly induced. Interestingly,
two acid-inducible genes code for small basic proteins with pIs of
>10.5, and six code for small acidic proteins with pIs ranging from
5.7 to 4.0; the roles of these small basic and acidic proteins in
acid resistance are unknown. The acid-induced genes represented only
five functional grouping categories, including eight genes involved
in metabolism, nine associated with cell envelope structures or modifications,
two encoding chaperones, six regulatory genes, and six unknown genes.
It is unlikely that all of these genes are involved in the glutamate-dependent
AR. However, nine acid-inducible genes are clustered in the gadA
region, including hdeA, which encodes a putative periplasmic chaperone,
and four putative regulatory genes. One of these putative regulators,
yhiE, was shown to significantly increase acid resistance when overexpressed
in cells that had not been preinduced by growth at pH 5.5, and mutation
of yhiE decreased acid resistance; yhiE could therefore encode an
activator of AR genes. Thus, the acid-inducible genes clustered in
the gadA region appear to be involved in glutatmate-dependent acid
resistance, although their specific roles remain to be elucidated.},
institution = {Advanced Center for Genome Technology, The University of Oklahoma,
Norman, Oklahoma 73069-0245, USA.},
keywords = {Culture Media; Escherichia coli; Escherichia coli Proteins; Gene Expression
Profiling; Gene Expression Regulation, Bacterial; Heat-Shock Response;
Hydrogen-Ion Concentration; Morpholines; Oligonucleotide Array Sequence
Analysis},
owner = {fantine},
pmid = {12426343},
timestamp = {2008.02.11}
}
@article{Tugcu2003Identification,
author = {Nihal Tugcu and Asif Ladiwala and Curt M Breneman and Steven M Cramer},
title = {Identification of chemically selective displacers using parallel
batch screening experiments and quantitative structure efficacy relationship
models.},
journal = {Anal {C}hem},
year = {2003},
volume = {75},
pages = {5806-16},
number = {21},
month = {Nov},
abstract = {Parallel batch screening experiments were carried out to examine how
displacer chemistry and salt counterions affect the selectivity of
batch protein displacements in anion exchange chromatographic systems.
{T}he results indicate that both salt type and displacer chemistry
can have a significant impact on the amount of protein displaced.
{I}mportantly, the results indicate that, by changing the displacer,
salt counterion, or both, one can induce significant selectivity
changes in the relative displacement of two model proteins. {T}his
indicates that highly selective separations can be developed in ion
exchange systems by the appropriate selection of displacer chemistry
and salt counterion. {T}he experimental batch screening data were
also used in conjunction with various molecular descriptors to generate
quantitative structure efficacy relationship ({QSER}) models based
on a support vector machine feature selection and regression tool.
{T}he models resulted in good correlations and successful predictions
for an external test set of displacers. {A} star plot approach was
shown to be a powerful tool to aid in the interpretation of the {QSER}
models. {T}hese results indicate that this modeling approach can
be employed for the a priori prediction of displacer efficacy as
well as for providing insight into displacer design and the selection
of proper mobile-phase conditions for highly selective separations.},
doi = {10.1021/ac0341564},
pdf = {../local/Tugcu2003Identification.pdf},
file = {Tugcu2003Identification.pdf:local/Tugcu2003Identification.pdf:PDF},
url = {http://dx.doi.org/10.1021/ac0341564}
}
@article{Tugcu2003Prediction,
author = {Nihal Tugcu and Minghu Song and Curt M Breneman and N. Sukumar and
Kristin P Bennett and Steven M Cramer},
title = {Prediction of the effect of mobile-phase salt type on protein retention
and selectivity in anion exchange systems.},
journal = {Anal {C}hem},
year = {2003},
volume = {75},
pages = {3563-72},
number = {14},
month = {Jul},
abstract = {This study examines the effect of different salt types on protein
retention and selectivity in anion exchange systems. {P}articularly,
linear retention data for various proteins were obtained on two structurally
different anion exchange stationary-phase materials in the presence
of three salts with different counterions. {T}he data indicated that
the effects are, for the most part, nonspecific, although various
specific effects could also be observed. {Q}uantitative structure
retention relationship ({QSRR}) models based on support vector machine
feature selection and regression models were developed using the
experimental chromatographic data in conjunction with various molecular
descriptors computed from protein crystal structure geometries. {S}tar
plots for each descriptor used in the final model were generated
to aid in interpretation. {T}he resulting {QSRR} models were predictive,
with cross-validated r2 values of 0.9445, 0.9676, and 0.8897 for
{S}ource 15{Q} and 0.9561, 0.9876, and 0.9760 for {Q} {S}epharose
resins in the presence of three different salts. {T}he predictive
power of these models was validated using a set of test proteins
that were not used in the generation of these models. {I}nterpretation
of the models revealed that particular trends for proteins and salts
could be captured using {QSRR} techniques.}
}
@article{Tung2007POPI:,
author = {Chun-Wei Tung and Shinn-Ying Ho},
title = {POPI: predicting immunogenicity of MHC class I binding peptides by
mining informative physicochemical properties.},
journal = {Bioinformatics},
year = {2007},
volume = {23},
pages = {942--949},
number = {8},
month = {Apr},
abstract = {MOTIVATION: Both modeling of antigen-processing pathway including
major histocompatibility complex (MHC) binding and immunogenicity
prediction of those MHC-binding peptides are essential to develop
a computer-aided system of peptide-based vaccine design that is one
goal of immunoinformatics. Numerous studies have dealt with modeling
the immunogenic pathway but not the intractable problem of immunogenicity
prediction due to complex effects of many intrinsic and extrinsic
factors. Moderate affinity of the MHC-peptide complex is essential
to induce immune responses, but the relationship between the affinity
and peptide immunogenicity is too weak to use for predicting immunogenicity.
This study focuses on mining informative physicochemical properties
from known experimental immunogenicity data to understand immune
responses and predict immunogenicity of MHC-binding peptides accurately.
RESULTS: This study proposes a computational method to mine a feature
set of informative physicochemical properties from MHC class I binding
peptides to design a support vector machine (SVM) based system (named
POPI) for the prediction of peptide immunogenicity. High performance
of POPI arises mainly from an inheritable bi-objective genetic algorithm,
which aims to automatically determine the best number m out of 531
physicochemical properties, identify these m properties and tune
SVM parameters simultaneously. The dataset consisting of 428 human
MHC class I binding peptides belonging to four classes of immunogenicity
was established from MHCPEP, a database of MHC-binding peptides (Brusic
et al., 1998). POPI, utilizing the m = 23 selected properties, performs
well with the accuracy of 64.72\% using leave-one-out cross-validation,
compared with two sequence alignment-based prediction methods ALIGN
(54.91\%) and PSI-BLAST (53.23\%). POPI is the first computational
system for prediction of peptide immunogenicity based on physicochemical
properties. AVAILABILITY: A web server for prediction of peptide
immunogenicity (POPI) and the used dataset of MHC class I binding
peptides (PEPMHCI) are available at http://iclab.life.nctu.edu.tw/POPI},
doi = {10.1093/bioinformatics/btm061},
keywords = {Algorithms; Artificial Intelligence; Binding Sites; Epitope Mapping;
Histocompatibility Antigens Class I; Oligopeptides; Pattern Recognition,
Automated; Protein Binding; Software; Structure-Activity Relationship},
owner = {laurent},
pii = {btm061},
pmid = {17384427},
timestamp = {2007.07.12},
url = {http://dx.doi.org/10.1093/bioinformatics/btm061}
}
@article{Tung2005GenSo-FDSS,
author = {W. L. Tung and C. Quek},
title = {Gen{S}o-{FDSS}: a neural-fuzzy decision support system for pediatric
{ALL} cancer subtype identification using gene expression data.},
journal = {Artif. {I}ntell. {M}ed.},
year = {2005},
volume = {33},
pages = {61-88},
number = {1},
month = {Jan},
abstract = {O{BJECTIVE}: {A}cute lymphoblastic leukemia ({ALL}) is the most common
malignancy of childhood, representing nearly one third of all pediatric
cancers. {C}urrently, the treatment of pediatric {ALL} is centered
on tailoring the intensity of the therapy applied to a patient's
risk of relapse, which is linked to the type of leukemia the patient
has. {H}ence, accurate and correct diagnosis of the various leukemia
subtypes becomes an important first step in the treatment process.
{R}ecently, gene expression profiling using {DNA} microarrays has
been shown to be a viable and accurate diagnostic tool to identify
the known prognostically important {ALL} subtypes. {T}hus, there
is currently a huge interest in developing autonomous classification
systems for cancer diagnosis using gene expression data. {T}his is
to achieve an unbiased analysis of the data and also partly to handle
the large amount of genetic information extracted from the {DNA}
microarrays. {METHODOLOGY}: {G}enerally, existing medical decision
support systems ({DSS}) for cancer classification and diagnosis are
based on traditional statistical methods such as {B}ayesian decision
theory and machine learning models such as neural networks ({NN})
and support vector machine ({SVM}). {T}hough high accuracies have
been reported for these systems, they fall short on certain critical
areas. {T}hese included (a) being able to present the extracted knowledge
and explain the computed solutions to the users; (b) having a logical
deduction process that is similar and intuitive to the human reasoning
process; and (c) flexible enough to incorporate new knowledge without
running the risk of eroding old but valid information. {O}n the other
hand, a neural fuzzy system, which is synthesized to emulate the
human ability to learn and reason in the presence of imprecise and
incomplete information, has the ability to overcome the above-mentioned
shortcomings. {H}owever, existing neural fuzzy systems have their
own limitations when used in the design and implementation of {DSS}.
{H}ence, this paper proposed the use of a novel neural fuzzy system:
the generic self-organising fuzzy neural network ({G}en{S}o{FNN})
with truth-value restriction ({TVR}) fuzzy inference, as a fuzzy
{DSS} (denoted as {G}en{S}o-{FDSS}) for the classification of {ALL}
subtypes using gene expression data. {RESULTS} {AND} {CONCLUSION}:
{T}he performance of the {G}en{S}o-{FDSS} system is encouraging when
benchmarked against those of {NN}, {SVM} and the {K}-nearest neighbor
({K}-{NN}) classifier. {O}n average, a classification rate of above
90\% has been achieved using the {G}en{S}o-{FDSS} system.},
doi = {10.1016/j.artmed.2004.03.009},
pdf = {../local/Tung2005GenSo-FDSS.pdf},
file = {Tung2005GenSo-FDSS.pdf:local/Tung2005GenSo-FDSS.pdf:PDF},
pii = {S0933-3657(04)00094-6},
url = {http://dx.doi.org/10.1016/j.artmed.2004.03.009}
}
@article{Turlach2005Simultaneous,
author = {Turlach, B. A. and Venables, W. N. and Wright, S. J.},
title = {Simultaneous variable selection},
journal = {Technometrics},
year = {2005},
volume = {47},
pages = {349--363},
number = {3},
publisher = {ASA}
}
@article{Turlin2001Regulation,
author = {E. Turlin and M. Perrotte-piquemal and A. Danchin and F. Biville},
title = {{R}egulation of the early steps of 3-phenylpropionate catabolism
in {E}scherichia coli.},
journal = {J. Mol. Microbiol. Biotechnol.},
year = {2001},
volume = {3},
pages = {127--133},
number = {1},
month = {Jan},
abstract = {Microbial catabolism of phenylpropanoid compounds plays a key role
in the degradation of aromatic molecules originating from the degradation
of proteins and plant constituents. In this study, the regulation
of the early steps in the utilisation of 3-phenylpropionate, a phenylpropanoid
compound, was investigated. Expression of the hcaA gene product,
which is involved in 3-phenylpropionate catabolism in Escherichia
coli, was positively regulated by HcaR, a regulatory protein similar
to members of the LysR regulators family. Remarkably, the expression
of hcaA in the presence of 3-phenylpropionate was sharply and transiently
induced at the end of the exponential growth phase. This occurred
in a rpoS-independent manner. This transient induction was also mediated
by HcaR. The expression of this positive regulator is negatively
autoregulated, as for other members of the LysR family. The expression
of hcaR is strongly repressed in the presence of glucose. Glucose-dependent
repression of hcaR expression could only be partially overcome by
adding exogenous cAMP.},
pmid = {11200225},
timestamp = {2008.02.12}
}
@article{Turner2002Cellular,
author = {Bryan M. Turner},
title = {Cellular Memory and the Histone Code},
journal = {Cell},
year = {2002},
volume = {111},
pages = {285-291},
keywords = {csbcbook}
}
@article{Turner2003POCUS,
author = {Turner, F. S. and Clutterbuck, D. R. and Semple, C. A. M.},
title = {POCUS: mining genomic sequence annotation to predict disease genes},
journal = {Genome Biol.},
year = {2003},
volume = {4},
pages = {R75},
number = {11},
abstract = {Here we present POCUS (prioritization of candidate genes using statistics),
a novel computational approach to prioritize candidate disease genes
that is based on over-representation of functional annotation between
loci for the same disease. We show that POCUS can provide high (up
to 81-fold) enrichment of real disease genes in the candidate-gene
shortlists it produces compared with the original large sets of positional
candidates. In contrast to existing methods, POCUS can also suggest
counterintuitive candidates.},
doi = {10.1186/gb-2003-4-11-r75},
pdf = {../local/Turner2003POCUS.pdf},
file = {Turner2003POCUS.pdf:Turner2003POCUS.pdf:PDF},
institution = {MRC Human Genetics Unit, Crewe Road, Western General Hospital, Edinburgh
EH4 2XU, UK.},
owner = {jp},
pii = {gb-2003-4-11-r75},
pmid = {14611661},
timestamp = {2009.03.18},
url = {http://dx.doi.org/10.1186/gb-2003-4-11-r75}
}
@article{Tuschl1999Targeted,
author = {Tuschl, T. and Zamore, P.D. and Lehmann, R. and Bartel, D.P. and
Sharp, P.A.},
title = {Targeted m{RNA} degradation by double-stranded {RNA} in vitro.},
journal = {Genes {D}ev.},
year = {1999},
volume = {13},
pages = {3191-7},
number = {24},
month = {Dec},
abstract = {Double-stranded {RNA} (ds{RNA}) directs gene-specific, post-transcriptional
silencing in many organisms, including vertebrates, and has provided
a new tool for studying gene function. {T}he biochemical mechanisms
underlying this ds{RNA} interference ({RNA}i) are unknown. {H}ere
we report the development of a cell-free system from syncytial blastoderm
{D}rosophila embryos that recapitulates many of the features of {RNA}i.
{T}he interference observed in this reaction is sequence specific,
is promoted by ds{RNA} but not single-stranded {RNA}, functions by
specific m{RNA} degradation, and requires a minimum length of ds{RNA}.
{F}urthermore, preincubation of ds{RNA} potentiates its activity.
{T}hese results demonstrate that {RNA}i can be mediated by sequence-specific
processes in soluble reactions.}
}
@article{Tusher2001Significance,
author = {Tusher, V. G. and Tibshirani, R. and Chu, G.},
title = {Significance analysis of microarrays applied to the ionizing radiation
response},
journal = {Proc. Natl. Acad. Sci. USA},
year = {2001},
volume = {98},
pages = {5116--5121},
number = {9},
month = {Apr},
abstract = {Microarrays can measure the expression of thousands of genes to identify
changes in expression between different biological states. Methods
are needed to determine the significance of these changes while accounting
for the enormous number of genes. We describe a method, Significance
Analysis of Microarrays (SAM), that assigns a score to each gene
on the basis of change in gene expression relative to the standard
deviation of repeated measurements. For genes with scores greater
than an adjustable threshold, SAM uses permutations of the repeated
measurements to estimate the percentage of genes identified by chance,
the false discovery rate (FDR). When the transcriptional response
of human cells to ionizing radiation was measured by microarrays,
SAM identified 34 genes that changed at least 1.5-fold with an estimated
FDR of 12\%, compared with FDRs of 60 and 84\% by using conventional
methods of analysis. Of the 34 genes, 19 were involved in cell cycle
regulation and 3 in apoptosis. Surprisingly, four nucleotide excision
repair genes were induced, suggesting that this repair pathway for
UV-damaged DNA might play a previously unrecognized role in repairing
DNA damaged by ionizing radiation.},
doi = {10.1073/pnas.091062498},
pdf = {../local/Tusher2001Significance.pdf},
file = {Tusher2001Significance.pdf:Tusher2001Significance.pdf:PDF},
institution = {Departments of Medicine and Biochemistry, Stanford University, 269
Campus Drive, Center for Clinical Sciences Research 1115, Stanford,
CA 94305-5151, USA.},
keywords = {csbcbook, csbcbook-ch4},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {091062498},
pmid = {11309499},
timestamp = {2011.04.07},
url = {http://dx.doi.org/10.1073/pnas.091062498}
}
@article{Tut2001Cyclin,
author = {Tut, V.M. and Braithwaite, K.L. and Angus, B. and Neal, D.E. and
Lunec, J. and Mellon, J.K.},
title = {Cyclin D1 expression in transitional cell carcinoma of the bladder:
correlation with p53, waf1, pRb and Ki67.},
journal = {Br J Cancer},
year = {2001},
volume = {84},
pages = {270-275},
owner = {lcalzone},
timestamp = {2010.04.27}
}
@article{Tuteja2009Extracting,
author = {Geetu Tuteja and Peter White and Jonathan Schug and Klaus H Kaestner},
title = {Extracting transcription factor targets from ChIP-Seq data.},
journal = {Nucleic Acids Res},
year = {2009},
volume = {37},
pages = {e113},
number = {17},
month = {Sep},
abstract = {ChIP-Seq technology, which combines chromatin immunoprecipitation
(ChIP) with massively parallel sequencing, is rapidly replacing ChIP-on-chip
for the genome-wide identification of transcription factor binding
events. Identifying bound regions from the large number of sequence
tags produced by ChIP-Seq is a challenging task. Here, we present
GLITR (GLobal Identifier of Target Regions), which accurately identifies
enriched regions in target data by calculating a fold-change based
on random samples of control (input chromatin) data. GLITR uses a
classification method to identify regions in ChIP data that have
a peak height and fold-change which do not resemble regions in an
input sample. We compare GLITR to several recent methods and show
that GLITR has improved sensitivity for identifying bound regions
closely matching the consensus sequence of a given transcription
factor, and can detect bona fide transcription factor targets missed
by other programs. We also use GLITR to address the issue of sequencing
depth, and show that sequencing biological replicates identifies
far more binding regions than re-sequencing the same sample.},
doi = {10.1093/nar/gkp536},
pdf = {../local/Tuteja2009Extracting.pdf},
file = {Tuteja2009Extracting.pdf:Tuteja2009Extracting.pdf:PDF},
institution = {Department of Genetics and Institute of Diabetes, Obesity and Metabolism,
University of Pennsylvania School of Medicine, Philadelphia, PA 19104,
USA.},
keywords = {ngs},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {gkp536},
pmid = {19553195},
timestamp = {2009.10.30},
url = {http://dx.doi.org/10.1093/nar/gkp536}
}
@article{Tzeng2004Predicting,
author = {Huey-Ming Tzeng and Jer-Guang Hsieh and Yih-Lon Lin},
title = {Predicting nurses' intention to quit with a support vector machine:
a new approach to set up an early warning mechanism in human resource
management.},
journal = {Comput {I}nform {N}urs},
year = {2004},
volume = {22},
pages = {232-42},
number = {4},
abstract = {This project developed a {S}upport {V}ector {M}achine for predicting
nurses' intention to quit, using working motivation, job satisfaction,
and stress levels as predictors. {T}his study was conducted in three
hospitals located in southern {T}aiwan. {T}he target population was
all nurses (389 valid cases). {F}or cross-validation, we randomly
split cases into four groups of approximately equal sizes, and performed
four training runs. {A}fter the training, the average percentage
of misclassification on the training data was 0.86, while that on
the testing data was 10.8, resulting in predictions with 89.2\% accuracy.
{T}his {S}upport {V}ector {M}achine can predict nurses' intention
to quit, without asking these nurses whether they have an intention
to quit.},
keywords = {Adolescent, Adult, Algorithms, Amino Acid Sequence, Amino Acids, Anatomic,
Attitude of Health Personnel, Bacterial Proteins, Bias (Epidemiology),
Brain, Brain Mapping, Burnout, Comparative Study, Computer Simulation,
Computer-Assisted, Data Interpretation, Diffusion Magnetic Resonance
Imaging, Facial Asymmetry, Facial Expression, Facial Paralysis, Female,
Gene Expression Profiling, Gram-Negative Bacteria, Gram-Positive
Bacteria, Hospital, Humans, Image Interpretation, Intention, Job
Satisfaction, Logistic Models, Magnetoencephalography, Male, Middle
Aged, Models, Motion, Neural Networks (Computer), Neural Pathways,
Non-U.S. Gov't, Nonlinear Dynamics, Nursing Administration Research,
Nursing Staff, Personnel Management, Personnel Turnover, Photography,
Predictive Value of Tests, Professional, Protein, Proteins, Proteome,
Psychological, Questionnaires, Regression Analysis, Reproducibility
of Results, Research Support, Retina, Risk Factors, Sequence Alignment,
Sequence Analysis, Severity of Illness Index, Software, Statistical,
Subcellular Fractions, Taiwan, Theoretical, Workplace, 15494654},
pii = {00024665-200407000-00012}
}
@inproceedings{Udupa04Algorithmic,
author = {Udupa, R. and Faruquie, T. A. and Maji, H. K.},
title = {An Algorithmic Framework for Solving the Decoding Problem in Statistical
Machine Translation },
booktitle = {Proceedings of Coling 2004 },
year = {2004},
pages = {631--637},
address = {Geneva, Switzerland},
month = {Aug 23--Aug 27},
publisher = {COLING}
}
@article{Ueda2005Probabilistic,
author = {Ueda, N. and Aoki-Kinoshita, K. F. and Yamaguchi, A. and Akutsu,
T. and Mamitsuka, H.},
title = {A {P}robabilistic {M}odel for {M}ining {L}abeled {O}rdered {T}rees:
{C}apturing {P}atterns in {C}arbohydrate {S}ugar {C}hains},
journal = {I{EEE} {T}ransactions on {K}nowledge and {D}ata {E}ngineering},
year = {2005},
volume = {17},
pages = {1051-1064},
number = {8},
abstract = {Glycans, or carbohydrate sugar chains, which play a number of important
roles in the development and functioning of multicellular organisms,
can be regarded as labeled ordered trees. {A} recent increase in
the documentation of glycan structures, especially in the form of
database curation, has made mining glycans important for the understanding
of living cells. {W}e propose a probabilistic model for mining labeled
ordered trees, and we further present an efficient learning algorithm
for this model, based on an {EM} algorithm. {T}he time and space
complexities of this algorithm are rather favorable, falling within
the practical limits set by a variety of existing probabilistic models,
including stochastic context-free grammars. {E}xperimental results
have shown that, in a supervised problem setting, the proposed method
outperformed five other competing methods by a statistically significant
factor in all cases. {W}e further applied the proposed method to
aligning multiple glycan trees, and we detected biologically significant
common subtrees in these alignments where the trees are automatically
classified into subtypes already known in glycobiology. {E}xtended
abstracts of parts of the work presented in this paper have appeared
in [35], [4], and [3].},
doi = {10.1109/TKDE.2005.117},
pdf = {../local/Ueda2005Probabilistic.pdf},
file = {Ueda2005Probabilistic.pdf:local/Ueda2005Probabilistic.pdf:PDF},
url = {http://doi.dox.org/10.1109/TKDE.2005.117}
}
@article{Uetz2000comprehensive,
author = {Uetz, P. and Giot, L. and Cagney, G. and Mansfield, T. A. and Judson,
R. S. and Knight, J. R. and Lockshon, D. and Narayan, V. and Srinivasan,
M. and Pochart, P. and Qureshi-Emili, A. and Li, Y. and Godwin, B.
and Conover, D. and Kalbfleish, T. and Vijayadamodar, G. and Yang,
M. and Johnston, M. and Fields, S. and Rothberg, J. M.},
title = {A comprehensive analysis of protein-protein interactions in {S}accharomyces
cerevisiae},
journal = {Nature},
year = {2000},
volume = {403},
pages = {623--627},
pdf = {../local/uetz00.pdf},
file = {uetz00.pdf:local/uetz00.pdf:PDF},
subject = {bionet},
url = {http://www.nature.com/cgi-taf/DynaPage.taf?file=/nature/journal/v403/n6770/full/403623a0_fs.html&content_filetype=pdf}
}
@article{Ui-Tei2004Guidelines,
author = {Ui-Tei, K. and Naito, Y. and Takahashi, F. and Haraguchi, T. and
Ohki-Hamazaki, H. and Juni, A. and Ueda, R. and Saigo, K.},
title = {Guidelines for the selection of highly effective si{RNA} sequences
for mammalian and chick {RNA} interference.},
journal = {Nucleic {A}cids {R}es.},
year = {2004},
volume = {32},
pages = {936-948},
number = {3},
month = {Feb},
abstract = {In the present study, the relationship between short interfering {RNA}
(si{RNA}) sequence and {RNA} interference ({RNA}i) effect was extensively
analyzed using 62 targets of four exogenous and two endogenous genes
and three mammalian and {D}rosophila cells. {W}e present the rules
that may govern si{RNA} sequence preference and in accordance with
which highly effective si{RNA}s essential for systematic mammalian
functional genomics can be readily designed. {T}hese rules indicate
that si{RNA}s which simultaneously satisfy all four of the following
sequence conditions are capable of inducing highly effective gene
silencing in mammalian cells: (i) {A}/{U} at the 5' end of the antisense
strand; (ii) {G}/{C} at the 5' end of the sense strand; (iii) at
least five {A}/{U} residues in the 5' terminal one-third of the antisense
strand; and (iv) the absence of any {GC} stretch of more than 9 nt
in length. si{RNA}s opposite in features with respect to the first
three conditions give rise to little or no gene silencing in mammalian
cells. {E}ssentially the same rules for si{RNA} sequence preference
were found applicable to {DNA}-based {RNA}i in mammalian cells and
in ovo {RNA}i using chick embryos. {I}n contrast to mammalian and
chick cells, little si{RNA} sequence preference could be detected
in {D}rosophila in vivo {RNA}i.},
doi = {10.1093/nar/gkh247},
keywords = {sirna},
url = {http://nar.oxfordjournals.org/cgi/content/abstract/32/3/936}
}
@article{Ulitsky2010DEGAS,
author = {Ulitsky, I. and Krishnamurthy, A. and Karp, R. M. and Shamir, R.},
title = {{DEGAS}: de novo discovery of dysregulated pathways in human diseases.},
journal = {PLoS One},
year = {2010},
volume = {5},
pages = {e13367},
number = {10},
abstract = {Molecular studies of the human disease transcriptome typically involve
a search for genes whose expression is significantly dysregulated
in sick individuals compared to healthy controls. Recent studies
have found that only a small number of the genes in human disease-related
pathways show consistent dysregulation in sick individuals. However,
those studies found that some pathway genes are affected in most
sick individuals, but genes can differ among individuals. While a
pathway is usually defined as a set of genes known to share a specific
function, pathway boundaries are frequently difficult to assign,
and methods that rely on such definition cannot discover novel pathways.
Protein interaction networks can potentially be used to overcome
these problems.We present DEGAS (DysrEgulated Gene set Analysis via
Subnetworks), a method for identifying connected gene subnetworks
significantly enriched for genes that are dysregulated in specimens
of a disease. We applied DEGAS to seven human diseases and obtained
statistically significant results that appear to home in on compact
pathways enriched with hallmarks of the diseases. In Parkinson's
disease, we provide novel evidence for involvement of mRNA splicing,
cell proliferation, and the 14-3-3 complex in the disease progression.
DEGAS is available as part of the MATISSE software package (http://acgt.cs.tau.ac.il/matisse).The
subnetworks identified by DEGAS can provide a signature of the disease
potentially useful for diagnosis, pinpoint possible pathways affected
by the disease, and suggest targets for drug intervention.},
doi = {10.1371/journal.pone.0013367},
pdf = {../local/Ulitsky2010DEGAS.pdf},
file = {Ulitsky2010DEGAS.pdf:Ulitsky2010DEGAS.pdf:PDF},
institution = {Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv,
Israel. ulitsky@wi.mit.edu},
language = {eng},
medline-pst = {epublish},
owner = {jp},
pmid = {20976054},
timestamp = {2011.10.09},
url = {http://dx.doi.org/10.1371/journal.pone.0013367}
}
@article{Ullman1976Algorithm,
author = {Ullmann, J. R.},
title = {An Algorithm for Subgraph Isomorphism},
journal = {J. ACM},
year = {1976},
volume = {23},
pages = {31--42},
number = {1},
address = {New York, NY, USA},
doi = {http://doi.acm.org/10.1145/321921.321925},
issn = {0004-5411},
publisher = {ACM}
}
@article{Umeyama1988eigendecomposition,
author = {Umeyama, S.},
title = {An eigendecomposition approach to weighted graph matching problems},
journal = {IEEE Trans. Pattern Anal. Mach. Intell.},
year = {1988},
volume = {10},
pages = {695--703},
number = {5},
month = {Sept. },
abstract = {An approximate solution to the weighted-graph-matching problem is
discussed for both undirected and directed graphs. The weighted-graph-matching
problem is that of finding the optimum matching between two weighted
graphs, which are graphs with weights at each arc. The proposed method
uses an analytic instead of a combinatorial or iterative approach
to the optimum matching problem. Using the eigendecompositions of
the adjacency matrices (in the case of the undirected-graph-matching
problem) or Hermitian matrices derived from the adjacency matrices
(in the case of the directed-graph-matching problem), a matching
close to the optimum can be found efficiently when the graphs are
sufficiently close to each other. Simulation results are given to
evaluate the performance of the proposed method.},
doi = {10.1109/34.6778},
owner = {jp},
timestamp = {2008.10.05},
url = {http://dx.doi.org/10.1109/34.6778}
}
@article{Vaidya2007Breast,
author = {Jayant S Vaidya},
title = {Breast cancer: an artistic view},
journal = {The Lancet Oncology},
year = {2007},
volume = {8},
pages = {583-585},
keywords = {csbcbook}
}
@article{Valentini2007Mosclust:,
author = {Giorgio Valentini},
title = {Mosclust: a software library for discovering significant structures
in bio-molecular data.},
journal = {Bioinformatics},
year = {2007},
volume = {23},
pages = {387--389},
number = {3},
month = {Feb},
abstract = {The R package mosclust (model order selection for clustering problems)
implements algorithms based on the concept of stability for discovering
significant structures in bio-molecular data. The software library
provides stability indices obtained through different data perturbations
methods (resampling, random projections, noise injection), as well
as statistical tests to assess the significance of multi-level structures
singled out from the data. Availability: http://homes.dsi.unimi.it/~valenti/SW/mosclust/download/mosclust_1.0.tar.gz.
Supplementary information: http://homes.dsi.unimi.it/~valenti/SW/mosclust.},
doi = {10.1093/bioinformatics/btl600},
institution = {DSI, Dipartimento di Scienze dell'Informazione, Università degli
Studi di Milano, Via Comelico 39, Italy. valentini@dsi.unimi.it},
keywords = {Algorithms; Artificial Intelligence; Cluster Analysis; Gene Expression
Profiling, methods; Oligonucleotide Array Sequence Analysis, methods;
Pattern Recognition, Automated, methods; Programming Languages; Proteome,
metabolism; Signal Transduction, physiology; Software},
language = {eng},
medline-pst = {ppublish},
owner = {philippe},
pii = {btl600},
pmid = {17127677},
timestamp = {2011.05.14},
url = {http://dx.doi.org/10.1093/bioinformatics/btl600}
}
@article{Valentini2002Gene,
author = {Valentini, G.},
title = {Gene expression data analysis of human lymphoma using support vector
machines and output coding ensembles.},
journal = {Artif. {I}ntell. {M}ed.},
year = {2002},
volume = {26},
pages = {281-304},
number = {3},
month = {Nov},
abstract = {The large amount of data generated by {DNA} microarrays was originally
analysed using unsupervised methods, such as clustering or self-organizing
maps. {R}ecently supervised methods such as decision trees, dot-product
support vector machines ({SVM}) and multi-layer perceptrons ({MLP})
have been applied in order to classify normal and tumoural tissues.
{W}e propose methods based on non-linear {SVM} with polynomial and
{G}aussian kernels, and output coding ({OC}) ensembles of learning
machines to separate normal from malignant tissues, to classify different
types of lymphoma and to analyse the role of sets of coordinately
expressed genes in carcinogenic processes of lymphoid tissues. {U}sing
gene expression data from "{L}ymphochip", a specialised {DNA} microarray
developed at {S}tanford {U}niversity {S}chool of {M}edicine, we show
that {SVM} can correctly separate normal from tumoural tissues, and
{OC} ensembles can be successfully used to classify different types
of lymphoma. {M}oreover, we identify a group of coordinately expressed
genes related to the separation of two distinct subgroups inside
diffuse large {B}-cell lymphoma ({DLBCL}), validating a previous
{A}lizadeh's hypothesis about the existence of two distinct diseases
inside {DLBCL}.},
doi = {10.1016/S0933-3657(02)00077},
pdf = {../local/Valentini2002Gene.pdf},
file = {Valentini2002Gene.pdf:local/Valentini2002Gene.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert}
}
@article{Vallabhaneni2004Motor,
author = {Anirudh Vallabhaneni and Bin He},
title = {Motor imagery task classification for brain computer interface applications
using spatiotemporal principle component analysis.},
journal = {Neurol {R}es},
year = {2004},
volume = {26},
pages = {282-7},
number = {3},
month = {Apr},
abstract = {Classification of single-trial imagined left- and right-hand movements
recorded through scalp {EEG} are explored in this study. {C}lassical
event-related desynchronization/synchronization ({ERD}/{ERS}) calculation
approach was utilized to extract {ERD} features from the raw scalp
{EEG} signal. {P}rinciple {C}omponent {A}nalysis ({PCA}) was used
for feature extraction and applied on spatial, as well as temporal
dimensions in two consecutive steps. {A} {S}upport {V}ector {M}achine
({SVM}) classifier using a linear decision function was used to classify
each trial as either left or right. {T}he present approach has yielded
good classification results and promises to have potential for further
refinement for increased accuracy as well as application in online
brain computer interface ({BCI}).},
doi = {10.1179/016164104225013950},
keywords = {Amino Acids, Antibodies, Artificial Intelligence, Biological, Brain,
Brain Mapping, Calibration, Comparative Study, Computational Biology,
Cysteine, Cystine, Electrodes, Electroencephalography, Evoked Potentials,
Female, Horseradish Peroxidase, Humans, Imagery (Psychotherapy),
Imagination, Laterality, Male, Monoclonal, Movement, Neoplasms, Non-P.H.S.,
Non-U.S. Gov't, P.H.S., Perception, Principal Component Analysis,
Protein, Protein Array Analysis, Proteins, Research Support, Sensitivity
and Specificity, Sequence Analysis, Tumor Markers, U.S. Gov't, User-Computer
Interface, 15142321},
url = {http://dx.doi.org/10.1179/016164104225013950}
}
@article{Vanunu2010Associating,
author = {Vanunu, O. and Magger, O. and Ruppin, E. and Shlomi, T. and Sharan,
R.},
title = {Associating genes and protein complexes with disease via network
propagation.},
journal = {PLoS Comput. Biol.},
year = {2010},
volume = {6},
pages = {e1000641},
number = {1},
month = {Jan},
abstract = {A fundamental challenge in human health is the identification of disease-causing
genes. Recently, several studies have tackled this challenge via
a network-based approach, motivated by the observation that genes
causing the same or similar diseases tend to lie close to one another
in a network of protein-protein or functional interactions. However,
most of these approaches use only local network information in the
inference process and are restricted to inferring single gene associations.
Here, we provide a global, network-based method for prioritizing
disease genes and inferring protein complex associations, which we
call PRINCE. The method is based on formulating constraints on the
prioritization function that relate to its smoothness over the network
and usage of prior information. We exploit this function to predict
not only genes but also protein complex associations with a disease
of interest. We test our method on gene-disease association data,
evaluating both the prioritization achieved and the protein complexes
inferred. We show that our method outperforms extant approaches in
both tasks. Using data on 1,369 diseases from the OMIM knowledgebase,
our method is able (in a cross validation setting) to rank the true
causal gene first for 34\% of the diseases, and infer 139 disease-related
complexes that are highly coherent in terms of the function, expression
and conservation of their member proteins. Importantly, we apply
our method to study three multi-factorial diseases for which some
causal genes have been found already: prostate cancer, alzheimer
and type 2 diabetes mellitus. PRINCE's predictions for these diseases
highly match the known literature, suggesting several novel causal
genes and protein complexes for further investigation.},
doi = {10.1371/journal.pcbi.1000641},
institution = {School of Computer Science, Tel-Aviv University, Tel-Aviv, Israel.},
keywords = {Algorithms; Alzheimer Disease; Databases, Genetic; Diabetes Mellitus;
Disease; Genes; Humans; Male; Multiprotein Complexes; Prostatic Neoplasms;
Protein Interaction Mapping; Proteins; Reproducibility of Results},
owner = {mordelet},
pmid = {20090828},
timestamp = {2010.09.27},
url = {http://dx.doi.org/10.1371/journal.pcbi.1000641}
}
@article{Vapnik2000Bounds,
author = {V. Vapnik and O. Chapelle},
title = {Bounds on error expectation for support vector machines.},
journal = {Neural {C}omput},
year = {2000},
volume = {12},
pages = {2013-36},
number = {9},
month = {Sep},
abstract = {We introduce the concept of span of support vectors ({SV}) and show
that the generalization ability of support vector machines ({SVM})
depends on this new geometrical concept. {W}e prove that the value
of the span is always smaller (and can be much smaller) than the
diameter of the smallest sphere containing the support vectors, used
in previous bounds ({V}apnik, 1998). {W}e also demonstrate experimentally
that the prediction of the test error given by the span is very accurate
and has direct application in model selection (choice of the optimal
parameters of the {SVM}).},
keywords = {Automated, Learning, Models, Neural Networks (Computer), Neurological,
Pattern Recognition, 10976137}
}
@misc{Vapnik1974Theory,
author = {V.N. Vapnik and A. Ya. Chervonenkis},
title = {Teoriya Raspoznavaniya Obrazov: Statisticheskie Problemy Obucheniya.
({R}ussian) [{T}heory of {P}attern {R}ecognition: {S}tatistical {P}roblems
of {L}earning]},
howpublished = {Moscow: Nauka},
year = {1974}
}
@book{Vapnik1998Statistical,
title = {Statistical {L}earning {T}heory},
publisher = {Wiley},
year = {1998},
author = {Vapnik, V. N.},
address = {New-York},
subject = {kernel}
}
@book{Vapnik1995nature,
title = {The nature of statistical learning theory},
publisher = {Springer-Verlag New York, Inc.},
year = {1995},
author = {Vapnik, Vladimir N.},
address = {New York, NY, USA},
citeulike-article-id = {254315},
citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=211359},
isbn = {0387945598},
keywords = {svms},
posted-at = {2005-07-13 13:49:01},
priority = {2},
url = {http://portal.acm.org/citation.cfm?id=211359}
}
@book{Varki1999Essentials,
title = {Essentials of glycobiology},
publisher = {Cold Spring Harbor Laboratory Press},
year = {1999},
author = {Varki, A. and Cummings, R. and Esko, J. and Freeze, H. and Hart,
G. and Marth, J.}
}
@article{Vassetzky2009Chromosome,
author = {Yegor Vassetzky and Alexey Gavrilov and Elvira Eivazova and Iryna
Priozhkova and Marc Lipinski and Sergey Razin},
title = {Chromosome conformation capture (from 3C to 5C) and its ChIP-based
modification.},
journal = {Methods Mol Biol},
year = {2009},
volume = {567},
pages = {171--188},
abstract = {Chromosome conformation capture (3C) methodology was developed to
study spatial organization of long genomic regions in living cells.
Briefly, chromatin is fixed with formaldehyde in vivo to cross-link
interacting sites, digested with a restriction enzyme and ligated
at a low DNA concentration so that ligation between cross-linked
fragments is favored over ligation between random fragments. Ligation
products are then analyzed and quantified by PCR. So far, semi-quantitative
PCR methods were widely used to estimate the ligation frequencies.
However, it is often important to estimate the ligation frequencies
more precisely which is only possible by using the real-time PCR.
At the same time, it is equally necessary to monitor the specificity
of PCR amplification. That is why the real-time PCR with TaqMan probes
is becoming more and more popular in 3C studies. In this chapter,
we describe the general protocol for 3C analysis with the subsequent
estimation of ligation frequencies by using the real-time PCR technology
with TaqMan probes. We discuss in details all steps of the experimental
procedure paying special attention to weak points and possible ways
to solve the problems. A special attention is also paid to the problems
in interpretation of the results and necessary control experiments.
Besides, in theory, we consider other approaches to analysis of the
ligation products used in frames of the so-called 4C and 5C methods.
The recently developed chromatin immunoprecipitation (ChIP)-loop
assay representing a combination of 3C and ChIP is also discussed.},
doi = {10.1007/978-1-60327-414-2\_12},
institution = {CNRS UMR-8126, Université Paris-Sud 11, Institut de Cancérologie
Gustave Roussy, 39, rue Camille-Desmoulins, 94805, Villejuif.},
keywords = {Chromatin Immunoprecipitation; Chromosome Mapping; Chromosomes; Cross-Linking
Reagents; Humans; Models, Biological; Nucleic Acid Conformation;
Polymerase Chain Reaction; Quality Control},
owner = {phupe},
pmid = {19588093},
timestamp = {2010.08.11},
url = {http://dx.doi.org/10.1007/978-1-60327-414-2\_12}
}
@article{Vasudevan2007Switching,
author = {Shobha Vasudevan and Yingchun Tong and Joan A Steitz},
title = {Switching from repression to activation: microRNAs can up-regulate
translation.},
journal = {Science},
year = {2007},
volume = {318},
pages = {1931--1934},
number = {5858},
month = {Dec},
abstract = {AU-rich elements (AREs) and microRNA target sites are conserved sequences
in messenger RNA (mRNA) 3' untranslated regions (3'UTRs) that control
gene expression posttranscriptionally. Upon cell cycle arrest, the
ARE in tumor necrosis factor-alpha (TNFalpha) mRNA is transformed
into a translation activation signal, recruiting Argonaute (AGO)
and fragile X mental retardation-related protein 1 (FXR1), factors
associated with micro-ribonucleoproteins (microRNPs). We show that
human microRNA miR369-3 directs association of these proteins with
the AREs to activate translation. Furthermore, we document that two
well-studied microRNAs-Let-7 and the synthetic microRNA miRcxcr4-likewise
induce translation up-regulation of target mRNAs on cell cycle arrest,
yet they repress translation in proliferating cells. Thus, activation
is a common function of microRNPs on cell cycle arrest. We propose
that translation regulation by microRNPs oscillates between repression
and activation during the cell cycle.},
doi = {10.1126/science.1149460},
institution = {Department of Molecular Biophysics and Biochemistry, Howard Hughes
Medical Institute, Yale University School of Medicine, Boyer Center
for Molecular Medicine, 295 Congress Avenue, New Haven, CT 06536,
USA.},
keywords = {sirna},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {1149460},
pmid = {18048652},
timestamp = {2009.10.28},
url = {http://dx.doi.org/10.1126/science.1149460}
}
@unpublished{Vazquez2001Modeling,
author = {Vazquez, A. and Flammini, A. and Maritan, A. and Vespignani, A.},
title = {Modeling of protein interaction networks},
note = {E-print cond-mat/0108043},
month = {Aug},
year = {2001},
pdf = {../local/vazq01.pdf},
file = {vazq01.pdf:local/vazq01.pdf:PDF},
subject = {bionetprot},
url = {http://xxx.lanl.gov/abs/cond-mat/0108043}
}
@article{Veber2002Molecular,
author = {Veber, D. F. and Johnson, S. R. and Cheng, H.-Y. and Smith, B. R.
and Ward, K. W. and Kopple, K. D.},
title = {Molecular properties that influence the oral bioavailability of drug
candidates},
journal = {J. Med. Chem.},
year = {2002},
volume = {45},
pages = {2615--2623},
number = {12},
month = {Jun},
abstract = {Oral bioavailability measurements in rats for over 1100 drug candidates
studied at SmithKline Beecham Pharmaceuticals (now GlaxoSmithKline)
have allowed us to analyze the relative importance of molecular properties
considered to influence that drug property. Reduced molecular flexibility,
as measured by the number of rotatable bonds, and low polar surface
area or total hydrogen bond count (sum of donors and acceptors) are
found to be important predictors of good oral bioavailability, independent
of molecular weight. That on average both the number of rotatable
bonds and polar surface area or hydrogen bond count tend to increase
with molecular weight may in part explain the success of the molecular
weight parameter in predicting oral bioavailability. The commonly
applied molecular weight cutoff at 500 does not itself significantly
separate compounds with poor oral bioavailability from those with
acceptable values in this extensive data set. Our observations suggest
that compounds which meet only the two criteria of (1) 10 or fewer
rotatable bonds and (2) polar surface area equal to or less than
140 A(2) (or 12 or fewer H-bond donors and acceptors) will have a
high probability of good oral bioavailability in the rat. Data sets
for the artificial membrane permeation rate and for clearance in
the rat were also examined. Reduced polar surface area correlates
better with increased permeation rate than does lipophilicity (C
log P), and increased rotatable bond count has a negative effect
on the permeation rate. A threshold permeation rate is a prerequisite
of oral bioavailability. The rotatable bond count does not correlate
with the data examined here for the in vivo clearance rate in the
rat.},
keywords = {chemogenomics},
owner = {laurent},
pii = {jm020017n},
pmid = {12036371},
timestamp = {2008.07.16}
}
@article{Veer2002Gene,
author = {van 't Veer, L. J. and Dai, H. and van de Vijver, M. J. and He, Y.
D. and Hart, A. A. M. and Mao, M. and Peterse, H. L. and van der
Kooy, K. and Marton, M. J. and Witteveen, A. T. and Schreiber, G.
J. and Kerkhoven, R. M. and Roberts, C. and Linsley, P. S. and Bernards,
R. and Friend, S. H.},
title = {Gene expression profiling predicts clinical outcome of breast cancers},
journal = {Nature},
year = {2002},
volume = {415},
pages = {530--536},
number = {6871},
month = {Jan},
abstract = {Breast cancer patients with the same stage of disease can have markedly
different treatment responses and overall outcome. The strongest
predictors for metastases (for example, lymph node status and histological
grade) fail to classify accurately breast tumours according to their
clinical behaviour. Chemotherapy or hormonal therapy reduces the
risk of distant metastases by approximately one-third; however, 70-80\%
of patients receiving this treatment would have survived without
it. None of the signatures of breast cancer gene expression reported
to date allow for patient-tailored therapy strategies. Here we used
DNA microarray analysis on primary breast tumours of 117 young patients,
and applied supervised classification to identify a gene expression
signature strongly predictive of a short interval to distant metastases
('poor prognosis' signature) in patients without tumour cells in
local lymph nodes at diagnosis (lymph node negative). In addition,
we established a signature that identifies tumours of BRCA1 carriers.
The poor prognosis signature consists of genes regulating cell cycle,
invasion, metastasis and angiogenesis. This gene expression profile
will outperform all currently used clinical parameters in predicting
disease outcome. Our findings provide a strategy to select patients
who would benefit from adjuvant therapy.},
doi = {10.1038/415530a},
pdf = {../local/Veer2002Gene.pdf},
file = {Veer2002Gene.pdf:Veer2002Gene.pdf:PDF},
institution = {Division of Diagnostic Oncology, The Netherlands Cancer Institute,
121 Plesmanlaan, 1066 CX Amsterdam, The Netherlands.},
keywords = {breastcancer, csbcbook, csbcbook-ch3},
owner = {jp},
pii = {415530a},
pmid = {11823860},
timestamp = {2008.11.16},
url = {http://dx.doi.org/10.1038/415530a}
}
@article{Venables2004Aberrant,
author = {Julian P. Venables},
title = {Aberrant and Alternative Splicing in Cancer},
journal = {Cancer Res.},
year = {2004},
volume = {64},
pages = {7647-7654},
keywords = {csbcbook}
}
@book{Venables2002Modern,
title = {{Modern Applied Statistics with S}},
publisher = {Springer},
year = {2002},
author = {Venables, W. N. and Ripley, B. D.},
address = {New York},
edition = {Fourth},
note = {ISBN 0-387-95457-0},
owner = {jp},
timestamp = {2012.07.31},
url = {http://www.stats.ox.ac.uk/pub/MASS4}
}
@article{Venet2011Most,
author = {Venet, D. and Dumont, J.E. and Detours, V.},
title = {Most random gene expression signatures are significantly associated
with breast cancer outcome},
journal = {PLoS computational biology},
year = {2011},
volume = {7},
pages = {e1002240},
number = {10},
publisher = {Public Library of Science}
}
@article{Venter2001Sequence,
author = {Venter, J. C. et al.},
title = {The {S}equence of the {H}uman {G}enome},
journal = {Science},
year = {2001},
volume = {291},
pages = {1304-1351},
number = {5507},
abstract = {A 2.91-billion base pair (bp) consensus sequence of the euchromatic
portion of the human genome was generated by the whole-genome shotgun
sequencing method. {T}he 14.8-billion bp {DNA} sequence was generated
over 9 months from 27,271,853 high-quality sequence reads (5.11-fold
coverage of the genome) from both ends of plasmid clones made from
the {DNA} of five individuals. {T}wo assembly strategies--a whole-genome
assembly and a regional chromosome assembly--were used, each combining
sequence data from {C}elera and the publicly funded genome effort.
{T}he public data were shredded into 550-bp segments to create a
2.9-fold coverage of those genome regions that had been sequenced,
without including biases inherent in the cloning and assembly procedure
used by the publicly funded group. {T}his brought the effective coverage
in the assemblies to eightfold, reducing the number and size of gaps
in the final assembly over what would be obtained with 5.11-fold
coverage. {T}he two assembly strategies yielded very similar results
that largely agree with independent mapping data. {T}he assemblies
effectively cover the euchromatic regions of the human chromosomes.
{M}ore than 90% of the genome is in scaffold assemblies of 100,000
bp or more, and 25% of the genome is in scaffolds of 10 million bp
or larger. {A}nalysis of the genome sequence revealed 26,588 protein-encoding
transcripts for which there was strong corroborating evidence and
an additional ~12,000 computationally derived genes with mouse matches
or other weak supporting evidence. {A}lthough gene-dense clusters
are obvious, almost half the genes are dispersed in low {G}+{C} sequence
separated by large tracts of apparently noncoding sequence. {O}nly
1.1% of the genome is spanned by exons, whereas 24% is in introns,
with 75% of the genome being intergenic {DNA}. {D}uplications of
segmental blocks, ranging in size up to chromosomal lengths, are
abundant throughout the genome and reveal a complex evolutionary
history. {C}omparative genomic analysis indicates vertebrate expansions
of genes associated with neuronal function, with tissue-specific
developmental regulation, and with the hemostasis and immune systems.
{DNA} sequence comparisons between the consensus sequence and publicly
funded genome data provided locations of 2.1 million single-nucleotide
polymorphisms ({SNP}s). {A} random pair of human haploid genomes
differed at a rate of 1 bp per 1250 on average, but there was marked
heterogeneity in the level of polymorphism across the genome. {L}ess
than 1% of all {SNP}s resulted in variation in proteins, but the
task of determining which {SNP}s have functional consequences remains
an open challenge.},
pdf = {../local/Venter2001Sequence.pdf},
file = {Venter2001Sequence.pdf:local/Venter2001Sequence.pdf:PDF},
keywords = {genomics bio},
owner = {vert},
url = {http://www.sciencemag.org/cgi/content/abstract/291/5507/1304}
}
@article{Vercoutere2001Rapid,
author = {W. Vercoutere and S. Winters-Hilt and H. Olsen and D. Deamer and
D. Haussler and M. Akeson},
title = {Rapid discrimination among individual {DNA} hairpin molecules at
single-nucleotide resolution using an ion channel.},
journal = {Nat {B}iotechnol},
year = {2001},
volume = {19},
pages = {248-52},
number = {3},
month = {Mar},
abstract = {R{NA} and {DNA} strands produce ionic current signatures when driven
through an alpha-hemolysin channel by an applied voltage. {H}ere
we combine this nanopore detector with a support vector machine ({SVM})
to analyze {DNA} hairpin molecules on the millisecond time scale.
{M}easurable properties include duplex stem length, base pair mismatches,
and loop length. {T}his nanopore instrument can discriminate between
individual {DNA} hairpins that differ by one base pair or by one
nucleotide.},
doi = {10.1038/85696},
pdf = {../local/Vercoutere2001Rapid.pdf},
file = {Vercoutere2001Rapid.pdf:local/Vercoutere2001Rapid.pdf:PDF},
keywords = {Acute, Acute Disease, Adenocarcinoma, Algorithms, Amino Acid Sequence,
Artificial Intelligence, Automated, B-Lymphocytes, Bacterial Proteins,
Base Pair Mismatch, Base Sequence, Bayes Theorem, Binding Sites,
Biological, Bone Marrow Cells, Cell Compartmentation, Chemistry,
Child, Chromosome Aberrations, Comparative Study, Computational Biology,
Computer Simulation, Computer-Assisted, DNA, Data Interpretation,
Databases, Decision Trees, Diagnosis, Discriminant Analysis, Electric
Conductivity, Electrophysiology, Escherichia coli Proteins, Factual,
Female, Fungal, Gastric Emptying, Gene Expression Profiling, Gene
Expression Regulation, Genes, Genetic, Genetic Markers, Hemolysins,
Humans, Ion Channels, Kinetics, Leukemia, Lipid Bilayers, Logistic
Models, Lymphocytic, Male, Markov Chains, Melanoma, Models, Molecular,
Myeloid, Neoplasm, Neoplastic, Neural Networks (Computer), Nevus,
Non-P.H.S., Non-U.S. Gov't, Nucleic Acid Conformation, Organ Specificity,
Organelles, P.H.S., Pattern Recognition, Physical, Pigmented, Predictive
Value of Tests, Promoter Regions (Genetics), Protein Folding, Protein
Structure, Proteins, Proteome, RNA, Reproducibility of Results, Research
Support, Saccharomyces cerevisiae, Secondary, Sensitivity and Specificity,
Sequence Alignment, Sex Characteristics, Skin Diseases, Skin Neoplasms,
Skin Pigmentation, Software, Statistical, Stomach Diseases, T-Lymphocytes,
Thermodynamics, Transcription, Transcription Factors, Tumor Markers,
U.S. Gov't, 11231558},
pii = {85696},
url = {http://dx.doi.org/10.1038/85696}
}
@article{de1993Multiplicites,
author = {de Verdi{\`e}re, Y. C.},
title = {Multiplicit{\'e}s des valeurs propres {L}aplaciens discrets et {L}aplaciens
continus},
journal = {Rendiconti di {M}atematica},
year = {1993},
volume = {13},
pages = {433--460},
subject = {net}
}
@article{Vermeulen2008Cancer,
author = {Vermeulen, L. and Sprick, M.R. and Kemper, K. and Stassi, G. and
Medema, J.P.},
title = {{{C}ancer stem cells - old concepts, new insights}},
journal = {Cell Death and Differentiation},
year = {2008},
volume = {15},
pages = {947-58},
keywords = {csbcbook}
}
@inproceedings{Vert2009High-level,
author = {Vert, J. P. and Matsui, T. and Satoh, S. and Uchiyama, Y.},
title = {High-level feature extraction using {SVM} with walk-based graph kernel},
booktitle = {Proceedings of the IEEE International Conference on Acoustic, Speech
and Signal Processing (ICASSP 2009)},
year = {2009},
pdf = {../local/Vert2009High-level.pdf},
file = {Vert2009High-level.pdf:Vert2009High-level.pdf:PDF},
owner = {jp},
timestamp = {2009.01.05}
}
@inproceedings{Vert2002Support,
author = {Vert, J.-P.},
title = {Support vector machine prediction of signal peptide cleavage site
using a new class of kernels for strings},
booktitle = {Proceedings of the {P}acific {S}ymposium on {B}iocomputing 2002},
year = {2002},
editor = {R. B. Altman and A. K. Dunker and L. Hunter and K. Lauerdale and
T. E. Klein},
pages = {649--660},
publisher = {World Scientific},
pdf = {../local/vert02.pdf},
file = {vert02.pdf:local/vert02.pdf:PDF},
keywords = {biosvm},
subject = {biokernel},
url = {http://www.smi.stanford.edu/projects/helix/psb02/vert.pdf}
}
@inproceedings{Vert2007Kernel,
author = {J.-P. Vert},
title = {Kernel methods in genomics and computational biology},
booktitle = {Kernel Methods in Bioengineering, Signal and Image Processing},
year = {2007},
editor = {G. Camps-Valls and J.-L. Rojo-Alvarez and M. Martinez-Ramon},
publisher = {IDEA Group},
owner = {vert},
timestamp = {2006.11.08}
}
@inproceedings{Vert2001Text,
author = {J.-P. Vert},
title = {Text categorization using adaptive context trees},
booktitle = {Proceedings of the CICLing-2001 conference},
year = {2001},
editor = {A. Gelbukh},
volume = {2004},
series = {LNCS},
pages = {423--436},
publisher = {Springer Verlag},
owner = {vert},
timestamp = {2006.11.08}
}
@techreport{Vert2008optimal,
author = {Vert, J.-P.},
title = {The optimal assignment kernel is not positive definite},
institution = {Arxiv},
year = {2008},
number = {0801.4061},
timestamp = {2008.01.25},
url = {http://hal.archives-ouvertes.fr/hal-00218278}
}
@unpublished{Vert2006Kernel,
author = {Vert, J.-P.},
title = {Kernel methods in bioinformatics : a survey},
note = {To appear},
year = {2006}
}
@techreport{Vert2005Kernel,
author = {Vert, J.-P.},
title = {Kernel methods in computational biology},
institution = {CNRS-HAL},
year = {2005},
number = {ccsd-00012124},
month = {Oct},
abstract = {Support vector machines and kernel methods are increasingly popular
in genomics and computational biology, due to their good performance
in real-world applications and strong modularity that makes them
suitable to a wide range of problems, from the classification of
tumors to the automatic annotation of proteins. {T}heir ability to
work in high dimension, to process non-vectorial data, and the natural
framework they provide to integrate heterogeneous data are particularly
relevant to various problems arising in computational biology. {I}n
this chapter we survey some of the most prominent applications published
so far, highlighting the particular developments in kernel methods
triggered by problems in biology, and mention a few promising research
directions likely to expand in the future.},
pdf = {../local/Vert2005Kernel.pdf},
file = {Vert2005Kernel.pdf:local/Vert2005Kernel.pdf:PDF},
keywords = {biosvm},
url = {http://hal.ccsd.cnrs.fr/ccsd-00012124}
}
@article{Vert2002tree,
author = {Vert, J.-P.},
title = {A tree kernel to analyze phylogenetic profiles},
journal = {Bioinformatics},
year = {2002},
volume = {18},
pages = {S276--S284},
pdf = {../local/vert02b.pdf},
file = {vert02b.pdf:local/vert02b.pdf:PDF},
keywords = {biosvm},
subject = {biokernel},
url = {http://cbio.ensmp.fr/~jvert/publi/ismb02/index.html}
}
@article{Vert2001Adaptive,
author = {Vert, J.-P. },
title = {Adaptive context trees and text clustering},
journal = {I{EEE} {T}rans. {I}nform. {T}heory},
year = {2001},
volume = {47},
pages = {1884-1901},
number = {5},
month = {Jul},
abstract = {In the finite-alphabet context we propose four alternatives to fixed-order
{M}arkov models to estimate a conditional distribution. {T}hey consist
in working with a large class of variable-length {M}arkov models
represented by context trees, and building an estimator of the conditional
distribution with a risk of the same order as the risk of the best
estimator for every model simultaneously, in a conditional {K}ullback-{L}eibler
sense. {S}uch estimators can be used to model complex objects like
texts written in natural language and define a notion of similarity
between them. {T}his idea is illustrated by experimental results
of unsupervised text clustering},
doi = {10.1109/18.930925},
pdf = {../local/Vert2001Adaptive.pdf},
file = {Vert2001Adaptive.pdf:local/Vert2001Adaptive.pdf:PDF},
owner = {vert},
url = {http://dx.doi.org/10.1109/18.930925}
}
@phdthesis{Vert2001Statistical,
author = {Vert, J.-P.},
title = {Statistical {M}ethods for {N}atural {L}anguage {M}odelling},
school = {Paris 6 University},
year = {2001},
pdf = {../local/these.pdf:http\://cg.ensmp.fr/~vert/publi/phd/these.pdf:PDF;these.pdf:http\},
file = {these.pdf:http\://cg.ensmp.fr/~vert/publi/phd/these.pdf:PDF;these.pdf:http\://cg.ensmp.fr/~vert/publi/phd/these.pdf:PDF},
owner = {vert}
}
@techreport{Vert2000Double,
author = {J.-P. Vert},
title = {Double mixture and universal inference},
institution = {Ecole Normale Sup{\'e}rieure},
year = {2000},
number = {DMA-00-15},
owner = {vert},
timestamp = {2006.11.08}
}
@misc{Vert2006Low-rank,
author = {Jean-Philippe Vert and Francis Bach and Theodoros Evgeniou},
title = {Low-rank matrix factorization with attributes},
year = {2006},
owner = {jacob}
}
@inproceedings{Vert2010Fast,
author = {Vert, J-P. and Bleakley, K.},
title = {Fast detection of multiple change-points shared by many signals using
group {LARS}},
booktitle = {Adv. Neural. Inform. Process Syst.},
year = {2010},
editor = {J. Lafferty and C. K. I. Williams and J. Shawe-Taylor and R.S. Zemel
and A. Culotta},
volume = {22},
pages = {2343--2352},
owner = {jp},
timestamp = {2010.10.12}
}
@article{Vert2006accurate,
author = {Vert, J.-P. and Foveau, N. and Lajaunie, C. and Vandenbrouck, Y.},
title = {An accurate and interpretable model for siRNA efficacy prediction},
journal = {BMC Bioinformatics},
year = {2006},
volume = {7},
pages = {520},
abstract = {Background
The use of exogenous small interfering RNAs (siRNAs) for gene silencing
has quickly become a widespread molecular tool providing a powerful
means for gene functional study and new drug target identification.
Although considerable progress has been made recently in understanding
how the RNAi pathway mediates gene silencing, the design of potent
siRNAs remains challenging.
Results
We propose a simple linear model combining basic features of siRNA
sequences for siRNA efficacy prediction. Trained and tested on a
large dataset of siRNA sequences made recently available, it performs
as well as more complex state-of-the-art models in terms of potency
prediction accuracy, with the advantage of being directly interpretable.
The analysis of this linear model allows us to detect and quantify
the effect of nucleotide preferences at particular positions, including
previously known and new observations. We also detect and quantify
a strong propensity of potent siRNAs to contain short asymmetric
motifs in their sequence, and show that, surprisingly, these motifs
alone contain at least as much relevant information for potency prediction
as the nucleotide preferences for particular positions.
Conclusion
The model proposed for prediction of siRNA potency is as accurate
as a state-of-the-art nonlinear model and is easily interpretable
in terms of biological features. It is freely available on the web
at http://cbio.ensmp.fr/dsir},
doi = {10.1186/1471-2105-7-520},
owner = {jp},
timestamp = {2008.11.30},
url = {http://dx.doi.org/10.1186/1471-2105-7-520}
}
@article{Vert2008Machine,
author = {Vert, J.-P. and Jacob, L.},
title = {Machine Learning for In Silico Virtual Screening and Chemical Genomics:
New Strategies},
journal = {Combinatorial Chemistry \& High Throughput Screening},
year = {2008},
volume = {11},
pages = {677--685},
number = {8},
month = {September},
doi = {10.2174/138620708785739899},
issn = {1386-2073},
keywords = {cheminformatics, virtual\_screening},
publisher = {Bentham Science Publishers},
url = {http://dx.doi.org/10.2174/138620708785739899}
}
@inproceedings{Vert2003Graph-driven,
author = {Vert, J.-P. and Kanehisa, M.},
title = {Graph-driven features extraction from microarray data using diffusion
kernels and kernel {CCA}},
booktitle = {Adv. {N}eural {I}nform. {P}rocess. {S}yst.},
year = {2003},
editor = {S. Becker and S. Thrun and K. Obermayer},
pages = {1449--1456},
publisher = {MIT Press},
pdf = {../local/Vert2003Graph-driven.pdf},
file = {Vert2003Graph-driven.pdf:local/Vert2003Graph-driven.pdf:PDF},
keywords = {biosvm}
}
@article{Vert2003Extracting,
author = {Vert, J.-P. and Kanehisa, M. },
title = {Extracting active pathways from gene expression data},
journal = {Bioinformatics},
year = {2003},
volume = {19},
pages = {238ii-234ii},
abstract = {Motivation: {A} promising way to make sense out of gene expression
profiles is to relate them to the activity of metabolic and signalling
pathways. {E}ach pathway usually involves many genes, such as enzymes,
which can themselves participate in many pathways. {T}he set of all
known pathways can therefore be represented by a complex network
of genes. {S}earching for regularities in the set of gene expression
profiles with respect to the topology of this gene network is a way
to automatically extract active pathways and their associated patterns
of activity. {M}ethod: {W}e present a method to perform this task,
which consists in encoding both the gene network and the set of profiles
into two kernel functions, and performing a regularized form of canonical
correlation analysis between the two kernels. {R}esults: {W}hen applied
to publicly available expression data the method is able to extract
biologically relevant expression patterns, as well as pathways with
related activity.},
pdf = {../local/Vert2003Extracting.pdf},
file = {Vert2003Extracting.pdf:local/Vert2003Extracting.pdf:PDF},
keywords = {biosvm},
owner = {vert},
url = {http://bioinformatics.oupjournals.org/cgi/content/abstract/19/suppl_2/ii238}
}
@techreport{Vert2002Graph-driven,
author = {Vert, J.-P. and Kanehisa, M.},
title = {Graph-driven features extraction from microarray data},
institution = {Arxiv physics},
year = {2002},
number = {0206055},
keywords = {biosvm}
}
@article{Vert2007new,
author = {Vert, J.-P. and Qiu, J. and Noble, W. S.},
title = {{A} new pairwise kernel for biological network inference with support
vector machines},
journal = {BMC Bioinformatics},
year = {2007},
volume = {8 Suppl 10},
pages = {S8},
abstract = {BACKGROUND: Much recent work in bioinformatics has focused on the
inference of various types of biological networks, representing gene
regulation, metabolic processes, protein-protein interactions, etc.
A common setting involves inferring network edges in a supervised
fashion from a set of high-confidence edges, possibly characterized
by multiple, heterogeneous data sets (protein sequence, gene expression,
etc.). RESULTS: Here, we distinguish between two modes of inference
in this setting: direct inference based upon similarities between
nodes joined by an edge, and indirect inference based upon similarities
between one pair of nodes and another pair of nodes. We propose a
supervised approach for the direct case by translating it into a
distance metric learning problem. A relaxation of the resulting convex
optimization problem leads to the support vector machine (SVM) algorithm
with a particular kernel for pairs, which we call the metric learning
pairwise kernel. This new kernel for pairs can easily be used by
most SVM implementations to solve problems of supervised classification
and inference of pairwise relationships from heterogeneous data.
We demonstrate, using several real biological networks and genomic
datasets, that this approach often improves upon the state-of-the-art
SVM for indirect inference with another pairwise kernel, and that
the combination of both kernels always improves upon each individual
kernel. CONCLUSION: The metric learning pairwise kernel is a new
formulation to infer pairwise relationships with SVM, which provides
state-of-the-art results for the inference of several biological
networks from heterogeneous genomic data.},
doi = {10.1186/1471-2105-8-S10-S8},
pii = {1471-2105-8-S10-S8},
pmid = {18269702},
timestamp = {2008.05.26},
url = {http://dx.doi.org/10.1186/1471-2105-8-S10-S8}
}
@incollection{Vert2004Local,
author = {Vert, J.-P. and Saigo, H. and Akutsu, T.},
title = {Local alignment kernels for biological sequences},
booktitle = {Kernel {M}ethods in {C}omputational {B}iology},
publisher = {MIT Press},
year = {2004},
editor = {Sch{\"o}lkopf, B. and Tsuda, K. and Vert, J.P.},
pages = {131-154},
address = {The MIT Press, Cambridge, Massachussetts},
pdf = {../local/saigo.pdf:http\},
file = {saigo.pdf:http\://cg.ensmp.fr/~vert/publi/04kmcbbook/saigo.pdf:PDF},
keywords = {biosvm},
owner = {vert}
}
@inproceedings{Vert2006Kernels,
author = {Vert, J.-P. and Thurman, R. and Noble, W. S.},
title = {Kernels for gene regulatory regions},
booktitle = {Adv. {N}eural. {I}nform. {P}rocess {S}yst.},
year = {2006},
editor = {Y. Weiss and B. Sch\"{o}lkopf and J. Platt},
volume = {18},
pages = {1401-1408},
address = {Cambridge, MA},
publisher = {MIT Press},
keywords = {biosvm}
}
@incollection{Vert2004primer,
author = {Vert, J.-P. and Tsuda, K. and Sch{\"o}lkopf, B.},
title = {A primer on kernel methods},
booktitle = {Kernel {M}ethods in {C}omputational {B}iology},
publisher = {MIT Press},
year = {2004},
editor = {Schölkopf, B. and Tsuda, K. and Vert, J.P.},
pages = {35-70},
keywords = {biosvm},
owner = {vert}
}
@inproceedings{Vert2005Supervised,
author = {Vert, J.-P. and Yamanishi, Y.},
title = {Supervised graph inference},
booktitle = {Adv. {N}eural {I}nform. {P}rocess. {S}yst.},
year = {2005},
editor = {Saul, L. K. and Weiss, Y. and Bottou, L.},
volume = {17},
pages = {1433-1440},
publisher = {MIT Press, Cambridge, MA},
pdf = {../local/nips2004.pdf:http\://cg.ensmp.fr/~vert/publi/04nips_yamanishi/nips2004.pdf:PDF;nips2004.pdf:http\},
file = {nips2004.pdf:http\://cg.ensmp.fr/~vert/publi/04nips_yamanishi/nips2004.pdf:PDF;nips2004.pdf:http\://cg.ensmp.fr/~vert/publi/04nips_yamanishi/nips2004.pdf:PDF},
keywords = {biosvm},
owner = {vert}
}
@article{Vert2006Consistency,
author = {Vert, R. and Vert, J.-P.},
title = {Consistency and convergence rates of one-class {SVM}s and related
algorithms},
journal = {J. Mach. Learn. Res.},
year = {2006},
volume = {7},
pages = {817--854},
abstract = {We determine the asymptotic behaviour of the function computed by
support vector machines (SVM) and related algorithms that minimize
a regularized empirical convex loss function in the reproducing kernel
Hilbert space of the Gaussian RBF kernel, in the situation where
the number of examples tends to infinity, the bandwidth of the Gaussian
kernel tends to 0, and the regularization parameter is held fixed.
Non-asymptotic convergence bounds to this limit in the L2 sense are
provided, together with upper bounds on the classification error
that is shown to converge to the Bayes risk, therefore proving the
Bayes-consistency of a variety of methods although the regularization
term does not vanish. These results are particularly relevant to
the one-class SVM, for which the regularization can not vanish by
construction, and which is shown for the first time to be a consistent
density level set estimator.},
owner = {jp},
timestamp = {2008.11.30},
url = {http://jmlr.csail.mit.edu/papers/v7/vert06a.html}
}
@techreport{Vert2005Consistency,
author = {Vert, R. and Vert, J.-P.},
title = {Consistency and convergence rates of one-class {SVM} and related
algorithms},
institution = {LRI, Universit{\'e} Paris-Sud},
year = {2005},
number = {1414},
abstract = {We determine the asymptotic limit of the function computed by support
vector machines ({SVM}) and related algorithms that minimize a regularized
empirical convex loss function in the reproducing kernel {H}ilbert
space of the {G}aussian {RBF} kernel, in the situation where the
number of examples tends to infinity, the bandwidth of the {G}aussian
kernel tends to 0, and the regularization parameter is held fixed.
{N}on-asymptotic convergence bounds to this limit in the {L}2 sense
are provided, together with upper bounds on the classification error
that is shown to converge to the {B}ayes risk, therefore proving
the {B}ayes-consistency of a variety of methods although the regularization
term does not vanish. {T}hese results are particularly relevant to
the one-class {SVM}, for which the regularization can not vanish
by construction, and which is shown for the first time to be a consistent
density level set estimator.},
pdf = {../local/Vert2005Consistency.pdf},
file = {Vert2005Consistency.pdf:local/Vert2005Consistency.pdf:PDF}
}
@article{Vickers2003Efficient,
author = {Vickers, T. A. and Koo, S. and Bennett, C. F. and Crooke, S. T. and
Dean, N. M. and Baker, B. F.},
title = {Efficient reduction of target {RNA}s by small interfering {RNA} and
{RN}ase {H}-dependent antisense agents. {A} comparative analysis.},
journal = {J. {B}iol. {C}hem.},
year = {2003},
volume = {278},
pages = {7108-18},
number = {9},
month = {Feb},
abstract = {R{NA} interference can be considered as an antisense mechanism of
action that utilizes a double-stranded {RN}ase to promote hydrolysis
of the target {RNA}. {W}e have performed a comparative study of optimized
antisense oligonucleotides designed to work by an {RNA} interference
mechanism to oligonucleotides designed to work by an {RN}ase {H}-dependent
mechanism in human cells. {T}he potency, maximal effectiveness, duration
of action, and sequence specificity of optimized {RN}ase {H}-dependent
oligonucleotides and small interfering {RNA} (si{RNA}) oligonucleotide
duplexes were evaluated and found to be comparable. {E}ffects of
base mismatches on activity were determined to be position-dependent
for both si{RNA} oligonucleotides and {RN}ase {H}-dependent oligonucleotides.
{I}n addition, we determined that the activity of both si{RNA} oligonucleotides
and {RN}ase {H}-dependent oligonucleotides is affected by the secondary
structure of the target m{RNA}. {T}o determine whether positions
on target {RNA} identified as being susceptible for {RN}ase {H}-mediated
degradation would be coincident with si{RNA} target sites, we evaluated
the effectiveness of si{RNA}s designed to bind the same position
on the target m{RNA} as {RN}ase {H}-dependent oligonucleotides. {E}xamination
of 80 si{RNA} oligonucleotide duplexes designed to bind to {RNA}
from four distinct human genes revealed that, in general, activity
correlated with the activity to {RN}ase {H}-dependent oligonucleotides
designed to the same site, although some exceptions were noted. {T}he
one major difference between the two strategies is that {RN}ase {H}-dependent
oligonucleotides were determined to be active when directed against
targets in the pre-m{RNA}, whereas si{RNA}s were not. {T}hese results
demonstrate that si{RNA} oligonucleotide- and {RN}ase {H}-dependent
antisense strategies are both valid strategies for evaluating function
of genes in cell-based assays.},
doi = {10.1074/jbc.M210326200},
keywords = {Animals, Antisense, Base Sequence, COS Cells, Calf Thymus, Cultured,
Dose-Response Relationship, Drug, Flow Cytometry, Humans, Intercellular
Adhesion Molecule-1, Introns, Luciferases, Messenger, Molecular Sequence
Data, Nucleic Acid Conformation, Oligonucleotides, PTEN Phosphohydrolase,
Phosphoric Monoester Hydrolases, Protein Structure, RNA, Ribonuclease
H, Small Interfering, Tertiary, Time Factors, Tumor Cells, Tumor
Suppressor Proteins, 12500975},
pii = {M210326200},
url = {http://dx.doi.org/10.1074/jbc.M210326200}
}
@article{Vijver2002gene-expression,
author = {van de Vijver, M. J. and He, Y. D. and van't Veer, L. J. and Dai,
H. and Hart, A. A. M. and Voskuil, D. W. and Schreiber, G. J. and
Peterse, J. L. and Roberts, C. and Marton, M. J. and Parrish, M.
and Atsma, D. and Witteveen, A. and Glas, A. and Delahaye, L. and
van der Velde, T. and Bartelink, H. and Rodenhuis, S. and Rutgers,
E. T. and Friend, S. H. and Bernards, R.},
title = {A gene-expression signature as a predictor of survival in breast
cancer},
journal = {N. Engl. J. Med.},
year = {2002},
volume = {347},
pages = {1999--2009},
number = {25},
month = {Dec},
abstract = {BACKGROUND: A more accurate means of prognostication in breast cancer
will improve the selection of patients for adjuvant systemic therapy.
METHODS: Using microarray analysis to evaluate our previously established
70-gene prognosis profile, we classified a series of 295 consecutive
patients with primary breast carcinomas as having a gene-expression
signature associated with either a poor prognosis or a good prognosis.
All patients had stage I or II breast cancer and were younger than
53 years old; 151 had lymph-node-negative disease, and 144 had lymph-node-positive
disease. We evaluated the predictive power of the prognosis profile
using univariable and multivariable statistical analyses. RESULTS:
Among the 295 patients, 180 had a poor-prognosis signature and 115
had a good-prognosis signature, and the mean (+/-SE) overall 10-year
survival rates were 54.6+/-4.4 percent and 94.5+/-2.6 percent, respectively.
At 10 years, the probability of remaining free of distant metastases
was 50.6+/-4.5 percent in the group with a poor-prognosis signature
and 85.2+/-4.3 percent in the group with a good-prognosis signature.
The estimated hazard ratio for distant metastases in the group with
a poor-prognosis signature, as compared with the group with the good-prognosis
signature, was 5.1 (95 percent confidence interval, 2.9 to 9.0; P<0.001).
This ratio remained significant when the groups were analyzed according
to lymph-node status. Multivariable Cox regression analysis showed
that the prognosis profile was a strong independent factor in predicting
disease outcome. CONCLUSIONS: The gene-expression profile we studied
is a more powerful predictor of the outcome of disease in young patients
with breast cancer than standard systems based on clinical and histologic
criteria.},
doi = {10.1056/NEJMoa021967},
pdf = {../local/Vijver2002gene-expression.pdf},
file = {Vijver2002gene-expression.pdf:local/Vijver2002gene-expression.pdf:PDF},
institution = {Division of Diagnostic Oncology, Netherlands Cancer Institute, Amsterdam,
The Netherlands.},
keywords = {breastcancer, csbcbook, csbcbook-ch3},
owner = {jp},
pii = {347/25/1999},
pmid = {12490681},
timestamp = {2008.11.16},
url = {http://dx.doi.org/10.1056/NEJMoa021967}
}
@article{Vinayagam2004Applying,
author = {Vinayagam, A. and König, R. and Moormann, J. and Schubert, F. and
Eils, R. and Glatting, K.-H. and Suhai, S.},
title = {Applying {S}upport {V}ector {M}achines for {G}ene {O}ntology based
gene function prediction.},
journal = {B{MC} {B}ioinformatics},
year = {2004},
volume = {5},
pages = {116},
number = {1},
month = {Aug},
abstract = {B{ACKGROUND}: {T}he current progress in sequencing projects calls
for rapid, reliable and accurate function assignments of gene products.
{A} variety of methods has been designed to annotate sequences on
a large scale. {H}owever, these methods can either only be applied
for specific subsets, or their results are not formalised, or they
do not provide precise confidence estimates for their predictions.
{RESULTS}: {W}e have developed a large-scale annotation system that
tackles all of these shortcomings. {I}n our approach, annotation
was provided through {G}ene {O}ntology terms by applying multiple
{S}upport {V}ector {M}achines ({SVM}) for the classification of correct
and false predictions. {T}he general performance of the system was
benchmarked with a large dataset. {A}n organism-wise cross-validation
was performed to define confidence estimates, resulting in an average
precision of 80\% for 74\% of all test sequences. {T}he validation
results show that the prediction performance was organism-independent
and could reproduce the annotation of other automated systems as
well as high-quality manual annotations. {W}e applied our trained
classification system to {X}enopus laevis sequences, yielding functional
annotation for more than half of the known expressed genome. {C}ompared
to the currently available annotation, we provided more than twice
the number of contigs with good quality annotation, and additionally
we assigned a confidence value to each predicted {GO} term. {CONCLUSIONS}:
{W}e present a complete automated annotation system that overcomes
many of the usual problems by applying a controlled vocabulary of
{G}ene {O}ntology and an established classification method on large
and well-described sequence data sets. {I}n a case study, the function
for {X}enopus laevis contig sequences was predicted and the results
are publicly available at ftp://genome.dkfz-heidelberg.de/pub/agd/gene_association.agd_{X}enopus.},
doi = {10.1186/1471-2105-5-116},
pdf = {../local/Vinayagam2004Applying.pdf},
file = {Vinayagam2004Applying.pdf:local/Vinayagam2004Applying.pdf:PDF},
keywords = {biosvm},
pii = {1471-2105-5-116},
url = {http://dx.doi.org/10.1186/1471-2105-5-116}
}
@article{Vincent-Salomon2008Integrated,
author = {Vincent-Salomon, A. and Lucchesi, C. and Gruel, N. and Raynal, V.
and Pierron, G. and Goudefroye, R. and Reyal, F. and Radvanyi, F.
and Salmon, R. and Thiery, J.-P. and Sastre-Garau, X. and Sigal-Zafrani,
B. and Fourquet, A. and Delattre, A.},
title = {Integrated genomic and transcriptomic analysis of ductal carcinoma
in situ of the breast},
journal = {Clin. Cancer Res.},
year = {2008},
volume = {14},
pages = {1956--1965},
number = {7},
month = {Apr},
abstract = {PURPOSE: To gain insight into genomic and transcriptomic subtypes
of ductal carcinomas in situ of the breast (DCIS). EXPERIMENTAL DESIGN:
We did a combined phenotypic and genomic analysis of a series of
57 DCIS integrated with gene expression profile analysis for 26 of
the 57 cases. RESULTS: Thirty-two DCIS exhibited a luminal phenotype;
21 were ERBB2 positive, and 4 were ERBB2/estrogen receptor (ER) negative
with 1 harboring a bona fide basal-like phenotype. Based on a CGH
analysis, genomic types were identified in this series of DCIS with
the 1q gain/16q loss combination observed in 3 luminal DCIS, the
mixed amplifier pattern including all ERBB2, 12 luminal and 2 ERBB2(-)/ER(-)
DCIS, and the complex copy number alteration profile encompassing
14 luminal and 1 ERBB2(-)/ER(-) DCIS. Eight cases (8 of 57; 14\%)
presented a TP53 mutation, all being amplifiers. Unsupervised analysis
of gene expression profiles of 26 of the 57 DCIS showed that luminal
and ERBB2-amplified, ER-negative cases clustered separately. We further
investigated the effect of high and low copy number changes on gene
expression. Strikingly, amplicons but also low copy number changes
especially on 1q, 8q, and 16q in DCIS regulated the expression of
a subset of genes in a very similar way to that recently described
in invasive ductal carcinomas. CONCLUSIONS: These combined approaches
show that the molecular heterogeneity of breast ductal carcinomas
exists already in in situ lesions and further indicate that DCIS
and invasive ductal carcinomas share genomic alterations with a similar
effect on gene expression profile.},
doi = {10.1158/1078-0432.CCR-07-1465},
pdf = {../local/Vincent-Salomon2008Integrated.pdf},
file = {Vincent-Salomon2008Integrated.pdf:Vincent-Salomon2008Integrated.pdf:PDF},
institution = {Institut Curie, Department of Tumor Biology, Paris, France.},
keywords = {breastcancer, cgh},
owner = {jp},
pii = {14/7/1956},
pmid = {18381933},
timestamp = {2008.12.09},
url = {http://dx.doi.org/10.1158/1078-0432.CCR-07-1465}
}
@inproceedings{Vinokourov2003Inferring,
author = {Vinokourov, A. and Shawe-Taylor, J. and Cristianini, N.},
title = {Inferring a semantic representation of text via cross-language correlation
analysis},
booktitle = {Adv. {N}eural {I}nform. {P}rocess. {S}yst.},
year = {2003},
editor = {Suzanna Becker and Sebastian Thrun and Klaus Obermayer},
publisher = {MIT Press},
pdf = {../local/Vinokourov2003Inferring.pdf},
file = {Vinokourov2003Inferring.pdf:local/Vinokourov2003Inferring.pdf:PDF}
}
@techreport{Vinokourov2002Finding,
author = {Vinokourov, A. and Shawe-Taylor, J. and Cristianini, N.},
title = {Finding {L}anguage-{I}ndependent {S}emantic {R}epresentation of {T}ext
using {K}ernel {C}anonical {C}orrelation {A}nalysis},
institution = {Neurocolt},
year = {2002},
note = {NeuroCOLT Technical Report NC-TR-02-119},
pdf = {../local/vino02.ps.gz},
file = {vino02.ps.gz:local/vino02.ps.gz:PostScript},
subject = {kernel},
url = {http://www.neurocolt.com/abs/2002/abs02119.html}
}
@inproceedings{Vishwanathan2007Fast,
author = {Vishwanathan, S.V.N. and Borgwardt, K. and Schraudolph, N.},
title = {Fast {C}omputation of {G}raph {K}ernels},
booktitle = {Adv. {N}eural {I}nform. {P}rocess. {S}yst.},
year = {2007},
editor = {B. Sch\"{o}lkopf and J. Platt and T. Hoffman},
volume = {19},
pages = {1-2},
address = {Cambridge, MA},
publisher = {MIT Press, Cambridge, MA}
}
@article{Vishwanathan2008Graph,
author = {S. V. N. Vishwanathan and K. M. Borgwardt and R. I. Kondor and N.
N. Schraudolph},
title = {Graph Kernels},
journal = {CoRR},
year = {2008},
volume = {abs/0807.0093},
bibsource = {DBLP, http://dblp.uni-trier.de},
ee = {http://arxiv.org/abs/0807.0093}
}
@article{Vishwanathan2009Graph,
author = {Vishwanathan, S. V. N. and Schraudolph, N. N. and Kondor, R. and
Borgwardt, K. M.},
title = {Graph Kernels},
journal = {J. Mach. Learn. Res.},
year = {2009},
volume = {10},
pages = {1--41},
bibsource = {DBLP, http://dblp.uni-trier.de},
ee = {http://arxiv.org/abs/0807.0093},
pdf = {../local/Vishwanathan2009Graph.pdf},
file = {Vishwanathan2009Graph.pdf:Vishwanathan2009Graph.pdf:PDF}
}
@incollection{Vishwanathan2004Fast,
author = {Vishwanathan, S. V. N. and Smola, A. J.},
title = {Fast kernels for string and tree matching},
booktitle = {Kernel methods in computational biology},
publisher = {MIT Press},
year = {2004},
editor = {Sch{\"o}lkopf, B. and Tsuda, K. and Vert, J.-P.},
pages = {113--130},
owner = {vert},
timestamp = {2007.08.01}
}
@article{Viterbi1973Error,
author = {Viterbi, A.},
title = {Error bounds for convolutional codes and an asymptotically optimum
decoding algorithm},
journal = {IEEE Trans. Inform. Theory},
year = {1973},
volume = {13},
pages = {260--269},
number = {2},
abstract = {The probability of error in decoding an optimal convolutional code
transmitted over a memoryless channel is bounded from above and below
as a function of the constraint length of the code. For all but pathological
channels the bounds are asymptotically (exponentially) tight for
rates aboveR_{0}, the computational cutoff rate of sequential decoding.
As a function of constraint length the performance of optimal convolutional
codes is shown to be superior to that of block codes of the same
length, the relative improvement increasing with rate. The upper
bound is obtained for a specific probabilistic nonsequential decoding
algorithm which is shown to be asymptotically optimum for rates aboveR_{0}and
whose performance bears certain similarities to that of sequential
decoding algorithms.},
owner = {jp},
timestamp = {2008.10.04},
url = {http://ieeexplore.ieee.org/search/wrapper.jsp?arnumber=1054010}
}
@article{Vlahovicek2005SBASE,
author = {Kristian Vlahovicek and László Kaján and Vilmos Agoston and Sándor
Pongor},
title = {The {SBASE} domain sequence resource, release 12: prediction of protein
domain-architecture using support vector machines.},
journal = {Nucleic {A}cids {R}es},
year = {2005},
volume = {33},
pages = {D223-5},
number = {Database issue},
month = {Jan},
abstract = {S{BASE} (http://www.icgeb.trieste.it/sbase) is an online resource
designed to facilitate the detection of domain homologies based on
sequence database search. {T}he present release of the {SBASE} {A}
library of protein domain sequences contains 972,397 protein sequence
segments annotated by structure, function, ligand-binding or cellular
topology, clustered into 8547 domain groups. {SBASE} {B} contains
169,916 domain sequences clustered into 2526 less well-characterized
groups. {D}omain prediction is based on an evaluation of database
search results in comparison with a 'similarity network' of inter-sequence
similarity scores, using support vector machines trained on similarity
search results of known domains.},
doi = {10.1093/nar/gki112},
pdf = {../local/Vlahovicek2005SBASE.pdf},
file = {Vlahovicek2005SBASE.pdf:local/Vlahovicek2005SBASE.pdf:PDF},
keywords = {biosvm},
pii = {33/suppl_1/D223},
url = {http://dx.doi.org/10.1093/nar/gki112}
}
@article{Vliet2012Integration,
author = {van Vliet, M.H. and Horlings, H.M. and van de Vijver, M.J. and Reinders,
M.J.T. and Wessels, L.F.A.},
title = {Integration of Clinical and Gene Expression Data Has a Synergetic
Effect on Predicting Breast Cancer Outcome},
journal = {PloS one},
year = {2012},
volume = {7},
pages = {e40358},
number = {7},
publisher = {Public Library of Science}
}
@article{Voduc2010Breast,
author = {K. David Voduc and Maggie C U Cheang and Scott Tyldesley and Karen
Gelmon and Torsten O Nielsen and Hagen Kennecke},
title = {Breast cancer subtypes and the risk of local and regional relapse.},
journal = {J Clin Oncol},
year = {2010},
volume = {28},
pages = {1684--1691},
number = {10},
month = {Apr},
abstract = {The risk of local and regional relapse associated with each breast
cancer molecular subtype was determined in a large cohort of patients
with breast cancer. Subtype assignment was accomplished using a validated
six-marker immunohistochemical panel applied to tissue microarrays.Semiquantitative
analysis of estrogen receptor (ER), progesterone receptor (PR), Ki-67,
human epidermal growth factor receptor 2 (HER2), epidermal growth
factor receptor (EGFR), and cytokeratin (CK) 5/6 was performed on
tissue microarrays constructed from 2,985 patients with early invasive
breast cancer. Patients were classified into the following categories:
luminal A, luminal B, luminal-HER2, HER2 enriched, basal-like, or
triple-negative phenotype-nonbasal. Multivariable Cox analysis was
used to determine the risk of local or regional relapse associated
the intrinsic subtypes, adjusting for standard clinicopathologic
factors.The intrinsic molecular subtype was successfully determined
in 2,985 tumors. The median follow-up time was 12 years, and there
have been a total of 325 local recurrences and 227 regional lymph
node recurrences. Luminal A tumors (ER or PR positive, HER2 negative,
Ki-67 < 1\%) had the best prognosis and the lowest rate of local
or regional relapse. For patients undergoing breast conservation,
HER2-enriched and basal subtypes demonstrated an increased risk of
regional recurrence, and this was statistically significant on multivariable
analysis. After mastectomy, luminal B, luminal-HER2, HER2-enriched,
and basal subtypes were all associated with an increased risk of
local and regional relapse on multivariable analysis.Luminal A tumors
are associated with a low risk of local or regional recurrence. Molecular
subtyping of breast tumors using a six-marker immunohistochemical
panel can identify patients at increased risk of local and regional
recurrence.},
doi = {10.1200/JCO.2009.24.9284},
pdf = {../local/Voduc2010Breast.pdf},
file = {Voduc2010Breast.pdf:Voduc2010Breast.pdf:PDF},
institution = {Department of Radiation Oncology, British Columbia Cancer Agency,
Vancouver, British Columbia, Canada V5Z 4E6. dvoduc@bccancer.bc.ca},
keywords = {Adult; Breast Neoplasms, mortality/pathology; Female; Humans; Ki-67
Antigen, metabolism; Lymphatic Metastasis; Middle Aged; Neoplasm
Metastasis; Neoplasm Recurrence, Local; Neoplasms, Hormone-Dependent;
Receptor, Epidermal Growth Factor, metabolism; Receptors, Estrogen,
analysis; Receptors, Progesterone, analysis; Tissue Array Analysis;
Tumor Markers, Biological, analysis},
language = {eng},
medline-pst = {ppublish},
owner = {phupe},
pii = {JCO.2009.24.9284},
pmid = {20194857},
timestamp = {2011.06.01},
url = {http://dx.doi.org/10.1200/JCO.2009.24.9284}
}
@article{Vogelstein2004Cancer,
author = {Vogelstein, B. and Kinzler, K. W.},
title = {Cancer genes and the pathways they control.},
journal = {Nat. Med.},
year = {2004},
volume = {10},
pages = {789--799},
number = {8},
month = {Aug},
abstract = {The revolution in cancer research can be summed up in a single sentence:
cancer is, in essence, a genetic disease. In the last decade, many
important genes responsible for the genesis of various cancers have
been discovered, their mutations precisely identified, and the pathways
through which they act characterized. The purposes of this review
are to highlight examples of progress in these areas, indicate where
knowledge is scarce and point out fertile grounds for future investigation.},
doi = {10.1038/nm1087},
pdf = {../local/Vogelstein2004Cancer.pdf},
file = {Vogelstein2004Cancer.pdf:Vogelstein2004Cancer.pdf:PDF},
institution = {Howard Hughes Medical Institute and The Sidney Kimmel Comprehensive
Cancer Center, The Johns Hopkins University Medical Institutions,
Baltimore, Maryland 21231, USA. vogelbe@welch.jhu.edu},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {nm1087},
pmid = {15286780},
timestamp = {2011.10.05},
url = {http://dx.doi.org/10.1038/nm1087}
}
@article{Vostrikova1981Detection,
author = {Vostrikova, L. J.},
title = {Detection of disorder in multidimensional stochastic processes},
journal = {Soviet Mathematics Doklady},
year = {1981},
volume = {24},
pages = {55--59},
owner = {jp},
timestamp = {2010.06.02}
}
@article{Waaijenborg2008Quantifying,
author = {Waaijenborg, S. and {Verselewel de Witt Hamer}, P. C. and Zwinderman,
A. H.},
title = {Quantifying the association between gene expressions and DNA-markers
by penalized canonical correlation analysis.},
journal = {Stat Appl Genet Mol Biol},
year = {2008},
volume = {7},
pages = {Article3},
number = {1},
abstract = {Multiple changes at the DNA level are at the basis of complex diseases.
Identifying the genetic networks that are influenced by these changes
might help in understanding the development of these diseases. Canonical
correlation analysis is used to associate gene expressions with DNA-markers
and thus reveals sets of co-expressed and co-regulated genes and
their associating DNA-markers. However, when the number of variables
gets high, e.g. in the case of microarray studies, interpretation
of these results can be difficult. By adapting the elastic net to
canonical correlation analysis the number of variables reduces, and
interpretation becomes easier, moreover, due to the grouping effect
of the elastic net co-regulated and co-expressed genes cluster. Additionally,
our adaptation works well in situations where the number of variables
exceeds by far the number of subjects.},
doi = {10.2202/1544-6115.1329},
institution = {Academic Medical Center / University of Amsterdam. s.waaijenborg@amc.uva.nl},
keywords = {Cluster Analysis; DNA, genetics; Gene Expression; Genetic Markers},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pmid = {18241193},
timestamp = {2012.02.29},
url = {http://dx.doi.org/10.2202/1544-6115.1329}
}
@article{Wadman2008James,
author = {Meredith Wadman},
title = {James Watson's genome sequenced at high speed.},
journal = {Nature},
year = {2008},
volume = {452},
pages = {788},
number = {7189},
month = {Apr},
doi = {10.1038/452788b},
keywords = {Genetic Counseling, trends; Genome, Human; Genomics, economics/trends;
History, 21st Century; Humans; Individuality; Male; Reference Standards;
Sequence Analysis, DNA, economics/trends; Time Factors},
language = {eng},
medline-pst = {ppublish},
owner = {philippe},
pmid = {18431822},
timestamp = {2010.07.28},
url = {http://dx.doi.org/10.1038/452788b}
}
@article{Wagener1995Autocorrelation,
author = {M. Wagener and J. Sadowski and J. Gasteiger},
title = {Autocorrelation of Molecular Surface Properties for Modeling. Corticosteroid
Binding Globulin and Cytosolic. Ah. Receptor. Activity by Neural
Networks},
journal = {J. Am. Chem. Soc.},
year = {1995},
volume = {117},
pages = {7769-7775},
owner = {mahe},
timestamp = {2006.09.13}
}
@article{Wagner2001Yeast,
author = {Wagner, A.},
title = {The {Y}east {P}rotein {I}nteraction {N}etwork {E}volves {R}apidly
and {C}ontains {F}ew {R}edundant {D}uplicate {G}enes},
journal = {Mol. {B}iol. {E}vol.},
year = {2001},
volume = {18},
pages = {1283--1292},
pdf = {../local/wagn01.pdf},
file = {wagn01.pdf:local/wagn01.pdf:PDF},
subject = {bionet},
url = {http://www.santafe.edu/sfi/publications/Abstracts/01-04-022abs.html}
}
@article{Wagner2003Protocols,
author = {Wagner, M. and Naik, D. and Pothen, A.},
title = {Protocols for disease classification from mass spectrometry data.},
journal = {Proteomics},
year = {2003},
volume = {3},
pages = {1692-1698},
number = {9},
abstract = {We report our results in classifying protein matrix-assisted laser
desorption/ionization-time of flight mass spectra obtained from serum
samples into diseased and healthy groups. {W}e discuss in detail
five of the steps in preprocessing the mass spectral data for biomarker
discovery, as well as our criterion for choosing a small set of peaks
for classifying the samples. {C}ross-validation studies with four
selected proteins yielded misclassification rates in the 10-15% range
for all the classification methods. {T}hree of these proteins or
protein fragments are down-regulated and one up-regulated in lung
cancer, the disease under consideration in this data set. {W}hen
cross-validation studies are performed, care must be taken to ensure
that the test set does not influence the choice of the peaks used
in the classification. {M}isclassification rates are lower when both
the training and test sets are used to select the peaks used in classification
versus when only the training set is used. {T}his expectation was
validated for various statistical discrimination methods when thirteen
peaks were used in cross-validation studies. {O}ne particular classification
method, a linear support vector machine, exhibited especially robust
performance when the number of peaks was varied from four to thirteen,
and when the peaks were selected from the training set alone. {E}xperiments
with the samples randomly assigned to the two classes confirmed that
misclassification rates were significantly higher in such cases than
those observed with the true data. {T}his indicates that our findings
are indeed significant. {W}e found closely matching masses in a database
for protein expression in lung cancer for three of the four proteins
we used to classify lung cancer. {D}ata from additional samples,
increased experience with the performance of various preprocessing
techniques, and affirmation of the biological roles of the proteins
that help in classification, will strengthen our conclusions in the
future.},
doi = {10.1002/pmic.200300519},
pdf = {../local/Wagner2003Protocols.pdf},
file = {Wagner2003Protocols.pdf:local/Wagner2003Protocols.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://dx.doi.org/10.1002/pmic.200300519}
}
@article{Wagner2004Computational,
author = {Wagner, M. and Naik, D.N. and Pothen, A. and Kasukurti, S. and Devineni,
R.R. and Adam, B.L. and Semmes, O.J. and Wright Jr, G.L.},
title = {Computational protein biomarker prediction: a case study for prostate
cancer},
journal = {B{MC} {B}ioinformatics},
year = {2004},
volume = {5},
number = {26},
abstract = {Background {R}ecent technological advances in mass spectrometry pose
challenges in computational mathematics and statistics to process
the mass spectral data into predictive models with clinical and biological
significance. {W}e discuss several classification-based approaches
to finding protein biomarker candidates using protein profiles obtained
via mass spectrometry, and we assess their statistical significance.
{O}ur overall goal is to implicate peaks that have a high likelihood
of being biologically linked to a given disease state, and thus to
narrow the search for biomarker candidates. {R}esults {T}horough
cross-validation studies and randomization tests are performed on
a prostate cancer dataset with over 300 patients, obtained at the
{E}astern {V}irginia {M}edical {S}chool using {SELDI}-{TOF} mass
spectrometry. {W}e obtain average classification accuracies of 87%
on a four-group classification problem using a two-stage linear {SVM}-based
procedure and just 13 peaks, with other methods performing comparably.
{C}onclusions {M}odern feature selection and classification methods
are powerful techniques for both the identification of biomarker
candidates and the related problem of building predictive models
from protein mass spectrometric profiles. {C}ross-validation and
randomization are essential tools that must be performed carefully
in order not to bias the results unfairly. {H}owever, only a biological
validation and identification of the underlying proteins will ultimately
confirm the actual value and power of any computational predictions.},
doi = {10.1186/1471-2105-5-26},
pdf = {../local/Wagner2004Computational.pdf},
file = {Wagner2004Computational.pdf:local/Wagner2004Computational.pdf:PDF},
keywords = {biosvm},
owner = {vert},
url = {http://www.biomedcentral.com/1471-2105/5/26}
}
@article{Wahba2002Soft,
author = {Grace Wahba},
title = {Soft and hard classification by reproducing kernel {H}ilbert space
methods.},
journal = {Proc {N}atl {A}cad {S}ci {U} {S} {A}},
year = {2002},
volume = {99},
pages = {16524-30},
number = {26},
month = {Dec},
abstract = {Reproducing kernel {H}ilbert space ({RKHS}) methods provide a unified
context for solving a wide variety of statistical modelling and function
estimation problems. {W}e consider two such problems: {W}e are given
a training set [yi, ti, i = 1, em leader, n], where yi is the response
for the ith subject, and ti is a vector of attributes for this subject.
{T}he value of y(i) is a label that indicates which category it came
from. {F}or the first problem, we wish to build a model from the
training set that assigns to each t in an attribute domain of interest
an estimate of the probability pj(t) that a (future) subject with
attribute vector t is in category j. {T}he second problem is in some
sense less ambitious; it is to build a model that assigns to each
t a label, which classifies a future subject with that t into one
of the categories or possibly "none of the above." {T}he approach
to the first of these two problems discussed here is a special case
of what is known as penalized likelihood estimation. {T}he approach
to the second problem is known as the support vector machine. {W}e
also note some alternate but closely related approaches to the second
problem. {T}hese approaches are all obtained as solutions to optimization
problems in {RKHS}. {M}any other problems, in particular the solution
of ill-posed inverse problems, can be obtained as solutions to optimization
problems in {RKHS} and are mentioned in passing. {W}e caution the
reader that although a large literature exists in all of these topics,
in this inaugural article we are selectively highlighting work of
the author, former students, and other collaborators.},
doi = {10.1073/pnas.242574899},
pdf = {../local/Wahba2002Soft.pdf},
file = {Wahba2002Soft.pdf:local/Wahba2002Soft.pdf:PDF},
keywords = {Acute, Algorithms, Animals, Automated, Base Pair Mismatch, Base Pairing,
Base Sequence, Biological, Biosensing Techniques, Classification,
Cluster Analysis, Comparative Study, Computational Biology, Computer-Assisted,
Cystadenoma, DNA, Drug, Drug Design, Eukaryotic Cells, Female, Gene
Expression, Gene Expression Profiling, Gene Expression Regulation,
Genes, Genetic, Genetic Markers, Hemolysins, Humans, Leukemia, Ligands,
Likelihood Functions, Lymphocytic, Markov Chains, Mathematics, Messenger,
Models, Molecular, Molecular Probe Techniques, Molecular Sequence
Data, Nanotechnology, Neoplasm, Neoplastic, Neural Networks (Computer),
Non-P.H.S., Non-U.S. Gov't, Nucleic Acid Conformation, Observer Variation,
Oligonucleotide Array Sequence Analysis, Ovarian Neoplasms, P.H.S.,
Pattern Recognition, Probability, Protein Binding, Proteins, Quality
Control, RNA, RNA Splicing, Receptors, Reference Values, Reproducibility
of Results, Research Support, Sensitivity and Specificity, Sequence
Analysis, Signal Processing, Statistical, Stomach Neoplasms, Thermodynamics,
Transcription, Tumor Markers, U.S. Gov't, 12477931},
pii = {242574899},
url = {http://dx.doi.org/10.1073/pnas.242574899}
}
@book{Wahba1990Spline,
title = {Spline {M}odels for {O}bservational {D}ata},
publisher = {{SIAM}},
year = {1990},
author = {Wahba, G.},
volume = {59},
series = {CBMS-NSF Regional Conference Series in Applied Mathematics},
address = {Philadelphia},
subject = {ml}
}
@article{Wainwright2009Information-Theoretic,
author = {Wainwright, M. J. },
title = {Information-Theoretic Limits on Sparsity Recovery in the High-Dimensional
and Noisy Setting},
journal = {IEEE T Inform Theory},
year = {2009},
volume = {55},
pages = {5728--5741},
number = {12},
doi = {10.1109/TIT.2009.2032816},
owner = {jp},
timestamp = {2011.09.19}
}
@article{Wainwright2009Sharp,
author = {Wainwright, M. J.},
title = {Sharp Thresholds for High-Dimensional and Noisy Sparsity Recovery
Using $\ell_1$-Constrained Quadratic Programming (Lasso)},
journal = {IEEE T. Inform. Theory.},
year = {2009},
volume = {55},
pages = {2183--2202},
number = {5},
doi = {10.1109/TIT.2009.2016018},
pdf = {../local/Wainwright2009Sharp.pdf},
file = {Wainwright2009Sharp.pdf:Wainwright2009Sharp.pdf:PDF},
owner = {jp},
timestamp = {2011.09.19},
url = {http://dx.doi.org/10.1109/TIT.2009.2016018}
}
@techreport{Wainwright2006Sharp,
author = {Wainwright, M. J.},
title = {Sharp thresholds for high-dimensional and noisy recovery of sparsity},
institution = {UC Berkeley, Department of Statistics},
year = {2006},
number = {709},
pdf = {../local/Wainwright2006Sharp.pdf},
file = {Wainwright2006Sharp.pdf:Wainwright2006Sharp.pdf:PDF},
owner = {jp},
timestamp = {2009.01.25},
url = {http://www.stat.berkeley.edu/tech-reports/709.pdf}
}
@article{Wallace2010Identification,
author = {Emma V B Wallace and David Stoddart and Andrew J Heron and Ellina
Mikhailova and Giovanni Maglia and Timothy J Donohoe and Hagan Bayley},
title = {Identification of epigenetic DNA modifications with a protein nanopore.},
journal = {Chem Commun (Camb)},
year = {2010},
volume = {46},
pages = {8195--8197},
number = {43},
month = {Nov},
abstract = {Two DNA bases, 5-methylcytosine (5mC) and 5-hydroxymethylcytosine
(hmC), marks of epigenetic modification, are recognized in immobilized
DNA strands and distinguished from G, A, T and C by nanopore current
recording. Therefore, if further aspects of nanopore sequencing can
be addressed, the approach will provide a means to locate epigenetic
modifications in unamplified genomic DNA.},
doi = {10.1039/c0cc02864a},
institution = {Department of Chemistry, University of Oxford, Chemistry Research
Laboratory, Mansfield Road, Oxford, UK OX1 3TA.},
keywords = {5-Methylcytosine, chemistry; Cyclodextrins, chemistry; Cytosine, analogs
/&/ derivatives/chemistry; DNA, chemistry; Epigenesis, Genetic; Hemolysin
Proteins, chemistry; Nanopores},
language = {eng},
medline-pst = {ppublish},
owner = {phupe},
pmid = {20927439},
timestamp = {2011.06.01},
url = {http://dx.doi.org/10.1039/c0cc02864a}
}
@article{Walter2003Detection,
author = {T. Walter and J.-C. Klein and P. Massin and A. Erignay},
title = {Detection of the median axis of vessels in retinal images},
journal = {European Journal of Ophthalmology},
year = {2003},
volume = {13},
number = {2}
}
@article{Walters2002Prediction,
author = {W. Patrick Walters and Mark A. Murcko},
title = {Prediction of 'drug-likeliness'},
journal = {Adv. {D}rug {D}eliv. {R}ev.},
year = {2002},
volume = {54},
pages = {255--271},
subject = {qsar}
}
@article{Wang2001Methylation,
author = {H. Wang and Z. Q. Huang and L. Xia and Q. Feng and H. Erdjument-Bromage
and B. D. Strahl and S. D. Briggs and C. D. Allis and J. Wong and
P. Tempst and Y. Zhang},
title = {Methylation of histone H4 at arginine 3 facilitating transcriptional
activation by nuclear hormone receptor.},
journal = {Science},
year = {2001},
volume = {293},
pages = {853--857},
number = {5531},
month = {Aug},
abstract = {Acetylation of core histone tails plays a fundamental role in transcription
regulation. In addition to acetylation, other posttranslational modifications,
such as phosphorylation and methylation, occur in core histone tails.
Here, we report the purification, molecular identification, and functional
characterization of a histone H4-specific methyltransferase PRMT1,
a protein arginine methyltransferase. PRMT1 specifically methylates
arginine 3 (Arg 3) of H4 in vitro and in vivo. Methylation of Arg
3 by PRMT1 facilitates subsequent acetylation of H4 tails by p300.
However, acetylation of H4 inhibits its methylation by PRMT1. Most
important, a mutation in the S-adenosyl-l-methionine-binding site
of PRMT1 substantially crippled its nuclear receptor coactivator
activity. Our finding reveals Arg 3 of H4 as a novel methylation
site by PRMT1 and indicates that Arg 3 methylation plays an important
role in transcriptional regulation.},
doi = {10.1126/science.1060781},
institution = {Department of Biochemistry and Biophysics, Lineberger Comprehensive
Cancer Center, University of North Carolina at Chapel Hill, Chapel
Hill, NC 27599-7295, USA.},
keywords = {Acetylation; Amino Acid Sequence; Animals; Arginine, metabolism; Binding
Sites; Cell Nucleus, metabolism; Hela Cells; Histones, chemistry/metabolism;
Humans; Hydroxamic Acids, pharmacology; Lysine, metabolism; Methylation;
Methyltransferases, chemistry/genetics/isolation /&/ purification/metabolism;
Molecular Sequence Data; Mutation; Oocytes; Receptors, Androgen,
metabolism; Recombinant Proteins, metabolism; S-Adenosylmethionine,
metabolism; Transcriptional Activation; Xenopus},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {1060781},
pmid = {11387442},
timestamp = {2010.11.23},
url = {http://dx.doi.org/10.1126/science.1060781}
}
@article{Wang2003Application,
author = {Haojun Wang and Chongxun Zheng and Ying Li and Huafeng Zhu and Xiangguo
Yan},
title = {Application of support vector machines to classification of blood
cells},
journal = {Sheng {W}u {Y}i {X}ue {G}ong {C}heng {X}ue {Z}a {Z}hi},
year = {2003},
volume = {20},
pages = {484-7},
number = {3},
month = {Sep},
abstract = {The support vector machine ({SVM}) is a new learning technique based
on the statistical learning theory. {I}t was originally developed
for two-class classification. {I}n this paper, the {SVM} approach
is extended to multi-class classification problems, a hierarchical
{SVM} is applied to classify blood cells in different maturation
stages from bone marrow. {B}ased on stepwise decomposition, a hierarchical
clustering method is presented to construct the architecture of the
hierarchical (tree-like) {SVM}, then the optimal control parameters
of {SVM} are determined by some criterion for each discriminant step.
{T}o verify the performances of classifiers, the {SVM} method is
compared with three classical classifiers using 3-fold cross validation.
{T}he preliminary results indicate that the proposed method avoids
the curse of dimensionality and has greater generalization. {T}hus,
the method can improve the classification correctness for blood cells
from bone marrow.}
}
@article{Wang2006Correspondence,
author = {Wang, H. F. and Hancock, E. R.},
title = {Correspondence matching using kernel principal components analysis
and label consistency constraints},
journal = {Pattern Recogn.},
year = {2006},
volume = {39},
pages = {1012--1025},
number = {6},
address = {New York, NY, USA},
doi = {http://dx.doi.org/10.1016/j.patcog.2005.05.013},
issn = {0031-3203},
publisher = {Elsevier Science Inc.}
}
@article{Wang2007new,
author = {Wang, J.Z. and Du, Z. and Payattakool, R. and Yu, P.S. and Chen,
C.F.},
title = {A new method to measure the semantic similarity of {GO} terms},
journal = {Bioinformatics},
year = {2007},
volume = {23},
pages = {1274},
number = {10},
doi = {10.1093/bioinformatics/btm087},
issn = {1367-4803},
owner = {jp},
publisher = {Oxford Univ Press},
timestamp = {2011.01.12},
url = {http://dx.doi.org/10.1093/bioinformatics/btm087}
}
@article{Wang2005Protein,
author = {Wang, J. and Sung, W.-K. and Krishnan, A. and Li, K.-B.},
title = {Protein subcellular localization prediction for {G}ram-negative bacteria
using amino acid subalphabets and a combination of multiple support
vector machines.},
journal = {B{MC} {B}ioinformatics},
year = {2005},
volume = {6},
pages = {174},
number = {1},
month = {Jul},
abstract = {B{ACKGROUND}: {P}redicting the subcellular localization of proteins
is important for determining the function of proteins. {P}revious
works focused on predicting protein localization in {G}ram-negative
bacteria obtained good results. {H}owever, these methods had relatively
low accuracies for the localization of extracellular proteins. {T}his
paper studies ways to improve the accuracy for predicting extracellular
localization in {G}ram-negative bacteria. {RESULTS}: {W}e have developed
a system for predicting the subcellular localization of proteins
for {G}ram-negative bacteria based on amino acid subalphabets and
a combination of multiple support vector machines. {T}he recall of
the extracellular site and overall recall of our predictor reach
86.0\% and 89.8\%, respectively, in 5-fold cross-validation. {T}o
the best of our knowledge, these are the most accurate results for
predicting subcellular localization in {G}ram-negative bacteria.
{CONCLUSIONS}: {C}lustering 20 amino acids into a few groups by the
proposed greedy algorithm provides a new way to extract features
from protein sequences to cover more adjacent amino acids and hence
reduce the dimensionality of the input vector of protein features.
{I}t was observed that a good amino acid grouping leads to an increase
in prediction performance. {F}urthermore, a proper choice of a subset
of complementary support vector machines constructed by different
features of proteins maximizes the prediction accuracy.},
doi = {10.1186/1471-2105-6-174},
pdf = {../local/Wang2005Protein.pdf},
file = {Wang2005Protein.pdf:local/Wang2005Protein.pdf:PDF},
keywords = {biosvm},
pii = {1471-2105-6-174},
url = {http://dx.doi.org/10.1186/1471-2105-6-174}
}
@article{Wang2008diploid,
author = {Wang, Jun and Wang, Wei and Li, Ruiqiang and Li, Yingrui and Tian,
Geng and Goodman, Laurie and Fan, Wei and Zhang, Junqing and Li,
Jun and Zhang, Juanbin and Guo, Yiran and Feng, Binxiao and Li, Heng
and Lu, Yao and Fang, Xiaodong and Liang, Huiqing and Du, Zhenglin
and Li, Dong and Zhao, Yiqing and Hu, Yujie and Yang, Zhenzhen and
Zheng, Hancheng and Hellmann, Ines and Inouye, Michael and Pool,
John and Yi, Xin and Zhao, Jing and Duan, Jinjie and Zhou, Yan and
Qin, Junjie and Ma, Lijia and Li, Guoqing and Yang, Zhentao and Zhang,
Guojie and Yang, Bin and Yu, Chang and Liang, Fang and Li, Wenjie
and Li, Shaochuan and Li, Dawei and Ni, Peixiang and Ruan, Jue and
Li, Qibin and Zhu, Hongmei and Liu, Dongyuan and Lu, Zhike and Li,
Ning and Guo, Guangwu and Zhang, J. and Ye, J. and Fang, L. and Hao,
Q. and Chen, Q. and Liang, Y. and Su, Y. and San, A. and Ping, C.
and Yang, S. and Chen, F. and Li, L. and Zhou, K. and Zheng, H. and
Ren, Y. and Yang, L. and Gao, Y. and Yang, G. and Li, Z. and Feng,
X. and Kristiansen, K. and Wong, G. K.-S. and Nielsen, R. and Durbin,
R. and Bolund, L. and Zhang, X. and Li, S. and Yang, H. and Wang,
J.},
title = {The diploid genome sequence of an {A}sian individual.},
journal = {Nature},
year = {2008},
volume = {456},
pages = {60--65},
number = {7218},
month = {Nov},
abstract = {Here we present the first diploid genome sequence of an Asian individual.
The genome was sequenced to 36-fold average coverage using massively
parallel sequencing technology. We aligned the short reads onto the
NCBI human reference genome to 99.97\% coverage, and guided by the
reference genome, we used uniquely mapped reads to assemble a high-quality
consensus sequence for 92\% of the Asian individual's genome. We
identified approximately 3 million single-nucleotide polymorphisms
(SNPs) inside this region, of which 13.6\% were not in the dbSNP
database. Genotyping analysis showed that SNP identification had
high accuracy and consistency, indicating the high sequence quality
of this assembly. We also carried out heterozygote phasing and haplotype
prediction against HapMap CHB and JPT haplotypes (Chinese and Japanese,
respectively), sequence comparison with the two available individual
genomes (J. D. Watson and J. C. Venter), and structural variation
identification. These variations were considered for their potential
biological impact. Our sequence data and analyses demonstrate the
potential usefulness of next-generation sequencing technologies for
personal genomics.},
doi = {10.1038/nature07484},
institution = {Beijing Genomics Institute at Shenzhen, Shenzhen 518000, China. wangj@genomics.org.cn},
keywords = {ngs},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {nature07484},
pmid = {18987735},
timestamp = {2011.10.28},
url = {http://dx.doi.org/10.1038/nature07484}
}
@article{Wang2005computer,
author = {J. F. Wang and C. Z. Cai and C. Y. Kong and Z. W. Cao and Y. Z. Chen},
title = {A computer method for validating traditional {C}hinese medicine herbal
prescriptions.},
journal = {Am {J} {C}hin {M}ed},
year = {2005},
volume = {33},
pages = {281-97},
number = {2},
abstract = {Traditional {C}hinese medicine ({TCM}) has been widely practiced and
is considered as an alternative to conventional medicine. {TCM} herbal
prescriptions contain a mixture of herbs that collectively exert
therapeutic actions and modulating effects. {T}raditionally defined
herbal properties, related to the pharmacodynamic, pharmacokinetic
and toxicological, as well as physicochemical properties of their
principal ingredients, have been used as the basis for formulating
{TCM} multi-herb prescriptions. {T}hese properties are used in this
work to develop a computer program for predicting whether a multi-herb
recipe is a valid {TCM} prescription. {T}his program is based on
a statistical learning method, support vector machine ({SVM}), and
it is trained by using 575 well-known {TCM} prescriptions and 1961
non-{TCM} recipes generated by random combination of {TCM} herbs.
{T}esting results by using 72 well-known {TCM} prescriptions and
5039 non-{TCM} recipes showed that 73.6\% of the {TCM} prescriptions
and 99.9\% of non-{TCM} recipes are correctly classified by this
system. {A} further test by using 48 {TCM} prescriptions published
in recent years found that 68.7\% of these are correctly classified.
{T}hese accuracies are comparable to those of {SVM} classification
of other biological systems. {O}ur study indicates the potential
of {SVM} for facilitating the analysis of {TCM} prescriptions.},
keywords = {Artificial Intelligence, Conservation of Natural Resources, Decision
Support Techniques, Ecosystem, Environment, Forestry, Regression
Analysis, Spain, 15974487}
}
@article{Wang2004Simple,
author = {Kai Wang and Ekachai Jenwitheesuk and Ram Samudrala and John E Mittler},
title = {Simple linear model provides highly accurate genotypic predictions
of {HIV}-1 drug resistance.},
journal = {Antivir {T}her},
year = {2004},
volume = {9},
pages = {343-52},
number = {3},
month = {Jun},
abstract = {Drug resistance is a major obstacle to the successful treatment of
{HIV}-1 infection. {G}enotypic assays are used widely to provide
indirect evidence of drug resistance, but the performance of these
assays has been mixed. {W}e used standard stepwise linear regression
to construct drug resistance models for seven protease inhibitors
and 10 reverse transcriptase inhibitors using data obtained from
the {S}tanford {HIV} drug resistance database. {W}e evaluated these
models by hold-one-out experiments and by tests on an independent
dataset. {O}ur linear model outperformed other publicly available
genotypic interpretation algorithms, including decision tree, support
vector machine and four rules-based algorithms ({HIV}db, {VGI}, {ANRS}
and {R}ega) under both tests. {I}nterestingly, our model did well
despite the absence of any terms for interactions between different
residues in protease or reverse transcriptase. {T}he resulting linear
models are easy to understand and can potentially assist in choosing
combination therapy regimens.},
keywords = {Algorithms, Computational Biology, Databases, Drug Resistance, Forecasting,
Genetic, Genotype, HIV Protease Inhibitors, HIV-1, Humans, Information
Management, Information Storage and Retrieval, Kinetics, Linear Models,
Microbial Sensitivity Tests, Models, Non-U.S. Gov't, P.H.S., Periodicals,
Point Mutation, Pyrimidinones, Research Support, Reverse Transcriptase
Inhibitors, Theoretical, U.S. Gov't, Viral, 15259897}
}
@article{Wang2004Predicting,
author = {Long-Hui Wang and Juan Liu and Yan-Fu Li and Huai-Bei Zhou},
title = {Predicting protein secondary structure by a support vector machine
based on a new coding scheme.},
journal = {Genome {I}nform {S}er {W}orkshop {G}enome {I}nform},
year = {2004},
volume = {15},
pages = {181-90},
number = {2},
abstract = {Protein structure prediction is one of the most important problems
in modern computational biology. {P}rotein secondary structure prediction
is a key step in prediction of protein tertiary structure. {T}here
have emerged many methods based on machine learning techniques, such
as neural networks ({NN}) and support vector machine ({SVM}) etc.,
to focus on the prediction of the secondary structures. {I}n this
paper, a new method was proposed based on {SVM}. {D}ifferent from
the existing methods, this method takes into account of the physical-chemical
properties and structure properties of amino acids. {W}hen tested
on the most popular dataset {CB}513, it achieved a {Q}(3) accuracy
of 0.7844, which illustrates that it is one of the top range methods
for protein of secondary structure prediction.},
keywords = {biosvm},
url = {http://www.jsbi.org/journal/GIW04/GIW04F019.html}
}
@article{Wang2005Using,
author = {M. Wang and J. Yang and K-C. Chou},
title = {Using string kernel to predict signal peptide cleavage site based
on subsite coupling model.},
journal = {Amino {A}cids},
year = {2005},
volume = {28},
pages = {395-402},
number = {4},
month = {Jun},
abstract = {Owing to the importance of signal peptides for studying the molecular
mechanisms of genetic diseases, reprogramming cells for gene therapy,
and finding new drugs for healing a specific defect, it is in great
demand to develop a fast and accurate method to identify the signal
peptides. {I}ntroduction of the so-called {-3,-1, +1} coupling model
({C}hou, {K}. {C}.: {P}rotein {E}ngineering, 2001, 14-2, 75-79) has
made it possible to take into account the coupling effect among some
key subsites and hence can significantly enhance the prediction quality
of peptide cleavage site. {B}ased on the subsite coupling model,
a kind of string kernels for protein sequence is introduced. {I}ntegrating
the biologically relevant prior knowledge, the constructed string
kernels can thus be used by any kernel-based method. {A} {S}upport
vector machines ({SVM}) is thus built to predict the cleavage site
of signal peptides from the protein sequences. {T}he current approach
is compared with the classical weight matrix method. {A}t small false
positive ratios, our method outperforms the classical weight matrix
method, indicating the current approach may at least serve as a powerful
complemental tool to other existing methods for predicting the signal
peptide cleavage site.{T}he software that generated the results reported
in this paper is available upon requirement, and will appear at http://www.pami.sjtu.edu.cn/wm.},
doi = {10.1007/s00726-005-0189-6},
pdf = {../local/Wang2005Using.pdf},
file = {Wang2005Using.pdf:local/Wang2005Using.pdf:PDF},
keywords = {biosvm},
url = {http://dx.doi.org/10.1007/s00726-005-0189-6}
}
@article{Wang2004Weighted-support,
author = {Wang, M. and Yang, J. and Liu, G.-P. and Xu, Z.-J. and Chou, K.-C.},
title = {Weighted-support vector machines for predicting membrane protein
types based on pseudo-amino acid composition},
journal = {Protein {E}ng. {D}es. {S}el.},
year = {2004},
volume = {17},
pages = {509-516},
number = {6},
abstract = {Membrane proteins are generally classified into the following five
types: (1) type {I} membrane proteins, (2) type {II} membrane proteins,
(3) multipass transmembrane proteins, (4) lipid chain-anchored membrane
proteins and (5) {GPI}-anchored membrane proteins. {P}rediction of
membrane protein types has become one of the growing hot topics in
bioinformatics. {C}urrently, we are facing two critical challenges
in this area: first, how to take into account the extremely complicated
sequence-order effects, and second, how to deal with the highly uneven
sizes of the subsets in a training dataset. {I}n this paper, stimulated
by the concept of using the pseudo-amino acid composition to incorporate
the sequence-order effects, the spectral analysis technique is introduced
to represent the statistical sample of a protein. {B}ased on such
a framework, the weighted support vector machine ({SVM}) algorithm
is applied. {T}he new approach has remarkable power in dealing with
the bias caused by the situation when one subset in the training
dataset contains many more samples than the other. {T}he new method
is particularly useful when our focus is aimed at proteins belonging
to small subsets. {T}he results obtained by the self-consistency
test, jackknife test and independent dataset test are encouraging,
indicating that the current approach may serve as a powerful complementary
tool to other existing methods for predicting the types of membrane
proteins.},
doi = {10.1093/protein/gzh061},
eprint = {http://peds.oupjournals.org/cgi/reprint/17/6/509.pdf},
pdf = {../local/Wang2004Weighted-support.pdf},
file = {Wang2004Weighted-support.pdf:local/Wang2004Weighted-support.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://dx.doi.org/10.1093/protein/gzh061}
}
@article{Wang2004Support,
author = {M-L. Wang and W-J. Li and M-L. Wang and W-B. Xu},
title = {Support vector machines for prediction of peptidyl prolyl cis/trans
isomerization.},
journal = {J {P}ept {R}es},
year = {2004},
volume = {63},
pages = {23-8},
number = {1},
month = {Jan},
abstract = {A new method for peptidyl prolyl cis/trans isomerization prediction
based on the theory of support vector machines ({SVM}) was introduced.
{T}he {SVM} represents a new approach to supervised pattern classification
and has been successfully applied to a wide range of pattern recognition
problems. {I}n this study, six training datasets consisting of different
length local sequence respectively were used. {T}he polynomial kernel
functions with different parameter d were chosen. {T}he test for
the independent testing dataset and the jackknife test were both
carried out. {W}hen the local sequence length was 20-residue and
the parameter d = 8, the {SVM} method archived the best performance
with the correct rate for the cis and trans forms reaching 70.4 and
69.7\% for the independent testing dataset, 76.7 and 76.6\% for the
jackknife test, respectively. {M}atthew's correlation coefficients
for the jackknife test could reach about 0.5. {T}he results obtained
through this study indicated that the {SVM} method would become a
powerful tool for predicting peptidyl prolyl cis/trans isomerization.},
keywords = {biosvm},
pii = {100}
}
@article{Wang2005Prediction,
author = {Ming-Lei Wang and Hui Yao and Wen-Bo Xu},
title = {Prediction by support vector machines and analysis by {Z}-score of
poly-{L}-proline type {II} conformation based on local sequence.},
journal = {Comput. {B}iol. {C}hem.},
year = {2005},
volume = {29},
pages = {95-100},
number = {2},
month = {Apr},
abstract = {In recent years, the poly-{L}-proline type {II} ({PPII}) conformation
has gained more and more importance. {T}his structure plays vital
roles in many biological processes. {B}ut few studies have been made
to predict {PPII} secondary structures computationally. {T}he support
vector machine ({SVM}) represents a new approach to supervised pattern
classification and has been successfully applied to a wide range
of pattern recognition problems. {I}n this paper, we present a {SVM}
prediction method of {PPII} conformation based on local sequence.
{T}he overall accuracy for both the independent testing set and estimate
of jackknife testing reached approximately 70\%. {M}atthew's correlation
coefficient ({MCC}) could reach 0.4. {B}y comparing the results of
training and testing datasets with different sequence identities,
we suggest that the performance of this method correlates with the
sequence identity of dataset. {T}he parameter of {SVM} kernel function
was an important factor to the performance of this method. {T}he
propensities of residues located at different positions were also
analyzed. {B}y computing {Z}-scores, we found that {P} and {G} were
the two most important residues to {PPII} structure conformation.},
doi = {10.1016/j.compbiolchem.2005.02.002},
pdf = {../local/Wang2005Prediction.pdf},
file = {Wang2005Prediction.pdf:local/Wang2005Prediction.pdf:PDF},
keywords = {biosvm},
pii = {S1476-9271(05)00017-4},
url = {http://dx.doi.org/10.1016/j.compbiolchem.2005.02.002}
}
@article{Wang1999Human,
author = {Wang, R. F.},
title = {{H}uman tumor antigens: implications for cancer vaccine development.},
journal = {J. Mol. Med.},
year = {1999},
volume = {77},
pages = {640--655},
number = {9},
month = {Sep},
abstract = {The adoptive transfer of tumor-infiltrating lymphocytes along with
interleukin 2 into autologous patients resulted in the objective
regression of tumor in about 30\% of patients with melanoma, indicating
that these T cells play a role in tumor rejection. To understand
the molecular basis of the T cell-cancer cell interaction we and
others started to search for tumor antigens expressed on cancer cells
recognized by T cells. This led to the identification of several
major histocompatibility complex (MHC) class I restricted tumor antigens.
These tumor antigens have been classified into several categories:
tissue-specific differentiation antigens, tumor-specific shared antigens,
and tumor-specific unique antigens. Because CD4+ T cells play a central
role in orchestrating the host immune response against cancer, infectious
diseases, and autoimmune diseases, a novel genetic approach has recently
been developed to identify these MHC class II restricted tumor antigens.
The identification of both MHC class I and II restricted tumor antigens
provides new opportunities for the development of therapeutic strategies
against cancer. This review summarizes the current status of tumor
antigens and their potential applications to cancer treatment.},
keywords = {immunoinformatics},
pmid = {10569202},
timestamp = {2007.01.25}
}
@article{Wang2004Objective,
author = {S. Wang and H. Li and F. Qi and Y. Zhao},
title = {Objective facial paralysis grading based on {P}face and eigenflow.},
journal = {Med {B}iol {E}ng {C}omput},
year = {2004},
volume = {42},
pages = {598-603},
number = {5},
month = {Sep},
abstract = {To provide physicians with an objective and quantitative measurement
of single-sided facial paralysis, the paper presents a computer-based
approach that is different from the nine existing, subjective and
hand-performed international scales, such as {H}ouse-{B}rackman.
{F}or voluntary expressions of a patient, this approach used {P}face,
which stems from {D}face, to measure the asymmetry between two sides
of the face and used eigenflow to measure the expression variations
between the patient and normal subjects. {T}he results from {P}face
and eigenflow were then combined by the support vector machine produce
to {P}degree. {A} study of 25 subjects revealed that {P}degree could
differentiate paralysis states ({P}degree > or = 0) and normal states
({P}degree < 0), with the ability to grade facial paralysis automatically.
{M}oreover, the {P}face of specific facial areas can be used in the
supervision of the rehabilitation process.},
keywords = {Adolescent, Adult, Computer-Assisted, Facial Asymmetry, Facial Expression,
Facial Paralysis, Female, Humans, Image Interpretation, Male, Middle
Aged, Motion, Photography, Severity of Illness Index, 15503959}
}
@article{Wang2005Gene-expression,
author = {Wang, Y. and Klijn, J.G.M. and Zhang, Y. and Sieuwerts, A.M. and
Look, M.P. and Yang, F. and Talantov, D. and Timmermans, M. and Meijer-van
Gelder, M.E. and Yu, J. and Jatkoe, T. and Berns, E.M.J.J. and Atkins,
D. and Foekens, J.A.},
title = {Gene-expression profiles to predict distant metastasis of lymph-node-negative
primary breast cancers},
journal = {Lancet},
year = {2005},
volume = {365},
pages = {671--679},
number = {9460},
abstract = {BACKGROUND: Genome-wide measures of gene expression can identify patterns
of gene activity that subclassify tumours and might provide a better
means than is currently available for individual risk assessment
in patients with lymph-node-negative breast cancer. METHODS: We analysed,
with Affymetrix Human U133a GeneChips, the expression of 22000 transcripts
from total RNA of frozen tumour samples from 286 lymph-node-negative
patients who had not received adjuvant systemic treatment. FINDINGS:
In a training set of 115 tumours, we identified a 76-gene signature
consisting of 60 genes for patients positive for oestrogen receptors
(ER) and 16 genes for ER-negative patients. This signature showed
93\% sensitivity and 48\% specificity in a subsequent independent
testing set of 171 lymph-node-negative patients. The gene profile
was highly informative in identifying patients who developed distant
metastases within 5 years (hazard ratio 5.67 [95\% CI 2.59-12.4]),
even when corrected for traditional prognostic factors in multivariate
analysis (5.55 [2.46-12.5]). The 76-gene profile also represented
a strong prognostic factor for the development of metastasis in the
subgroups of 84 premenopausal patients (9.60 [2.28-40.5]), 87 postmenopausal
patients (4.04 [1.57-10.4]), and 79 patients with tumours of 10-20
mm (14.1 [3.34-59.2]), a group of patients for whom prediction of
prognosis is especially difficult. INTERPRETATION: The identified
signature provides a powerful tool for identification of patients
at high risk of distant recurrence. The ability to identify patients
who have a favourable prognosis could, after independent confirmation,
allow clinicians to avoid adjuvant systemic therapy or to choose
less aggressive therapeutic options.},
doi = {10.1016/S0140-6736(05)17947-1},
pdf = {../local/Wang2005Gene-expression.pdf},
file = {Wang2005Gene-expression.pdf:local/Wang2005Gene-expression.pdf:PDF},
keywords = {microarray, breastcancer},
owner = {jp},
pii = {S0140673605179471},
pmid = {15894094},
timestamp = {2006.07.06},
url = {http://dx.doi.org/10.1016/S0140-6736(05)17947-1}
}
@inproceedings{Wang2004Bipartie,
author = {Y. Wang and F. Makedon and J. Ford},
title = {A Bipartite Graph Matching Framework for Finding Correspondences
between Structural Elements in Two Proteins},
booktitle = {Proceedings of the 26th Annual International Conference of the IEEE
Engineering in Medicine and Biology Society},
year = {2004},
abstract = {A protein molecule consists one or more chains of amino acid sequences
that fold into a complex three-dimensional structure. A protein's
functions are often determined by its 3D structure, and so comparing
the similarity of 3D structures between proteins is an important
problem. To accomplish such comparison, one must align two proteins
properly with rotation and translation in 3D space. Finding the correspondences
between structural elements in the two proteins is the key step in
many protein structure alignment algorithms. In this paper, we introduce
a new graph theoretic framework based on bipartite graph matching
for finding sufficiently good correspondences. It is capable of providing
both sequence-dependent and sequence-independent correspondences.
It is a general framework for pair-wise matching of atoms, amino
acids residues or secondary structure elements. },
date = {September 1--5},
url = {http://www.cs.dartmouth.edu/~wyh/papers/embs04_alignment.pdf}
}
@article{Wang2005Gene,
author = {Yu Wang and Igor V Tetko and Mark A Hall and Eibe Frank and Axel
Facius and Klaus F X Mayer and Hans W Mewes},
title = {Gene selection from microarray data for cancer classification--a
machine learning approach.},
journal = {Comput. {B}iol. {C}hem.},
year = {2005},
volume = {29},
pages = {37-46},
number = {1},
month = {Feb},
abstract = {A {DNA} microarray can track the expression levels of thousands of
genes simultaneously. {P}revious research has demonstrated that this
technology can be useful in the classification of cancers. {C}ancer
microarray data normally contains a small number of samples which
have a large number of gene expression levels as features. {T}o select
relevant genes involved in different types of cancer remains a challenge.
{I}n order to extract useful gene information from cancer microarray
data and reduce dimensionality, feature selection algorithms were
systematically investigated in this study. {U}sing a correlation-based
feature selector combined with machine learning algorithms such as
decision trees, naïve {B}ayes and support vector machines, we show
that classification performance at least as good as published results
can be obtained on acute leukemia and diffuse large {B}-cell lymphoma
microarray data sets. {W}e also demonstrate that a combined use of
different classification and feature selection approaches makes it
possible to select relevant genes with high confidence. {T}his is
also the first paper which discusses both computational and biological
evidence for the involvement of zyxin in leukaemogenesis.},
doi = {10.1016/j.compbiolchem.2004.11.001},
pdf = {../local/Wang2005Gene.pdf},
file = {Wang2005Gene.pdf:local/Wang2005Gene.pdf:PDF},
keywords = {biosvm microarray},
pii = {S1476-9271(04)00108-2},
url = {http://dx.doi.org/10.1016/j.compbiolchem.2004.11.001}
}
@article{Wang2003Nonlinear,
author = {Yongmei Michelle Wang and Robert T Schultz and R. Todd Constable
and Lawrence H Staib},
title = {Nonlinear estimation and modeling of f{MRI} data using spatio-temporal
support vector regression.},
journal = {Inf {P}rocess {M}ed {I}maging},
year = {2003},
volume = {18},
pages = {647-59},
month = {Jul},
abstract = {This paper presents a new and general nonlinear framework for f{MRI}
data analysis based on statistical learning methodology: support
vector machines. {U}nlike most current methods which assume a linear
model for simplicity, the estimation and analysis of f{MRI} signal
within the proposed framework is nonlinear, which matches recent
findings on the dynamics underlying neural activity and hemodynamic
physiology. {T}he approach utilizes spatio-temporal support vector
regression ({SVR}), within which the intrinsic spatio-temporal autocorrelations
in f{MRI} data are reflected. {T}he novel formulation of the problem
allows merging model-driven with data-driven methods, and therefore
unifies these two currently separate modes of f{MRI} analysis. {I}n
addition, multiresolution signal analysis is achieved and developed.
{O}ther advantages of the approach are: avoidance of interpolation
after motion estimation, embedded removal of low-frequency noise
components, and easy incorporation of multi-run, multi-subject, and
multi-task studies into the framework.}
}
@article{Wang2009RNA,
author = {Wang, Z. and Gerstein, M. and Snyder, M.},
title = {{RNA-Seq}: a revolutionary tool for transcriptomics.},
journal = {Nat. Rev. Genet.},
year = {2009},
volume = {10},
pages = {57--63},
number = {1},
month = {Jan},
abstract = {RNA-Seq is a recently developed approach to transcriptome profiling
that uses deep-sequencing technologies. Studies using this method
have already altered our view of the extent and complexity of eukaryotic
transcriptomes. RNA-Seq also provides a far more precise measurement
of levels of transcripts and their isoforms than other methods. This
article describes the RNA-Seq approach, the challenges associated
with its application, and the advances made so far in characterizing
several eukaryote transcriptomes.},
doi = {10.1038/nrg2484},
pdf = {../local/Wang2009RNA.pdf},
file = {Wang2009RNA.pdf:Wang2009RNA.pdf:PDF},
institution = {Department of Molecular, Cellular and Developmental Biology, Yale
University, 219 Prospect Street, New Haven, Connecticut 06520, USA.},
keywords = {ngs, rnaseq},
owner = {ljacob},
pii = {nrg2484},
pmid = {19015660},
timestamp = {2009.09.14},
url = {http://dx.doi.org/10.1038/nrg2484}
}
@article{Ward2003Secondary,
author = {Ward, J. J. and McGuffin, L. J. and Buxton, B. F. and Jones, D. T.},
title = {Secondary structure prediction with support vector machines},
journal = {Bioinformatics},
year = {2003},
volume = {19},
pages = {1650-1655},
number = {13},
abstract = {Motivation: {A} new method that uses support vector machines ({SVM}s)
to predict protein secondary structure is described and evaluated.
{T}he study is designed to develop a reliable prediction method using
an alternative technique and to investigate the applicability of
{SVM}s to this type of bioinformatics problem. {M}ethods: {B}inary
{SVM}s are trained to discriminate between two structural classes.
{T}he binary classifiers are combined in several ways to predict
multi-class secondary structure. {R}esults: {T}he average three-state
prediction accuracy per protein ({Q}3) is estimated by cross-validation
to be 77.07 {+/-} 0.26% with a segment overlap ({S}ov) score of 73.32
{+/-} 0.39%. {T}he {SVM} performs similarly to the 'state-of-the-art'
{PSIPRED} prediction method on a non-homologous test set of 121 proteins
despite being trained on substantially fewer examples. {A} simple
consensus of the {SVM}, {PSIPRED} and {PROF}sec achieves significantly
higher prediction accuracy than the individual methods. {A}vailability:
{T}he {SVM} classifier is available from the authors. {W}ork is in
progress to make the method available on-line and to integrate the
{SVM} predictions into the {PSIPRED} server.},
pdf = {../local/Ward2003Secondary.pdf},
file = {Ward2003Secondary.pdf:local/Ward2003Secondary.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://bioinformatics.oupjournals.org/cgi/content/abstract/19/13/1650}
}
@article{Waring2005Face,
author = {Christopher A Waring and Xiuwen Liu},
title = {Face detection using spectral histograms and {SVM}s.},
journal = {I{EEE} {T}rans {S}yst {M}an {C}ybern {B} {C}ybern},
year = {2005},
volume = {35},
pages = {467-76},
number = {3},
month = {Jun},
abstract = {We present a face detection method using spectral histograms and support
vector machines ({SVM}s). {E}ach image window is represented by its
spectral histogram, which is a feature vector consisting of histograms
of filtered images. {U}sing statistical sampling, we show systematically
the representation groups face images together; in comparison, commonly
used representations often do not exhibit this necessary and desirable
property. {B}y using an {SVM} trained on a set of 4500 face and 8000
nonface images, we obtain a robust classifying function for face
and non-face patterns. {W}ith an effective illumination-correction
algorithm, our system reliably discriminates face and nonface patterns
in images under different kinds of conditions. {O}ur method on two
commonly used data sets give the best performance among recent face-detection
ones. {W}e attribute the high performance to the desirable properties
of the spectral histogram representation and good generalization
of {SVM}s. {S}everal further improvements in computation time and
in performance are discussed.}
}
@article{Waring2004Interlaboratory,
author = {Jeffrey F Waring and Roger G Ulrich and Nick Flint and David Morfitt
and Arno Kalkuhl and Frank Staedtler and Michael Lawton and Johanna
M Beekman and Laura Suter},
title = {Interlaboratory evaluation of rat hepatic gene expression changes
induced by methapyrilene.},
journal = {Environ {H}ealth {P}erspect},
year = {2004},
volume = {112},
pages = {439-48},
number = {4},
month = {Mar},
abstract = {Several studies using microarrays have shown that changes in gene
expression provide information about the mechanism of toxicity induced
by xenobiotic agents. {N}evertheless, the issue of whether gene expression
profiles are reproducible across different laboratories remains to
be determined. {T}o address this question, several members of the
{H}epatotoxicity {W}orking {G}roup of the {I}nternational {L}ife
{S}ciences {I}nstitute {H}ealth and {E}nvironmental {S}ciences {I}nstitute
evaluated the liver gene expression profiles of rats treated with
methapyrilene ({MP}). {A}nimals were treated at one facility, and
{RNA} was distributed to five different sites for gene expression
analysis. {A} preliminary evaluation of the number of modulated genes
uncovered striking differences between the five different sites.
{H}owever, additional data analysis demonstrated that these differences
had an effect on the absolute gene expression results but not on
the outcome of the study. {F}or all users, unsupervised algorithms
showed that gene expression allows the distinction of the high dose
of {MP} from controls and low dose. {I}n addition, the use of a supervised
analysis method (support vector machines) made it possible to correctly
classify samples. {I}n conclusion, the results show that, despite
some variability, robust gene expression changes were consistent
between sites. {I}n addition, key expression changes related to the
mechanism of {MP}-induced hepatotoxicity were identified. {T}hese
results provide critical information regarding the consistency of
microarray results across different laboratories and shed light on
the strengths and limitations of expression profiling in drug safety
analysis.},
keywords = {biosvm}
}
@article{Warmke1994family,
author = {J. W. Warmke and B. Ganetzky},
title = {{A} family of potassium channel genes related to eag in {D}rosophila
and mammals.},
journal = {Proc. Natl. Acad. Sci. U. S. A.},
year = {1994},
volume = {91},
pages = {3438--3442},
number = {8},
month = {Apr},
abstract = {We have identified a conserved family of genes related to Drosophila
eag, which encodes a distinct type of voltage-activated K+ channel.
Three related genes were recovered in screens of cDNA libraries from
Drosophila, mouse, and human tissues. One gene is the mouse counterpart
of eag; the other two represent additional subfamilies. The human
gene maps to chromosome 7. Family members share at least 47\% amino
acid identity in their hydrophobic cores and all contain a segment
homologous to a cyclic nucleotide-binding domain. Sequence comparisons
indicate that members of this family are most closely related to
vertebrate cyclic nucleotide-gated cation channels and plant inward-rectifying
K+ channels. The existence of another family of K+ channel structural
genes further extends the known diversity of K+ channels and has
important implications for the structure, function, and evolution
of the superfamily of voltage-sensitive ion channels.},
pdf = {../local/Warmke1994family.pdf},
file = {Warmke1994family.pdf:local/Warmke1994family.pdf:PDF},
pmid = {8159766},
timestamp = {2006.10.05},
url = {http://www.pnas.org/cgi/reprint/91/8/3438}
}
@article{Warmuth2003Active,
author = {Warmuth, M. K. and Liao, J. and R{\"a}tsch, G. and Mathieson, M.
and Putta, S. and Lemmen, C.},
title = {Active learning with support vector machines in the drug discovery
process.},
journal = {J {C}hem {I}nf {C}omput {S}ci},
year = {2003},
volume = {43},
pages = {667-673},
number = {2},
abstract = {We investigate the following data mining problem from computer-aided
drug design: {F}rom a large collection of compounds, find those that
bind to a target molecule in as few iterations of biochemical testing
as possible. {I}n each iteration a comparatively small batch of compounds
is screened for binding activity toward this target. {W}e employed
the so-called "active learning paradigm" from {M}achine {L}earning
for selecting the successive batches. {O}ur main selection strategy
is based on the maximum margin hyperplane-generated by "{S}upport
{V}ector {M}achines". {T}his hyperplane separates the current set
of active from the inactive compounds and has the largest possible
distance from any labeled compound. {W}e perform a thorough comparative
study of various other selection strategies on data sets provided
by {D}u{P}ont {P}harmaceuticals and show that the strategies based
on the maximum margin hyperplane clearly outperform the simpler ones.},
doi = {10.1021/ci025620t},
pdf = {../local/Warmuth2003Active.pdf},
file = {Warmuth2003Active.pdf:local/Warmuth2003Active.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://dx.doi.org/10.1021/ci025620t}
}
@inproceedings{Warmuth2002Active,
author = {Warmuth, M. K. and R{\"a}tsch, G. and Mathieson, M. and Liao, L.
and Lemmen, C.},
title = {Active learning in the drug discovery process},
booktitle = {Adv. {N}eural {I}nform. {P}rocess. {S}yst.},
year = {2002},
editor = {T.G. Dietterich and S. Becker and Z. Ghahramani},
volume = {14},
pages = {1449--1456},
publisher = {MIT Press},
keywords = {biosvm},
subject = {qsar}
}
@article{Warren2006Critical,
author = {Warren, G.L. and Andrews, C.W. and Capelli, A.M. and Clarke, B. and
LaLonde, J. and Lambert, M.H. and Lindvall, M. and Nevins, N. and
Semus, S.F. and Senger, S. and Tedesco, G. and Wall, I.D. and Woolven,
J.M. and Peishoff, C.E. and Head, M.S.},
title = {A critical assessment of docking programs and scoring functions.},
journal = {J. Med. Chem.},
year = {2006},
volume = {49},
pages = {5912--5931},
number = {20},
month = {Oct},
abstract = {Docking is a computational technique that samples conformations of
small molecules in protein binding sites; scoring functions are used
to assess which of these conformations best complements the protein
binding site. An evaluation of 10 docking programs and 37 scoring
functions was conducted against eight proteins of seven protein types
for three tasks: binding mode prediction, virtual screening for lead
identification, and rank-ordering by affinity for lead optimization.
All of the docking programs were able to generate ligand conformations
similar to crystallographically determined protein/ligand complex
structures for at least one of the targets. However, scoring functions
were less successful at distinguishing the crystallographic conformation
from the set of docked poses. Docking programs identified active
compounds from a pharmaceutically relevant pool of decoy compounds;
however, no single program performed well for all of the targets.
For prediction of compound affinity, none of the docking programs
or scoring functions made a useful prediction of ligand binding affinity.},
owner = {vero},
pmid = {17004707},
timestamp = {2009.02.04}
}
@article{Wassermann2009Ligand,
author = {Anne Mai Wassermann and Hanna Geppert and Jürgen Bajorath},
title = {Ligand prediction for orphan targets using support vector machines
and various target-ligand kernels is dominated by nearest neighbor
effects.},
journal = {J Chem Inf Model},
year = {2009},
volume = {49},
pages = {2155--2167},
number = {10},
month = {Oct},
abstract = {Support vector machine (SVM) calculations combining protein and small
molecule information have been applied to identify ligands for simulated
orphan targets (i.e., targets for which no ligands were available).
The combination of protein and ligand information was facilitated
through the design of target-ligand kernel functions that account
for pairwise ligand and target similarity. The design and biological
information content of such kernel functions was expected to play
a major role for target-directed ligand prediction. Therefore, a
variety of target-ligand kernels were implemented to capture different
types of target information including sequence, secondary structure,
tertiary structure, biophysical properties, ontologies, or structural
taxonomy. These kernels were tested in ligand predictions for simulated
orphan targets in two target protein systems characterized by the
presence of different intertarget relationships. Surprisingly, although
there were target- and set-specific differences in prediction rates
for alternative target-ligand kernels, the performance of these kernels
was overall similar and also similar to SVM linear combinations.
Test calculations designed to better understand possible reasons
for these observations revealed that ligand information provided
by nearest neighbors of orphan targets significantly influenced SVM
performance, much more so than the inclusion of protein information.
As long as ligands of closely related neighbors of orphan targets
were available for SVM learning, orphan target ligands could be well
predicted, regardless of the type and sophistication of the kernel
function that was used. These findings suggest simplified strategies
for SVM-based ligand prediction for orphan targets.},
doi = {10.1021/ci9002624},
pdf = {../local/Wassermann2009Ligand.pdf},
file = {Wassermann2009Ligand.pdf:Wassermann2009Ligand.pdf:PDF},
institution = {Department of Life Science Informatics, B-IT, LIMES Program Unit
Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität
Bonn, Dahlmannstrasse 2, D-53113 Bonn, Germany.},
keywords = {chemogenomics, chemoinformatics},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pmid = {19780576},
timestamp = {2009.10.30},
url = {http://dx.doi.org/10.1021/ci9002624}
}
@article{Watanabe2008Endogenous,
author = {Watanabe, T. and Totoki, Y. and Toyoda, A. and Kaneda, M. and Kuramochi-Miyagawa,
S. and Obata, Y. and Chiba, H. and Kohara, Y. and Kono, T. and Nakano,
T. and Surani, M.A. and Sakaki, Y. and Sasaki, H.},
title = {{E}ndogenous si{R}{N}{A}s from naturally formed ds{R}{N}{A}s regulate
transcripts in mouse oocytes},
journal = {Nature},
year = {2008},
volume = {453},
pages = {539--543},
keywords = {csbcbook}
}
@article{Waterman2003Transcriptional,
author = {Waterman, S.R. and Small, P.L.C.},
title = {Transcriptional expression of Escherichia coli glutamate-dependent
acid resistance genes gadA and gadBC in an hns rpoS mutant.},
journal = {J. Bacteriol.},
year = {2003},
volume = {185},
pages = {4644--4647},
number = {15},
month = {Aug},
abstract = {Resistance to being killed by acidic environments with pH values lower
than 3 is an important feature of both pathogenic and nonpathogenic
Escherichia coli. The most potent E. coli acid resistance system
utilizes two isoforms of glutamate decarboxylase encoded by gadA
and gadB and a putative glutamate:gamma-aminobutyric acid antiporter
encoded by gadC. The gad system is controlled by two repressors (H-NS
and CRP), one activator (GadX), one repressor-activator (GadW), and
two sigma factors (sigma(S) and sigma(70)). In contrast to results
of previous reports, we demonstrate that gad transcription can be
detected in an hns rpoS mutant strain of E. coli K-12, indicating
that gad promoters can be initiated by sigma(70) in the absence of
H-NS.},
institution = {Division of Human Immunology, Hanson Institute, Institute of Medical
and Veterinary Science, Adelaide, South Australia, 5000, Australia.
scott.waterman@imvs.sa.gov.au},
keywords = {Bacterial Proteins; DNA-Binding Proteins; Drug Resistance, Bacterial;
Escherichia coli; Escherichia coli Proteins; Gene Expression Regulation,
Bacterial; Glutamate Decarboxylase; Glutamates; Hydrogen-Ion Concentration;
Membrane Proteins; Mutation; Sigma Factor; Transcription, Genetic},
owner = {fantine},
pmid = {12867478},
timestamp = {2008.02.11}
}
@incollection{Watkins2000Dynamic,
author = {C. Watkins},
title = {Dynamic alignment kernels},
booktitle = {Advances in {L}arge {M}argin {C}lassifiers},
publisher = {MIT Press},
year = {2000},
editor = {A.J. Smola and P.L. Bartlett and B. Sch{\"o}lkopf and D. Schuurmans},
pages = {39--50},
address = {Cambridge, MA},
pdf = {../local/Watkins2000Dynamic.pdf},
file = {Watkins2000Dynamic.pdf:local/Watkins2000Dynamic.pdf:PDF},
keywords = {biosvm},
subject = {kernel},
url = {http://www.cs.rhbnc.ac.uk/home/chrisw/dynk.ps.gz}
}
@article{Watson1953Structure,
author = {Watson, J. D. and Crick, F. H. C.},
title = {A {S}tructure for {D}eoxyribose {N}ucleic {A}cid},
journal = {Nature},
year = {1953},
volume = {171},
pages = {737},
pdf = {../local/Watson1953Structure.pdf},
file = {Watson1953Structure.pdf:local/Watson1953Structure.pdf:PDF},
keywords = { bio},
owner = {vert},
url = {http://www.nature.com/genomics/human/watson-crick/index.html}
}
@article{Watts1998Collective,
author = {Watts, D. J. and Strogatz, S. H.},
title = {Collective dynamics of 'small-world' networks},
journal = {Nature},
year = {1998},
volume = {393},
pages = {440-442},
pdf = {../local/watt98.pdf},
file = {watt98.pdf:local/watt98.pdf:PDF},
subject = {compnet},
url = {http://www.nature.com/cgi-taf/DynaPage.taf?file=/nature/journal/v393/n6684/full/393440a0_fs.html&content_filetype=PDF}
}
@article{Weathers2004Reduced,
author = {Weathers, E. A. and Paulaitis, M. E. and Woolf, T. B. and Hoh, J.
H.},
title = {Reduced amino acid alphabet is sufficient to accurately recognize
intrinsically disordered protein.},
journal = {F{EBS} {L}ett.},
year = {2004},
volume = {576},
pages = {348-352},
number = {3},
abstract = {Intrinsically disordered proteins are an important class of proteins
with unique functions and properties. {H}ere, we have applied a support
vector machine ({SVM}) trained on naturally occurring disordered
and ordered proteins to examine the contribution of various parameters
(vectors) to recognizing proteins that contain disordered regions.
{W}e find that a {SVM} that incorporates only amino acid composition
has a recognition accuracy of 87+/-2%. {T}his result suggests that
composition alone is sufficient to accurately recognize disorder.
{I}nterestingly, {SVM}s using reduced sets of amino acids based on
chemical similarity preserve high recognition accuracy. {A} set as
small as four retains an accuracy of 84+/-2%; this suggests that
general physicochemical properties rather than specific amino acids
are important factors contributing to protein disorder.},
doi = {10.1016/j.febslet.2004.09.036},
pdf = {../local/Weathers2004Reduced.pdf},
file = {Weathers2004Reduced.pdf:local/Weathers2004Reduced.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://dx.doi.org/10.1016/j.febslet.2004.09.036}
}
@article{Weber2002Cancer,
author = {Weber, B. L.},
title = {Cancer genomics},
journal = {Cancer {C}ell},
year = {2002},
volume = {1},
pages = {37-47},
number = {1},
abstract = {The draft human genome sequence and the dissemination of high throughput
technology provides opportunities for systematic analysis of cancer
cells. {G}enome-wide mutation screens, high resolution analysis of
chromosomal abberations and expression profiling all give comprehensive
views of genetic alterations in cancer cells. {F}rom these analyses
will come a complete list of the genetic changes that drive malignant
transformation and of the therapeutic targets that may be exploited
for clinical benefit.},
doi = {doi:10.1016/S1535-6108(02)00026-0},
pdf = {../local/Weber2002Cancer.pdf},
file = {Weber2002Cancer.pdf:local/Weber2002Cancer.pdf:PDF},
url = {http://dx.doi.org/10.1016/S1535-6108(02)00026-0}
}
@article{Weber2002Building,
author = {Griffin Weber and Staal Vinterbo and Lucila Ohno-Machado},
title = {Building an asynchronous web-based tool for machine learning classification.},
journal = {Proc {AMIA} {S}ymp},
year = {2002},
pages = {869-73},
abstract = {Various unsupervised and supervised learning methods including support
vector machines, classification trees, linear discriminant analysis
and nearest neighbor classifiers have been used to classify high-throughput
gene expression data. {S}impler and more widely accepted statistical
tools have not yet been used for this purpose, hence proper comparisons
between classification methods have not been conducted. {W}e developed
free software that implements logistic regression with stepwise variable
selection as a quick and simple method for initial exploration of
important genetic markers in disease classification. {T}o implement
the algorithm and allow our collaborators in remote locations to
evaluate and compare its results against those of other methods,
we developed a user-friendly asynchronous web-based application with
a minimal amount of programming using free, downloadable software
tools. {W}ith this program, we show that classification using logistic
regression can perform as well as other more sophisticated algorithms,
and it has the advantages of being easy to interpret and reproduce.
{B}y making the tool freely and easily available, we hope to promote
the comparison of classification methods. {I}n addition, we believe
our web application can be used as a model for other bioinformatics
laboratories that need to develop web-based analysis tools in a short
amount of time and on a limited budget.},
keywords = {Acute, Algorithms, Animals, Artificial Intelligence, Automated, Base
Pair Mismatch, Base Pairing, Base Sequence, Biological, Biosensing
Techniques, Classification, Cluster Analysis, Comparative Study,
Computational Biology, Computer-Assisted, Cystadenoma, DNA, Drug,
Drug Design, Eukaryotic Cells, Female, Gene Expression, Gene Expression
Profiling, Gene Expression Regulation, Genes, Genetic, Genetic Markers,
Hemolysins, Humans, Internet, Leukemia, Ligands, Likelihood Functions,
Logistic Models, Lymphocytic, Markov Chains, Mathematics, Messenger,
Models, Molecular, Molecular Probe Techniques, Molecular Sequence
Data, Nanotechnology, Neoplasm, Neoplasms, Neoplastic, Neural Networks
(Computer), Non-P.H.S., Non-U.S. Gov't, Nucleic Acid Conformation,
Observer Variation, Oligonucleotide Array Sequence Analysis, Ovarian
Neoplasms, P.H.S., Pattern Recognition, Probability, Protein Binding,
Proteins, Quality Control, RNA, RNA Splicing, Receptors, Reference
Values, Reproducibility of Results, Research Support, Sensitivity
and Specificity, Sequence Analysis, Signal Processing, Software,
Statistical, Stomach Neoplasms, Thermodynamics, Transcription, Tumor
Markers, U.S. Gov't, 12463949},
pii = {D020001919}
}
@article{Wei2005study,
author = {Liyang Wei and Yongyi Yang and Robert M Nishikawa and Yulei Jiang},
title = {A study on several machine-learning methods for classification of
malignant and benign clustered microcalcifications.},
journal = {I{EEE} {T}rans {M}ed {I}maging},
year = {2005},
volume = {24},
pages = {371-80},
number = {3},
month = {Mar},
abstract = {In this paper, we investigate several state-of-the-art machine-learning
methods for automated classification of clustered microcalcifications
({MC}s). {T}he classifier is part of a computer-aided diagnosis ({CAD}x)
scheme that is aimed to assisting radiologists in making more accurate
diagnoses of breast cancer on mammograms. {T}he methods we considered
were: support vector machine ({SVM}), kernel {F}isher discriminant
({KFD}), relevance vector machine ({RVM}), and committee machines
(ensemble averaging and {A}da{B}oost), of which most have been developed
recently in statistical learning theory. {W}e formulated differentiation
of malignant from benign {MC}s as a supervised learning problem,
and applied these learning methods to develop the classification
algorithm. {A}s input, these methods used image features automatically
extracted from clustered {MC}s. {W}e tested these methods using a
database of 697 clinical mammograms from 386 cases, which included
a wide spectrum of difficult-to-classify cases. {W}e analyzed the
distribution of the cases in this database using the multidimensional
scaling technique, which reveals that in the feature space the malignant
cases are not trivially separable from the benign ones. {W}e used
receiver operating characteristic ({ROC}) analysis to evaluate and
to compare classification performance by the different methods. {I}n
addition, we also investigated how to combine information from multiple-view
mammograms of the same case so that the best decision can be made
by a classifier. {I}n our experiments, the kernel-based methods (i.e.,
{SVM}, {KFD}, and {RVM}) yielded the best performance ({A}z = 0.85,
{SVM}), significantly outperforming a well-established, clinically-proven
{CAD}x approach that is based on neural network ({A}z = 0.80).}
}
@article{Weigelt2010Breast,
author = {Britta Weigelt and Alan Mackay and Roger A'hern and Rachael Natrajan
and David S P Tan and Mitch Dowsett and Alan Ashworth and Jorge S
Reis-Filho},
title = {Breast cancer molecular profiling with single sample predictors:
a retrospective analysis.},
journal = {Lancet Oncol},
year = {2010},
volume = {11},
pages = {339--349},
number = {4},
month = {Apr},
abstract = {Microarray expression profiling classifies breast cancer into five
molecular subtypes: luminal A, luminal B, basal-like, HER2, and normal
breast-like. Three microarray-based single sample predictors (SSPs)
have been used to define molecular classification of individual samples.
We aimed to establish agreement between these SSPs for identification
of breast cancer molecular subtypes.Previously described microarray-based
SSPs were applied to one in-house (n=53) and three publicly available
(n=779) breast cancer datasets. Agreement was analysed between SSPs
for the whole classification system and for the five molecular subtypes
individually in each cohort.Fair-to-substantial agreement between
every pair of SSPs in each cohort was recorded (kappa=0.238-0.740).
Of the five molecular subtypes, only basal-like cancers consistently
showed almost-perfect agreement (kappa>0.812). The proportion of
cases classified as basal-like in each cohort was consistent irrespective
of the SSP used; however, the proportion of each remaining molecular
subtype varied substantially. Assignment of individual cases to luminal
A, luminal B, HER2, and normal breast-like subtypes was dependent
on the SSP used. The significance of associations with outcome of
each molecular subtype, other than basal-like and luminal A, varied
depending on SSP used. However, different SSPs produced broadly similar
survival curves.Although every SSP identifies molecular subtypes
with similar survival, they do not reliably assign the same patients
to the same molecular subtypes. For molecular subtype classification
to be incorporated into routine clinical practice and treatment decision
making, stringent standardisation of methodologies and definitions
for identification of breast cancer molecular subtypes is needed.Breakthrough
Breast Cancer, Cancer Research UK.},
doi = {10.1016/S1470-2045(10)70008-5},
institution = {Cancer Research UK, London Research Institute, London, UK.},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {S1470-2045(10)70008-5},
pmid = {20181526},
timestamp = {2012.02.29},
url = {http://dx.doi.org/10.1016/S1470-2045(10)70008-5}
}
@article{Weigelt2010Molecular,
author = {Weigelt, B. and Reis-Filho, J. S.},
title = {Molecular profiling currently offers no more than tumour morphology
and basic immunohistochemistry},
journal = {Breast Cancer Res.},
year = {2010},
volume = {12},
pages = {S5},
number = {S4},
doi = {10.1186/bcr2734},
pdf = {../local/Weigelt2010Molecular.pdf},
file = {Weigelt2010Molecular.pdf:Weigelt2010Molecular.pdf:PDF},
owner = {jp},
timestamp = {2011.01.13},
url = {http://dx.doi.org/10.1186/bcr2734}
}
@article{Weill2009Development,
author = {Nathanael Weill and Didier Rognan},
title = {Development and {V}alidation of a {N}ovel {P}rotein-- {L}igand {F}ingerprint
{T}o {M}ine {C}hemogenomic {S}pace: {A}pplication to {G} {P}rotein-{C}oupled
{R}eceptors and {T}heir {L}igands},
journal = {Journal of {C}hemical {I}nformation and {M}odeling},
year = {2009},
volume = {49},
pages = {1049--1062},
number = {4}
}
@article{Weinberg2010Point,
author = {Weinberg, R.},
title = {Point: Hypotheses first.},
journal = {Nature},
year = {2010},
volume = {464},
pages = {678},
number = {7289},
month = {Apr},
doi = {10.1038/464678a},
institution = {Whitehead Institute for Biomedical Research, Department of Biology,
Massachusetts Institute of Technology, Cambridge, Massachusetts 02142,
USA. weinberg@wi.mit.edu},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {464678a},
pmid = {20360718},
timestamp = {2010.10.12},
url = {http://dx.doi.org/10.1038/464678a}
}
@book{Weinberg2007Biology,
title = {The biology of cancer},
publisher = {Garland Science, Taylor \& Francis Group, LLC},
year = {2007},
author = {R A Weinberg},
pages = {864 pages},
keywords = {csbcbook}
}
@inproceedings{Weinberger2005Nonlinear,
author = {Weinberger, K. and Packer, B. and Saul, L.},
title = {Nonlinear {D}imensionality {R}eduction by {S}emidefinite {P}rogramming
and {K}ernel {M}atrix {F}actorization},
booktitle = {Proceedings of the {T}enth {I}nternational {W}orkshop on {A}rtificial
{I}ntelligence and {S}tatistics, {J}an 6-8, 2005, {S}avannah {H}otel,
{B}arbados},
year = {2005},
editor = {Cowell, R. G. and Ghahramani, Z.},
pages = {381-388},
publisher = {Society for Artificial Intelligence and Statistics},
abstract = {We describe an algorithm for nonlinear dimensionality reduction based
on semidefinite programming and kernel matrix factorization. {T}he
algorithm learns a kernel matrix for high dimensional data that lies
on or near a low dimensional manifold. {I}n earlier work, the kernel
matrix was learned by maximizing the variance in feature space while
preserving the distances and angles between nearest neighbors. {I}n
this paper, adapting recent ideas from semi-supervised learning on
graphs, we show that the full kernel matrix can be very well approximated
by a product of smaller matrices. {R}epresenting the kernel matrix
in this way, we can reformulate the semidefinite program in terms
of a much smaller submatrix of inner products between randomly chosen
landmarks. {T}he new framework leads to order-of-magnitude reductions
in computation time and makes it possible to study much larger problems
in manifold learning.},
pdf = {../local/We describe an algorithm for nonlinear dimensionality reduction based on semidefinite programming and kernel matrix factorization. The algorithm learns a kernel matrix for high dimensional data that lies on or near a low dimensional manifold. In earlier work, the kernel matrix was learned by maximizing the variance in feature space while preserving the distances and angles between nearest neighbors. In this paper, adapting recent ideas from semi-supervised learning on graphs, we show that the full kernel matrix can be very well approximated by a product of smaller matrices. Representing the kernel matrix in this way, we can reformulate the semidefinite program in terms of a much smaller submatrix of inner products between randomly chosen landmarks. The new framework leads to order-of-magnitude reductions in computation time and makes it possible to study much larger problems in manifold learning.},
file = {We describe an algorithm for nonlinear dimensionality reduction based on semidefinite programming and kernel matrix factorization. The algorithm learns a kernel matrix for high dimensional data that lies on or near a low dimensional manifold. In earlier work, the kernel matrix was learned by maximizing the variance in feature space while preserving the distances and angles between nearest neighbors. In this paper, adapting recent ideas from semi-supervised learning on graphs, we show that the full kernel matrix can be very well approximated by a product of smaller matrices. Representing the kernel matrix in this way, we can reformulate the semidefinite program in terms of a much smaller submatrix of inner products between randomly chosen landmarks. The new framework leads to order-of-magnitude reductions in computation time and makes it possible to study much larger problems in manifold learning.:We describe an algorithm for nonlinear dimensionality reduction based on semidefinite programming and kernel matrix factorization. The algorithm learns a kernel matrix for high dimensional data that lies on or near a low dimensional manifold. In earlier work, the kernel matrix was learned by maximizing the variance in feature space while preserving the distances and angles between nearest neighbors. In this paper, adapting recent ideas from semi-supervised learning on graphs, we show that the full kernel matrix can be very well approximated by a product of smaller matrices. Representing the kernel matrix in this way, we can reformulate the semidefinite program in terms of a much smaller submatrix of inner products between randomly chosen landmarks. The new framework leads to order-of-magnitude reductions in computation time and makes it possible to study much larger problems in manifold learning.:PDF},
keywords = {dimred}
}
@inproceedings{Weinberger2006Distance,
author = {Weinberger, K. Q. and Blitzer, J. and Saul, L. K.},
title = {Distance metric learning for large margin nearest neighbor classification},
booktitle = {Adv. Neural. Inform. Process Syst.},
year = {2006},
editor = {Weiss, Y. and Schoelkopf, B. and Platt, J.},
volume = {18},
address = {Cambridge, MA},
publisher = {MIT Press},
timestamp = {2007.06.06}
}
@inproceedings{Weinberger2004Unsupervised,
author = {Weinberger, K. Q. and Saul, L. K.},
title = {Unsupervised {L}earning of {I}mage {M}anifolds by {S}emidefinite
{P}rogramming},
booktitle = {2004 {IEEE} {C}omputer {S}ociety {C}onference on {C}omputer {V}ision
and {P}attern {R}ecognition ({CVPR}'04)},
year = {2004},
volume = {2},
number = {2},
pages = {988-995},
abstract = {Can we detect low dimensional structure in high dimensional data sets
of images and video? {T}he problem of dimensionality reduction arises
often in computer vision and pattern recognition. {I}n this paper,
we propose a new solution to this problem based on semidefinite programming.
{O}ur algorithm can be used to analyze high dimensional data that
lies on or near a low dimensional manifold. {I}t overcomes certain
limitations of previous work in manifold learning, such as {I}somap
and locally linear embedding. {W}e illustrate the algorithm on easily
visualized examples of curves and surfaces, as well as on actual
images of faces, handwritten digits, and solid objects.},
doi = {doi.ieeecomputersociety.org/10.1109/CVPR.2004.256},
pdf = {../local/sdeFinal_cvpr04.pdf:http\://www.seas.upenn.edu/~kilianw/publications/PDFs/sdeFinal_cvpr04.pdf:PDF;sdeFinal_cvpr04.pdf:http\},
file = {sdeFinal_cvpr04.pdf:http\://www.seas.upenn.edu/~kilianw/publications/PDFs/sdeFinal_cvpr04.pdf:PDF;sdeFinal_cvpr04.pdf:http\://www.seas.upenn.edu/~kilianw/publications/PDFs/sdeFinal_cvpr04.pdf:PDF},
keywords = {dimred}
}
@inproceedings{Weinberger2004Learning,
author = {Weinberger, K. Q. and Sha, F. and Saul, L. K.},
title = {Learning a kernel matrix for nonlinear dimensionality reduction},
booktitle = {I{CML} '04: {T}wenty-first international conference on {M}achine
learning},
year = {2004},
address = {New York, NY, USA},
publisher = {ACM Press},
abstract = {We investigate how to learn a kernel matrix for high dimensional data
that lies on or near a low dimensional manifold. {N}oting that the
kernel matrix implicitly maps the data into a nonlinear feature space,
we show how to discover a mapping that "unfolds" the underlying manifold
from which the data was sampled. {T}he kernel matrix is constructed
by maximizing the variance in feature space subject to local constraints
that preserve the angles and distances between nearest neighbors.
{T}he main optimization involves an instance of semidefinite programming---a
fundamentally different computation than previous algorithms for
manifold learning, such as {I}somap and locally linear embedding.
{T}he optimized kernels perform better than polynomial and {G}aussian
kernels for problems in manifold learning, but worse for problems
in large margin classification. {W}e explain these results in terms
of the geometric properties of different kernels and comment on various
interpretations of other manifold learning algorithms as kernel methods.},
doi = {http://doi.acm.org/10.1145/1015330.1015345},
pdf = {../local/kernel_icml04.pdf:http\://www.seas.upenn.edu/~kilianw/publications/PDFs/kernel_icml04.pdf:PDF;kernel_icml04.pdf:http\},
file = {kernel_icml04.pdf:http\://www.seas.upenn.edu/~kilianw/publications/PDFs/kernel_icml04.pdf:PDF;kernel_icml04.pdf:http\://www.seas.upenn.edu/~kilianw/publications/PDFs/kernel_icml04.pdf:PDF},
keywords = {dimred}
}
@article{Weinberger1994Optimal,
author = {Weinberger, M. J. and Merhav, N. and Feder, M.},
title = {Optimal sequential probability assignment for individual sequences},
journal = {I{EEE} {T}rans. {I}nform. {T}heory},
year = {1994},
volume = {40},
pages = {384-396},
number = {2},
month = {Mar},
abstract = {The problem of sequential probability assignment for individual sequences
is investigated. {T}he authors compare the probabilities assigned
by any sequential scheme to the performance of the best ?batch? scheme
(model) in some class. {F}or the class of finite-state schemes and
other related families, they derive a deterministic performance bound,
analogous to the classical (probabilistic) minimum description length
({MDL}) bound. {I}t holds for ?most? sequences, similarly to the
probabilistic setting, where the bound holds for ?most? sources in
a class. {I}t is shown that the bound can be attained both pointwise
and sequentially for any model family in the reference class and
without any prior knowledge of its order. {T}his is achieved by a
universal scheme based on a mixing approach. {T}he bound and its
sequential achievability establish a completely deterministic significance
to the concept of predictive {MDL} },
pdf = {../local/Weinberger1994Optimal.pdf},
file = {Weinberger1994Optimal.pdf:local/Weinberger1994Optimal.pdf:PDF},
keywords = {information-theory},
owner = {vert}
}
@article{Weinberger1995universal,
author = {Weinberger, M. J. and Rissanen, J. J. and Feder, M.},
title = {A universal finite memory source},
journal = {I{EEE} {T}rans. {I}nform. {T}heory},
year = {1995},
volume = {41},
pages = {643-652},
number = {3},
month = {May},
abstract = {An irreducible parameterization for a finite memory source is constructed
in the form of a tree machine. {A} universal information source for
the set of finite memory sources is constructed by a predictive modification
of an earlier studied algorithm-{C}ontext. {I}t is shown that this
universal source incorporates any minimal data-generating tree machine
in an asymptotically optimal manner in the following sense: the negative
logarithm of the probability it assigns to any long typical sequence,
generated by any tree machine, approaches that assigned by the tree
machine at the best possible rate },
pdf = {../local/Weinberger1995universal.pdf},
file = {Weinberger1995universal.pdf:local/Weinberger1995universal.pdf:PDF},
keywords = {information-theory},
owner = {vert}
}
@article{Weis2008Structural,
author = {William I Weis and Brian K Kobilka},
title = {Structural insights into {G}-protein-coupled receptor activation.},
journal = {Curr Opin Struct Biol},
year = {2008},
volume = {18},
pages = {734--740},
number = {6},
month = {Dec},
abstract = {G-protein-coupled receptors (GPCRs) are the largest family of eukaryotic
plasma membrane receptors, and are responsible for the majority of
cellular responses to external signals. GPCRs share a common architecture
comprising seven transmembrane (TM) helices. Binding of an activating
ligand enables the receptor to catalyze the exchange of GTP for GDP
in a heterotrimeric G protein. GPCRs are in a conformational equilibrium
between inactive and activating states. Crystallographic and spectroscopic
studies of the visual pigment rhodopsin and two beta-adrenergic receptors
have defined some of the conformational changes associated with activation.},
doi = {10.1016/j.sbi.2008.09.010},
institution = { Cellular Physiology, Stanford University School of Medicine, USA.
bill.weis@stanford.edu},
keywords = {Animals; Crystallography; Humans; Membrane Proteins; Models, Molecular;
Receptors, Adrenergic, beta; Receptors, G-Protein-Coupled; Rhodopsin},
owner = {ljacob},
pii = {S0959-440X(08)00147-4},
pmid = {18957321},
timestamp = {2009.11.09},
url = {http://dx.doi.org/10.1016/j.sbi.2008.09.010}
}
@book{Weisberg1981Applied,
title = {Applied linear regression},
publisher = {Wiley},
year = {1981},
author = {Weisberg, S.},
address = {New-York},
owner = {jp},
timestamp = {2012.02.12}
}
@article{Wellings1973origin,
author = {Wellings, S. R. and Jensen, H. M.},
title = {On the origin and progression of ductal carcinoma in the human breast},
journal = {J. Natl. Cancer Inst.},
year = {1973},
volume = {50},
pages = {1111--1118},
number = {5},
month = {May},
keywords = {breastcancer},
owner = {jp},
pmid = {4123242},
timestamp = {2009.02.04}
}
@article{Weskamp2007Multiple,
author = {N. Weskamp and E. Hullermeier and D. Kuhn and G. Klebe},
title = {Multiple Graph Alignment for the Structural Analysis of Protein Active
Sites},
journal = {IEEE/ACM Trans. Comput. Biol. Bioinformatics},
year = {2007},
volume = {4},
pages = {310--320},
number = {2},
address = {Los Alamitos, CA, USA},
doi = {http://dx.doi.org/10.1109/TCBB.2007.358301},
issn = {1545-5963},
publisher = {IEEE Computer Society Press}
}
@book{Westfall1993Resampling,
title = {Resampling-based multiple testing: Examples and methods for p-value
adjustment.},
publisher = {John Wiley and Sons},
year = {1993},
author = {Westfall, P. H. and Young, S. S.},
owner = {jp},
timestamp = {2012.03.07}
}
@article{Weston2003Feature,
author = {Weston, J. and P{\'e}rez-Cruz, F. and Bousquet, O. and Chapelle,
O. and Elisseeff, A. and Sch{\"o}lkopf, B.},
title = {Feature selection and transduction for prediction of molecular bioactivity
for drug design},
journal = {Bioinformatics},
year = {2003},
volume = {19},
pages = {764-771},
number = {6},
abstract = {Motivation: {I}n drug discovery a key task is to identify characteristics
that separate active (binding) compounds from inactive (non-binding)
ones. {A}n automated prediction system can help reduce resources
necessary to carry out this task. {R}esults: {T}wo methods for prediction
of molecular bioactivity for drug design are introduced and shown
to perform well in a data set previously studied as part of the {KDD}
({K}nowledge {D}iscovery and {D}ata {M}ining) {C}up 2001. {T}he data
is characterized by very few positive examples, a very large number
of features (describing three-dimensional properties of the molecules)
and rather different distributions between training and test data.
{T}wo techniques are introduced specifically to tackle these problems:
a feature selection method for unbalanced data and a classifier which
adapts to the distribution of the the unlabeled test data (a so-called
transductive method). {W}e show both techniques improve identification
performance and in conjunction provide an improvement over using
only one of the techniques. {O}ur results suggest the importance
of taking into account the characteristics in this data which may
also be relevant in other problems of a similar type. {A}vailability:
{M}atlab source code is available at http://www.kyb.tuebingen.mpg.de/bs/people/weston/kdd/kdd.html
{C}ontact: jason.weston@tuebingen.mpg.de {S}upplementary information:
{S}upplementary material is available at http://www.kyb.tuebingen.mpg.de/bs/people/weston/kdd/kdd.html.},
pdf = {../local/Weston2003Feature.pdf},
file = {Weston2003Feature.pdf:local/Weston2003Feature.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://bioinformatics.oupjournals.org/cgi/content/abstract/19/6/764}
}
@article{Wheeler2008Complete,
author = {David A Wheeler and Maithreyan Srinivasan and Michael Egholm and
Yufeng Shen and Lei Chen and Amy McGuire and Wen He and Yi-Ju Chen
and Vinod Makhijani and G. Thomas Roth and Xavier Gomes and Karrie
Tartaro and Faheem Niazi and Cynthia L Turcotte and Gerard P Irzyk
and James R Lupski and Craig Chinault and Xing-zhi Song and Yue Liu
and Ye Yuan and Lynne Nazareth and Xiang Qin and Donna M Muzny and
Marcel Margulies and George M Weinstock and Richard A Gibbs and Jonathan
M Rothberg},
title = {The complete genome of an individual by massively parallel DNA sequencing.},
journal = {Nature},
year = {2008},
volume = {452},
pages = {872--876},
number = {7189},
month = {Apr},
abstract = {The association of genetic variation with disease and drug response,
and improvements in nucleic acid technologies, have given great optimism
for the impact of 'genomic medicine'. However, the formidable size
of the diploid human genome, approximately 6 gigabases, has prevented
the routine application of sequencing methods to deciphering complete
individual human genomes. To realize the full potential of genomics
for human health, this limitation must be overcome. Here we report
the DNA sequence of a diploid genome of a single individual, James
D. Watson, sequenced to 7.4-fold redundancy in two months using massively
parallel sequencing in picolitre-size reaction vessels. This sequence
was completed in two months at approximately one-hundredth of the
cost of traditional capillary electrophoresis methods. Comparison
of the sequence to the reference genome led to the identification
of 3.3 million single nucleotide polymorphisms, of which 10,654 cause
amino-acid substitution within the coding sequence. In addition,
we accurately identified small-scale (2-40,000 base pair (bp)) insertion
and deletion polymorphism as well as copy number variation resulting
in the large-scale gain and loss of chromosomal segments ranging
from 26,000 to 1.5 million base pairs. Overall, these results agree
well with recent results of sequencing of a single individual by
traditional methods. However, in addition to being faster and significantly
less expensive, this sequencing technology avoids the arbitrary loss
of genomic sequences inherent in random shotgun sequencing by bacterial
cloning because it amplifies DNA in a cell-free system. As a result,
we further demonstrate the acquisition of novel human sequence, including
novel genes not previously identified by traditional genomic sequencing.
This is the first genome sequenced by next-generation technologies.
Therefore it is a pilot for the future challenges of 'personalized
genome sequencing'.},
doi = {10.1038/nature06884},
institution = {Human Genome Sequencing Center, Baylor College of Medicine, One Baylor
Plaza, Houston, Texas 77030, USA.},
keywords = {Alleles; Computational Biology; Genetic Predisposition to Disease,
genetics; Genetic Variation, genetics; Genome, Human, genetics; Genomics,
economics/methods/trends; Genotype; Humans; Individuality; Male;
Oligonucleotide Array Sequence Analysis; Polymorphism, Single Nucleotide,
genetics; Reproducibility of Results; Sensitivity and Specificity;
Sequence Alignment; Sequence Analysis, DNA, economics/methods; Software},
language = {eng},
medline-pst = {ppublish},
owner = {philippe},
pii = {nature06884},
pmid = {18421352},
timestamp = {2010.07.28},
url = {http://dx.doi.org/10.1038/nature06884}
}
@article{Wheeler2008Homologene,
author = {Wheeler, D. L. and Barrett,T and Benson,D.A and Bryant, S.H.},
title = {Database resources of the National Center for Biotechnology Information},
journal = {Nucleic {A}cids {R}es.},
year = {2006},
volume = {31},
pages = {28-33}
}
@article{Whitfield2006Common,
author = {Whitfield, M.L. and George, L.K. and Grant, G.D. and Perou, C.M.},
title = {Common markers of proliferation},
journal = {Nature Reviews Cancer},
year = {2006},
volume = {6},
pages = {99--106},
number = {2},
publisher = {Nature Publishing Group}
}
@article{Whitney1932Congruent,
author = {H. Whitney},
title = {Congruent graphs and the connectivity of graphs},
journal = {Amer. J. Math.},
year = {1932},
volume = {54},
pages = {150--168}
}
@article{Whitney1930Nonseparable,
author = {H. Whitney},
title = {Non-Separable and Planar Graphs},
journal = {Proc. Natl. Acad. Sci.},
year = {1930},
volume = {93},
pages = {415--443}
}
@misc{wiki:tsp,
author = {{Wikipedia}},
title = {{Travelling Salesman Problem --- Wikipedia{,} The Free Encyclopedia}},
year = {2009},
note = {[Online; accessed 5-May-2009]}
}
@article{Wilbur2000Boosting,
author = {W. J. Wilbur},
title = {Boosting naive {B}ayesian learning on a large subset of {MEDLINE}.},
journal = {Proc {AMIA} {S}ymp},
year = {2000},
pages = {918-22},
abstract = {We are concerned with the rating of new documents that appear in a
large database ({MEDLINE}) and are candidates for inclusion in a
small specialty database ({REBASE}). {T}he requirement is to rank
the new documents as nearly in order of decreasing potential to be
added to the smaller database as possible, so as to improve the coverage
of the smaller database without increasing the effort of those who
manage this specialty database. {T}o perform this ranking task we
have considered several machine learning approaches based on the
naï ve {B}ayesian algorithm. {W}e find that adaptive boosting outperforms
naï ve {B}ayes, but that a new form of boosting which we term staged
{B}ayesian retrieval outperforms adaptive boosting. {S}taged {B}ayesian
retrieval involves two stages of {B}ayesian retrieval and we further
find that if the second stage is replaced by a support vector machine
we again obtain a significant improvement over the strictly {B}ayesian
approach.},
keywords = {Acute, Acute Disease, Adenocarcinoma, Algorithms, Amino Acid Sequence,
Animals, Artificial Intelligence, Automated, B-Lymphocytes, Bacterial
Proteins, Base Pair Mismatch, Base Sequence, Bayes Theorem, Binding
Sites, Biological, Bone Marrow Cells, Brachyura, Cell Compartmentation,
Chemistry, Child, Chromosome Aberrations, Classification, Codon,
Colonic Neoplasms, Comparative Study, Computational Biology, Computer
Simulation, Computer-Assisted, DNA, Data Interpretation, Databases,
Decision Trees, Diabetes Mellitus, Diagnosis, Discriminant Analysis,
Discrimination Learning, Electric Conductivity, Electrophysiology,
Escherichia coli Proteins, Factual, Feedback, Female, Fungal, Gastric
Emptying, Gene Expression Profiling, Gene Expression Regulation,
Genes, Genetic, Genetic Markers, Genetic Predisposition to Disease,
Genomics, Hemolysins, Humans, Indians, Information Storage and Retrieval,
Initiator, Ion Channels, Kinetics, Leukemia, Likelihood Functions,
Lipid Bilayers, Logistic Models, Lymphocytic, MEDLINE, Male, Markov
Chains, Melanoma, Models, Molecular, Myeloid, Neoplasm, Neoplasms,
Neoplastic, Neural Networks (Computer), Neurological, Nevus, Non-P.H.S.,
Non-U.S. Gov't, Nonlinear Dynamics, Normal Distribution, North American,
Nucleic Acid Conformation, Oligonucleotide Array Sequence Analysis,
Organ Specificity, Organelles, Ovarian Neoplasms, Ovary, P.H.S.,
Pattern Recognition, Physical, Pigmented, Predictive Value of Tests,
Promoter Regions (Genetics), Protein Biosynthesis, Protein Folding,
Protein Structure, Proteins, Proteome, RNA, Reproducibility of Results,
Research Support, Saccharomyces cerevisiae, Secondary, Sensitivity
and Specificity, Sequence Alignment, Sequence Analysis, Sex Characteristics,
Skin Diseases, Skin Neoplasms, Skin Pigmentation, Software, Sound
Spectrography, Statistical, Stomach Diseases, T-Lymphocytes, Thermodynamics,
Transcription, Transcription Factors, Tumor Markers, Type 2, U.S.
Gov't, Vertebrates, 11080018},
pii = {D200250}
}
@article{Wilcoxon1945Individual,
author = {Wilcoxon, F.},
title = {Individual comparisons by ranking methods},
journal = {Biometrics Bulletin},
year = {1945},
volume = {1},
pages = {80--83},
number = {6},
publisher = {JSTOR}
}
@article{Wilhelm2004Analysis,
author = {Wilhelm, T. and Behre, J. and Schuster, S.},
title = {Analysis of structural robustness of metabolic networks.},
journal = {Syst Biol},
year = {2004},
volume = {1},
pages = {114--120},
number = {1},
month = {Jun},
abstract = {We study the structural robustness of metabolic networks on the basis
of the concept of elementary flux modes. It is shown that the number
of elementary modes itself is not an appropriate measure of structural
robustness. Instead, we introduce three new robustness measures.
These are based on the relative number of elementary modes remaining
after the knockout of enzymes. We discuss the relevance of these
measures with the help of simple examples, as well as with larger,
realistic metabolic networks. Thereby we demonstrate quantitatively
that the metabolism of Escherichia coli, which must be able to adapt
to varying conditions, is more robust than the metabolism of the
human erythrocyte, which lives under much more homeostatic conditions.},
pdf = {../local/Wilhelm2004Analysis.pdf},
file = {Wilhelm2004Analysis.pdf:Wilhelm2004Analysis.pdf:PDF},
institution = {Institute of Molecular Biotechnology, Theoretical Systems Biology
Group, Jena, Germany.},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pmid = {17052121},
timestamp = {2013.01.25}
}
@article{Wilkins1996From,
author = {M. R. Wilkins and C. Pasquali and R. D. Appel and K. Ou and O. Golaz
and J. C. Sanchez and J. X. Yan and A. A. Gooley and G. Hughes and
I. Humphery-Smith and K. L. Williams and D. F. Hochstrasser},
title = {From proteins to proteomes: large scale protein identification by
two-dimensional electrophoresis and amino acid analysis.},
journal = {Biotechnology (N Y)},
year = {1996},
volume = {14},
pages = {61--65},
number = {1},
month = {Jan},
abstract = {Separation and identification of proteins by two-dimensional (2-D)
electrophoresis can be used for protein-based gene expression analysis.
In this report single protein spots, from polyvinylidene difluoride
blots of micropreparative E. coli 2-D gels, were rapidly and economically
identified by matching their amino acid composition, estimated pI
and molecular weight against all E. coli entries in the SWISS-PROT
database. Thirty proteins from an E. coli 2-D map were analyzed and
identities assigned. Three of the proteins were unknown. By protein
sequencing analysis, 20 of the 27 proteins were correctly identified.
Importantly, correct identifications showed unambiguous "correct"
score patterns. While incorrect protein identifications also showed
distinctive score patterns, indicating that protein must be identified
by other means. These techniques allow large-scale screening of the
protein complement of simple organisms, or tissues in normal and
disease states. The computer program described here is accessible
via the World Wide Web at URL address (http:@expasy.hcuge.ch/).},
institution = {Macquarie University Centre for Analytical Biotechnology, Macquarie
University, Sydney, NSW, Australia.},
keywords = {Amino Acids; Bacterial Proteins; Blood Proteins; Databases, Factual;
Electrophoresis, Gel, Two-Dimensional; Escherichia coli; Humans;
Microchemistry; Molecular Weight; Multienzyme Complexes; Proteins;
Reproducibility of Results; Software; Time Factors},
owner = {ljacob},
pmid = {9636313},
timestamp = {2009.09.14}
}
@article{Willems1996Coding,
author = {Willems, F. M. J.},
title = {Coding for a {B}inary {I}ndependent {P}iecewise-{I}dentically {D}istributed
{S}ource},
journal = {I{EEE} {T}rans. {I}nform. {T}heory},
year = {1996},
volume = {42},
pages = {2210--2217},
month = {nov},
pdf = {../local/will96b.pdf},
file = {will96b.pdf:local/will96b.pdf:PDF},
subject = {it},
url = {http://ei1.ei.ele.tue.nl/~frans/bipid.ps}
}
@article{Willems1989Universal,
author = {Willems, F. M. J.},
title = {Universal data compression and repetition times},
journal = {I{EEE} {T}rans. {I}nform. {T}heory},
year = {1989},
volume = {35},
pages = {54-58},
number = {1},
month = {Jan},
abstract = {A novel universal data compression algorithm is described. {T}his
algorithm encodes {L} source symbols at a time. {A}n upper limit
for the number of bits per source symbol is given for the class of
binary stationary sources. {I}n the author's analysis, a property
of repetition times turns out to be of crucial importance },
doi = {10.1109/18.42176},
pdf = {../local/Willems1989Universal.pdf},
file = {Willems1989Universal.pdf:local/Willems1989Universal.pdf:PDF},
owner = {vert},
url = {http://dx.doi.org/10.1109/18.42176}
}
@article{Willems1996Context,
author = {Willems, F. M. J. and Shtarkov, Y. M. and Tjalkens, T. J.},
title = {Context {W}eighting for {G}eneral {F}inite {C}ontext {S}ources},
journal = {I{EEE} {T}rans. {I}nform. {T}heory},
year = {1996},
volume = {42},
pages = {1514--1520},
number = {5},
pdf = {../local/Willems1996Context.pdf},
file = {Willems1996Context.pdf:local/Willems1996Context.pdf:PDF},
subject = {it},
url = {http://ei1.ei.ele.tue.nl/~frans/gcw.ps}
}
@article{Willems1995Context,
author = {Willems, F. M. J. and Shtarkov, Y. M. and Tjalkens, T. J.},
title = {The {C}ontext {T}ree {W}eighting {M}ethod: {B}asic {P}roperties},
journal = {I{EEE} {T}rans. {I}nform. {T}heory},
year = {1995},
volume = {41},
pages = {653--664},
number = {3},
month = {May},
abstract = {Describes a sequential universal data compression procedure for binary
tree sources that performs the ?double mixture.? {U}sing a context
tree, this method weights in an efficient recursive way the coding
distributions corresponding to all bounded memory tree sources, and
achieves a desirable coding distribution for tree sources with an
unknown model and unknown parameters. {C}omputational and storage
complexity of the proposed procedure are both linear in the source
sequence length. {T}he authors derive a natural upper bound on the
cumulative redundancy of the method for individual sequences. {T}he
three terms in this bound can be identified as coding, parameter,
and model redundancy, {T}he bound holds for all source sequence lengths,
not only for asymptotically large lengths. {T}he analysis that leads
to this bound is based on standard techniques and turns out to be
extremely simple. {T}he upper bound on the redundancy shows that
the proposed context-tree weighting procedure is optimal in the sense
that it achieves the {R}issanen (1984) lower bound },
pdf = {../local/Willems1995Context.pdf},
file = {Willems1995Context.pdf:local/Willems1995Context.pdf:PDF},
subject = {it},
url = {http://ei1.ei.ele.tue.nl/~frans/ctw1.ps}
}
@article{Willett2008From,
author = {Willett, P.},
title = {From chemical documentation to chemoinformatics: 50 years of chemical
information science},
journal = {J. Inf. Sci.},
year = {2008},
volume = {34},
pages = {477--499},
number = {4},
address = {Thousand Oaks, CA, USA},
doi = {http://dx.doi.org/10.1177/0165551507084631},
issn = {0165-5515},
publisher = {Sage Publications, Inc.}
}
@article{Willett2006Similarity-based,
author = {Willett, P.},
title = {Similarity-based virtual screening using 2D fingerprints.},
journal = {Drug Discov Today},
year = {2006},
volume = {11},
pages = {1046--1053},
number = {23-24},
month = {Dec},
abstract = {This paper summarizes recent work at the University of Sheffield on
virtual screening methods that use 2D fingerprint measures of structural
similarity. A detailed comparison of a large number of similarity
coefficients demonstrates that the well-known Tanimoto coefficient
remains the method of choice for the computation of fingerprint-based
similarity, despite possessing some inherent biases related to the
sizes of the molecules that are being sought. Group fusion involves
combining the results of similarity searches based on multiple reference
structures and a single similarity measure. We demonstrate the effectiveness
of this approach to screening, and also describe an approximate form
of group fusion, turbo similarity searching, that can be used when
just a single reference structure is available.},
doi = {10.1016/j.drudis.2006.10.005},
pdf = {../local/Willett2006Similarity-based.pdf},
file = {Willett2006Similarity-based.pdf:Willett2006Similarity-based.pdf:PDF},
institution = {Krebs Institute for Biomolecular Research and Department of Information
Studies, University of Sheffield, 211 Portobello, Sheffield S1 4DP,
UK. p.willett@sheffield.ac.uk},
keywords = {PUlearning, chemoinformatics},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {S1359-6446(06)00419-3},
pmid = {17129822},
timestamp = {2010.04.01},
url = {http://dx.doi.org/10.1016/j.drudis.2006.10.005}
}
@article{Willett1998Chemical,
author = {P. Willett},
title = {Chemical {S}imilarity {S}earching},
journal = {J Chem Inf Comput Sci},
year = {1998},
volume = {38},
pages = {983-996},
owner = {mahe},
timestamp = {2006.09.01}
}
@article{Willett1986Implementation,
author = {P. Willett and V. Winterman and D. Bawden},
title = {Implementation of nearest-neighbor searching in an online chemical
structure search system},
journal = {J. Chem. Inform. Comput. Sci.},
year = {1986},
volume = {26},
pages = {36-41},
number = {1},
bibsource = {DBLP, http://dblp.uni-trier.de},
ee = {http://pubs.acs.org/cgi-bin/archive.cgi/jcisd8/1986/26/i01/f-pdf/f_ci00049a008.pdf}
}
@incollection{Williams1998Prediction,
author = {Williams, C.K.I. },
title = {Prediction with {G}aussian {P}rocesses: {F}rom {L}inear {R}egression
to {L}inear {P}rediction and {B}eyond},
booktitle = {Learning and {I}nference in {G}raphical {M}odels},
publisher = {Kluwer Academic Press},
year = {1998},
editor = {Jordan, M.I. },
owner = {vert}
}
@article{Williams2004Prognostic,
author = {Williams, R.D. and Hing, S.N. and Greer, B.T. and Whiteford, C.C.
and Wei, J.S. and Natrajan, R. and Kelsey, A. and Rogers, S. and
Campbell, C. and Pritchard-Jones, K. and Khan, J.},
title = {Prognostic classification of relapsing favorable histology {W}ilms
tumor using c{DNA} microarray expression profiling and support vector
machines.},
journal = {Genes {C}hromosomes {C}ancer},
year = {2004},
volume = {41},
pages = {65-79},
number = {1},
month = {Sep},
abstract = {Treatment of {W}ilms tumor has a high success rate, with some 85%
of patients achieving long-term survival. {H}owever, late effects
of treatment and management of relapse remain significant clinical
problems. {I}f accurate prognostic methods were available, effective
risk-adapted therapies could be tailored to individual patients at
diagnosis. {F}ew molecular prognostic markers for {W}ilms tumor are
currently defined, though previous studies have linked allele loss
on 1p or 16q, genomic gain of 1q, and overexpression from 1q with
an increased risk of relapse. {T}o identify specific patterns of
gene expression that are predictive of relapse, we used high-density
(30 k) c{DNA} microarrays to analyze {RNA} samples from 27 favorable
histology {W}ilms tumors taken from primary nephrectomies at the
time of initial diagnosis. {T}hirteen of these tumors relapsed within
2 years. {G}enes differentially expressed between the relapsing and
nonrelapsing tumor classes were identified by statistical scoring
(t test). {T}hese genes encode proteins with diverse molecular functions,
including transcription factors, developmental regulators, apoptotic
factors, and signaling molecules. {U}se of a support vector machine
classifier, feature selection, and test evaluation using cross-validation
led to identification of a generalizable expression signature, a
small subset of genes whose expression potentially can be used to
predict tumor outcome in new samples. {S}imilar methods were used
to identify genes that are differentially expressed between tumors
with and without genomic 1q gain. {T}his set of discriminators was
highly enriched in genes on 1q, indicating close agreement between
data obtained from expression profiling with data from genomic copy
number analyses.},
doi = {10.1002/gcc.20060Â },
pdf = {../local/Williams2004Prognostic.pdf},
file = {Williams2004Prognostic.pdf:local/Williams2004Prognostic.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://dx.doi.org/10.1002/gcc.20060}
}
@inproceedings{Williamson2000Entropy,
author = {R.C. Williamson and A.J. Smola and B. Schoelkopf},
title = {Entropy {N}umbers of {L}inear {F}unction {C}lasses},
booktitle = {Proc. 13th {A}nnu. {C}onference on {C}omput. {L}earning {T}heory},
year = {2000},
pages = {309--319},
publisher = {Morgan Kaufmann, San Francisco},
pdf = {../local/Williamson2000Entropy.pdf},
file = {Williamson2000Entropy.pdf:local/Williamson2000Entropy.pdf:PDF}
}
@article{Wilton2003Comparison,
author = {D. Wilton and P. Willett and K. Lawson and G. Mullier},
title = {Comparison of ranking methods for virtual screening in lead-discovery
programs.},
journal = {J {C}hem {I}nf {C}omput {S}ci},
year = {2003},
volume = {43},
pages = {469-74},
number = {2},
abstract = {This paper discusses the use of several rank-based virtual screening
methods for prioritizing compounds in lead-discovery programs, given
a training set for which both structural and bioactivity data are
available. {S}tructures from the {NCI} {AIDS} data set and from the
{S}yngenta corporate database were represented by two types of fragment
bit-string and by sets of high-level molecular features. {T}hese
representations were processed using binary kernel discrimination,
similarity searching, substructural analysis, support vector machine,
and trend vector analysis, with the effectiveness of the methods
being judged by the extent to which active test set molecules were
clustered toward the top of the resultant rankings. {T}he binary
kernel discrimination approach yielded consistently superior rankings
and would appear to have considerable potential for chemical screening
applications.},
doi = {10.1021/ci025586i},
pdf = {../local/Wilton2003Comparison.pdf},
file = {Wilton2003Comparison.pdf:local/Wilton2003Comparison.pdf:PDF},
keywords = {biosvm},
url = {http://dx.doi.org/10.1021/ci025586i}
}
@article{Winters-Hilt2004Nanopore,
author = {Stephen Winters-Hilt and Mark Akeson},
title = {Nanopore cheminformatics.},
journal = {D{NA} {C}ell {B}iol},
year = {2004},
volume = {23},
pages = {675-83},
number = {10},
month = {Oct},
abstract = {A cheminformatics method is described for classification, and biophysical
examination, of individual molecules. {A} novel molecular detector
is used--one based on current blockade measurements through a nanometer-scale
ion channel (alpha-hemolysin). {C}lassification results are described
for blockades caused by {DNA} molecules in the alpha-hemolysin nanopore
detector, with signal analysis and pattern recognition performed
using a combination of methods from bioinformatics and machine learning.
{D}ue to the size of the alpha-hemolysin protein channel, the blockade
events report on one {DNA} molecule at a time, which enables a variety
of reproducible, single-molecule biophysical experiments. {T}o capture
the full sensitivity of the nanopore detector's blockade signal,
{H}idden {M}arkov {M}odels ({HMM}s) were used with {E}xpectation/{M}aximization
for denoising and for associating a feature vector with the ionic
current blockade of each captured {DNA} molecule. {S}upport {V}ector
{M}achines ({SVM}s) that employ novel kernel designs were then used
as discriminators. {W}ith {SVM} training performed off-line, and
economical {HMM} processing on-line, blockade classification was
possible during capture. {HMM}s were also used in conjunction with
a time-domain finite state automaton (off-line) for feature discovery
and kinetics analysis. {A}nalysis of the {DNA} data indicates a variety
of binding ({DNA}-protein), fraying, and conformational shifts that
are consistent with data obtained from thermodynamic analyses (melting
curves), {X}-ray crystallography, and {NMR} studies. {T}he software
tools are designed for analysis of generic blockades in ionic channels,
including those in other biological pore-forming toxins, other biological
channels in general, and semiconductor-based channels.},
doi = {10.1089/1044549042476893},
keywords = {Algorithms, Artificial Intelligence, Ascomycota, Automated, Base Sequence,
Chromosome Mapping, Codon, Comparative Study, Crystallography, DNA,
DNA Primers, Hordeum, Host-Parasite Relations, Informatics, Kinetics,
Magnetic Resonance Spectroscopy, Nanotechnology, Non-U.S. Gov't,
Pattern Recognition, Plant, Plants, Research Support, Sequence Alignment,
Sequence Analysis, Thermodynamics, X-Ray, 15585125},
url = {http://dx.doi.org/10.1089/1044549042476893}
}
@article{Winters-Hilt2003Highly,
author = {Winters-Hilt, S. and Vercoutere, W. and DeGuzman, V.S. and Deamer,
D. and Akeson, M. and Haussler, D.},
title = {Highly accurate classification of {W}atson-{C}rick basepairs on termini
of single {DNA} molecules.},
journal = {Biophys. {J}.},
year = {2003},
volume = {84},
pages = {967-976},
number = {2},
abstract = {We introduce a computational method for classification of individual
{DNA} molecules measured by an{alpha} -hemolysin channel detector.
{W}e show classification with better than 99% accuracy for {DNA}
hairpin molecules that differ only in their terminal {W}atson-{C}rick
basepairs. {S}ignal classification was done in silico to establish
performance metrics (i.e., where train and test data were of known
type, via single-species data files). {I}t was then performed in
solution to assay real mixtures of {DNA} hairpins. {H}idden {M}arkov
{M}odels ({HMM}s) were used with {E}xpectation/{M}aximization for
denoising and for associating a feature vector with the ionic current
blockade of the {DNA} molecule. {S}upport {V}ector {M}achines ({SVM}s)
were used as discriminators, and were the focus of off-line training.
{A} multiclass {SVM} architecture was designed to place less discriminatory
load on weaker discriminators, and novel {SVM} kernels were used
to boost discrimination strength. {T}he tuning on {HMM}s and {SVM}s
enabled biophysical analysis of the captured molecule states and
state transitions; structure revealed in the biophysical analysis
was used for better feature selection.},
pdf = {../local/Winters-Hilt2003Highly.pdf},
file = {Winters-Hilt2003Highly.pdf:local/Winters-Hilt2003Highly.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://www.biophysj.org/cgi/content/abstract/84/2/967}
}
@article{Winzeler2006Applied,
author = {E. A Winzeler},
title = {{A}pplied systems biology and malaria.},
journal = {Nat. Rev. Microbiol.},
year = {2006},
volume = {4},
pages = {145--151},
number = {2},
month = {Feb},
abstract = {One of the goals of systems-biology research is to discover networks
and interactions by integrating diverse data sets. So far, systems-biology
research has focused on model organisms, which are well characterized
and therefore suited to testing new methods. Systems biology has
great potential for use in the search for therapies for disease.
Here, the potential of systems-biology approaches in the search for
new drugs and vaccines to treat malaria is examined.},
doi = {10.1038/nrmicro1327},
pdf = {../local/Winzeler2006Applied.pdf},
file = {Winzeler2006Applied.pdf:local/Winzeler2006Applied.pdf:PDF},
keywords = {plasmodium},
pii = {nrmicro1327},
pmid = {16362033},
timestamp = {2006.04.13},
url = {http://dx.doi.org/10.1038/nrmicro1327}
}
@article{Wirapati2008Meta-analysis,
author = {Wirapati, P. and Sotiriou, C. and Kunkel, S. and Farmer, P. and Pradervand,
S. and Haibe-Kains, B. and Desmedt, C. and Ignatiadis, M. and Sengstag,
T. and Sch\"utz, F. and Goldstein, D. R. and Piccart, M. and Delorenzi,
M.},
title = {Meta-analysis of gene expression profiles in breast cancer: toward
a unified understanding of breast cancer subtyping and prognosis
signatures.},
journal = {Breast Cancer Res.},
year = {2008},
volume = {10},
pages = {R65},
number = {4},
abstract = {INTRODUCTION: Breast cancer subtyping and prognosis have been studied
extensively by gene expression profiling, resulting in disparate
signatures with little overlap in their constituent genes. Although
a previous study demonstrated a prognostic concordance among gene
expression signatures, it was limited to only one dataset and did
not fully elucidate how the different genes were related to one another
nor did it examine the contribution of well-known biological processes
of breast cancer tumorigenesis to their prognostic performance. METHOD:
To address the above issues and to further validate these initial
findings, we performed the largest meta-analysis of publicly available
breast cancer gene expression and clinical data, which are comprised
of 2,833 breast tumors. Gene coexpression modules of three key biological
processes in breast cancer (namely, proliferation, estrogen receptor
[ER], and HER2 signaling) were used to dissect the role of constituent
genes of nine prognostic signatures. RESULTS: Using a meta-analytical
approach, we consolidated the signatures associated with ER signaling,
ERBB2 amplification, and proliferation. Previously published expression-based
nomenclature of breast cancer 'intrinsic' subtypes can be mapped
to the three modules, namely, the ER-/HER2- (basal-like), the HER2+
(HER2-like), and the low- and high-proliferation ER+/HER2- subtypes
(luminal A and B). We showed that all nine prognostic signatures
exhibited a similar prognostic performance in the entire dataset.
Their prognostic abilities are due mostly to the detection of proliferation
activity. Although ER- status (basal-like) and ERBB2+ expression
status correspond to bad outcome, they seem to act through elevated
expression of proliferation genes and thus contain only indirect
information about prognosis. Clinical variables measuring the extent
of tumor progression, such as tumor size and nodal status, still
add independent prognostic information to proliferation genes. CONCLUSION:
This meta-analysis unifies various results of previous gene expression
studies in breast cancer. It reveals connections between traditional
prognostic factors, expression-based subtyping, and prognostic signatures,
highlighting the important role of proliferation in breast cancer
prognosis.},
doi = {10.1186/bcr2124},
pdf = {../local/Wirapati2008Meta-analysis.pdf},
file = {Wirapati2008Meta-analysis.pdf:Wirapati2008Meta-analysis.pdf:PDF},
institution = {Swiss Institute of Bioinformatics, 'Batiment Genopode', University
of Lausanne, 1015 Lausanne, Switzerland. Pratyaksha.Wirapati@isb-sib.ch},
keywords = {microarray, breastcancer},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {bcr2124},
pmid = {18662380},
timestamp = {2010.10.13},
url = {http://dx.doi.org/10.1186/bcr2124}
}
@article{Witten2011New,
author = {Witten, D. M. and Friedman, J. H. and Simon, N.},
title = {New insights and faster computations for the graphical lasso},
journal = {J. Comput. Graph. Stat.},
year = {2011},
volume = {20},
pages = {892--900},
number = {4},
abstract = {We consider the graphical lasso formulation for estimating a Gaussian
graphical model in the high-dimensional setting. This approach entails
estimating the inverse covariance matrix under a multivariate normal
model by maximizing the ℓ1-penalized log-likelihood. We present a
very simple necessary and sufficient condition that can be used to
identify the connected components in the graphical lasso solution.
The condition can be employed to determine whether the estimated
inverse covariance matrix will be block diagonal, and if so, then
to identify the blocks. This in turn can lead to drastic speed improvements,
since one can simply apply a standard graphical lasso algorithm to
each block separately. Moreover, the necessary and sufficient condition
provides insight into the graphical lasso solution: the set of connected
nodes at any given tuning parameter value is a superset of the set
of connected nodes at any larger tuning parameter value. This article
has supplementary material online.},
doi = {10.1198/jcgs.2011.11051a},
pdf = {../local/Witten2011New.pdf},
file = {Witten2011New.pdf:Witten2011New.pdf:PDF},
owner = {jp},
timestamp = {2012.04.14},
url = {http://dx.doi.org/10.1198/jcgs.2011.11051a}
}
@article{Witten2009Covariance-regularized,
author = {Witten, D. M. and Tibshirani, R.},
title = {Covariance-regularized regression and classification for high dimensional
problems},
journal = {J. R. Stat. Soc. Ser. B},
year = {2009},
volume = {71},
number = {3},
doi = {10.1111/j.1467-9868.2009.00699.x},
pdf = {../local/Witten2009Covariance-regularized.pdf},
file = {Witten2009Covariance-regularized.pdf:Witten2009Covariance-regularized.pdf:PDF},
owner = {jp},
timestamp = {2009.03.12},
url = {http://dx.doi.org/10.1111/j.1467-9868.2009.00699.x}
}
@article{Witten2009penalized,
author = {Witten, Daniela M. and Tibshirani, Robert and Hastie, Trevor},
title = {A penalized matrix decomposition, with applications to sparse principal
components and canonical correlation analysis.},
journal = {Biostatistics},
year = {2009},
volume = {10},
pages = {515--534},
number = {3},
month = {Jul},
abstract = {We present a penalized matrix decomposition (PMD), a new framework
for computing a rank-K approximation for a matrix. We approximate
the matrix X as circumflexX = sigma(k=1)(K) d(k)u(k)v(k)(T), where
d(k), u(k), and v(k) minimize the squared Frobenius norm of X - circumflexX,
subject to penalties on u(k) and v(k). This results in a regularized
version of the singular value decomposition. Of particular interest
is the use of L(1)-penalties on u(k) and v(k), which yields a decomposition
of X using sparse vectors. We show that when the PMD is applied using
an L(1)-penalty on v(k) but not on u(k), a method for sparse principal
components results. In fact, this yields an efficient algorithm for
the "SCoTLASS" proposal (Jolliffe and others 2003) for obtaining
sparse principal components. This method is demonstrated on a publicly
available gene expression data set. We also establish connections
between the SCoTLASS method for sparse principal component analysis
and the method of Zou and others (2006). In addition, we show that
when the PMD is applied to a cross-products matrix, it results in
a method for penalized canonical correlation analysis (CCA). We apply
this penalized CCA method to simulated data and to a genomic data
set consisting of gene expression and DNA copy number measurements
on the same set of samples.},
doi = {10.1093/biostatistics/kxp008},
pdf = {../local/Witten2009penalized.pdf},
file = {Witten2009penalized.pdf:Witten2009penalized.pdf:PDF},
institution = {Department of Statistics, Stanford University, Stanford, CA 94305,
USA. dwitten@stanford.edu},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {kxp008},
pmid = {19377034},
timestamp = {2012.02.28},
url = {http://dx.doi.org/10.1093/biostatistics/kxp008}
}
@article{Wolf2003Learning,
author = {Wolf, L. and Shashua, A.},
title = {Learning over {S}ets using {K}ernel {P}rincipal {A}ngles},
journal = {J. {M}ach. {L}earn. {R}es.},
year = {2003},
volume = {4},
pages = {913-931},
keywords = {kernel-theory},
owner = {mahe},
timestamp = {2006.08.09},
url = {http://jmlr.csail.mit.edu/papers/v4/wolf03a.html}
}
@article{Wu2003Comparison,
author = {Wu, B. and Abbott, T. and Fishman, D. and McMurray, W. and Mor, G.
and Stone, K. and Ward, D. and Williams, K. and Zhao, H.},
title = {Comparison of statistical methods for classification of ovarian cancer
using mass spectrometry data},
journal = {Bioinformatics},
year = {2003},
volume = {19},
pages = {1636-1643},
number = {13},
abstract = {Motivation: {N}ovel methods, both molecular and statistical, are urgently
needed to take advantage of recent advances in biotechnology and
the human genome project for disease diagnosis and prognosis. {M}ass
spectrometry ({MS}) holds great promise for biomarker identification
and genome-wide protein profiling. {I}t has been demonstrated in
the literature that biomarkers can be identified to distinguish normal
individuals from cancer patients using {MS} data. {S}uch progress
is especially exciting for the detection of early-stage ovarian cancer
patients. {A}lthough various statistical methods have been utilized
to identify biomarkers from {MS} data, there has been no systematic
comparison among these approaches in their relative ability to analyze
{MS} data. {R}esults: {W}e compare the performance of several classes
of statistical methods for the classification of cancer based on
{MS} spectra. {T}hese methods include: linear discriminant analysis,
quadratic discriminant analysis, k-nearest neighbor classifier, bagging
and boosting classification trees, support vector machine, and random
forest ({RF}). {T}he methods are applied to ovarian cancer and control
serum samples from the {N}ational {O}varian {C}ancer {E}arly {D}etection
{P}rogram clinic at {N}orthwestern {U}niversity {H}ospital. {W}e
found that {RF} outperforms other methods in the analysis of {MS}
data. {S}upplementary information: http://bioinformatics.med.yale.edu/proteomics/{B}io{S}upp1.html},
pdf = {../local/Wu2003Comparison.pdf},
file = {Wu2003Comparison.pdf:local/Wu2003Comparison.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://bioinformatics.oupjournals.org/cgi/content/abstract/19/13/1636}
}
@article{Wu2002Natural,
author = {Jiann-Ming Wu},
title = {Natural discriminant analysis using interactive {P}otts models.},
journal = {Neural {C}omput},
year = {2002},
volume = {14},
pages = {689-713},
number = {3},
month = {Mar},
abstract = {Natural discriminant analysis based on interactive {P}otts models
is developed in this work. {A} generative model composed of piece-wise
multivariate gaussian distributions is used to characterize the input
space, exploring the embedded clustering and mixing structures and
developing proper internal representations of input parameters. {T}he
maximization of a log-likelihood function measuring the fitness of
all input parameters to the generative model, and the minimization
of a design cost summing up square errors between posterior outputs
and desired outputs constitutes a mathematical framework for discriminant
analysis. {W}e apply a hybrid of the mean-field annealing and the
gradient-descent methods to the optimization of this framework and
obtain multiple sets of interactive dynamics, which realize coupled
{P}otts models for discriminant analysis. {T}he new learning process
is a whole process of component analysis, clustering analysis, and
labeling analysis. {I}ts major improvement compared to the radial
basis function and the support vector machine is described by using
some artificial examples and a real-world application to breast cancer
diagnosis.},
doi = {10.1162/089976602317250951},
url = {http://dx.doi.org/10.1162/089976602317250951}
}
@article{Wu2008Network-based,
author = {Wu, X. and Jiang, R. and Zhang, M.Q. and Li, S.},
title = {Network-based global inference of human disease genes.},
journal = {Mol. Syst. Biol.},
year = {2008},
volume = {4},
pages = {189},
abstract = {Deciphering the genetic basis of human diseases is an important goal
of biomedical research. On the basis of the assumption that phenotypically
similar diseases are caused by functionally related genes, we propose
a computational framework that integrates human protein-protein interactions,
disease phenotype similarities, and known gene-phenotype associations
to capture the complex relationships between phenotypes and genotypes.
We develop a tool named CIPHER to predict and prioritize disease
genes, and we show that the global concordance between the human
protein network and the phenotype network reliably predicts disease
genes. Our method is applicable to genetically uncharacterized phenotypes,
effective in the genome-wide scan of disease genes, and also extendable
to explore gene cooperativity in complex diseases. The predicted
genetic landscape of over 1000 human phenotypes, which reveals the
global modular organization of phenotype-genotype relationships.
The genome-wide prioritization of candidate genes for over 5000 human
phenotypes, including those with under-characterized disease loci
or even those lacking known association, is publicly released to
facilitate future discovery of disease genes.},
doi = {10.1038/msb.2008.27},
institution = {MOE Key Laboratory of Bioinformatics and Bioinformatics Division,
TNLIST/Department of Automation, Tsinghua University, Beijing, China.},
keywords = {BRCA1 Protein; Bias (Epidemiology); Breast Neoplasms; Disease; Female;
Gene Regulatory Networks; Genes; Genome, Human; Genotype; Humans;
Linkage (Genetics); Phenotype; Software},
owner = {mordelet},
pii = {msb200827},
pmid = {18463613},
timestamp = {2010.09.27},
url = {http://dx.doi.org/10.1038/msb.2008.27}
}
@techreport{Wu2003Model,
author = {Z Wu and R A Irizarry and R Gentleman and F M Murillo and F Spencer},
title = {A Model Based Background Adjustment for Oligonucleotide Expression
Arrays},
institution = {John Hopkins University, Department of Biostatistics Working Papers,
Baltimore, MD},
year = {2003},
keywords = {csbcbook, csbcbook-ch2}
}
@article{Wyner1989Some,
author = {Wyner, A.D. and Ziv, J.},
title = {Some asymptotic properties of the entropy of a stationary ergodic
data source with applications to data compression},
journal = {I{EEE} {T}rans. {I}nform. {T}heory},
year = {1989},
volume = {35},
pages = {1250-1258},
number = {6},
month = {Nov},
abstract = {Theorems concerning the entropy of a stationary ergodic information
source are derived and used to obtain insight into the workings of
certain data-compression coding schemes, in particular the {L}empel-{S}iv
data compression algorithm},
pdf = {../local/Wyner1989Some.pdf},
file = {Wyner1989Some.pdf:local/Wyner1989Some.pdf:PDF},
keywords = {information-theory},
owner = {vert}
}
@article{Xenarios2002DIP,
author = {Xenarios, I. and Salwínski, L. and Duan, X,J. and Higney, P. and
Kim, S-M and Eisenberg, D.},
title = {DIP, the Database of Interacting Proteins: a research tool for studying
cellular networks of protein interactions.},
journal = {Nucleic Acids Res},
year = {2002},
volume = {30},
pages = {303--305},
number = {1},
month = {Jan},
abstract = {The Database of Interacting Proteins (DIP: http://dip.doe-mbi.ucla.edu)
is a database that documents experimentally determined protein-protein
interactions. It provides the scientific community with an integrated
set of tools for browsing and extracting information about protein
interaction networks. As of September 2001, the DIP catalogs approximately
11 000 unique interactions among 5900 proteins from >80 organisms;
the vast majority from yeast, Helicobacter pylori and human. Tools
have been developed that allow users to analyze, visualize and integrate
their own experimental data with the information about protein-protein
interactions available in the DIP database.},
institution = {UCLA-DOE Laboratory of Structural Biology and Molecular Medicine,
Molecular Biology Institute, PO Box 951570, UCLA, Los Angeles, CA
90095-1570, USA.},
owner = {fantine},
pmid = {11752321},
timestamp = {2010.10.21}
}
@article{Xia2004RNAi,
author = {Xia, H. and Mao, Q. and Eliason, S. L. and Harper, S. Q. and Martins,
I. H. and Orr, H. T. and Paulson, H. L. and Yang, L. and Kotin, R.
M. and Davidson, B. L.},
title = {{RNA}i suppresses polyglutamine-induced neurodegeneration in a model
of spinocerebellar ataxia.},
journal = {Nat. Med.},
year = {2004},
volume = {10},
pages = {816--820},
number = {8},
month = {Aug},
abstract = {The dominant polyglutamine expansion diseases, which include spinocerebellar
ataxia type 1 (SCA1) and Huntington disease, are progressive, untreatable,
neurodegenerative disorders. In inducible mouse models of SCA1 and
Huntington disease, repression of mutant allele expression improves
disease phenotypes. Thus, therapies designed to inhibit expression
of the mutant gene would be beneficial. Here we evaluate the ability
of RNA interference (RNAi) to inhibit polyglutamine-induced neurodegeneration
caused by mutant ataxin-1 in a mouse model of SCA1. Upon intracerebellar
injection, recombinant adeno-associated virus (AAV) vectors expressing
short hairpin RNAs profoundly improved motor coordination, restored
cerebellar morphology and resolved characteristic ataxin-1 inclusions
in Purkinje cells of SCA1 mice. Our data demonstrate in vivo the
potential use of RNAi as therapy for dominant neurodegenerative disease.},
doi = {10.1038/nm1076},
keywords = {Adenoviridae, Animal, Animals, Blotting, Brain, Cells, Comparative
Study, Cultured, Disease Models, Gene Expression, Genetic, Glutamine,
Immunohistochemistry, Messenger, Mice, Nerve Degeneration, Nerve
Tissue Proteins, Non-U.S. Gov't, Northern, Nuclear Proteins, P.H.S.,
Plasmids, Psychomotor Performance, Purkinje Cells, RNA, RNA Interference,
Research Support, Reverse Transcriptase Polymerase Chain Reaction,
Small Interfering, Spinocerebellar Ataxias, Transduction, Transgenic,
U.S. Gov't, 15286770},
owner = {vert},
pii = {nm1076},
pmid = {15286770},
timestamp = {2006.03.28},
url = {http://dx.doi.org/10.1038/nm1076}
}
@article{Xia2006IntNetDB,
author = {Xia, K. and Dong, D. and Han, J-D.J.},
title = {IntNetDB v1.0: an integrated protein-protein interaction network
database generated by a probabilistic model.},
journal = {BMC Bioinformatics},
year = {2006},
volume = {7},
pages = {508},
abstract = {BACKGROUND: Although protein-protein interaction (PPI) networks have
been explored by various experimental methods, the maps so built
are still limited in coverage and accuracy. To further expand the
PPI network and to extract more accurate information from existing
maps, studies have been carried out to integrate various types of
functional relationship data. A frequently updated database of computationally
analyzed potential PPIs to provide biological researchers with rapid
and easy access to analyze original data as a biological network
is still lacking. RESULTS: By applying a probabilistic model, we
integrated 27 heterogeneous genomic, proteomic and functional annotation
datasets to predict PPI networks in human. In addition to previously
studied data types, we show that phenotypic distances and genetic
interactions can also be integrated to predict PPIs. We further built
an easy-to-use, updatable integrated PPI database, the Integrated
Network Database (IntNetDB) online, to provide automatic prediction
and visualization of PPI network among genes of interest. The networks
can be visualized in SVG (Scalable Vector Graphics) format for zooming
in or out. IntNetDB also provides a tool to extract topologically
highly connected network neighborhoods from a specific network for
further exploration and research. Using the MCODE (Molecular Complex
Detections) algorithm, 190 such neighborhoods were detected among
all the predicted interactions. The predicted PPIs can also be mapped
to worm, fly and mouse interologs. CONCLUSION: IntNetDB includes
180,010 predicted protein-protein interactions among 9,901 human
proteins and represents a useful resource for the research community.
Our study has increased prediction coverage by five-fold. IntNetDB
also provides easy-to-use network visualization and analysis tools
that allow biological researchers unfamiliar with computational biology
to access and analyze data over the internet. The web interface of
IntNetDB is freely accessible at http://hanlab.genetics.ac.cn/IntNetDB.htm.
Visualization requires Mozilla version 1.8 (or higher) or Internet
Explorer with installation of SVGviewer.},
doi = {10.1186/1471-2105-7-508},
institution = {Chinese Academy of Sciences Key Laboratory of Developmental Biology,
Center for Molecular Systems Biology, Institute of Genetics and Developmental
Biology, Chinese Academy of Sciences, Beijing, China. kxia@genetics.ac.cn},
owner = {fantine},
pii = {1471-2105-7-508},
pmid = {17112386},
timestamp = {2010.10.21},
url = {http://dx.doi.org/10.1186/1471-2105-7-508}
}
@article{Xia1998Thermodynamic,
author = {Xia, T. and SantaLucia, J. and Burkard, M. E. and Kierzek, R. and
Schroeder, S. J. and Jiao, X. and Cox, C. and Turner, D. H.},
title = {Thermodynamic parameters for an expanded nearest-neighbor model for
formation of {RNA} duplexes with {W}atson-{C}rick base pairs.},
journal = {Biochemistry},
year = {1998},
volume = {37},
pages = {14719-35},
number = {42},
month = {Oct},
abstract = {Improved thermodynamic parameters for prediction of {RNA} duplex formation
are derived from optical melting studies of 90 oligoribonucleotide
duplexes containing only {W}atson-{C}rick base pairs. {T}o test end
or base composition effects, new sets of duplexes are included that
have identical nearest neighbors, but different base compositions
and therefore different ends. {D}uplexes with terminal {GC} pairs
are more stable than duplexes with the same nearest neighbors but
terminal {AU} pairs. {P}enalizing terminal {AU} base pairs by 0.45
kcal/mol relative to terminal {GC} base pairs significantly improves
predictions of {D}elta{G} degrees37 from a nearest-neighbor model.
{A} physical model is suggested in which the differential treatment
of {AU} and {GC} ends accounts for the dependence of the total number
of {W}atson-{C}rick hydrogen bonds on the base composition of a duplex.
{O}n average, the new parameters predict {D}elta{G} degrees37, {D}elta{H}
degrees, {D}elta{S} degrees, and {TM} within 3.2\%, 6.0\%, 6.8\%,
and 1.3 degrees{C}, respectively. {T}hese predictions are within
the limit of the model, based on experimental results for duplexes
predicted to have identical thermodynamic parameters.},
doi = {10.1021/bi9809425},
pii = {bi9809425},
url = {http://dx.doi.org/10.1021/bi9809425}
}
@article{Xia2004one-layer,
author = {Youshen Xia and Jun Wang},
title = {A one-layer recurrent neural network for support vector machine learning.},
journal = {I{EEE} {T}rans {S}yst {M}an {C}ybern {B} {C}ybern},
year = {2004},
volume = {34},
pages = {1261-9},
number = {2},
month = {Apr},
abstract = {This paper presents a one-layer recurrent neural network for support
vector machine ({SVM}) learning in pattern classification and regression.
{T}he {SVM} learning problem is first converted into an equivalent
formulation, and then a one-layer recurrent neural network for {SVM}
learning is proposed. {T}he proposed neural network is guaranteed
to obtain the optimal solution of support vector classification and
regression. {C}ompared with the existing two-layer neural network
for the {SVM} classification, the proposed neural network has a low
complexity for implementation. {M}oreover, the proposed neural network
can converge exponentially to the optimal solution of {SVM} learning.
{T}he rate of the exponential convergence can be made arbitrarily
high by simply turning up a scaling parameter. {S}imulation examples
based on benchmark problems are discussed to show the good performance
of the proposed neural network for {SVM} learning.}
}
@article{Xia2011NSMAP,
author = {Xia, Z. and Wen, J. and Chang, C.-C. and Zhou, X.},
title = {{NSMAP}: a method for spliced isoforms identification and quantification
from {RNA-Seq}.},
journal = {BMC Bioinformatics},
year = {2011},
volume = {12},
pages = {162},
abstract = {The development of techniques for sequencing the messenger RNA (RNA-Seq)
enables it to study the biological mechanisms such as alternative
splicing and gene expression regulation more deeply and accurately.
Most existing methods employ RNA-Seq to quantify the expression levels
of already annotated isoforms from the reference genome. However,
the current reference genome is very incomplete due to the complexity
of the transcriptome which hiders the comprehensive investigation
of transcriptome using RNA-Seq. Novel study on isoform inference
and estimation purely from RNA-Seq without annotation information
is desirable.A Nonnegativity and Sparsity constrained Maximum APosteriori
(NSMAP) model has been proposed to estimate the expression levels
of isoforms from RNA-Seq data without the annotation information.
In contrast to previous methods, NSMAP performs identification of
the structures of expressed isoforms and estimation of the expression
levels of those expressed isoforms simultaneously, which enables
better identification of isoforms. In the simulations parameterized
by two real RNA-Seq data sets, more than 77\% expressed isoforms
are correctly identified and quantified. Then, we apply NSMAP on
two RNA-Seq data sets of myelodysplastic syndromes (MDS) samples
and one normal sample in order to identify differentially expressed
known and novel isoforms in MDS disease.NSMAP provides a good strategy
to identify and quantify novel isoforms without the knowledge of
annotated reference genome which can further realize the potential
of RNA-Seq technique in transcriptome analysis. NSMAP package is
freely available at https://sites.google.com/site/nsmapforrnaseq.},
doi = {10.1186/1471-2105-12-162},
pdf = {../local/Xia2011NSMAP.pdf},
file = {Xia2011NSMAP.pdf:Xia2011NSMAP.pdf:PDF},
institution = {Department of Radiology, The Methodist Hospital Research Institute,
Houston, TX 77030, USA.},
language = {eng},
medline-pst = {epublish},
owner = {jp},
pii = {1471-2105-12-162},
pmid = {21575225},
timestamp = {2012.03.06},
url = {http://dx.doi.org/10.1186/1471-2105-12-162}
}
@article{Xie2009CNV-seq,
author = {Chao Xie and Martti T Tammi},
title = {CNV-seq, a new method to detect copy number variation using high-throughput
sequencing.},
journal = {BMC Bioinformatics},
year = {2009},
volume = {10},
pages = {80},
abstract = {BACKGROUND: DNA copy number variation (CNV) has been recognized as
an important source of genetic variation. Array comparative genomic
hybridization (aCGH) is commonly used for CNV detection, but the
microarray platform has a number of inherent limitations. RESULTS:
Here, we describe a method to detect copy number variation using
shotgun sequencing, CNV-seq. The method is based on a robust statistical
model that describes the complete analysis procedure and allows the
computation of essential confidence values for detection of CNV.
Our results show that the number of reads, not the length of the
reads is the key factor determining the resolution of detection.
This favors the next-generation sequencing methods that rapidly produce
large amount of short reads. CONCLUSION: Simulation of various sequencing
methods with coverage between 0.1x to 8x show overall specificity
between 91.7 - 99.9\%, and sensitivity between 72.2 - 96.5\%. We
also show the results for assessment of CNV between two individual
human genomes.},
doi = {10.1186/1471-2105-10-80},
pdf = {../local/Xie2009CNV-seq.pdf},
file = {Xie2009CNV-seq.pdf:Xie2009CNV-seq.pdf:PDF},
institution = {Department of Biological Sciences, National University of Singapore,
Singapore. xie@nus.edu.sg},
keywords = {ngs},
owner = {jp},
pii = {1471-2105-10-80},
pmid = {19267900},
timestamp = {2009.10.09},
url = {http://dx.doi.org/10.1186/1471-2105-10-80}
}
@article{Xie2005LOCSVMPSI,
author = {Dan Xie and Ao Li and Minghui Wang and Zhewen Fan and Huanqing Feng},
title = {L{OCSVMPSI}: a web server for subcellular localization of eukaryotic
proteins using {SVM} and profile of {PSI}-{BLAST}.},
journal = {Nucleic {A}cids {R}es.},
year = {2005},
volume = {33},
pages = {W105-10},
number = {Web Server issue},
month = {Jul},
abstract = {Subcellular location of a protein is one of the key functional characters
as proteins must be localized correctly at the subcellular level
to have normal biological function. {I}n this paper, a novel method
named {LOCSVMPSI} has been introduced, which is based on the support
vector machine ({SVM}) and the position-specific scoring matrix generated
from profiles of {PSI}-{BLAST}. {W}ith a jackknife test on the {RH}2427
data set, {LOCSVMPSI} achieved a high overall prediction accuracy
of 90.2\%, which is higher than the prediction results by {S}ub{L}oc
and {ESL}pred on this data set. {I}n addition, prediction performance
of {LOCSVMPSI} was evaluated with 5-fold cross validation test on
the {PK}7579 data set and the prediction results were consistently
better than the previous method based on several {SVM}s using composition
of both amino acids and amino acid pairs. {F}urther test on the {SWISSPROT}
new-unique data set showed that {LOCSVMPSI} also performed better
than some widely used prediction methods, such as {PSORTII}, {T}arget{P}
and {LOC}net. {A}ll these results indicate that {LOCSVMPSI} is a
powerful tool for the prediction of eukaryotic protein subcellular
localization. {A}n online web server (current version is 1.3) based
on this method has been developed and is freely available to both
academic and commercial users, which can be accessed by at http://{B}ioinformatics.ustc.edu.cn/{LOCSVMPSI}/{LOCSVMPSI}.php.},
doi = {10.1093/nar/gki359},
pdf = {../local/Xie2005LOCSVMPSI.pdf},
file = {Xie2005LOCSVMPSI.pdf:local/Xie2005LOCSVMPSI.pdf:PDF},
keywords = {biosvm},
pii = {33/suppl_2/W105},
url = {http://dx.doi.org/10.1093/nar/gki359}
}
@article{Xie2009Unified,
author = {Lei Xie and Li Xie and Philip E Bourne},
title = {A unified statistical model to support local sequence order independent
similarity searching for ligand-binding sites and its application
to genome-based drug discovery.},
journal = {Bioinformatics},
year = {2009},
volume = {25},
pages = {i305--i312},
number = {12},
month = {Jun},
abstract = {Functional relationships between proteins that do not share global
structure similarity can be established by detecting their ligand-binding-site
similarity. For a large-scale comparison, it is critical to accurately
and efficiently assess the statistical significance of this similarity.
Here, we report an efficient statistical model that supports local
sequence order independent ligand-binding-site similarity searching.
Most existing statistical models only take into account the matching
vertices between two sites that are defined by a fixed number of
points. In reality, the boundary of the binding site is not known
or is dependent on the bound ligand making these approaches limited.
To address these shortcomings and to perform binding-site mapping
on a genome-wide scale, we developed a sequence-order independent
profile-profile alignment (SOIPPA) algorithm that is able to detect
local similarity between unknown binding sites a priori. The SOIPPA
scoring integrates geometric, evolutionary and physical information
into a unified framework. However, this imposes a significant challenge
in assessing the statistical significance of the similarity because
the conventional probability model that is based on fixed-point matching
cannot be applied. Here we find that scores for binding-site matching
by SOIPPA follow an extreme value distribution (EVD). Benchmark studies
show that the EVD model performs at least two-orders faster and is
more accurate than the non-parametric statistical method in the previous
SOIPPA version. Efficient statistical analysis makes it possible
to apply SOIPPA to genome-based drug discovery. Consequently, we
have applied the approach to the structural genome of Mycobacterium
tuberculosis to construct a protein-ligand interaction network. The
network reveals highly connected proteins, which represent suitable
targets for promiscuous drugs.},
doi = {10.1093/bioinformatics/btp220},
institution = {San Diego Supercomputer Center, University of California, San Diego,
La Jolla, CA 92093, USA. lxie@sdsc.edu},
keywords = {Binding Sites; Computational Biology, methods; Drug Discovery, methods;
Genome; Ligands; Models, Statistical; Mycobacterium tuberculosis,
genetics/metabolism; Proteins, chemistry},
language = {eng},
medline-pst = {ppublish},
owner = {bricehoffmann},
pii = {btp220},
pmid = {19478004},
timestamp = {2009.07.27},
url = {http://dx.doi.org/10.1093/bioinformatics/btp220}
}
@article{Xie2000Asymptotic,
author = {Xie, Q. and Barron, A.R.},
title = {Asymptotic minimax regret for data compression, gambling, and prediction},
journal = {I{EEE} {T}rans. {I}nform. {T}heory},
year = {2000},
volume = {46},
pages = {431 - 445},
number = {2},
month = {Mar},
abstract = {For problems of data compression, gambling, and prediction of individual
sequences x1, ···, xn the following questions arise. {G}iven a
target family of probability mass functions p(x1, ···, x n|&thetas;),
how do we choose a probability mass function q(x 1, ···, xn) so
that it approximately minimizes the maximum regret/belowdisplayskip10ptminus6pt
max (log1/q(x1, ···, xn)-log1/p(x1, ···, xn |&thetas;?)) and
so that it achieves the best constant {C} in the asymptotics of the
minimax regret, which is of the form (d/2)log(n/2?)+{C}+o(1), where
d is the parameter dimension? {A}re there easily implementable strategies
q that achieve those asymptotics? {A}nd how does the solution to
the worst case sequence problem relate to the solution to the corresponding
expectation version minq max 0 {E}0(log1/q(x1, ···, xn)-log1/p(x1,
···, xn|&thetas;))? {I}n the discrete memoryless case, with a
given alphabet of size m, the {B}ayes procedure with the {D}irichlet(1/2,
···, 1/2) prior is asymptotically maximin. {S}imple modifications
of it are shown to be asymptotically minimax. {T}he best constant
is {C}m=log(?(1/2)m/(?(m/2)) which agrees with the logarithm of the
integral of the square root of the determinant of the {F}isher information.
{M}oreover, our asymptotically optimal strategies for the worst case
problem are also asymptotically optimal for the expectation version.
{A}nalogous conclusions are given for the case of prediction, gambling,
and compression when, for each observation, one has access to side
information from an alphabet of size k. {I}n this setting the minimax
regret is shown to be k(m-1)/2logn/2?k+k{C}m+o(1)},
pdf = {../local/Xie2000Asymptotic.pdf},
file = {Xie2000Asymptotic.pdf:local/Xie2000Asymptotic.pdf:PDF},
keywords = {information-theory},
owner = {vert}
}
@article{Xie1997Minimax,
author = {Xie, Q. and Barron, A.R.},
title = {Minimax redundancy for the class of memoryless sources},
journal = {I{EEE} {T}rans. {I}nform. {T}heory},
year = {1997},
volume = {43},
pages = {646-657},
number = {2},
month = {Mar},
abstract = {Let {X}n=({X}1,...,{X}n) be a memoryless source with unknown distribution
on a finite alphabet of size k. {W}e identify the asymptotic minimax
coding redundancy for this class of sources, and provide a sequence
of asymptotically minimax codes. {E}quivalently, we determine the
limiting behavior of the minimax relative entropy min{QX}n maxp{X}n
{D}({PX}n∥{QX}n), where the maximum is over all independent and
identically distributed (i.i.d.) source distributions and the minimum
is over all joint distributions. {W}e show in this paper that the
minimax redundancy minus ((k-1)/2) log(n/(2?e)) converges to log??(det
{I}(&thetas;))d&thetas;=log (?(1/2)k/?(k/2)), where {I}(&thetas;)
is the {F}isher information and the integral is over the whole probability
simplex. {T}he {B}ayes strategy using {J}effreys' prior is shown
to be asymptotically maximin but not asymptotically minimax in our
setting. {T}he boundary risk using {J}effreys' prior is higher than
that of interior points. {W}e provide a sequence of modifications
of {J}effreys' prior that put some prior mass near the boundaries
of the probability simplex to pull down that risk to the asymptotic
minimax level in the limit },
pdf = {../local/Xie1997Minimax.pdf},
file = {Xie1997Minimax.pdf:local/Xie1997Minimax.pdf:PDF},
keywords = {information-theory},
owner = {vert}
}
@article{Xing2004MotifPrototyper,
author = {Xing, E. and Karp, R.},
title = {MotifPrototyper: A Bayesian profile model for motif families},
journal = {PNAS},
year = {2004},
volume = {101},
pages = {10523--10528},
number = {29}
}
@inproceedings{Xing2003Distance,
author = {E.P. Xing and A.Y. Ng and M.I. Jordan and S. Russell},
title = {Distance Metric Learning with Application to Clustering with Side-Information},
booktitle = {Adv. Neural. Inform. Process Syst.},
year = {2003},
editor = {S. Becker, S. Thrun and K. Obermayer},
volume = {15},
pages = {505--512},
address = {Cambridge, MA},
publisher = {MIT Press},
owner = {vert},
timestamp = {2006.06.09}
}
@article{Xing2004LOGOS,
author = {Xing, E. P. and Wu, W. and Jordan, M. I. and Karp, R. M.},
title = {L{OGOS}: {A} modular {B}ayesian model for de novo motif detection},
journal = {J. {B}ioinform. {C}omput. {B}iol.},
year = {2004},
volume = {2},
pages = {127--154},
abstract = {The complexity of the global organization and internal structure of
motifs in higher eukaryotic organisms raises significant challenges
for motif detection techniques. {T}o achieve successful de novo motif
detection, it is necessary to model the complex dependencies within
and among motifs and to incorporate biological prior knowledge. {I}n
this paper, we present {LOGOS}, an integrated {LO}cal and {G}l{O}bal
motif {S}equence model for biopolymer sequences, which provides a
principled framework for developing, modularizing, extending and
computing expressive motif models for complex biopolymer sequence
analysis. {LOGOS} consists of two interacting submodels: {HMDM},
a local alignment model capturing biological prior knowledge and
positional dependency within the motif local structure; and {HMM},
a global motif distribution model modeling frequencies and dependencies
of motif occurrences. {M}odel parameters can be fit using training
motifs within an empirical {B}ayesian framework. {A} variational
{EM} algorithm is developed for de novo motif detection. {LOGOS}
improves over existing models that ignore biological priors and dependencies
in motif structures and motif occurrences, and demonstrates superior
performance on both semi-realistic test data and cis-regulatory sequences
from yeast and {D}rosophila genomes with regard to sensitivity, specificity,
flexibility and extensibility.},
doi = {10.1142/S0219720004000508},
pdf = {../local/Xing2004LOGOS.pdf},
file = {Xing2004LOGOS.pdf:Xing2004LOGOS.pdf:PDF},
keywords = {biogm},
owner = {vert},
timestamp = {2006.01.18},
url = {http://dx.doi.org/10.1142/S0219720004000508}
}
@article{Xiong2001Biomarker,
author = {Xiong, M. and Fang, X. and Zhao, J.},
title = {Biomarker {I}dentification by {F}eature {W}rappers},
journal = {Genome {R}es.},
year = {2001},
volume = {11},
pages = {1878-1887},
number = {11},
abstract = {Gene expression studies bridge the gap between {DNA} information and
trait information by dissecting biochemical pathways into intermediate
components between genotype and phenotype. {T}hese studies open new
avenues for identifying complex disease genes and biomarkers for
disease diagnosis and for assessing drug efficacy and toxicity. {H}owever,
the majority of analytical methods applied to gene expression data
are not efficient for biomarker identification and disease diagnosis.
{I}n this paper, we propose a general framework to incorporate feature
(gene) selection into pattern recognition in the process to identify
biomarkers. {U}sing this framework, we develop three feature wrappers
that search through the space of feature subsets using the classification
error as measure of goodness for a particular feature subset being
"wrapped around": linear discriminant analysis, logistic regression,
and support vector machines. {T}o effectively carry out this computationally
intensive search process, we employ sequential forward search and
sequential forward floating search algorithms. {T}o evaluate the
performance of feature selection for biomarker identification we
have applied the proposed methods to three data sets. {T}he preliminary
results demonstrate that very high classification accuracy can be
attained by identified composite classifiers with several biomarkers.},
pdf = {../local/Xiong2001Biomarker.pdf},
file = {Xiong2001Biomarker.pdf:local/Xiong2001Biomarker.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://www.genome.org/cgi/content/abstract/11/11/1878}
}
@article{Xu2002Chemoinformatics,
author = {J. Xu and A. Hagler},
title = {Chemoinformatics and {D}rug {Discovery}},
journal = {Molecules},
year = {2002},
volume = {7},
pages = {566-600},
keywords = {chemoinformatics},
owner = {mahe},
timestamp = {2006.08.12},
url = {http://www.mdpi.org/molecules/papers/70800566.pdf}
}
@inproceedings{Xu94pca,
author = {L. Xu and I. King},
title = {A PCA Approach for Fast Retrieval of Structural Patterns in Attributed
Graphs},
booktitle = {Humboldt University Berlin},
year = {1994}
}
@article{Xu2004Molecular,
author = {Xiu-Qin Xu and Chon K Leow and Xin Lu and Xuegong Zhang and Jun S
Liu and Wing-Hung Wong and Arndt Asperger and Sören Deininger and
Hon-Chiu Eastwood Leung},
title = {Molecular classification of liver cirrhosis in a rat model by proteomics
and bioinformatics.},
journal = {Proteomics},
year = {2004},
volume = {4},
pages = {3235-45},
number = {10},
month = {Oct},
abstract = {Liver cirrhosis is a worldwide health problem. {R}eliable, noninvasive
methods for early detection of liver cirrhosis are not available.
{U}sing a three-step approach, we classified sera from rats with
liver cirrhosis following different treatment insults. {T}he approach
consisted of: (i) protein profiling using surface-enhanced laser
desorption/ionization ({SELDI}) technology; (ii) selection of a statistically
significant serum biomarker set using machine learning algorithms;
and (iii) identification of selected serum biomarkers by peptide
sequencing. {W}e generated serum protein profiles from three groups
of rats: (i) normal (n=8), (ii) thioacetamide-induced liver cirrhosis
(n=22), and (iii) bile duct ligation-induced liver fibrosis (n=5)
using a weak cation exchanger surface. {P}rofiling data were further
analyzed by a recursive support vector machine algorithm to select
a panel of statistically significant biomarkers for class prediction.
{S}ensitivity and specificity of classification using the selected
protein marker set were higher than 92\%. {A} consistently down-regulated
3495 {D}a protein in cirrhosis samples was one of the selected significant
biomarkers. {T}his 3495 {D}a protein was purified on-chip and trypsin
digested. {F}urther structural characterization of this biomarkers
candidate was done by using cross-platform matrix-assisted laser
desorption/ionization mass spectrometry ({MALDI}-{MS}) peptide mass
fingerprinting ({PMF}) and matrix-assisted laser desorption/ionization
time of flight/time of flight ({MALDI}-{TOF}/{TOF}) tandem mass spectrometry
({MS}/{MS}). {C}ombined data from {PMF} and {MS}/{MS} spectra of
two tryptic peptides suggested that this 3495 {D}a protein shared
homology to a histidine-rich glycoprotein. {T}hese results demonstrated
a novel approach to discovery of new biomarkers for early detection
of liver cirrhosis and classification of liver diseases.},
doi = {10.1002/pmic.200400839},
pdf = {../local/Xu2004Molecular.pdf},
file = {Xu2004Molecular.pdf:Xu2004Molecular.pdf:PDF},
keywords = {biosvm},
url = {http://dx.doi.org/10.1002/pmic.200400839}
}
@article{Xue2004Support,
author = {C. X. Xue and R. S. Zhang and H. X. Liu and M. C. Liu and Z. D. Hu
and B. T. Fan},
title = {Support vector machines-based quantitative structure-property relationship
for the prediction of heat capacity.},
journal = {J {C}hem {I}nf {C}omput {S}ci},
year = {2004},
volume = {44},
pages = {1267-74},
number = {4},
abstract = {The support vector machine ({SVM}), as a novel type of learning machine,
for the first time, was used to develop a {Q}uantitative {S}tructure-{P}roperty
{R}elationship ({QSPR}) model of the heat capacity of a diverse set
of 182 compounds based on the molecular descriptors calculated from
the structure alone. {M}ultiple linear regression ({MLR}) and radial
basis function networks ({RBFNN}s) were also utilized to construct
quantitative linear and nonlinear models to compare with the results
obtained by {SVM}. {T}he root-mean-square (rms) errors in heat capacity
predictions for the whole data set given by {MLR}, {RBFNN}s, and
{SVM} were 4.648, 4.337, and 2.931 heat capacity units, respectively.
{T}he prediction results are in good agreement with the experimental
value of heat capacity; also, the results reveal the superiority
of the {SVM} over {MLR} and {RBFNN}s models.},
doi = {10.1021/ci049934n},
pdf = {../local/Xue2004Support.pdf},
file = {Xue2004Support.pdf:local/Xue2004Support.pdf:PDF},
keywords = {biosvm},
url = {http://dx.doi.org/10.1021/ci049934n}
}
@article{Xue2004accurate,
author = {C. X. Xue and R. S. Zhang and H. X. Liu and X. J. Yao and M. C. Liu
and Z. D. Hu and B. T. Fan},
title = {An accurate {QSPR} study of {O}-{H} bond dissociation energy in substituted
phenols based on support vector machines.},
journal = {J {C}hem {I}nf {C}omput {S}ci},
year = {2004},
volume = {44},
pages = {669-77},
number = {2},
abstract = {The support vector machine ({SVM}), as a novel type of learning machine,
was used to develop a {Q}uantitative {S}tructure-{P}roperty {R}elationship
({QSPR}) model of the {O}-{H} bond dissociation energy ({BDE}) of
78 substituted phenols. {T}he six descriptors calculated solely from
the molecular structures of compounds selected by forward stepwise
regression were used as inputs for the {SVM} model. {T}he root-mean-square
(rms) errors in {BDE} predictions for the training, test, and overall
data sets were 3.808, 3.320, and 3.713 {BDE} units (k{J} mol(-1)),
respectively. {T}he results obtained by {G}aussian-kernel {SVM} were
much better than those obtained by multiple linear regression, radial
basis function neural networks, linear-kernel {SVM}, and other {QSPR}
approaches.},
doi = {10.1021/ci034248u},
pdf = {../local/Xue2004accurate.pdf},
file = {Xue2004accurate.pdf:local/Xue2004accurate.pdf:PDF},
keywords = {biosvm},
url = {http://dx.doi.org/10.1021/ci034248u}
}
@article{Xue2004QSAR,
author = {C. X. Xue and R. S. Zhang and H. X. Liu and X. J. Yao and M. C. Liu
and Z. D. Hu and B. T. Fan},
title = {Q{SAR} models for the prediction of binding affinities to human serum
albumin using the heuristic method and a support vector machine.},
journal = {J {C}hem {I}nf {C}omput {S}ci},
year = {2004},
volume = {44},
pages = {1693-700},
number = {5},
abstract = {The binding affinities to human serum albumin for 94 diverse drugs
and drug-like compounds were modeled with the descriptors calculated
from the molecular structure alone using a quantitative structure-activity
relationship ({QSAR}) technique. {T}he heuristic method ({HM}) and
support vector machine ({SVM}) were utilized to construct the linear
and nonlinear prediction models, leading to a good correlation coefficient
({R}2) of 0.86 and 0.94 and root-mean-square errors (rms) of 0.212
and 0.134 albumin drug binding affinity units, respectively. {F}urthermore,
the models were evaluated by a 10 compound external test set, yielding
{R}2 of 0.71 and 0.89 and rms error of 0.430 and 0.222. {T}he specific
information described by the heuristic linear model could give some
insights into the factors that are likely to govern the binding affinity
of the compounds and be used as an aid to the drug design process;
however, the prediction results of the nonlinear {SVM} model seem
to be better than that of the {HM}.},
doi = {10.1021/ci049820b},
pdf = {../local/Xue2004QSAR.pdf},
file = {Xue2004QSAR.pdf:local/Xue2004QSAR.pdf:PDF},
keywords = {biosvm},
url = {http://dx.doi.org/10.1021/ci049820b}
}
@article{Xue2004Study,
author = {C. X. Xue and R. S. Zhang and M. C. Liu and Z. D. Hu and B. T. Fan},
title = {Study of the quantitative structure-mobility relationship of carboxylic
acids in capillary electrophoresis based on support vector machines.},
journal = {J {C}hem {I}nf {C}omput {S}ci},
year = {2004},
volume = {44},
pages = {950-7},
number = {3},
abstract = {The support vector machines ({SVM}), as a novel type of learning machine,
were used to develop a quantitative structure-mobility relationship
({QSMR}) model of 58 aliphatic and aromatic carboxylic acids based
on molecular descriptors calculated from the structure alone. {M}ultiple
linear regression ({MLR}) and radial basis function neural networks
({RBFNN}s) were also utilized to construct the linear and the nonlinear
model to compare with the results obtained by {SVM}. {T}he root-mean-square
errors in absolute mobility predictions for the whole data set given
by {MLR}, {RBFNN}s, and {SVM} were 1.530, 1.373, and 0.888 mobility
units (10(-5) cm(2) {S}(-1) {V}(-1)), respectively, which indicated
that the prediction result agrees well with the experimental values
of these compounds and also revealed the superiority of {SVM} over
{MLR} and {RBFNN}s models for the prediction of the absolute mobility
of carboxylic acids. {M}oreover, the models we proposed could also
provide some insight into what structural features are related to
the absolute mobility of aliphatic and aromatic carboxylic acids.},
doi = {10.1021/ci034280o},
pdf = {../local/Xue2004Study.pdf},
file = {Xue2004Study.pdf:local/Xue2004Study.pdf:PDF},
keywords = {biosvm},
url = {http://dx.doi.org/10.1021/ci034280o}
}
@article{Xue2000Molecular,
author = {L. Xue and J. Bajorath},
title = {Molecular descriptors in chemoinformatics, computational combinatorial
chemistry, and virtual screening.},
journal = {Comb. {C}hem. {H}igh. {T}hroughput {S}creen.},
year = {2000},
volume = {3},
pages = {363--372},
number = {5},
month = {Oct},
abstract = {Many contemporary applications in computer-aided drug discovery and
chemoinformatics depend on representations of molecules by descriptors
that capture their structural characteristics and properties. {S}uch
applications include, among others, diversity analysis, library design,
and virtual screening. {H}undreds of molecular descriptors have been
reported in the literature, ranging from simple bulk properties to
elaborate three-dimensional formulations and complex molecular fingerprints,
which sometimes consist of thousands of bit positions. {K}nowledge-based
selection of descriptors that are suitable for specific applications
is an important task in chemoinformatics research. {I}f descriptors
are to be selected on rational grounds, rather than guesses or chemical
intuition, detailed evaluation of their performance is required.
{A} number of studies have been reported that investigate the performance
of molecular descriptors in specific applications and/or introduce
novel types of descriptors. {P}rogress made in this area is reviewed
here in the context of other computational developments in combinatorial
chemistry and compound screening.},
keywords = {chemoinformatics},
owner = {mahe},
pmid = {11032954},
timestamp = {2006.02.03}
}
@article{Xue2001Fingerprint,
author = {L. Xue and F. L. Stahura and J. W. Godden and J. Bajorath},
title = {{F}ingerprint scaling increases the probability of identifying molecules
with similar activity in virtual screening calculations.},
journal = {J Chem Inf Comput Sci},
year = {2001},
volume = {41},
pages = {746--753},
number = {3},
abstract = {Results of systematic virtual screening calculations using a structural
key-type fingerprint are reported for compounds belonging to 14 activity
classes added to randomly selected synthetic molecules. For each
class, a fingerprint profile was calculated to monitor the relative
occupancy of fingerprint bit positions. Consensus bit patterns were
determined consisting of all bits that were always set on in compounds
belonging to a specific activity class. In virtual screening calculations,
scale factors were applied to each consensus bit position in fingerprints
of query molecules. This technique, called "fingerprint scaling",
effectively increases the weight of consensus bit positions in fingerprint
comparisons. Although overall prediction accuracy was satisfactory
using unscaled calculations, scaling significantly increased the
number of correct predictions but only slightly increased the rate
of false positives. These observations suggest that fingerprint scaling
is an attractive approach to increase the probability of identifying
molecules with similar activity by virtual screening. It requires
the availability of a series of related compounds and can be easily
applied to any keyed fingerprint representation that associates bit
positions with specific molecular features.},
keywords = {16S, Algae, Algorithms, Animals, Archaeal, Automation, Bacteria, Biodiversity,
Chemical, Colorimetry, Computational Biology, Computer Terminals,
DNA, DNA Fingerprinting, Daphnia, Databases, Ecosystem, Euryarchaeota,
Factual, Fresh Water, Hazardous Substances, Humans, Information Storage
and Retrieval, Methane, Models, Non-U.S. Gov't, Oxidoreductases,
Perciformes, Photic Stimulation, Photometry, Polymorphism, Quantitative
Structure-Activity Relationship, RNA, Research Support, Restriction
Fragment Length, Ribosomal, Seasons, Soil Microbiology, Spain, Sulfur,
Theoretical, Time Factors, Toxicity Tests, Water Microbiology, Water
Pollutants, 11410055},
owner = {mahe},
pii = {ci000311t},
pmid = {11410055},
timestamp = {2006.09.03}
}
@article{Xue2001Mini-fingerprints,
author = {L. Xue and F. L. Stahura and J. W. Godden and J. Bajorath},
title = {{M}ini-fingerprints detect similar activity of receptor ligands previously
recognized only by three-dimensional pharmacophore-based methods.},
journal = {J Chem Inf Comput Sci},
year = {2001},
volume = {41},
pages = {394--401},
number = {2},
abstract = {Mini-fingerprints (MFPs) are short binary bit string representations
of molecular structure and properties, composed of few selected two-dimensional
(2D) descriptors and a number of structural keys. MFPs were specifically
designed to recognize compounds with similar activity. Here we report
that MFPs are capable of detecting similar activities of some druglike
molecules, including endothelin A antagonists and alpha(1)-adrenergic
receptor ligands, the recognition of which was previously thought
to depend on the use of multiple point three-dimensional (3D) pharmacophore
methods. Thus, in these cases, MFPs and pharmacophore fingerprints
produce similar results, although they define, in terms of their
complexity, opposite ends of the spectrum of methods currently used
to study molecular similarity or diversity. For each of the studied
compound classes, comparison of MFP bit settings identified a consensus
or signature pattern. Scaling factors can be applied to these bits
in order to increase the probability of finding compounds with similar
activity by virtual screening.},
keywords = {Adrenergic, Angiotensin II, Cell Surface, Combinatorial Chemistry
Techniques, Databases, Drug Evaluation, Endothelins, Environmental
Pollutants, Factual, Information Management, Ligands, Molecular Structure,
Pharmaceutical Preparations, Platelet Glycoprotein GPIIb-IIIa Complex,
Preclinical, Receptors, Serine Proteinase Inhibitors, Structure-Activity
Relationship, User-Computer Interface, alpha-1, 11277728},
owner = {mahe},
pii = {ci000305x},
pmid = {11277728},
timestamp = {2006.08.22}
}
@inproceedings{Xue2007The,
author = {Xue, Ya and Dunson, David and Carin, Lawrence},
title = {The matrix stick-breaking process for flexible multi-task learning},
booktitle = {ICML '07: Proceedings of the 24th international conference on Machine
learning},
year = {2007},
pages = {1063--1070},
address = {New York, NY, USA},
month = {June},
publisher = {ACM},
doi = {http://doi.acm.org/10.1145/1273496.1273630},
isbn = {978-1-59593-793-3},
location = {Corvalis, Oregon}
}
@article{Xue2004Effect,
author = {Y. Xue and Z. R. Li and C. W. Yap and L. Z. Sun and X. Chen and Y.
Z. Chen},
title = {Effect of molecular descriptor feature selection in support vector
machine classification of pharmacokinetic and toxicological properties
of chemical agents.},
journal = {J {C}hem {I}nf {C}omput {S}ci},
year = {2004},
volume = {44},
pages = {1630-8},
number = {5},
abstract = {Statistical-learning methods have been developed for facilitating
the prediction of pharmacokinetic and toxicological properties of
chemical agents. {T}hese methods employ a variety of molecular descriptors
to characterize structural and physicochemical properties of molecules.
{S}ome of these descriptors are specifically designed for the study
of a particular type of properties or agents, and their use for other
properties or agents might generate noise and affect the prediction
accuracy of a statistical learning system. {T}his work examines to
what extent the reduction of this noise can improve the prediction
accuracy of a statistical learning system. {A} feature selection
method, recursive feature elimination ({RFE}), is used to automatically
select molecular descriptors for support vector machines ({SVM})
prediction of {P}-glycoprotein substrates ({P}-gp), human intestinal
absorption of molecules ({HIA}), and agents that cause torsades de
pointes ({T}d{P}), a rare but serious side effect. {RFE} significantly
reduces the number of descriptors for each of these properties thereby
increasing the computational speed for their classification. {T}he
{SVM} prediction accuracies of {P}-gp and {HIA} are substantially
increased and that of {T}d{P} remains unchanged by {RFE}. {T}hese
prediction accuracies are comparable to those of earlier studies
derived from a selective set of descriptors. {O}ur study suggests
that molecular feature selection is useful for improving the speed
and, in some cases, the accuracy of statistical learning methods
for the prediction of pharmacokinetic and toxicological properties
of chemical agents.},
doi = {10.1021/ci049869h},
pdf = {../local/Xue2004Effect.pdf},
file = {Xue2004Effect.pdf:local/Xue2004Effect.pdf:PDF},
keywords = {biosvm},
url = {http://dx.doi.org/10.1021/ci049869h}
}
@article{Xue2007Multi-task,
author = {Ya Xue and Xuejun Liao and Lawrence Carin and Balaji Krishnapuram},
title = {Multi-task learning for classification with dirichlet process priors},
journal = {Journal of Machine Learning Research},
year = {2007},
volume = {8},
pages = {2007},
month = {January}
}
@article{Xue2004Prediction,
author = {Y. Xue and C. W. Yap and L. Z. Sun and Z. W. Cao and J. F. Wang and
Y. Z. Chen},
title = {Prediction of {P}-glycoprotein substrates by a support vector machine
approach.},
journal = {J {C}hem {I}nf {C}omput {S}ci},
year = {2004},
volume = {44},
pages = {1497-505},
number = {4},
abstract = {P-glycoproteins ({P}-gp) actively transport a wide variety of chemicals
out of cells and function as drug efflux pumps that mediate multidrug
resistance and limit the efficacy of many drugs. {M}ethods for facilitating
early elimination of potential {P}-gp substrates are useful for facilitating
new drug discovery. {A} computational ensemble pharmacophore model
has recently been used for the prediction of {P}-gp substrates with
a promising accuracy of 63\%. {I}t is desirable to extend the prediction
range beyond compounds covered by the known pharmacophore models.
{F}or such a purpose, a machine learning method, support vector machine
({SVM}), was explored for the prediction of {P}-gp substrates. {A}
set of 201 chemical compounds, including 116 substrates and 85 nonsubstrates
of {P}-gp, was used to train and test a {SVM} classification system.
{T}his {SVM} system gave a prediction accuracy of at least 81.2\%
for {P}-gp substrates based on two different evaluation methods,
which is substantially improved against that obtained from the multiple-pharmacophore
model. {T}he prediction accuracy for nonsubstrates of {P}-gp is 79.2\%
using 5-fold cross-validation. {T}hese accuracies are slightly better
than those obtained from other statistical classification methods,
including k-nearest neighbor (k-{NN}), probabilistic neural networks
({PNN}), and {C}4.5 decision tree, that use the same sets of data
and molecular descriptors. {O}ur study indicates the potential of
{SVM} in facilitating the prediction of {P}-gp substrates.},
doi = {10.1021/ci049971e},
pdf = {../local/Xue2004Prediction.pdf},
file = {Xue2004Prediction.pdf:local/Xue2004Prediction.pdf:PDF},
keywords = {biosvm},
url = {http://dx.doi.org/10.1021/ci049971e}
}
@article{Yabuki2005GRIFFIN,
author = {Yabuki, Y. and Muramatsu, T. and Hirokawa, T. and Mukai, H. and Suwa,
M.},
title = {G{RIFFIN}: a system for predicting {GPCR}-{G}-protein coupling selectivity
using a support vector machine and a hidden {M}arkov model.},
journal = {Nucleic {A}cids {R}es.},
year = {2005},
volume = {33},
pages = {W148-53},
number = {Web Server issue},
month = {Jul},
abstract = {We describe a novel system, {GRIFFIN} ({G}-protein and {R}eceptor
{I}nteraction {F}eature {F}inding {IN}strument), that predicts {G}-protein
coupled receptor ({GPCR}) and {G}-protein coupling selectivity based
on a support vector machine ({SVM}) and a hidden {M}arkov model ({HMM})
with high sensitivity and specificity. {B}ased on our assumption
that whole structural segments of ligands, {GPCR}s and {G}-proteins
are essential to determine {GPCR} and {G}-protein coupling, various
quantitative features were selected for ligands, {GPCR}s and {G}-protein
complex structures, and those parameters that are the most effective
in selecting {G}-protein type were used as feature vectors in the
{SVM}. {T}he main part of {GRIFFIN} includes a hierarchical {SVM}
classifier using the feature vectors, which is useful for {C}lass
{A} {GPCR}s, the major family. {F}or the opsins and olfactory subfamilies
of {C}lass {A} and other minor families ({C}lasses {B}, {C}, frizzled
and smoothened), the binding {G}-protein is predicted with high accuracy
using the {HMM}. {A}pplying this system to known {GPCR} sequences,
each binding {G}-protein is predicted with high sensitivity and specificity
(>85\% on average). {GRIFFIN} (http://griffin.cbrc.jp/) is freely
available and allows users to easily execute this reliable prediction
of {G}-proteins.},
doi = {10.1093/nar/gki495},
pdf = {../local/Yabuki2005GRIFFIN.pdf},
file = {Yabuki2005GRIFFIN.pdf:local/Yabuki2005GRIFFIN.pdf:PDF},
keywords = {biosvm},
pii = {33/suppl_2/W148},
url = {http://dx.doi.org/10.1093/nar/gki495}
}
@article{Yaffe2011Probabilistic,
author = {Yaffe, E. and Tanay, A.},
title = {Probabilistic modeling of {Hi-C} contact maps eliminates systematic
biases to characterize global chromosomal architecture},
journal = {Nat. Genet.},
year = {2011},
volume = {43},
pages = {1059--1065},
number = {11},
abstract = {Hi-C experiments measure the probability of physical proximity between
pairs of chromosomal loci on a genomic scale. We report on several
systematic biases that substantially affect the Hi-C experimental
procedure, including the distance between restriction sites, the
GC content of trimmed ligation junctions and sequence uniqueness.
To address these biases, we introduce an integrated probabilistic
background model and develop algorithms to estimate its parameters
and renormalize Hi-C data. Analysis of corrected human lymphoblast
contact maps provides genome-wide evidence for interchromosomal aggregation
of active chromatin marks, including DNase-hypersensitive sites and
transcriptionally active foci. We observe extensive long-range (up
to 400 kb) cis interactions at active promoters and derive asymmetric
contact profiles next to transcription start sites and CTCF binding
sites. Clusters of interacting chromosomal domains suggest physical
separation of centromere-proximal and centromere-distal regions.
These results provide a computational basis for the inference of
chromosomal architectures from Hi-C experiments.},
doi = {10.1038/ng.947},
pdf = {../local/Yaffe2011Probabilistic.pdf},
file = {Yaffe2011Probabilistic.pdf:Yaffe2011Probabilistic.pdf:PDF},
issn = {1061-4036},
keywords = {hic, ngs},
owner = {nelle},
url = {http://dx.doi.org/10.1038/ng.947},
urldate = {2012-01-11}
}
@article{Yakoby2008combinatorial,
author = {Yakoby, Nir and Bristow, Christopher A. and Gong, Danielle and Schafer,
Xenia and Lembong, Jessica and Zartman, Jeremiah J. and Halfon, Marc
S. and Schüpbach, Trudi and Shvartsman, Stanislav Y.},
title = {A combinatorial code for pattern formation in Drosophila oogenesis.},
journal = {Dev Cell},
year = {2008},
volume = {15},
pages = {725--737},
number = {5},
month = {Nov},
abstract = {Two-dimensional patterning of the follicular epithelium in Drosophila
oogenesis is required for the formation of three-dimensional eggshell
structures. Our analysis of a large number of published gene expression
patterns in the follicle cells suggests that they follow a simple
combinatorial code based on six spatial building blocks and the operations
of union, difference, intersection, and addition. The building blocks
are related to the distribution of inductive signals, provided by
the highly conserved epidermal growth factor receptor and bone morphogenetic
protein signaling pathways. We demonstrate the validity of the code
by testing it against a set of patterns obtained in a large-scale
transcriptional profiling experiment. Using the proposed code, we
distinguish 36 distinct patterns for 81 genes expressed in the follicular
epithelium and characterize their joint dynamics over four stages
of oogenesis. The proposed combinatorial framework allows systematic
analysis of the diversity and dynamics of two-dimensional transcriptional
patterns and guides future studies of gene regulation.},
doi = {10.1016/j.devcel.2008.09.008},
pdf = {../local/Yakoby2008combinatorial.pdf},
file = {Yakoby2008combinatorial.pdf:Yakoby2008combinatorial.pdf:PDF},
institution = {Lewis-Sigler Institute for Integrative Genomics and Department of
Chemical Engineering, Princeton University, Princeton, NJ 08544,
USA.},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {S1534-5807(08)00390-0},
pmid = {19000837},
timestamp = {2012.10.23},
url = {http://dx.doi.org/10.1016/j.devcel.2008.09.008}
}
@article{Yamada2005Accelerated,
author = {Yamada, T. and Morishita, S.},
title = {Accelerated off-target search algorithm for si{RNA}.},
journal = {Bioinformatics},
year = {2005},
volume = {21},
pages = {1316-24},
number = {8},
month = {Apr},
abstract = {M{OTIVATION}: {D}esigning highly effective short interfering {RNA}
(si{RNA}) sequences with maximum target-specificity for mammalian
{RNA} interference ({RNA}i) is one of the hottest topics in molecular
biology. {T}he relationship between si{RNA} sequences and {RNA}i
activity has been studied extensively to establish rules for selecting
highly effective sequences. {H}owever, there is a pressing need to
compute si{RNA} sequences that minimize off-target silencing effects
efficiently and to match any non-targeted sequences with mismatches.
{RESULTS}: {T}he enumeration of potential cross-hybridization candidates
is non-trivial, because si{RNA} sequences are short, ca. 19 nt in
length, and at least three mismatches with non-targets are required.
{W}ith at least three mismatches, there are typically four or five
contiguous matches, so that a {BLAST} search frequently overlooks
off-target candidates. {B}y contrast, existing accurate approaches
are expensive to execute; thus we need to develop an accurate, efficient
algorithm that uses seed hashing, the pigeonhole principle, and combinatorics
to identify mismatch patterns. {T}ests show that our method can list
potential cross-hybridization candidates for any si{RNA} sequence
of selected human gene rapidly, outperforming traditional methods
by orders of magnitude in terms of computational performance. {AVAILABILITY}:
http://design.{RNA}i.jp {CONTACT}: yamada@cb.k.u-tokyo.ac.jp.},
doi = {10.1093/bioinformatics/bti155},
keywords = {sirna},
pii = {bti155},
url = {http://dx.doi.org/10.1093/bioinformatics/bti155}
}
@article{Yamanishi2008Prediction,
author = {Yamanishi, Y. and Araki, M. and Gutteridge, A. and Honda, W. and
Kanehisa, M.},
title = {Prediction of drug-target interaction networks from the integration
of chemical and genomic spaces},
journal = {Bioinformatics},
year = {2008},
volume = {24},
pages = {i232--i240},
number = {13},
month = {Jul},
abstract = {MOTIVATION: The identification of interactions between drugs and target
proteins is a key area in genomic drug discovery. Therefore, there
is a strong incentive to develop new methods capable of detecting
these potential drug-target interactions efficiently. RESULTS: In
this article, we characterize four classes of drug-target interaction
networks in humans involving enzymes, ion channels, G-protein-coupled
receptors (GPCRs) and nuclear receptors, and reveal significant correlations
between drug structure similarity, target sequence similarity and
the drug-target interaction network topology. We then develop new
statistical methods to predict unknown drug-target interaction networks
from chemical structure and genomic sequence information simultaneously
on a large scale. The originality of the proposed method lies in
the formalization of the drug-target interaction inference as a supervised
learning problem for a bipartite graph, the lack of need for 3D structure
information of the target proteins, and in the integration of chemical
and genomic spaces into a unified space that we call 'pharmacological
space'. In the results, we demonstrate the usefulness of our proposed
method for the prediction of the four classes of drug-target interaction
networks. Our comprehensively predicted drug-target interaction networks
enable us to suggest many potential drug-target interactions and
to increase research productivity toward genomic drug discovery.
AVAILABILITY: Softwares are available upon request. SUPPLEMENTARY
INFORMATION: Datasets and all prediction results are available at
http://web.kuicr.kyoto-u.ac.jp/supp/yoshi/drugtarget/.},
doi = {10.1093/bioinformatics/btn162},
pdf = {../local/Yamanishi2008Prediction.pdf},
file = {Yamanishi2008Prediction.pdf:Yamanishi2008Prediction.pdf:PDF},
institution = {Bioinformatics Center, Institute for Chemical Research, Kyoto University,
Gokasho, Uji, Kyoto 611-0011, Japan. Yoshihiro.Yamanishi@ensmp.fr},
owner = {jp},
pii = {btn162},
pmid = {18586719},
timestamp = {2008.12.24},
url = {http://dx.doi.org/10.1093/bioinformatics/btn162}
}
@article{Yamanishi2007Glycan,
author = {Yamanishi, Y. and Bach, F. and Vert, J.-P.},
title = {Glycan classification with tree kernels},
journal = {Bioinformatics},
year = {2007},
volume = {23},
pages = {1211--1216},
number = {10},
month = {May},
abstract = {MOTIVATION: Glycans are covalent assemblies of sugar that play crucial
roles in many cellular processes. Recently, comprehensive data about
the structure and function of glycans have been accumulated, therefore
the need for methods and algorithms to analyze these data is growing
fast. RESULTS: This article presents novel methods for classifying
glycans and detecting discriminative glycan motifs with support vector
machines (SVM). We propose a new class of tree kernels to measure
the similarity between glycans. These kernels are based on the comparison
of tree substructures, and take into account several glycan features
such as the sugar type, the sugar bound type or layer depth. The
proposed methods are tested on their ability to classify human glycans
into four blood components: leukemia cells, erythrocytes, plasma
and serum. They are shown to outperform a previously published method.
We also applied a feature selection approach to extract glycan motifs
which are characteristic of each blood component. We confirmed that
some leukemia-specific glycan motifs detected by our method corresponded
to several results in the literature. AVAILABILITY: Softwares are
available upon request. SUPPLEMENTARY INFORMATION: Datasets are available
at the following website: http://web.kuicr.kyoto-u.ac.jp/supp/yoshi/glycankernel/},
doi = {10.1093/bioinformatics/btm090},
institution = {Bioinformatics Center, Institute for Chemical Research, Kyoto University,
Gokasho, Uji, Kyoto 611-0011, Japan. yoshi@kuicr.kyoto-u.ac.jp},
owner = {jp},
pii = {btm090},
pmid = {17344232},
timestamp = {2008.11.30},
url = {http://dx.doi.org/10.1093/bioinformatics/btm090}
}
@incollection{Yamanishi2004Heterogeneous,
author = {Yamanishi, Y. and Vert, J.-P. and Kanehisa, M.},
title = {Heterogeneous data comparison and gene selection with kernel canonical
correlation analysis},
booktitle = {Kernel {M}ethods in {C}omputational {B}iology},
publisher = {MIT Press},
year = {2004},
editor = {Schölkopf, B. and Tsuda, K. and Vert, J.P.},
pages = {209-230},
pdf = {../local/heterogeneous.pdf:http\://cg.ensmp.fr/~vert/publi/04kmcbbook/heterogeneous.pdf:PDF;heterogeneous.pdf:http\},
file = {heterogeneous.pdf:http\://cg.ensmp.fr/~vert/publi/04kmcbbook/heterogeneous.pdf:PDF;heterogeneous.pdf:http\://cg.ensmp.fr/~vert/publi/04kmcbbook/heterogeneous.pdf:PDF},
keywords = {biosvm},
owner = {vert}
}
@article{Yamanishi2005Supervised,
author = {Yamanishi, Y. and Vert, J.-P. and Kanehisa, M.},
title = {Supervised enzyme network inference from the integration of genomic
data and chemical information},
journal = {Bioinformatics},
year = {2005},
volume = {21},
pages = {i468-i477},
abstract = {Motivation: {T}he metabolic network is an important biological network
which relates enzyme proteins and chemical compounds. {A} large number
of metabolic pathways remain unknown nowadays, and many enzymes are
missing even in known metabolic pathways. {T}here is, therefore,
an incentive to develop methods to reconstruct the unknown parts
of the metabolic network and to identify genes coding for missing
enzymes. {R}esults: {T}his paper presents new methods to infer enzyme
networks from the integration of multiple genomic data and chemical
information, in the framework of supervised graph inference. {T}he
originality of the methods is the introduction of chemical compatibility
as a constraint for refining the network predicted by the network
inference engine. {T}he chemical compatibility between two enzymes
is obtained automatically from the information encoded by their {E}nzyme
{C}ommission ({EC}) numbers. {T}he proposed methods are tested and
compared on their ability to infer the enzyme network of the yeast
{S}accharomyces cerevisiae from four datasets for enzymes with assigned
{EC} numbers: gene expression data, protein localization data, phylogenetic
profiles and chemical compatibility information. {I}t is shown that
the prediction accuracy of the network reconstruction consistently
improves owing to the introduction of chemical constraints, the use
of a supervised approach and the weighted integration of multiple
datasets. {F}inally, we conduct a comprehensive prediction of a global
enzyme network consisting of all enzyme candidate proteins of the
yeast to obtain new biological findings.},
doi = {10.1093/bioinformatics/bti1012},
pdf = {../local/Yamanishi2005Supervised.pdf},
file = {Yamanishi2005Supervised.pdf:local/Yamanishi2005Supervised.pdf:PDF},
keywords = {biosvm},
url = {http://dx.doi.org/10.1093/bioinformatics/bti1012}
}
@article{Yamanishi2004Protein,
author = {Yamanishi, Y. and Vert, J.-P. and Kanehisa, M.},
title = {Protein network inference from multiple genomic data: a supervised
approach},
journal = {Bioinformatics},
year = {2004},
volume = {20},
pages = {i363-i370},
abstract = {Motivation: {A}n increasing number of observations support the hypothesis
that most biological functions involve the interactions between many
proteins, and that the complexity of living systems arises as a result
of such interactions. {I}n this context, the problem of inferring
a global protein network for a given organism, using all available
genomic data about the organism, is quickly becoming one of the main
challenges in current computational biology. {R}esults: {T}his paper
presents a new method to infer protein networks from multiple types
of genomic data. {B}ased on a variant of kernel canonical correlation
analysis, its originality is in the formalization of the protein
network inference problem as a supervised learning problem, and in
the integration of heterogeneous genomic data within this framework.
{W}e present promising results on the prediction of the protein network
for the yeast {S}accharomyces cerevisiae from four types of widely
available data: gene expressions, protein interactions measured by
yeast two-hybrid systems, protein localizations in the cell and protein
phylogenetic profiles. {T}he method is shown to outperform other
unsupervised protein network inference methods. {W}e finally conduct
a comprehensive prediction of the protein network for all proteins
of the yeast, which enables us to propose protein candidates for
missing enzymes in a biosynthesis pathway. {A}vailability: {S}oftwares
are available upon request.},
pdf = {../local/Yamanishi2004Protein.pdf},
file = {Yamanishi2004Protein.pdf:local/Yamanishi2004Protein.pdf:PDF},
keywords = {biosvm},
owner = {vert},
url = {http://bioinformatics.oupjournals.org/cgi/reprint/19/suppl\_1/i323}
}
@article{Yamanishi2003Extraction,
author = {Yamanishi, Y. and Vert, J.-P. and Nakaya, A. and Kanehisa, M.},
title = {Extraction of correlated gene clusters from multiple genomic data
by generalized kernel canonical correlation analysis},
journal = {Bioinformatics},
year = {2003},
volume = {19},
pages = {i323-i330},
number = {Suppl. 1},
abstract = {Motivation: {A} major issue in computational biology is the reconstruction
of pathways from several genomic datasets, such as expression data,
protein interaction data and phylogenetic profiles. {A}s a first
step toward this goal, it is important to investigate the amount
of correlation which exists between these data. {R}esults: {T}hese
methods are successfully tested on their ability to recognize operons
in the {E}scherichia coli genome, from the comparison of three datasets
corresponding to functional relationships between genes in metabolic
pathways, geometrical relationships along the chromosome, and co-expression
relationships as observed by gene expression data. {C}ontact: yoshi@kuicr.kyoto-u.ac.jp},
pdf = {../local/Yamanishi2003Extraction.pdf},
file = {Yamanishi2003Extraction.pdf:local/Yamanishi2003Extraction.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://bioinformatics.oupjournals.org/cgi/content/abstract/19/suppl_1/i323}
}
@article{Yan2004two-stage,
author = {Yan, C. and Dobbs, D. and Honavar, V.},
title = {A two-stage classifier for identification of protein-protein interface
residues},
journal = {Bioinformatics},
year = {2004},
volume = {20},
pages = {i371-i378},
number = {Suppl. 1},
abstract = {Motivation: {T}he ability to identify protein-protein interaction
sites and to detect specific amino acid residues that contribute
to the specificity and affinity of protein interactions has important
implications for problems ranging from rational drug design to analysis
of metabolic and signal transduction networks. {R}esults: {W}e have
developed a two-stage method consisting of a support vector machine
({SVM}) and a {B}ayesian classifier for predicting surface residues
of a protein that participate in protein-protein interactions. {T}his
approach exploits the fact that interface residues tend to form clusters
in the primary amino acid sequence. {O}ur results show that the proposed
two-stage classifier outperforms previously published sequence-based
methods for predicting interface residues. {W}e also present results
obtained using the two-stage classifier on an independent test set
of seven {CAPRI} ({C}ritical {A}ssessment of {PR}edicted {I}nteractions)
targets. {T}he success of the predictions is validated by examining
the predictions in the context of the three-dimensional structures
of protein complexes. {S}upplementary information: http://www.public.iastate.edu/~chhyan/{ISMB}2004/list.html},
pdf = {../local/Yan2004two-stage.pdf},
file = {Yan2004two-stage.pdf:local/Yan2004two-stage.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://bioinformatics.oupjournals.org/cgi/content/abstract/20/suppl_1/i371}
}
@article{Yan2004Identification,
author = {Yan, C. and Honavar, V. and Dobbs, D.},
title = {Identification of interface residues in protease-inhibitor and antigen-antibody
complexes: a support vector machine},
journal = {Neural {C}omput. \& {A}pplic.},
year = {2004},
volume = {13},
pages = {123-129},
doi = {10.1007/s00521-004-0414-3},
pdf = {../local/Yan2004Identification.pdf},
file = {Yan2004Identification.pdf:local/Yan2004Identification.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert}
}
@article{Yan2007Determining,
author = {Yan, Mingjin and Ye, Keying},
title = {Determining the number of clusters using the weighted gap statistic.},
journal = {Biometrics},
year = {2007},
volume = {63},
pages = {1031--1037},
number = {4},
month = {Dec},
abstract = {Estimating the number of clusters in a data set is a crucial step
in cluster analysis. In this article, motivated by the gap method
(Tibshirani, Walther, and Hastie, 2001, Journal of the Royal Statistical
Society B63, 411-423), we propose the weighted gap and the difference
of difference-weighted (DD-weighted) gap methods for estimating the
number of clusters in data using the weighted within-clusters sum
of errors: a measure of the within-clusters homogeneity. In addition,
we propose a "multilayer" clustering approach, which is shown to
be more accurate than the original gap method, particularly in detecting
the nested cluster structure of the data. The methods are applicable
when the input data contain continuous measurements and can be used
with any clustering method. Simulation studies and real data are
investigated and compared among these proposed methods as well as
with the original gap method.},
doi = {10.1111/j.1541-0420.2007.00784.x},
institution = {Medtronic Sofamor Danek, 1800 Pyramid Place, Memphis, Tennessee 38132,
USA. mingjin.yan@medtronic.com},
keywords = {Algorithms; Biometry, methods; Cluster Analysis; Computer Simulation;
Data Interpretation, Statistical; Models, Biological; Models, Statistical;
Pattern Recognition, Automated, methods},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {BIOM784},
pmid = {17425640},
timestamp = {2011.12.29},
url = {http://dx.doi.org/10.1111/j.1541-0420.2007.00784.x}
}
@inproceedings{Yan2007Machine,
author = {Yan, R. J. and Ling, C. X.},
title = {Machine learning for stock selection},
booktitle = {KDD '07 Proceedings of the 13th ACM SIGKDD international conference
on Knowledge discovery and data mining},
year = {2007},
pages = {1038--1042},
address = {New York, NY, USA},
publisher = {ACM Press},
abstract = {In this paper, we propose a new method called Prototype Ranking (PR)
designed for the stock selection problem. PR takes into account the
huge size of real-world stock data and applies a modified competitive
learning technique to predict the ranks of stocks. The primary target
of PR is to select the top performing stocks among many ordinary
stocks. PR is designed to perform the learning and testing in a noisy
stocks sample set where the top performing stocks are usually the
minority. The performance of PR is evaluated by a trading simulation
of the real stock data. Each week the stocks with the highest predicted
ranks are chosen to construct a portfolio. In the period of 1978-2004,
PR's portfolio earns a much higher average return as well as a higher
risk-adjusted return than Cooper's method, which shows that the PR
method leads to a clear profit improvement.},
doi = {10.1145/1281192.1281307},
pdf = {../local/Yan2007Machine.pdf},
file = {Yan2007Machine.pdf:Yan2007Machine.pdf:PDF},
owner = {jp},
timestamp = {2011.04.08},
url = {http://dx.doi.org/10.1145/1281192.1281307}
}
@inproceedings{Yang2010Fast,
author = {Yang, A. and Ganesh, A. and Sastry, S. and Ma, Y.},
title = {Fast l1-minimization algorithms and an application in robust face
recognition: a review},
booktitle = {Proceedings of the International Conference on Image Processing},
year = {2010},
pages = {1849--1852}
}
@article{Yang1999Minimax,
author = {Yang, Y.},
title = {Minimax nonparametric classification --- {P}art {I}: rates of convergence},
journal = {I{EEE} {T}rans. {I}nform. {T}heory},
year = {1999},
volume = {45},
pages = {2271-2284},
number = {7},
doi = {10.1109/18.796368},
pdf = {../local/Yang1999Minimax.pdf},
file = {Yang1999Minimax.pdf:local/Yang1999Minimax.pdf:PDF},
owner = {jeanphilippevert},
url = {http://dx.doi.org/10.1109/18.796368}
}
@article{Yang2002Normalization,
author = {Yang, Y. H. and Dudoit, S. and Luu, P. and Lin, D. M. and Peng, V.
and Ngai, J. and Speed, T. P.},
title = {Normalization for cDNA microarray data: a robust composite method
addressing single and multiple slide systematic variation.},
journal = {Nucleic Acids Res.},
year = {2002},
volume = {30},
number = {4},
month = {February},
abstract = {There are many sources of systematic variation in cDNA microarray
experiments which affect the measured gene expression levels (e.g.
differences in labeling efficiency between the two fluorescent dyes).
The term normalization refers to the process of removing such variation.
A constant adjustment is often used to force the distribution of
the intensity log ratios to have a median of zero for each slide.
However, such global normalization approaches are not adequate in
situations where dye biases can depend on spot overall intensity
and/or spatial location within the array. This article proposes normalization
methods that are based on robust local regression and account for
intensity and spatial dependence in dye biases for different types
of cDNA microarray experiments. The selection of appropriate controls
for normalization is discussed and a novel set of controls (microarray
sample pool, MSP) is introduced to aid in intensity-dependent normalization.
Lastly, to allow for comparisons of expression levels across slides,
a robust method based on maximum likelihood estimation is proposed
to adjust for scale differences among slides.},
address = {Department of Statistics, Helen Wills Neuroscience Institute, University
of California, Berkeley, CA 94720-3860, USA.},
doi = {10.1093/nar/30.4.e15},
issn = {1362-4962},
url = {http://dx.doi.org/10.1093/nar/30.4.e15}
}
@article{Yang2002Design,
author = {Yee Hwa Yang and Terry Speed},
title = {Design issues for cDNA microarray experiments.},
journal = {Nat Rev Genet},
year = {2002},
volume = {3},
pages = {579--588},
number = {8},
month = {Aug},
doi = {10.1038/nrg863},
institution = {Department of Statistics and Program in Biostatistics, 367 Evans
Hall, 3860, University of California, Berkeley, California 94720-3860,
USA.},
keywords = {Gene Expression Profiling; Gene Expression Regulation; Humans; Oligonucleotide
Array Sequence Analysis, methods; Research Design; Time Factors},
language = {eng},
medline-pst = {ppublish},
owner = {phupe},
pii = {nrg863},
pmid = {12154381},
timestamp = {2011.04.08},
url = {http://dx.doi.org/10.1038/nrg863}
}
@article{Yang2005Prediction,
author = {Zheng Rong Yang},
title = {Prediction of caspase cleavage sites using {B}ayesian bio-basis function
neural networks.},
journal = {Bioinformatics},
year = {2005},
volume = {21},
pages = {1831-7},
number = {9},
month = {May},
abstract = {M{OTIVATION}: {A}poptosis has drawn the attention of researchers because
of its importance in treating some diseases through finding a proper
way to block or slow down the apoptosis process. {H}aving understood
that caspase cleavage is the key to apoptosis, we find novel methods
or algorithms are essential for studying the specificity of caspase
cleavage activity and this helps the effective drug design. {A}s
bio-basis function neural networks have proven to outperform some
conventional neural learning algorithms, there is a motivation, in
this study, to investigate the application of bio-basis function
neural networks for the prediction of caspase cleavage sites. {RESULTS}:
{T}hirteen protein sequences with experimentally determined caspase
cleavage sites were downloaded from {NCBI}. {B}ayesian bio-basis
function neural networks are investigated and the comparisons with
single-layer perceptrons, multilayer perceptrons, the original bio-basis
function neural networks and support vector machines are given. {T}he
impact of the sliding window size used to generate sub-sequences
for modelling on prediction accuracy is studied. {T}he results show
that the {B}ayesian bio-basis function neural network with two {G}aussian
distributions for model parameters (weights) performed the best and
the highest prediction accuracy is 97.15 +/- 1.13\%. {AVAILABILITY}:
{T}he package of {B}ayesian bio-basis function neural network can
be obtained by request to the author.},
doi = {10.1093/bioinformatics/bti281},
pdf = {../local/Yang2005Prediction.pdf},
file = {Yang2005Prediction.pdf:local/Yang2005Prediction.pdf:PDF},
pii = {bti281},
url = {http://dx.doi.org/10.1093/bioinformatics/bti281}
}
@article{Yang2004Biological,
author = {Zheng Rong Yang},
title = {Biological applications of support vector machines.},
journal = {Brief {B}ioinform},
year = {2004},
volume = {5},
pages = {328-38},
number = {4},
month = {Dec}
}
@article{Yang2004Bio-support,
author = {Yang, Z. R. and Chou, K.-C.},
title = {Bio-support vector machines for computational proteomics},
journal = {Bioinformatics},
year = {2004},
volume = {20},
pages = {735-741},
number = {5},
abstract = {Motivation: {O}ne of the most important issues in computational proteomics
is to produce a prediction model for the classification or annotation
of biological function of novel protein sequences. {I}n order to
improve the prediction accuracy, much attention has been paid to
the improvement of the performance of the algorithms used, few is
for solving the fundamental issue, namely, amino acid encoding as
most existing pattern recognition algorithms are unable to recognize
amino acids in protein sequences. {I}mportantly, the most commonly
used amino acid encoding method has the flaw that leads to large
computational cost and recognition bias. {R}esults: {B}y replacing
kernel functions of support vector machines ({SVM}s) with amino acid
similarity measurement matrices, we have modified {SVM}s, a new type
of pattern recognition algorithm for analysing protein sequences,
particularly for proteolytic cleavage site prediction. {W}e refer
to the modified {SVM}s as bio-support vector machine. {W}hen applied
to the prediction of {HIV} protease cleavage sites, the new method
has shown a remarkable advantage in reducing the model complexity
and enhancing the model robustness.},
pdf = {../local/Yang2004Bio-support.pdf},
file = {Yang2004Bio-support.pdf:local/Yang2004Bio-support.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://bioinformatics.oupjournals.org/cgi/content/abstract/20/5/735}
}
@article{Yano2001Evaluating,
author = {Yano, Y. and Beal, S.L. and Sheiner, L.B. },
title = {Evaluating pharmacokinetic/pharmacodynamic models using the {P}osterior
{P}redictive {C}heck.},
journal = {J Pharmacokin Pharmacodynam},
year = {2001},
volume = {28},
pages = {171-192},
number = {2},
abstract = {The posterior predictive check (PPC) is a model evaluation tool. It
assigns a value (p PPC ) to the probability that the value of a given
statistic computed from data arising under an analysis model is as
or more extreme than the value computed from the real data themselves.
If this probability is too small, the analysis model is regarded
as invalid for the given statistic. Properties of the PPC for pharmacokinetic
(PK) and pharmacodynamic (PD) model evaluation are examined herein
for a particularly simple simulation setting: extensive sampling
of a single individual's data arising from simple PK/PD and error
models. To test the performance characteristics of the PPC, repeatedly,
ldquorealrdquo data are simulated and for a variety of statistics,
the PPC is applied to an analysis model, which may (null hypothesis)
or may not (alternative hypothesis) be identical to the simulation
model. Five models are used here: (PK1) mono-exponential with proportional
error, (PK2) biexponential with proportional error, (PK2epsi) biexponential
with additive error, (PD1) E max model with additive error under
the logit transform, and (PD2) sigmoid E max model with additive
error under the logit transform. Six simulation/analysis settings
are studied. The first three, (PK1/PK1), (PK2/PK2), and (PD1/PD1)
evaluate whether the PPC has appropriate type-I error level, whereas
the second three (PK2/PK1), (PK2epsi/PK2), and (PD2/PD1) evaluate
whether the PPC has adequate power. For a set of 100 data sets simulated/analyzed
under each model pair according to a stipulated extensive sampling
design, the p PPC is computed for a number of statistics in three
different ways (each way uses a different approximation to the posterior
distribution on the model parameters). We find that in general; (i)
The PPC is conservative under the null in the sense that for many
statistics, prob(p PPC leagr)agr for small agr. With respect to such
statistics, this means that useful models will rarely be regarded
incorrectly as invalid. A high correlation of a statistic with the
parameter estimates obtained from the same data used to compute the
statistic (a measure of statistical ldquosufficiencyrdquo) tends
to identify the most conservative statistics. (ii) Power is not very
great, at least for the alternative models we tested, and it is especially
poor with ldquostatisticsrdquo that are in part a function of parameters
as well as data. Although there is a tendency for nonsufficient statistics
(as we have measured this) to have greater power, this is by no means
an infallible diagnostic. (iii) No clear advantage for one or another
method of approximating the posterior distribution on model parameters
is found.},
owner = {kb}
}
@inproceedings{Yanover2005Predicting,
author = {Chen Yanover and Tomer Hertz},
title = {Predicting Protein-Peptide Binding Affinity by Learning Peptide-Peptide
Distance Functions.},
booktitle = {RECOMB},
year = {2005},
pages = {456-471},
bibsource = {DBLP, http://dblp.uni-trier.de},
ee = {http://dx.doi.org/10.1007/11415770_34}
}
@article{Yao2006Coupling,
author = {Yao, X. and Parnot, C. and Deupi, X. and Ratnala, V. R. P. and Swaminath,
G. and Farrens, D. and Kobilka, B.},
title = {Coupling ligand structure to specific conformational switches in
the beta2-adrenoceptor.},
journal = {Nat. Chem. Biol.},
year = {2006},
volume = {2},
pages = {417--422},
number = {8},
month = {Aug},
abstract = {G protein-coupled receptors (GPCRs) regulate a wide variety of physiological
functions in response to structurally diverse ligands ranging from
cations and small organic molecules to peptides and glycoproteins.
For many GPCRs, structurally related ligands can have diverse efficacy
profiles. To investigate the process of ligand binding and activation,
we used fluorescence spectroscopy to study the ability of ligands
having different efficacies to induce a specific conformational change
in the human beta2-adrenoceptor (beta2-AR). The 'ionic lock' is a
molecular switch found in rhodopsin-family GPCRs that has been proposed
to link the cytoplasmic ends of transmembrane domains 3 and 6 in
the inactive state. We found that most partial agonists were as effective
as full agonists in disrupting the ionic lock. Our results show that
disruption of this important molecular switch is necessary, but not
sufficient, for full activation of the beta2-AR.},
doi = {10.1038/nchembio801},
keywords = {chemogenomics},
owner = {laurent},
pii = {nchembio801},
pmid = {16799554},
timestamp = {2008.07.16},
url = {http://dx.doi.org/10.1038/nchembio801}
}
@article{Yao2004Comparative,
author = {X. J. Yao and A. Panaye and J. P. Doucet and R. S. Zhang and H. F.
Chen and M. C. Liu and Z. D. Hu and B. T. Fan},
title = {Comparative study of {QSAR}/{QSPR} correlations using support vector
machines, radial basis function neural networks, and multiple linear
regression.},
journal = {J {C}hem {I}nf {C}omput {S}ci},
year = {2004},
volume = {44},
pages = {1257-66},
number = {4},
abstract = {Support vector machines ({SVM}s) were used to develop {QSAR} models
that correlate molecular structures to their toxicity and bioactivities.
{T}he performance and predictive ability of {SVM} are investigated
and compared with other methods such as multiple linear regression
and radial basis function neural network methods. {I}n the present
study, two different data sets were evaluated. {T}he first one involves
an application of {SVM} to the development of a {QSAR} model for
the prediction of toxicities of 153 phenols, and the second investigation
deals with the {QSAR} model between the structures and the activities
of a set of 85 cyclooxygenase 2 ({COX}-2) inhibitors. {F}or each
application, the molecular structures were described using either
the physicochemical parameters or molecular descriptors. {I}n both
studied cases, the predictive ability of the {SVM} model is comparable
or superior to those obtained by {MLR} and {RBFNN}. {T}he results
indicate that {SVM} can be used as an alternative powerful modeling
tool for {QSAR} studies.},
doi = {10.1021/ci049965i},
pdf = {../local/Yao2004Comparative.pdf},
file = {Yao2004Comparative.pdf:local/Yao2004Comparative.pdf:PDF},
keywords = {biosvm chemoinformatics},
url = {http://dx.doi.org/10.1021/ci049965i}
}
@article{Yao1988Estimating,
author = {Yao, Y. C.},
title = {Estimating the number of change-points via Schwarz criterion},
journal = {Stat. Probab. Lett.},
year = {1988},
volume = {6},
pages = {181--189},
owner = {jp},
timestamp = {2010.06.02}
}
@article{Yao1989Least,
author = {Yao, Y.-C. and Au, S. T.},
title = {Least-Squares Estimation of a Step Function},
journal = {Sankhy\={a}: The Indian Journal of Statistics, Series A},
year = {1989},
volume = {51},
pages = {370--381},
number = {3},
owner = {jp},
timestamp = {2012.10.03},
url = {http://www.jstor.org/stable/25050759}
}
@article{Yap2004Prediction,
author = {C. W. Yap and C. Z. Cai and Y. Xue and Y. Z. Chen},
title = {Prediction of torsade-causing potential of drugs by support vector
machine approach.},
journal = {Toxicol {S}ci},
year = {2004},
volume = {79},
pages = {170-7},
number = {1},
month = {May},
abstract = {In an effort to facilitate drug discovery, computational methods for
facilitating the prediction of various adverse drug reactions ({ADR}s)
have been developed. {S}o far, attention has not been sufficiently
paid to the development of methods for the prediction of serious
{ADR}s that occur less frequently. {S}ome of these {ADR}s, such as
torsade de pointes ({T}d{P}), are important issues in the approval
of drugs for certain diseases. {T}hus there is a need to develop
tools for facilitating the prediction of these {ADR}s. {T}his work
explores the use of a statistical learning method, support vector
machine ({SVM}), for {T}d{P} prediction. {T}d{P} involves multiple
mechanisms and {SVM} is a method suitable for such a problem. {O}ur
{SVM} classification system used a set of linear solvation energy
relationship ({LSER}) descriptors and was optimized by leave-one-out
cross validation procedure. {I}ts prediction accuracy was evaluated
by using an independent set of agents and by comparison with results
obtained from other commonly used classification methods using the
same dataset and optimization procedure. {T}he accuracies for the
{SVM} prediction of {T}d{P}-causing agents and non-{T}d{P}-causing
agents are 97.4 and 84.6\% respectively; one is substantially improved
against and the other is comparable to the results obtained by other
classification methods useful for multiple-mechanism prediction problems.
{T}his indicates the potential of {SVM} in facilitating the prediction
of {T}d{P}-causing risk of small molecules and perhaps other {ADR}s
that involve multiple mechanisms.},
doi = {10.1093/toxsci/kfh082},
pdf = {../local/Yap2004Prediction.pdf},
file = {Yap2004Prediction.pdf:local/Yap2004Prediction.pdf:PDF},
keywords = {biosvm chemoinformatics},
pii = {kfh082},
url = {http://dx.doi.org/10.1093/toxsci/kfh082}
}
@article{Yap2005Prediction,
author = {C. W. Yap and Y. Z. Chen},
title = {Prediction of {C}ytochrome {P}450 3{A}4, 2{D}6, and 2{C}9 {I}nhibitors
and {S}ubstrates by {U}sing {S}upport {V}ector {M}achines.},
journal = {J {C}hem {I}nf {M}odel},
year = {2005},
volume = {45},
pages = {982-92},
number = {4},
abstract = {Statistical learning methods have been used in developing filters
for predicting inhibitors of two {P}450 isoenzymes, {CYP}3{A}4 and
{CYP}2{D}6. {T}his work explores the use of different statistical
learning methods for predicting inhibitors of these enzymes and an
additional {P}450 enzyme, {CYP}2{C}9, and the substrates of the three
{P}450 isoenzymes. {T}wo consensus support vector machine ({CSVM})
methods, "positive majority" ({PM}-{CSVM}) and "positive probability"
({PP}-{CSVM}), were used in this work. {T}hese methods were first
tested for the prediction of inhibitors of {CYP}3{A}4 and {CYP}2{D}6
by using a significantly higher number of inhibitors and noninhibitors
than that used in earlier studies. {T}hey were then applied to the
prediction of inhibitors of {CYP}2{C}9 and substrates of the three
enzymes. {B}oth methods predict inhibitors of {CYP}3{A}4 and {CYP}2{D}6
at a similar level of accuracy as those of earlier studies. {F}or
classification of inhibitors of {CYP}2{C}9, the best {CSVM} method
gives an accuracy of 88.9\% for inhibitors and 96.3\% for noninhibitors.
{T}he accuracies for classification of substrates and nonsubstrates
of {CYP}3{A}4, {CYP}2{D}6, and {CYP}2{C}9 are 98.2 and 90.9\%, 96.6
and 94.4\%, and 85.7 and 98.8\%, respectively. {B}oth {CSVM} methods
are potentially useful as filters for predicting inhibitors and substrates
of {P}450 isoenzymes. {T}hese methods generally give better accuracies
than single {SVM} classification systems, and the performance of
the {PP}-{CSVM} method is slightly better than that of the {PM}-{CSVM}
method.},
doi = {10.1021/ci0500536},
pdf = {../local/Yap2005Prediction.pdf},
file = {Yap2005Prediction.pdf:local/Yap2005Prediction.pdf:PDF},
keywords = {biosvm chemoinformatics},
url = {http://dx.doi.org/10.1021/ci0500536}
}
@article{Yeang2001Molecular,
author = {Yeang, C.H. and Ramaswamy, S. and Tamayo, P. and Mukherjee, S. and
Rifkin, R.M. and Angelo, M. and Reich, M. and Lander, E. and Mesirov,
J. and Golub, T.},
title = {Molecular classification of multiple tumor types},
journal = {Bioinformatics},
year = {2001},
volume = {17},
pages = {S316--S322},
number = {Suppl. 1},
abstract = {Using gene expression data to classify tumor types is a very promising
tool in cancer diagnosis. {P}revious works show several pairs of
tumor types can be successfully distinguished by their gene expression
patterns ({G}olub et al. 1999, {B}en-{D}or et al. 2000, {A}lizadeh
et al. 2000). {H}owever, the simultaneous classification across a
heterogeneous set of tumor types has not been well studied yet. {W}e
obtained 190 samples from 14 tumor classes and generated a combined
expression dataset containing 16063 genes for each of those samples.
{W}e performed multi-class classification by combining the outputs
of binary classifiers. {T}hree binary classifiers (k-nearest neighbors,
weighted voting, and support vector machines) were applied in conjunction
with three combination scenarios (one-vs-all, all-pairs, hierarchical
partitioning). {W}e achieved the best cross validation error rate
of 18.75% and the best test error rate of 21.74% by using the one-vs-all
support vector machine algorithm. {T}he results demonstrate the feasibility
of performing clinically useful classification from samples of multiple
tumor types.},
pdf = {../local/Yeang2001Molecular.pdf},
file = {Yeang2001Molecular.pdf:local/Yeang2001Molecular.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://bioinformatics.oupjournals.org/cgi/content/abstract/17/suppl_1/S316}
}
@article{Yeh2005Liver,
author = {Wen-Chun Yeh and Yung-Ming Jeng and Cheng-Han Li and Po-Huang Lee
and Pai-Chi Li},
title = {Liver steatosis classification using high-frequency ultrasound.},
journal = {Ultrasound {M}ed {B}iol},
year = {2005},
volume = {31},
pages = {599-605},
number = {5},
month = {May},
abstract = {High-frequency {B}-mode images of 19 fresh human liver samples were
obtained to evaluate their usefulness in determining the steatosis
grade. {T}he images were acquired by a mechanically controlled single-crystal
probe at 25 {MH}z. {I}mage features derived from gray-level concurrence
and nonseparable wavelet transform were extracted to classify steatosis
grade using a classifier known as the support vector machine. {A}
subsequent histologic examination of each liver sample graded the
steatosis from 0 to 3. {T}he four grades were then combined into
two, three and four classes. {T}he classification results were correlated
with histology. {T}he best classification accuracies of the two,
three and four classes were 90.5\%, 85.8\% and 82.6\%, respectively,
which were markedly better than those at 7 {MH}z. {T}hese results
indicate that liver steatosis can be more accurately characterized
using high-frequency {B}-mode ultrasound. {L}imitations and their
potential solutions of applying high-frequency ultrasound to liver
imaging are also discussed.},
doi = {10.1016/j.ultrasmedbio.2005.01.009},
pdf = {../local/Yeh2005Liver.pdf},
file = {Yeh2005Liver.pdf:local/Yeh2005Liver.pdf:PDF},
pii = {S0301-5629(05)00050-5},
url = {http://dx.doi.org/10.1016/j.ultrasmedbio.2005.01.009}
}
@article{Yeung2002Reverse,
author = {Yeung, M. K. Stephen and Tegn\'{e}r, Jesper and Collins, James J.},
title = {Reverse engineering gene networks using singular value decomposition
and robust regression},
journal = {Proc. {N}atl. {A}cad. {S}ci. {USA}},
year = {2002},
volume = {99},
pages = {6163-6168},
number = {9},
abstract = {We propose a scheme to reverse-engineer gene networks on a genome-wide
scale using a relatively small amount of gene expression data from
microarray experiments. Our method is based on the empirical observation
that such networks are typically large and sparse. It uses singular
value decomposition to construct a family of candidate solutions
and then uses robust regression to identify the solution with the
smallest number of connections as the most likely solution. Our algorithm
has O(log N) sampling complexity and O(N4) computational complexity.
We test and validate our approach in a series of in numero experiments
on model gene networks.},
doi = {10.1073/pnas.092576199},
eprint = {http://www.pnas.org/content/99/9/6163.full.pdf+html},
url = {http://www.pnas.org/content/99/9/6163.abstract}
}
@article{Yewdell1999Immunodominance,
author = {Yewdell, J. W. and Bennink, J. R.},
title = {{I}mmunodominance in major histocompatibility complex class {I}-restricted
{T} lymphocyte responses.},
journal = {Annu. Rev. Immunol.},
year = {1999},
volume = {17},
pages = {51--88},
abstract = {Of the many thousands of peptides encoded by a complex foreign antigen
that can potentially be presented to CD8+ T cells (TCD8+), only a
small fraction induce measurable responses in association with any
given major histocompatibility complex class I allele. To design
vaccines that elicit optimal TCD8+ responses, a thorough understanding
of this phenomenon, known as immunodominance, is imperative. Here
we review recent progress in unraveling the molecular and cellular
basis for immunodominance. Of foremost importance is peptide binding
to class I molecules; only approximately 1/200 of potential determinants
bind at greater than the threshold affinity (Kd > 500 nM) associated
with immunogenicity. Limitations in the TCD8+ repertoire render approximately
half of these peptides nonimmunogenic, and inefficient antigen processing
further thins the ranks by approximately four fifths. As a result,
only approximately 1/2000 of the peptides in a foreign antigen expressed
by an appropriate antigen presenting cell achieve immunodominant
status with a given class I allele. A roughly equal fraction of peptides
have subdominant status, i.e. they induce weak-to-nondetectable primary
TCD8+ responses in the context of their natural antigen. Subdominant
determinants may be expressed at or above levels of immunodominant
determinants, at least on antigen presenting cells in vitro. The
immunogenicity of subdominant determinants is often limited by immunodomination:
suppression mediated by TCD8+ specific for immunodominant determinants.
Immunodomination is a central feature of TCD8+ responses, as it even
occurs among clones responding to the same immunodominant determinant.
Little is known about how immunodominant and subdominant determinants
are distinguished by the TCD8+ repertoire, or how (and why) immunodomination
occurs, but new tools are available to address these questions.},
doi = {10.1146/annurev.immunol.17.1.51},
keywords = {immunoinformatics},
pmid = {10358753},
timestamp = {2007.01.25},
url = {http://dx.doi.org/10.1146/annurev.immunol.17.1.51}
}
@article{Yi2007Strategy,
author = {Yi, Y. and Li, C. and Miller, C. and George, A. L.},
title = {Strategy for encoding and comparison of gene expression signatures.},
journal = {Genome Biol.},
year = {2007},
volume = {8},
pages = {R133},
number = {7},
abstract = {EXALT (EXpression signature AnaLysis Tool) is a computational system
enabling comparisons of microarray data across experimental platforms
and different laboratories http://seq.mc.vanderbilt.edu/exalt/. An
essential feature of EXALT is a database holding thousands of gene
expression signatures extracted from the Gene Expression Omnibus,
and encoded in a searchable format. This novel approach to performing
global comparisons of shared microarray data may have enormous value
when coupled directly with a shared data repository.},
doi = {10.1186/gb-2007-8-7-r133},
institution = {Department of Medicine, Vanderbilt University, Nashville, Tennessee
37232-0275, USA.},
owner = {jp},
pii = {gb-2007-8-7-r133},
pmid = {17612401},
timestamp = {2008.12.09},
url = {http://dx.doi.org/10.1186/gb-2007-8-7-r133}
}
@article{Yin2008Activity,
author = {Yin, J. and Yang, Q. and Shen, D. and Li, Z.-N.},
title = {Activity recognition via user-trace segmentation},
journal = {ACM Trans. Sen. Netw.},
year = {2008},
volume = {4},
pages = {19:1--19:34},
number = {4},
month = {September},
doi = {10.1145/1387663.1387665},
pdf = {../local/Yin2008Activity.pdf},
file = {Yin2008Activity.pdf:Yin2008Activity.pdf:PDF},
owner = {jp},
timestamp = {2010.12.07},
url = {http://dx.doi.org/10.1145/1387663.1387665}
}
@article{Yiu2005Filtering,
author = {Yiu, S. M. and Wong, Prudence W. H. and Lam, T.W. and Mui, Y.C. and
Kung, H. F. and Lin, Marie and Cheung, Y. T.},
title = {Filtering of {I}neffective si{RNA}s and {I}mproved si{RNA} {D}esign
{T}ool},
journal = {Bioinformatics},
year = {2005},
volume = {21},
pages = {144-151},
number = {2},
month = {Jan},
note = {To appear},
abstract = {Motivation: {S}hort interfering {RNA}s (si{RNA}s) can be used to suppress
gene expression and possess many potential applications in therapy,
but how to design an effective si{RNA} is still not clear. {B}ased
on the {MPI} ({M}ax-{P}lanck-{I}nstitute) basic principles, a number
of si{RNA} design tools have been developed recently. {T}he set of
candidates reported by these tools is usually large and often contains
ineffective si{RNA}s. {I}n view of this, we initiate the study of
filtering ineffective si{RNA}s. {R}esults: {T}he contribution of
this paper is 2-fold. {F}irst, we propose a fair scheme to compare
existing design tools based on real data in the literature. {S}econd,
we attempt to improve the {MPI} principles and existing tools by
an algorithm that can filter ineffective si{RNA}s. {T}he algorithm
is based on some new observations on the secondary structure, which
we have verified by {AI} techniques (decision trees and support vector
machines). {W}e have tested our algorithm together with the {MPI}
principles and the existing tools. {T}he results show that our filtering
algorithm is effective. {A}vailability: {T}he si{RNA} design software
tool can be found in the website http://www.cs.hku.hk/~sirna/ {C}ontact:
smyiu@cs.hku.hk},
doi = {10.1093/bioinformatics/bth498},
pdf = {../local/Yiu2005Filtering.pdf},
file = {Yiu2005Filtering.pdf:local/Yiu2005Filtering.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://bioinformatics.oupjournals.org/cgi/content/abstract/21/2/144}
}
@article{Ylstra2006BAC,
author = {Ylstra, Bauke and Van den Ijssel, Paul and Carvalho, Beatriz and
Brakenhoff, Ruud H and Meijer, Gerrit A},
title = {{BAC to the future! or oligonucleotides: a perspective for micro
array comparative genomic hybridization (array CGH)}},
journal = {Nucleic Acids Res.},
year = {2006},
volume = {34},
pages = {445--450},
keywords = {csbcbook, csbcbook-ch2}
}
@article{Yook2001Weighted,
author = {S. H. Yook and H. Jeong and Y. Tu and A.-L. Barab{\'a}si},
title = {Weighted evolution networks},
journal = {Phys. {R}ev. {L}ett.},
year = {2001},
volume = {86},
pages = {5835--5838},
number = {25},
pdf = {../local/yook01.pdf},
file = {yook01.pdf:local/yook01.pdf:PDF},
subject = {compnet},
url = {http://www.nd.edu/~networks/Papers/weighted.pdf}
}
@article{Yoon2009Sensitive,
author = {Seungtai Yoon and Zhenyu Xuan and Vladimir Makarov and Kenny Ye and
Jonathan Sebat},
title = {Sensitive and accurate detection of copy number variants using read
depth of coverage.},
journal = {Genome Res.},
year = {2009},
volume = {19},
pages = {1586--1592},
number = {9},
month = {Sep},
abstract = {Methods for the direct detection of copy number variation (CNV) genome-wide
have become effective instruments for identifying genetic risk factors
for disease. The application of next-generation sequencing platforms
to genetic studies promises to improve sensitivity to detect CNVs
as well as inversions, indels, and SNPs. New computational approaches
are needed to systematically detect these variants from genome sequence
data. Existing sequence-based approaches for CNV detection are primarily
based on paired-end read mapping (PEM) as reported previously by
Tuzun et al. and Korbel et al. Due to limitations of the PEM approach,
some classes of CNVs are difficult to ascertain, including large
insertions and variants located within complex genomic regions. To
overcome these limitations, we developed a method for CNV detection
using read depth of coverage. Event-wise testing (EWT) is a method
based on significance testing. In contrast to standard segmentation
algorithms that typically operate by performing likelihood evaluation
for every point in the genome, EWT works on intervals of data points,
rapidly searching for specific classes of events. Overall false-positive
rate is controlled by testing the significance of each possible event
and adjusting for multiple testing. Deletions and duplications detected
in an individual genome by EWT are examined across multiple genomes
to identify polymorphism between individuals. We estimated error
rates using simulations based on real data, and we applied EWT to
the analysis of chromosome 1 from paired-end shotgun sequence data
(30x) on five individuals. Our results suggest that analysis of read
depth is an effective approach for the detection of CNVs, and it
captures structural variants that are refractory to established PEM-based
methods.},
doi = {10.1101/gr.092981.109},
pdf = {../local/Yoon2009Sensitive.pdf},
file = {Yoon2009Sensitive.pdf:Yoon2009Sensitive.pdf:PDF},
institution = {Cold Spring Harbor Laboratory, Cold Spring Harbor, New York 11724,
USA.},
keywords = {ngs},
owner = {jp},
pii = {gr.092981.109},
pmid = {19657104},
timestamp = {2009.10.09},
url = {http://dx.doi.org/10.1101/gr.092981.109}
}
@article{Yoon2003Analysis,
author = {Yoon, Y. and Song, J. and Hong, S.H. and Kim, J.Q.},
title = {Analysis of multiple single nucleotide polymorphisms of candidate
genes related to coronary heart disease susceptibility by using support
vector machines},
journal = {Clin. {C}hem. {L}ab. {M}ed.},
year = {2003},
volume = {41},
pages = {529-534},
number = {4},
abstract = {Coronary heart disease ({CHD}) is a complex genetic disease involving
gene-environment interaction. {M}any association studies between
single nucleotide polymorphisms ({SNP}s) of candidate genes and {CHD}
have been reported. {W}e have applied a new method to analyze such
relationships using support vector machines ({SVM}s), which is one
of the methods for artificial neuronal network. {W}e assumed that
common haplotype implicit in genotypes will differ between cases
and controls, and that this will allow {SVM}-derived patterns to
be classifiable according to subject genotypes. {F}ourteen {SNP}s
of ten candidate genes in 86 {CHD} patients and 119 controls were
investigated. {G}enotypes were transformed to a numerical vector
by giving scores based on difference between the genotypes of each
subject and the reference genotypes, which represent the healthy
normal population. {O}verall classification accuracy by {SVM}s was
64.4% with a receiver operating characteristic ({ROC}) area of 0.639.
{B}y conventional analysis using the chi2 test, the association between
{CHD} and the {SNP} of the scavenger receptor {B}1 gene was most
significant in terms of allele frequencies in cases vs. controls
(p = 0.0001). {I}n conclusion, we suggest that the application of
{SVM}s for association studies of {SNP}s in candidate genes shows
considerable promise and that further work could be usefully performed
upon the estimation of {CHD} susceptibility in individuals of high
risk.},
pdf = {../local/Yoon2003Analysis.pdf},
file = {Yoon2003Analysis.pdf:local/Yoon2003Analysis.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://www.degruyter.de/journals/cclm/abs/10592.html}
}
@article{Yosef2008Improved,
author = {N. Yosef and R. Sharan and W. S. Noble},
title = {Improved network-based identification of protein orthologs},
journal = {Bioinformatics},
year = {2008},
volume = {24},
pages = {i200--i206},
number = {16},
month = {Aug},
abstract = {MOTIVATION: Identifying protein orthologs is an important task that
is receiving growing attention in the bioinformatics literature.
Orthology detection provides a fundamental tool towards understanding
protein evolution, predicting protein functions and interactions,
aligning protein-protein interaction (PPI) networks of different
species and detecting conserved modules within these networks. RESULTS:
Here, we present a novel diffusion-based framework that builds on
the Rankprop algorithm for protein orthology detection and enhances
it in several important ways. Specifically, we enhance the Rankprop
algorithm to account for the presence of multiple paralogs, utilize
PPI, and consider multiple (>2) species in parallel. We comprehensively
benchmarked our framework using a variety of training datasets and
experimental settings. The results, based on the yeast, fly and human
proteomes, show that the novel enhancements of Rankprop provide substantial
improvements over its original formulation as well as over a number
of state of the art methods for network-based orthology detection.
AVAILABILITY: datasets and source code are available upon request.},
doi = {10.1093/bioinformatics/btn277},
pdf = {../local/Yosef2008Improved.pdf},
file = {Yosef2008Improved.pdf:local/Yosef2008Improved.pdf:PDF},
institution = {School of Computer Science, Tel-Aviv University, Tel-Aviv, Israel.
niryosef@post.tau.ac.il},
owner = {jp},
pii = {btn277},
pmid = {18689825},
timestamp = {2008.10.02},
url = {http://dx.doi.org/10.1093/bioinformatics/btn277}
}
@article{Young2005Plasmodium,
author = {Jason A Young and Quinton L Fivelman and Peter L Blair and Patricia
de la Vega and Karine G Le Roch and Yingyao Zhou and Daniel J Carucci
and David A Baker and Elizabeth A Winzeler},
title = {{T}he {P}lasmodium falciparum sexual development transcriptome: a
microarray analysis using ontology-based pattern identification.},
journal = {Mol. Biochem. Parasitol.},
year = {2005},
volume = {143},
pages = {67--79},
number = {1},
month = {Sep},
abstract = {The sexual stages of malarial parasites are essential for the mosquito
transmission of the disease and therefore are the focus of transmission-blocking
drug and vaccine development. In order to better understand genes
important to the sexual development process, the transcriptomes of
high-purity stage I-V Plasmodium falciparum gametocytes were comprehensively
profiled using a full-genome high-density oligonucleotide microarray.
The interpretation of this transcriptional data was aided by applying
a novel knowledge-based data-mining algorithm termed ontology-based
pattern identification (OPI) using current information regarding
known sexual stage genes as a guide. This analysis resulted in the
identification of a sexual development cluster containing 246 genes,
of which approximately 75\% were hypothetical, exhibiting highly-correlated,
gametocyte-specific expression patterns. Inspection of the upstream
promoter regions of these 246 genes revealed putative cis-regulatory
elements for sexual development transcriptional control mechanisms.
Furthermore, OPI analysis was extended using current annotations
provided by the Gene Ontology Consortium to identify 380 statistically
significant clusters containing genes with expression patterns characteristic
of various biological processes, cellular components, and molecular
functions. Collectively, these results, available as part of a web-accessible
OPI database (http://carrier.gnf.org/publications/Gametocyte), shed
light on the components of molecular mechanisms underlying parasite
sexual development and other areas of malarial parasite biology.},
doi = {10.1016/j.molbiopara.2005.05.007},
pdf = {../local/Young2005Plasmodium.pdf},
file = {Young2005Plasmodium.pdf:local/Young2005Plasmodium.pdf:PDF},
keywords = {plasmodium},
pii = {S0166-6851(05)00162-3},
pmid = {16005087},
timestamp = {2006.04.13},
url = {http://dx.doi.org/10.1016/j.molbiopara.2005.05.007}
}
@article{Young2005Using,
author = {J. A. Young and E. A. Winzeler},
title = {{U}sing expression information to discover new drug and vaccine targets
in the malaria parasite {P}lasmodium falciparum.},
journal = {Pharmacogenomics},
year = {2005},
volume = {6},
pages = {17--26},
number = {1},
month = {Jan},
abstract = {The recent completion of the malaria parasite Plasmodium falciparum
genome has opened the door for applying a variety of genomic-based
systems biology approaches that complement existing gene-by-gene
methods of investigation. Transcriptomic analyses of P.falciparum
using DNA microarrays has allowed for the rapid elucidation of gene
function, parasite drug response, and invivo expression profiles,
as well as general mechanisms guiding the parasite life cycle that
are vital to disease pathogenesis. The results of these studies have
identified promising novel gene targets for the development of new
drug and vaccine therapies.},
keywords = {plasmodium},
pii = {PGS060105},
pmid = {15723602},
timestamp = {2006.04.13}
}
@article{Yu1996Lower,
author = {Yu, B.},
title = {Lower bounds on expected redundancy for nonparametric classes},
journal = {I{EEE} {T}rans. {I}nform. {T}heory},
year = {1996},
volume = {42},
pages = {272-275},
number = {1},
month = {Jan},
abstract = {The article focuses on lower bound results on expected redundancy
for universal coding of independent and identically distributed data
on [0, 1] from parametric and nonparametric families. {A}fter reviewing
existing lower bounds, we provide a new proof for minimax lower bounds
on expected redundancy over nonparametric density classes. {T}his
new proof is based on the calculation of a mutual information quantity,
or it utilizes the relationship between redundancy and {S}hannon
capacity. {I}t therefore unifies the minimax redundancy lower bound
proofs in the parametric and nonparametric cases },
doi = {10.1109/18.481802},
pdf = {../local/Yu1996Lower.pdf},
file = {Yu1996Lower.pdf:local/Yu1996Lower.pdf:PDF},
keywords = {information-theory},
owner = {vert},
url = {http://dx.doi.org/10.1109/18.481802}
}
@article{Yu2003Fine-grained,
author = {Yu, C.S. and Wang, J.Y. and Yang, J.M. and Lyu, P.C. and Lin, C.J.
and Hwang, J.K.},
title = {Fine-grained protein fold assignment by support vector machines using
generalized npeptide coding schemes and jury voting from multiple-parameter
sets.},
journal = {Proteins},
year = {2003},
volume = {50},
pages = {531},
number = {4},
month = {6},
abstract = {In the coarse-grained fold assignment of major protein classes, such
as all-alpha, all-beta, alpha + beta, alpha/beta proteins, one can
easily achieve high prediction accuracy from primary amino acid sequences.
{H}owever, the fine-grained assignment of folds, such as those defined
in the {S}tructural {C}lassification of {P}roteins ({SCOP}) database,
presents a challenge due to the larger amount of folds available.
{R}ecent study yielded reasonable prediction accuracy of 56.0% on
an independent set of 27 most populated folds. {I}n this communication,
we apply the support vector machine ({SVM}) method, using a combination
of protein descriptors based on the properties derived from the composition
of n-peptide and jury voting, to the fine-grained fold prediction,
and are able to achieve an overall prediction accuracy of 69.6% on
the same independent set-significantly higher than the previous results.
{O}n 10-fold cross-validation, we obtained a prediction accuracy
of 65.3%. {O}ur results show that {SVM} coupled with suitable global
sequence-coding schemes can significantly improve the fine-grained
fold prediction. {O}ur approach should be useful in structure prediction
and modeling.},
doi = {10.1002/prot.10313},
pdf = {../local/Yu2003Fine-grained.pdf},
file = {Yu2003Fine-grained.pdf:local/Yu2003Fine-grained.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://dx.doi.org/10.1002/prot.10313}
}
@article{Yu2005Classifying,
author = {Yu, C. and Zavaljevski, N. and Stevens, F. J. and Yackovich, K. and
Reifman, J.},
title = {Classifying noisy protein sequence data: a case study of immunoglobulin
light chains.},
journal = {Bioinformatics},
year = {2005},
volume = {21},
pages = {i495-i501},
number = {Supp 1},
month = {Jun},
abstract = {S{UMMARY}: {T}he classification of protein sequences obtained from
patients with various immunoglobulin-related conformational diseases
may provide insight into structural correlates of pathogenicity.
{H}owever, clinical data are very sparse and, in the case of antibody-related
proteins, the collected sequences have large variability with only
a small subset of variations relevant to the protein pathogenicity
(function). {O}n this basis, these sequences represent a model system
for development of strategies to recognize the small subset of function-determining
variations among the much larger number of primary structure diversifications
introduced during evolution. {U}nder such conditions, most protein
classification algorithms have limited accuracy. {T}o address this
problem, we propose a support vector machine ({SVM})-based classifier
that combines sequence and 3{D} structural averaging information.
{E}ach amino acid in the sequence is represented by a set of six
physicochemical properties: hydrophobicity, hydrophilicity, volume,
surface area, bulkiness and refractivity. {E}ach position in the
sequence is described by the properties of the amino acid at that
position and the properties of its neighbors in 3{D} space or in
the sequence. {A} structure template is selected to determine neighbors
in 3{D} space and a window size is used to determine the neighbors
in the sequence. {T}he test data consist of 209 proteins of human
antibody immunoglobulin light chains, each represented by aligned
sequences of 120 amino acids. {T}he methodology is applied to the
classification of protein sequences collected from patients with
and without amyloidosis, and indicates that the proposed modified
classifiers are more robust to sequence variability than standard
{SVM} classifiers, improving classification error between 5 and 25\%
and sensitivity between 9 and 17\%. {T}he classification results
might also suggest possible mechanisms for the propensity of immunoglobulin
light chains to amyloid formation. {CONTACT}: cyu@bioanalysis.org.},
doi = {10.1093/bioinformatics/bti1024},
pdf = {../local/Yu2005Classifying.pdf},
file = {Yu2005Classifying.pdf:local/Yu2005Classifying.pdf:PDF},
keywords = {biosvm},
pii = {21/suppl_1/i495},
url = {http://dx.doi.org/10.1093/bioinformatics/bti1024}
}
@article{Yu2004Predicting,
author = {Yu, C.-S. and Lin, C.-J. and Hwang, J.-K.},
title = {Predicting subcellular localization of proteins for {G}ram-negative
bacteria by support vector machines based on n-peptide compositions},
journal = {Protein {S}ci.},
year = {2004},
volume = {13},
pages = {1402-1406},
number = {5},
abstract = {Gram-negative bacteria have five major subcellular localization sites:
the cytoplasm, the periplasm, the inner membrane, the outer membrane,
and the extracellular space. {T}he subcellular location of a protein
can provide valuable information about its function. {W}ith the rapid
increase of sequenced genomic data, the need for an automated and
accurate tool to predict subcellular localization becomes increasingly
important. {W}e present an approach to predict subcellular localization
for {G}ram-negative bacteria. {T}his method uses the support vector
machines trained by multiple feature vectors based on n-peptide compositions.
{F}or a standard data set comprising 1443 proteins, the overall prediction
accuracy reaches 89%, which, to the best of our knowledge, is the
highest prediction rate ever reported. {O}ur prediction is 14% higher
than that of the recently developed multimodular {PSORT}-{B}. {B}ecause
of its simplicity, this approach can be easily extended to other
organisms and should be a useful tool for the high-throughput and
large-scale analysis of proteomic and genomic data.},
doi = {10.1110/ps.03479604},
pdf = {../local/Yu2004Predicting.pdf},
file = {Yu2004Predicting.pdf:local/Yu2004Predicting.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://www.proteinscience.org/cgi/content/abstract/13/5/1402}
}
@article{Yu2008High-quality,
author = {Yu, H. and Braun, P. and Yildirim, M. A. and Lemmens, I. and Venkatesan,
K. and Sahalie, J. and Hirozane-Kishikawa, T. and Gebreab, F. and
Li, N. and Simonis, N. and Hao, T. and Rual, J.-F. and Dricot, A.
and Vazquez, A. and Murray, R. R. and Simon, C. and Tardivo, L. and
Tam, S. and Svrzikapa, N. and Fan, C. and de Smet, A.-S. and Motyl,
A. and Hudson, M. E. and Park, J. and Xin, X. and Cusick, M. E. and
Moore, T. and Boone, C. and Snyder, M. and Roth, F. R. and Barab{\'a}si,
A.-L. and Tavernier, J. and Hill, D. E. and Vidal, M.},
title = {High-quality binary protein interaction map of the yeast interactome
network},
journal = {Science},
year = {2008},
volume = {322},
pages = {104--110},
number = {5898},
month = {Oct},
abstract = {Current yeast interactome network maps contain several hundred molecular
complexes with limited and somewhat controversial representation
of direct binary interactions. We carried out a comparative quality
assessment of current yeast interactome data sets, demonstrating
that high-throughput yeast two-hybrid (Y2H) screening provides high-quality
binary interaction information. Because a large fraction of the yeast
binary interactome remains to be mapped, we developed an empirically
controlled mapping framework to produce a "second-generation" high-quality,
high-throughput Y2H data set covering approximately 20\% of all yeast
binary interactions. Both Y2H and affinity purification followed
by mass spectrometry (AP/MS) data are of equally high quality but
of a fundamentally different and complementary nature, resulting
in networks with different topological and biological properties.
Compared to co-complex interactome models, this binary map is enriched
for transient signaling interactions and intercomplex connections
with a highly significant clustering between essential proteins.
Rather than correlating with essentiality, protein connectivity correlates
with genetic pleiotropy.},
doi = {10.1126/science.1158684},
pdf = {../local/Yu2008High-quality.pdf},
file = {Yu2008High-quality.pdf:Yu2008High-quality.pdf:PDF},
institution = {Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute,
Boston, MA 02115, USA.},
owner = {jp},
pii = {1158684},
pmid = {18719252},
timestamp = {2009.02.13},
url = {http://dx.doi.org/10.1126/science.1158684}
}
@article{Yu2004PEBL,
author = {Yu, H. and Han, J. and Chang, K. C.-C.},
title = {{PEBL}: Web Page Classification without Negative Examples},
journal = {IEEE Trans. Knowl. Data Eng.},
year = {2004},
volume = {16},
pages = {70--81},
number = {1},
doi = {10.1109/TKDE.2004.1264823},
pdf = {../local/Yu2004PEBL.pdf},
file = {Yu2004PEBL.pdf:Yu2004PEBL.pdf:PDF},
keywords = {PUlearning},
owner = {fantine},
timestamp = {2009.06.09},
url = {http://dx.doi.org/10.1109/TKDE.2004.1264823}
}
@article{Yu2004integrated,
author = {Yu, J.K. and Chen, Y.D. and Zheng, S.},
title = {An integrated approach to the detection of colorectal cancer utilizing
proteomics and bioinformatics},
journal = {World {J}. {G}astroenterol.},
year = {2004},
volume = {10},
pages = {3127-3131},
number = {21},
abstract = {A{IM}: {T}o find new potential biomarkers and to establish patterns
for early detection of colorectal cancer. {METHODS}: {O}ne hundred
and eighty-two serum samples including 55 from colorectal cancer
({CRC}) patients, 35 from colorectal adenoma ({CRA}) patients and
92 from healthy persons ({HP}) were detected by surface-enhanced
laser desorption/ionization mass spectrometry ({SELDI}-{MS}). {T}he
data of spectra were analyzed by bioinformatics tools like artificial
neural network ({ANN}) and support vector machine ({SVM}). {RESULTS}:
{T}he diagnostic pattern combined with 7 potential biomarkers could
differentiate {CRC} patients from {CRA} patients with a specificity
of 83%, sensitivity of 89% and positive predictive value of 89%.
{T}he diagnostic pattern combined with 4 potential biomarkers could
differentiate {CRC} patients from {HP} with a specificity of 92%,
sensitivity of 89% and positive predictive value of 86%. {CONCLUSION}:
{T}he combination of {SELDI} with bioinformatics tools could help
find new biomarkers and establish patterns with high sensitivity
and specificity for the detection of {CRC}.},
pdf = {../local/Yu2004integrated.pdf},
file = {Yu2004integrated.pdf:local/Yu2004integrated.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert}
}
@article{Yu2007Pathway,
author = {Yu, J. and Sieuwerts, A. and Zhang, Y. and Martens, J. and Smid,
M. and Klijn, J. and Wang, Y. and Foekens, J.},
title = {Pathway analysis of gene signatures predicting metastasis of node-negative
primary breast cancer},
journal = {BMC cancer},
year = {2007},
volume = {7},
pages = {182},
number = {1},
publisher = {BioMed Central Ltd}
}
@article{Yu2004Advances,
author = {Yu, J. and Smith, V.A. and Wang, P.P. and Hartemink, A.J. and Jarvis,
E.D.},
title = {Advances to Bayesian network inference for generating causal networks
from observational biological data.},
journal = {Bioinformatics},
year = {2004},
volume = {20},
pages = {3594--3603},
number = {18},
month = {Dec},
abstract = {MOTIVATION: Network inference algorithms are powerful computational
tools for identifying putative causal interactions among variables
from observational data. Bayesian network inference algorithms hold
particular promise in that they can capture linear, non-linear, combinatorial,
stochastic and other types of relationships among variables across
multiple levels of biological organization. However, challenges remain
when applying these algorithms to limited quantities of experimental
data collected from biological systems. Here, we use a simulation
approach to make advances in our dynamic Bayesian network (DBN) inference
algorithm, especially in the context of limited quantities of biological
data. RESULTS: We test a range of scoring metrics and search heuristics
to find an effective algorithm configuration for evaluating our methodological
advances. We also identify sampling intervals and levels of data
discretization that allow the best recovery of the simulated networks.
We develop a novel influence score for DBNs that attempts to estimate
both the sign (activation or repression) and relative magnitude of
interactions among variables. When faced with limited quantities
of observational data, combining our influence score with moderate
data interpolation reduces a significant portion of false positive
interactions in the recovered networks. Together, our advances allow
DBN inference algorithms to be more effective in recovering biological
networks from experimentally collected data. AVAILABILITY: Source
code and simulated data are available upon request. SUPPLEMENTARY
INFORMATION: http://www.jarvislab.net/Bioinformatics/BNAdvances/},
doi = {10.1093/bioinformatics/bth448},
institution = {>},
keywords = {Algorithms; Bayes Theorem; Computer Simulation; Gene Expression Profiling;
Gene Expression Regulation; Models, Genetic; Models, Statistical;
Oligonucleotide Array Sequence Analysis; Signal Transduction; Software},
owner = {fantine},
pii = {bth448},
pmid = {15284094},
timestamp = {2010.10.21},
url = {http://dx.doi.org/10.1093/bioinformatics/bth448}
}
@article{Yu2005Ovarian,
author = {J. S. Yu and S. Ongarello and R. Fiedler and X. W. Chen and G. Toffolo
and C. Cobelli and Z. Trajanoski},
title = {Ovarian cancer identification based on dimensionality reduction for
high-throughput mass spectrometry data.},
journal = {Bioinformatics},
year = {2005},
volume = {21},
pages = {2200-9},
number = {10},
month = {May},
abstract = {M{OTIVATION}: {H}igh-throughput and high-resolution mass spectrometry
instruments are increasingly used for disease classification and
therapeutic guidance. {H}owever, the analysis of immense amount of
data poses considerable challenges. {W}e have therefore developed
a novel method for dimensionality reduction and tested on a published
ovarian high-resolution {SELDI}-{TOF} dataset. {RESULTS}: {W}e have
developed a four-step strategy for data preprocessing based on: (1)
binning, (2) {K}olmogorov-{S}mirnov test, (3) restriction of coefficient
of variation and (4) wavelet analysis. {S}ubsequently, support vector
machines were used for classification. {T}he developed method achieves
an average sensitivity of 97.38\% (sd = 0.0125) and an average specificity
of 93.30\% (sd = 0.0174) in 1000 independent k-fold cross-validations,
where k = 2, ..., 10. {AVAILABILITY}: {T}he software is available
for academic and non-commercial institutions.},
doi = {10.1093/bioinformatics/bti370},
pdf = {../local/Yu2005Ovarian.pdf},
file = {Yu2005Ovarian.pdf:local/Yu2005Ovarian.pdf:PDF},
keywords = {biosvm proteomics},
pii = {bti370},
url = {http://dx.doi.org/10.1093/bioinformatics/bti370}
}
@article{Yu2005integrated,
author = {Yu, J.-k. and Zheng, S. and Tang, Y. and Li, L.},
title = {An integrated approach utilizing proteomics and bioinformatics to
detect ovarian cancer.},
journal = {J {Z}hejiang {U}niv {S}ci {B}},
year = {2005},
volume = {6},
pages = {227-31},
number = {4},
month = {Apr},
abstract = {O{BJECTIVE}: {T}o find new potential biomarkers and establish the
patterns for the detection of ovarian cancer. {METHODS}: {S}ixty
one serum samples including 32 ovarian cancer patients and 29 healthy
people were detected by surface-enhanced laser desorption/ionization
mass spectrometry ({SELDI}-{MS}). {T}he protein fingerprint data
were analyzed by bioinformatics tools. {T}en folds cross-validation
support vector machine ({SVM}) was used to establish the diagnostic
pattern. {RESULTS}: {F}ive potential biomarkers were found (2085
{D}a, 5881 {D}a, 7564 {D}a, 9422 {D}a, 6044 {D}a), combined with
which the diagnostic pattern separated the ovarian cancer from the
healthy samples with a sensitivity of 96.7\%, a specificity of 96.7\%
and a positive predictive value of 96.7\%. {CONCLUSIONS}: {T}he combination
of {SELDI} with bioinformatics tools could find new biomarkers and
establish patterns with high sensitivity and specificity for the
detection of ovarian cancer.},
doi = {10.1631/jzus.2005.B0227},
pdf = {../local/Yu2005integrated.pdf},
file = {Yu2005integrated.pdf:local/Yu2005integrated.pdf:PDF},
keywords = {biosvm},
url = {http://dx.doi.org/10.1631/jzus.2005.B0227}
}
@article{Yu2002Methods,
author = {Kun Yu and Nikolai Petrovsky and Christian Schönbach and Judice
Y L Koh and Vladimir Brusic},
title = {Methods for prediction of peptide binding to {MHC} molecules: a comparative
study.},
journal = {Mol Med},
year = {2002},
volume = {8},
pages = {137--148},
number = {3},
month = {Mar},
abstract = {BACKGROUND: A variety of methods for prediction of peptide binding
to major histocompatibility complex (MHC) have been proposed. These
methods are based on binding motifs, binding matrices, hidden Markov
models (HMM), or artificial neural networks (ANN). There has been
little prior work on the comparative analysis of these methods. MATERIALS
AND METHODS: We performed a comparison of the performance of six
methods applied to the prediction of two human MHC class I molecules,
including binding matrices and motifs, ANNs, and HMMs. RESULTS: The
selection of the optimal prediction method depends on the amount
of available data (the number of peptides of known binding affinity
to the MHC molecule of interest), the biases in the data set and
the intended purpose of the prediction (screening of a single protein
versus mass screening). When little or no peptide data are available,
binding motifs are the most useful alternative to random guessing
or use of a complete overlapping set of peptides for selection of
candidate binders. As the number of known peptide binders increases,
binding matrices and HMM become more useful predictors. ANN and HMM
are the predictive methods of choice for MHC alleles with more than
100 known binding peptides. CONCLUSION: The ability of bioinformatic
methods to reliably predict MHC binding peptides, and thereby potential
T-cell epitopes, has major implications for clinical immunology,
particularly in the area of vaccine design.},
keywords = {Amino Acid Motifs; Computational Biology; Histocompatibility Antigens
Class I; Humans; Models, Molecular; Peptides; Protein Binding},
owner = {laurent},
pii = {S152836580230137X},
pmid = {12142545},
timestamp = {2007.01.27}
}
@inproceedings{Yu2005Learning,
author = {Yu, Kai and Tresp, Volker and Schwaighofer, Anton},
title = {Learning Gaussian processes from multiple tasks},
booktitle = {ICML '05: Proceedings of the 22nd international conference on Machine
learning},
year = {2005},
pages = {1012--1019},
address = {New York, NY, USA},
publisher = {ACM},
doi = {http://doi.acm.org/10.1145/1102351.1102479},
isbn = {1-59593-180-5},
location = {Bonn, Germany}
}
@inproceedings{Yu2008Stable,
author = {Yu, L. and Ding, C. and Loscalzo, S.},
title = {Stable feature selection via dense feature groups},
booktitle = {Proceeding of the 14th ACM SIGKDD international conference on Knowledge
discovery and data mining},
year = {2008},
pages = {803--811},
organization = {ACM}
}
@article{Yu2004Efficient,
author = {Yu, L. and Liu, H.},
title = {Efficient feature selection via analysis of relevance and redundancy},
journal = {The Journal of Machine Learning Research},
year = {2004},
volume = {5},
pages = {1205--1224},
publisher = {JMLR. org}
}
@article{Yu2010L2-norm,
author = {Yu, S. and Falck, T. and Daemen, A. and Tranchevent, L-C and Suykens,
Y.Ak. and De Moor, B. and Moreau, Y.},
title = {L2-norm multiple kernel learning and its application to biomedical
data fusion.},
journal = {BMC Bioinformatics},
year = {2010},
volume = {11},
pages = {309},
abstract = {BACKGROUND: This paper introduces the notion of optimizing different
norms in the dual problem of support vector machines with multiple
kernels. The selection of norms yields different extensions of multiple
kernel learning (MKL) such as L(infinity), L1, and L2 MKL. In particular,
L2 MKL is a novel method that leads to non-sparse optimal kernel
coefficients, which is different from the sparse kernel coefficients
optimized by the existing L(infinity) MKL method. In real biomedical
applications, L2 MKL may have more advantages over sparse integration
method for thoroughly combining complementary information in heterogeneous
data sources. RESULTS: We provide a theoretical analysis of the relationship
between the L2 optimization of kernels in the dual problem with the
L2 coefficient regularization in the primal problem. Understanding
the dual L2 problem grants a unified view on MKL and enables us to
extend the L2 method to a wide range of machine learning problems.
We implement L2 MKL for ranking and classification problems and compare
its performance with the sparse L(infinity) and the averaging L1
MKL methods. The experiments are carried out on six real biomedical
data sets and two large scale UCI data sets. L2 MKL yields better
performance on most of the benchmark data sets. In particular, we
propose a novel L2 MKL least squares support vector machine (LSSVM)
algorithm, which is shown to be an efficient and promising classifier
for large scale data sets processing. CONCLUSIONS: This paper extends
the statistical framework of genomic data fusion based on MKL. Allowing
non-sparse weights on the data sources is an attractive option in
settings where we believe most data sources to be relevant to the
problem at hand and want to avoid a "winner-takes-all" effect seen
in L(infinity) MKL, which can be detrimental to the performance in
prospective studies. The notion of optimizing L2 kernels can be straightforwardly
extended to ranking, classification, regression, and clustering algorithms.
To tackle the computational burden of MKL, this paper proposes several
novel LSSVM based MKL algorithms. Systematic comparison on real data
sets shows that LSSVM MKL has comparable performance as the conventional
SVM MKL algorithms. Moreover, large scale numerical experiments indicate
that when cast as semi-infinite programming, LSSVM MKL can be solved
more efficiently than SVM MKL. AVAILABILITY: The MATLAB code of algorithms
implemented in this paper is downloadable from http://homes.esat.kuleuven.be/~sistawww/bioi/syu/l2lssvm.html.},
doi = {10.1186/1471-2105-11-309},
institution = {Bioinformatics Group, Department of Electrical Engineering, Katholieke
Universiteit Leuven, Kasteelpark Arenberg 10, Heverlee B-3001, Belgium.
shee.yu@gmail.com},
owner = {mordelet},
pii = {1471-2105-11-309},
pmid = {20529363},
timestamp = {2010.09.27},
url = {http://dx.doi.org/10.1186/1471-2105-11-309}
}
@inproceedings{Yu2007Robust,
author = {Shipeng Yu and Volker Tresp and Kai Yu},
title = {Robust multi-task learning with t-processes},
booktitle = {ICML '07: Proceedings of the 24th international conference on Machine
learning},
year = {2007},
pages = {1103--1110},
address = {New York, NY, USA},
publisher = {ACM},
doi = {http://doi.acm.org/10.1145/1273496.1273635},
isbn = {978-1-59593-793-3},
location = {Corvalis, Oregon}
}
@article{Yuan2007Model,
author = {Ming Yuan and Yi Lin},
title = {Model selection and estimation in the Gaussian graphical model},
journal = {Biometrika},
year = {2007},
volume = {94},
pages = {19-35},
number = {1},
pdf = {../local/Yuan2007Model.pdf},
file = {Yuan2007Model.pdf:Yuan2007Model.pdf:PDF},
url = {http://ideas.repec.org/a/oup/biomet/v94y2007i1p19-35.html}
}
@article{Yuan2007On,
author = {Ming Yuan and Yi Lin},
title = {On the non-negative garrotte estimator},
journal = {Journal Of The Royal Statistical Society Series B},
year = {2007},
volume = {69},
pages = {143-161},
number = {2},
abstract = { We study the non-negative garrotte estimator from three different
aspects: consistency, computation and flexibility. We argue that
the non-negative garrotte is a general procedure that can be used
in combination with estimators other than the original least squares
estimator as in its original form. In particular, we consider using
the lasso, the elastic net and ridge regression along with ordinary
least squares as the initial estimate in the non-negative garrotte.
We prove that the non-negative garrotte has the nice property that,
with probability tending to 1, the solution path contains an estimate
that correctly identifies the set of important variables and is consistent
for the coefficients of the important variables, whereas such a property
may not be valid for the initial estimators. In general, we show
that the non-negative garrotte can turn a consistent estimate into
an estimate that is not only consistent in terms of estimation but
also in terms of variable selection. We also show that the non-negative
garrotte has a piecewise linear solution path. Using this fact, we
propose an efficient algorithm for computing the whole solution path
for the non-negative garrotte. Simulations and a real example demonstrate
that the non-negative garrotte is very effective in improving on
the initial estimator in terms of variable selection and estimation
accuracy. Copyright 2007 Royal Statistical Society.},
url = {http://ideas.repec.org/a/bla/jorssb/v69y2007i2p143-161.html}
}
@article{Yuan2006Model,
author = {Yuan, M. and Lin, Y.},
title = {Model selection and estimation in regression with grouped variables},
journal = {J. R. Stat. Soc. Ser. B},
year = {2006},
volume = {68},
pages = {49--67},
number = {1},
pdf = {../local/Yuan2006Model.pdf},
file = {Yuan2006Model.pdf:Yuan2006Model.pdf:PDF},
owner = {jp},
timestamp = {2008.12.05}
}
@article{Yuan2007Predicting,
author = {Yuan, Y. and Guo, L. and Shen, L. and Liu, J. S.},
title = {Predicting Gene Expression from Sequence: A Reexamination},
journal = {PLoS Comput. Biol.},
year = {2007},
volume = {3},
pages = {e243},
number = {11},
doi = {10.1371/journal.pcbi.0030243},
pdf = {../local/Yuan2007Predicting.pdf},
file = {Yuan2007Predicting.pdf:Yuan2007Predicting.pdf:PDF},
owner = {jp},
timestamp = {2009.01.05},
url = {http://dx.doi.org/10.1371/journal.pcbi.0030243}
}
@article{Yuan2002Prediction,
author = {Yuan, Z. and Burrage, K. and Mattick, J.S.},
title = {Prediction of protein solvent accessibility using support vector
machines},
journal = {Proteins},
year = {2002},
volume = {48},
pages = {566-570},
number = {3},
abstract = {A {S}upport {V}ector {M}achine learning system has been trained to
predict protein solvent accessibility from the primary structure.
{D}ifferent kernel functions and sliding window sizes have been explored
to find how they affect the prediction performance. {U}sing a cut-off
threshold of 15% that splits the dataset evenly (an equal number
of exposed and buried residues), this method was able to achieve
a prediction accuracy of 70.1% for single sequence input and 73.9%
for multiple alignment sequence input, respectively. {T}he prediction
of three and more states of solvent accessibility was also studied
and compared with other methods. {T}he prediction accuracies are
better than, or comparable to, those obtained by other methods such
as neural networks, {B}ayesian classification, multiple linear regression,
and information theory. {I}n addition, our results further suggest
that this system may be combined with other prediction methods to
achieve more reliable results, and that the {S}upport {V}ector {M}achine
method is a very useful tool for biological sequence analysis.},
doi = {10.1002/prot.10176},
pdf = {../local/Yuan2002Prediction.pdf},
file = {Yuan2002Prediction.pdf:local/Yuan2002Prediction.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://dx.doi.org/10.1002/prot.10176}
}
@article{Yuan2004SVMtm,
author = {Yuan, Z. and Mattick, J.S. and Teasdale, R.D.},
title = {{{SVM}tm}: support vector machines to predict transmembrane segments.},
journal = {J. {C}omput. {C}hem.},
year = {2004},
volume = {25},
pages = {632},
number = {5},
month = {6},
abstract = {A new method has been developed for prediction of transmembrane helices
using support vector machines. {D}ifferent coding schemes of protein
sequences were explored, and their performances were assessed by
crossvalidation tests. {T}he best performance method can predict
the transmembrane helices with sensitivity of 93.4% and precision
of 92.0%. {F}or each predicted transmembrane segment, a score is
given to show the strength of transmembrane signal and the prediction
reliability. {I}n particular, this method can distinguish transmembrane
proteins from soluble proteins with an accuracy of approximately
99%. {T}his method can be used to complement current transmembrane
helix prediction methods and can be used for consensus analysis of
entire proteomes. {T}he predictor is located at http://genet.imb.uq.edu.au/predictors/{SVM}tm.},
doi = {10.1002/jcc.10411},
pdf = {../local/Yuan2004SVMtm.pdf},
file = {Yuan2004SVMtm.pdf:local/Yuan2004SVMtm.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://dx.doi.org/10.1002/jcc.10411}
}
@article{Zheng2006Robust,
author = {Yefeng Z. and Doermann, D.},
title = {Robust point matching for nonrigid shapes by preserving local neighborhood
structures},
journal = {IEEE Trans. Pattern Anal. Mach. Intell.},
year = {2006},
volume = {28},
pages = {643--649},
number = {4},
month = {April },
doi = {10.1109/TPAMI.2006.81},
owner = {michael},
timestamp = {2009.11.17}
}
@article{Zaki2005Application,
author = {Zaki, N. M. and Deris, S. and Illias, R.},
title = {Application of string kernels in protein sequence classification.},
journal = {Appl. {B}ioinformatics},
year = {2005},
volume = {4},
pages = {45-52},
number = {1},
abstract = {I{NTRODUCTION}: {T}he production of biological information has become
much greater than its consumption. {T}he key issue now is how to
organise and manage the huge amount of novel information to facilitate
access to this useful and important biological information. {O}ne
core problem in classifying biological information is the annotation
of new protein sequences with structural and functional features.
{METHOD}: {T}his article introduces the application of string kernels
in classifying protein sequences into homogeneous families. {A} string
kernel approach used in conjunction with support vector machines
has been shown to achieve good performance in text categorisation
tasks. {W}e evaluated and analysed the performance of this approach,
and we present experimental results on three selected families from
the {SCOP} ({S}tructural {C}lassification of {P}roteins) database.
{W}e then compared the overall performance of this method with the
existing protein classification methods on benchmark {SCOP} datasets.
{RESULTS}: {A}ccording to the {F}1 performance measure and the rate
of false positive ({RFP}) measure, the string kernel method performs
well in classifying protein sequences. {T}he method outperformed
all the generative-based methods and is comparable with the {SVM}-{F}isher
method. {DISCUSSION}: {A}lthough the string kernel approach makes
no use of prior biological knowledge, it still captures sufficient
biological information to enable it to outperform some of the state-of-the-art
methods.},
keywords = {biosvm},
pii = {415}
}
@article{Zamore2000RNAi,
author = {Zamore, P.D. and Tuschl, T. and Sharp, P.A. and Bartel, D.P.},
title = {R{NA}i: double-stranded {RNA} directs the {ATP}-dependent cleavage
of m{RNA} at 21 to 23 nucleotide intervals.},
journal = {Cell},
year = {2000},
volume = {101},
pages = {25-33},
number = {1},
month = {Mar},
abstract = {Double-stranded {RNA} (ds{RNA}) directs the sequence-specific degradation
of m{RNA} through a process known as {RNA} interference ({RNA}i).
{U}sing a recently developed {D}rosophila in vitro system, we examined
the molecular mechanism underlying {RNA}i. {W}e find that {RNA}i
is {ATP} dependent yet uncoupled from m{RNA} translation. {D}uring
the {RNA}i reaction, both strands of the ds{RNA} are processed to
{RNA} segments 21-23 nucleotides in length. {P}rocessing of the ds{RNA}
to the small {RNA} fragments does not require the targeted m{RNA}.
{T}he m{RNA} is cleaved only within the region of identity with the
ds{RNA}. {C}leavage occurs at sites 21-23 nucleotides apart, the
same interval observed for the ds{RNA} itself, suggesting that the
21-23 nucleotide fragments from the ds{RNA} are guiding m{RNA} cleavage.},
doi = {10.1016/S0092-8674(00)80620-0},
keywords = {sirna},
pii = {S0092-8674(00)80620-0},
url = {http://dx.doi.org/10.1016/S0092-8674(00)80620-0}
}
@article{Zanella2010High,
author = {Fabian Zanella and James B Lorens and Wolfgang Link},
title = {High content screening: seeing is believing.},
journal = {Trends Biotechnol},
year = {2010},
volume = {28},
pages = {237--245},
number = {5},
month = {May},
abstract = {High content screening (HCS) combines the efficiency of high-throughput
techniques with the ability of cellular imaging to collect quantitative
data from complex biological systems. HCS technology is integrated
into all aspects of contemporary drug discovery, including primary
compound screening, post-primary screening capable of supporting
structure-activity relationships, and early evaluation of ADME (absorption,
distribution, metabolism and excretion)/toxicity properties and complex
multivariate drug profiling. Recently, high content approaches have
been used extensively to interrogate stem cell biology. Despite these
dramatic advances, a number of significant challenges remain related
to the use of more biology- and disease-relevant cell systems, the
development of informative reagents to measure and manipulate cellular
events, and the integration of data management and informatics.},
doi = {10.1016/j.tibtech.2010.02.005},
institution = {Experimental Therapeutics Program, Centro Nacional de Investigaciones
Oncologicas, Melchor Fernandez Almagro 3, 28029 Madrid, Spain.},
keywords = {Animals; Drug Evaluation, Preclinical, instrumentation/methods; High-Throughput
Screening Assays, instrumentation/methods; Humans; Stem Cells, drug
effects; Structure-Activity Relationship},
language = {eng},
medline-pst = {ppublish},
owner = {philippe},
pii = {S0167-7799(10)00035-1},
pmid = {20346526},
timestamp = {2010.07.26},
url = {http://dx.doi.org/10.1016/j.tibtech.2010.02.005}
}
@article{Zangwill2004Heidelberg,
author = {Linda M Zangwill and Kwokleung Chan and Christopher Bowd and Jicuang
Hao and Te-Won Lee and Robert N Weinreb and Terrence J Sejnowski
and Michael H Goldbaum},
title = {Heidelberg retina tomograph measurements of the optic disc and parapapillary
retina for detecting glaucoma analyzed by machine learning classifiers.},
journal = {Invest {O}phthalmol {V}is {S}ci},
year = {2004},
volume = {45},
pages = {3144-51},
number = {9},
month = {Sep},
abstract = {P{URPOSE}: {T}o determine whether topographical measurements of the
parapapillary region analyzed by machine learning classifiers can
detect early to moderate glaucoma better than similarly processed
measurements obtained within the disc margin and to improve methods
for optimization of machine learning classifier feature selection.
{METHODS}: {O}ne eye of each of 95 patients with early to moderate
glaucomatous visual field damage and of each of 135 normal subjects
older than 40 years participating in the longitudinal {D}iagnostic
{I}nnovations in {G}laucoma {S}tudy ({DIGS}) were included. {H}eidelberg
{R}etina {T}omograph ({HRT}; {H}eidelberg {E}ngineering, {D}ossenheim,
{G}ermany) mean height contour was measured in 36 equal sectors,
both along the disc margin and in the parapapillary region (at a
mean contour line radius of 1.7 mm). {E}ach sector was evaluated
individually and in combination with other sectors. {G}aussian support
vector machine ({SVM}) learning classifiers were used to interpret
{HRT} sector measurements along the disc margin and in the parapapillary
region, to differentiate between eyes with normal and glaucomatous
visual fields and to compare the results with global and regional
{HRT} parameter measurements. {T}he area under the receiver operating
characteristic ({ROC}) curve was used to measure diagnostic performance
of the {HRT} parameters and to evaluate the cross-validation strategies
and forward selection and backward elimination optimization techniques
that were used to generate the reduced feature sets. {RESULTS}: {T}he
area under the {ROC} curve for mean height contour of the 36 sectors
along the disc margin was larger than that for the mean height contour
in the parapapillary region (0.97 and 0.85, respectively). {O}f the
36 individual sectors along the disc margin, those in the inferior
region between 240 degrees and 300 degrees, had the largest area
under the {ROC} curve (0.85-0.91). {W}ith {SVM} {G}aussian techniques,
the regional parameters showed the best ability to discriminate between
normal eyes and eyes with glaucomatous visual field damage, followed
by the global parameters, mean height contour measures along the
disc margin, and mean height contour measures in the parapapillary
region. {T}he area under the {ROC} curve was 0.98, 0.94, 0.93, and
0.85, respectively. {C}ross-validation and optimization techniques
demonstrated that good discrimination (99\% of peak area under the
{ROC} curve) can be obtained with a reduced number of {HRT} parameters.
{CONCLUSIONS}: {M}ean height contour measurements along the disc
margin discriminated between normal and glaucomatous eyes better
than measurements obtained in the parapapillary region.},
doi = {10.1167/iovs.04-0202},
pdf = {../local/Zangwill2004Heidelberg.pdf},
file = {Zangwill2004Heidelberg.pdf:local/Zangwill2004Heidelberg.pdf:PDF},
pii = {45/9/3144},
url = {http://dx.doi.org/10.1167/iovs.04-0202}
}
@inproceedings{Zaslavskiy2008path,
author = {Zaslavskiy, M. and Bach, F. and Vert, J. P.},
title = {A path following algorithm for graph matching},
booktitle = {Image and Signal Processing, Proceedings of the 3rd International
Conference, ICISP 2008},
year = {2008},
editor = {Elmoataz, A. and Lezoray, O. and Nouboud, F. and Mammass, D.},
volume = {5099},
series = {LNCS},
pages = {329--337},
publisher = {Springer Berlin / Heidelberg},
abstract = {We propose a convex-concave programming approach for the labelled
weighted graph matching problem. The convex-concave programming formulation
is obtained by rewriting the graph matching problem as a least-square
problem on the set of permutation matrices and relaxing it to two
different optimization problems: a quadratic convex and a quadratic
concave optimization problem on the set of doubly stochastic matrices.
The concave relaxation has the same global minimum as the initial
graph matching problem, but the search for its global minimum is
aslo a complex combinatorial problem. We therefore construct an approximation
of the concave problem solution by following a solution path of the
convex-concave problem obtained by linear interpolation of the convex
and concave formulations, starting from the convex relaxation. The
algorithm is compared with some of the best performing graph matching
methods on three datasets: simulated graphs, QAPLib and handwritten
chinese characters.},
doi = {10.1007/978-3-540-69905-7_38},
owner = {jp},
timestamp = {2008.10.03},
url = {http://dx.doi.org/10.1007/978-3-540-69905-7_38}
}
@misc{Zaslavskiy2008GraphM,
author = {M. Zaslavskiy and F. Bach and J. P. Vert},
title = {{GRAPHM}: Graph matching package},
year = {2008},
note = {Available at \texttt{http://cbio.ensmp.fr/graphm}},
owner = {michael},
timestamp = {2008.10.02},
url = {http://cbio.ensmp.fr/graphm/}
}
@inproceedings{Zaslavskiy2010Many-to-Many,
author = {Zaslavskiy, M. and Bach, F. and Vert, J.-P.},
title = {Many-to-Many Graph Matching: A Continuous Relaxation Approach},
booktitle = {Machine Learning and Knowledge Discovery in Databases},
year = {2010},
editor = {Balc{\'a}zar, J. and Bonchi, F. and Gionis, A. and Sebag, M.},
volume = {6323},
series = {Lecture Notes in Computer Science},
pages = {515-530},
publisher = {Springer Berlin / Heidelberg},
doi = {10.1007/978-3-642-15939-8_33},
owner = {jp},
timestamp = {2010.09.22},
url = {http://dx.doi.org/10.1007/978-3-642-15939-8_33}
}
@article{Zaslavskiy2009Path,
author = {Zaslavskiy, M. and Bach, F. and Vert, J.-P.},
title = {A Path Following Algorithm for the Graph Matching Problem},
journal = {IEEE Trans. Pattern Anal. Mach. Intell.},
year = {2009},
volume = {31},
pages = {2227--2242},
number = {12},
abstract = {We propose a convex-concave programming approach for the labeled weighted
graph matching problem. The convex-concave programming formulation
is obtained by rewriting the weighted graph matching problem as a
least-square problem on the set of permutation matrices and relaxing
it to two different optimization problems: a quadratic convex and
a quadratic concave optimization problem on the set of doubly stochastic
matrices. The concave relaxation has the same global minimum as the
initial graph matching problem, but the search for its global minimum
is also a hard combinatorial problem. We, therefore, construct an
approximation of the concave problem solution by following a solution
path of a convex-concave problem obtained by linear interpolation
of the convex and concave formulations, starting from the convex
relaxation. This method allows to easily integrate the information
on graph label similarities into the optimization problem, and therefore,
perform labeled weighted graph matching. The algorithm is compared
with some of the best performing graph matching methods on four data
sets: simulated graphs, QAPLib, retina vessel images, and handwritten
Chinese characters. In all cases, the results are competitive with
the state of the art.},
doi = {10.1109/TPAMI.2008.245},
pdf = {../local/Zaslavskiy2009Path.pdf},
file = {Zaslavskiy2009Path.pdf:Zaslavskiy2009Path.pdf:PDF},
owner = {jp},
timestamp = {2009.10.22},
url = {http://dx.doi.org/10.1109/TPAMI.2008.245}
}
@techreport{Zaslavskiy2008patha,
author = {Zaslavskiy, M. and Bach, F. and Vert, J.-P.},
title = {A path following algorithm for the graph matching problem},
institution = {HAL},
year = {2008},
number = {00232851},
note = {To appear in IEEE Trans. Pattern Anal. Mach. Intell.},
abstract = {We propose a convex-concave programming approach for the labeled weighted
graph matching problem. The convex-concave programming formulation
is obtained by rewriting the weighted graph matching problem as a
least-square problem on the set of permutation matrices and relaxing
it to two different optimization problems: a quadratic convex and
a quadratic concave optimization problem on the set of doubly stochastic
matrices. The concave relaxation has the same global minimum as the
initial graph matching problem, but the search for its global minimum
is also a hard combinatorial problem. We therefore construct an approximation
of the concave problem solution by following a solution path of a
convex-concave problem obtained by linear interpolation of the convex
and concave formulations, starting from the convex relaxation. This
method allows to easily integrate the information on graph label
similarities into the optimization problem, and therefore to perform
labeled weighted graph matching. The algorithm is compared with some
of the best performing graph matching methods on four datasets: simulated
graphs, QAPLib, retina vessel images and handwritten chinese characters.
In all cases, the results are competitive with the state-of-the-art.},
timestamp = {2008.07.29},
url = {http://hal.archives-ouvertes.fr/hal-00232851}
}
@inproceedings{Zaslavskiy2009Phrase,
author = {Zaslavskiy, M. and Dymetman, M. and Cancedda, N.},
title = {Phrase-Based Statistical Machine Translation as a Traveling Salesman
Problem},
booktitle = {Proceedings of the Joint Conference of the 47th Annual Meeting of
the ACL and the 4th International Joint Conference on Natural Language
Processing of the AFNLP},
year = {2009},
pages = {333--341},
address = {Suntec, Singapore},
month = {August},
publisher = {Association for Computational Linguistics},
url = {http://www.aclweb.org/anthology/P/P09/P09-1038}
}
@article{Zaslavskiy2009Global,
author = {M. Zaslavskiyi and F. Bach and J-P. Vert},
title = {Global alignment of protein-protein interaction networks by graph
matching methods},
journal = {Bioinformatics},
year = {2009},
volume = {25},
number = {12},
bibsource = {DBLP, http://dblp.uni-trier.de},
ee = {http://dx.doi.org/10.1093/bioinformatics/btp196}
}
@article{Zavaljevski2002Support,
author = {Zavaljevski, N. and Stevens, F.J. and Reifman, J.},
title = {Support vector machines with selective kernel scaling for protein
classification and identification of key amino acid positions },
journal = {Bioinformatics},
year = {2002},
volume = {18},
pages = {689--696},
number = {5},
abstract = {Motivation: {D}ata that characterize primary and tertiary structures
of proteins are now accumulating at a rapid and accelerating rate
and require automated computational tools to extract critical information
relating amino acid changes with the spectrum of functionally attributes
exhibited by a protein. {W}e propose that immunoglobulin-type beta-domains,
which are found in approximate 400 functionally distinct forms in
humans alone, provide the immense genetic variation within limited
conformational changes that might facilitate the development of new
computational tools. {A}s an initial step, we describe here an approach
based on {S}upport {V}ector {M}achine ({SVM}) technology to identify
amino acid variations that contribute to the functional attribute
of pathological self-assembly by some human antibody light chains
produced during plasma cell diseases. {R}esults: {W}e demonstrate
that {SVM}s with selective kernel scaling are an effective tool in
discriminating between benign and pathologic human immunoglobulin
light chains. {I}nitial results compare favorably against manual
classification performed by experts and indicate the capability of
{SVM}s to capture the underlying structure of the data. {T}he data
set consists of 70 proteins of human antibody 1 light chains, each
represented by aligned sequences of 120 amino acids. {W}e perform
feature selection based on a first-order adaptive scaling algorithm,
which confirms the importance of changes in certain amino acid positions
and identifies other positions that are key in the characterization
of protein function.},
pdf = {../local/zava02.pdf},
file = {zava02.pdf:local/zava02.pdf:PDF},
keywords = {biosvm},
subject = {biokernel},
url = {http://bioinformatics.oupjournals.org/cgi/content/abstract/18/5/689}
}
@article{Zellner1962An,
author = {Zellner, Arnold},
title = {An Efficient Method of Estimating Seemingly Unrelated Regressions
and Tests for Aggregation Bias},
journal = {J. Am. Stat. Assoc.},
year = {1962},
volume = {57},
pages = {348--368},
number = {298},
abstract = {In this paper a method of estimating the parameters of a set of regression
equations is reported which involves application of Aitken's generalized
least-squares [1] to the whole system of equations. Under conditions
generally encountered in practice, it is found that the regression
coefficient estimators so obtained are at least asymptotically more
efficient than those obtained by an equation-by-equation application
of least squares. This gain in efficiency can be quite large if \"{i}ndependent"
variables in different equations are not highly correlated and if
disturbance terms in different equations are highly correlated. Further,
tests of the hypothesis that all regression equation coefficient
vectors are equal, based on "micro" and "macro" data, are described.
If this hypothesis is accepted, there will be no aggregation bias.
Finally, the estimation procedure and the "micro-test" for aggregation
bias are applied in the analysis of annual investment data, 1935-1954,
for two firms.},
citeulike-article-id = {1211699},
citeulike-linkout-0 = {http://dx.doi.org/10.2307/2281644},
citeulike-linkout-1 = {http://www.jstor.org/stable/2281644},
doi = {10.2307/2281644},
keywords = {480-3, econometrics, sur},
posted-at = {2007-04-06 06:07:30},
priority = {2},
url = {http://dx.doi.org/10.2307/2281644}
}
@article{Zernov2003Drug,
author = {V. V. Zernov and K. V. Balakin and A. A. Ivaschenko and N. P. Savchuk
and I. V. Pletnev},
title = {Drug discovery using support vector machines. {T}he case studies
of drug-likeness, agrochemical-likeness, and enzyme inhibition predictions.},
journal = {J {C}hem {I}nf {C}omput {S}ci},
year = {2003},
volume = {43},
pages = {2048-56},
number = {6},
abstract = {Support {V}ector {M}achines ({SVM}) is a powerful classification and
regression tool that is becoming increasingly popular in various
machine learning applications. {W}e tested the ability of {SVM},
in comparison with well-known neural network techniques, to predict
drug-likeness and agrochemical-likeness for large compound collections.
{F}or both kinds of data, {SVM} outperforms various neural networks
using the same set of descriptors. {W}e also used {SVM} for estimating
the activity of {C}arbonic {A}nhydrase {II} ({CA} {II}) enzyme inhibitors
and found that the prediction quality of our {SVM} model is better
than that reported earlier for conventional {QSAR}. {M}odel characteristics
and data set features were studied in detail.},
doi = {10.1021/ci0340916},
pdf = {../local/Zernov2003Drug.pdf},
file = {Zernov2003Drug.pdf:local/Zernov2003Drug.pdf:PDF},
keywords = {biosvm chemoinformatics},
url = {http://dx.doi.org/10.1021/ci0340916}
}
@article{Zhang2009Penalized,
author = {Zhang, D. and Lin, Y. and Zhang, M.},
title = {Penalized orthogonal-components regression for large p small n data},
journal = {Electron. J. Statist.},
year = {2009},
volume = {3},
pages = {781--796},
abstract = {Here we propose a penalized orthogonal-components regression (POCRE)
for large p small n data. Orthogonal components are sequentially
constructed to maximize, upon standardization, their correlation
to the response residuals. A new penalization framework, implemented
via empirical Bayes thresholding, is presented to effectively identify
sparse predictors of each component. POCRE is computationally efficient
owing to its sequential construction of leading sparse principal
components. In addition, such construction offers other properties
such as grouping highly correlated predictors and allowing for collinear
or nearly collinear predictors. With multivariate responses, POCRE
can construct common components and thus build up latent-variable
models for large p small n data.},
doi = {10.1214/09-EJS354},
owner = {jp},
review = {A Bayesian formulation of sparse PLS},
timestamp = {2010.01.10},
url = {http://dx.doi.org/10.1214/09-EJS354}
}
@article{Zhang2005MULTIPRED,
author = {Zhang, G. L. and Khan, A. M. and Srinivasan, K. N. and August, J.
T. and Brusic, V.},
title = {{MULTIPRED}: a computational system for prediction of promiscuous
{HLA} binding peptides.},
journal = {Nucleic Acids Res/},
year = {2005},
volume = {33},
pages = {W172--W179},
number = {Web Server issue},
month = {Jul},
abstract = {MULTIPRED is a web-based computational system for the prediction of
peptide binding to multiple molecules (proteins) belonging to human
leukocyte antigens (HLA) class I A2, A3 and class II DR supertypes.
It uses hidden Markov models and artificial neural network methods
as predictive engines. A novel data representation method enables
MULTIPRED to predict peptides that promiscuously bind multiple HLA
alleles within one HLA supertype. Extensive testing was performed
for validation of the prediction models. Testing results show that
MULTIPRED is both sensitive and specific and it has good predictive
ability (area under the receiver operating characteristic curve A(ROC)
> 0.80). MULTIPRED can be used for the mapping of promiscuous T-cell
epitopes as well as the regions of high concentration of these targets--termed
T-cell epitope hotspots. MULTIPRED is available at http://antigen.i2r.a-star.edu.sg/multipred/.},
doi = {10.1093/nar/gki452},
keywords = {Algorithms, Amino Acid Sequence, Antigen-Antibody Complex, Automated,
Binding Sites, Computational Biology, Drug Delivery Systems, Drug
Design, Epitopes, HLA Antigens, HLA-A Antigens, HLA-DR Antigens,
Humans, Internet, Markov Chains, Molecular Sequence Data, Neural
Networks (Computer), Pattern Recognition, Peptides, Protein, Protein
Binding, Protein Interaction Mapping, Sequence Analysis, Software,
T-Lymphocyte, User-Computer Interface, Viral Vaccines, 15980449},
pii = {33/suppl_2/W172},
pmid = {15980449},
timestamp = {2007.01.25},
url = {http://dx.doi.org/10.1093/nar/gki452}
}
@article{Zhang2008Variable,
author = {Zhang, H. H. and Liu, Y. and Wu, Y. and Zhu, J.},
title = {Variable selection for multicategory {SVM} via adaptive sup-norm
regularization},
journal = {Electronic Journal of Statistics},
year = {2008},
volume = {2},
pages = {149--167},
doi = {10.1214/08-EJS122},
keywords = {lasso},
owner = {jp},
timestamp = {2009.01.05},
url = {http://dx.doi.org/10.1214/08-EJS122}
}
@article{Zhang2009Maximum,
author = {Zhang, K. and Tsang, I.W. and Kwok, J.T.},
title = {Maximum Margin Clustering Made Practical},
journal = {IEEE T. Neural Networ.},
year = {2009},
volume = {20},
pages = {583--596},
number = {4},
doi = {10.1109/TNN.2008.2010620},
issn = {1045-9227},
keywords = {learning (artificial intelligence), optimisation, pattern clustering,
Laplacian/square loss, alternating optimization, maximum margin clustering,
nonconvex optimization problem, nonconvex problem, semidefinite programs,
supervised learning, Large margin methods, maximum margin clustering
(MMC), scalability, unsupervised learning},
owner = {mordelet},
timestamp = {2009.10.22}
}
@article{Zhang2004Intrusion,
author = {Lian-hua Zhang and Guan-hua Zhang and Jie Zhang and Ying-cai Bai},
title = {Intrusion detection using rough set classification.},
journal = {J {Z}hejiang {U}niv {S}ci},
year = {2004},
volume = {5},
pages = {1076-86},
number = {9},
month = {Sep},
abstract = {Recently machine learning-based intrusion detection approaches have
been subjected to extensive researches because they can detect both
misuse and anomaly. {I}n this paper, rough set classification ({RSC}),
a modern learning algorithm, is used to rank the features extracted
for detecting intrusions and generate intrusion detection models.
{F}eature ranking is a very critical step when building the model.
{RSC} performs feature ranking before generating rules, and converts
the feature ranking to minimal hitting set problem addressed by using
genetic algorithm ({GA}). {T}his is done in classical approaches
using {S}upport {V}ector {M}achine ({SVM}) by executing many iterations,
each of which removes one useless feature. {C}ompared with those
methods, our method can avoid many iterations. {I}n addition, a hybrid
genetic algorithm is proposed to increase the convergence speed and
decrease the training time of {RSC}. {T}he models generated by {RSC}
take the form of "{IF}-{THEN}" rules, which have the advantage of
explication. {T}ests and comparison of {RSC} with {SVM} on {DARPA}
benchmark data showed that for {P}robe and {D}o{S} attacks both {RSC}
and {SVM} yielded highly accurate results (greater than 99\% accuracy
on testing set).},
doi = {10.1631/jzus.2004.1076},
pdf = {../local/Zhang2004Intrusion.pdf},
file = {Zhang2004Intrusion.pdf:local/Zhang2004Intrusion.pdf:PDF},
url = {http://dx.doi.org/10.1631/jzus.2004.1076}
}
@article{Zhang2004Hidden,
author = {Li Zhang and Weida Zhou and Licheng Jiao},
title = {Hidden space support vector machines.},
journal = {I{EEE} {T}rans {N}eural {N}etw},
year = {2004},
volume = {15},
pages = {1424-34},
number = {6},
month = {Nov},
abstract = {Hidden space support vector machines ({HSSVM}s) are presented in this
paper. {T}he input patterns are mapped into a high-dimensional hidden
space by a set of hidden nonlinear functions and then the structural
risk is introduced into the hidden space to construct {HSSVM}s. {M}oreover,
the conditions for the nonlinear kernel function in {HSSVM}s are
more relaxed, and even differentiability is not required. {C}ompared
with support vector machines ({SVM}s), {HSSVM}s can adopt more kinds
of kernel functions because the positive definite property of the
kernel function is not a necessary condition. {T}he performance of
{HSSVM}s for pattern recognition and regression estimation is also
analyzed. {E}xperiments on artificial and real-world domains confirm
the feasibility and the validity of our algorithms.},
doi = {10.1109/TNN.2004.831161},
pdf = {../local/Zhang2004Hidden.pdf},
file = {Zhang2004Hidden.pdf:local/Zhang2004Hidden.pdf:PDF},
url = {http://dx.doi.org/10.1109/TNN.2004.831161}
}
@article{Zhang2004Wavelet,
author = {Li Zhang and Weida Zhou and Licheng Jiao},
title = {Wavelet support vector machine.},
journal = {I{EEE} {T}rans {S}yst {M}an {C}ybern {B} {C}ybern},
year = {2004},
volume = {34},
pages = {34-9},
number = {1},
month = {Feb},
abstract = {An admissible support vector ({SV}) kernel (the wavelet kernel), by
which we can construct a wavelet support vector machine ({SVM}),
is presented. {T}he wavelet kernel is a kind of multidimensional
wavelet function that can approximate arbitrary nonlinear functions.
{T}he existence of wavelet kernels is proven by results of theoretic
analysis. {C}omputer simulations show the feasibility and validity
of wavelet support vector machines ({WSVM}s) in regression and pattern
recognition.},
doi = {10.1109/TSMCB.2003.811113},
pdf = {../local/Zhang2004Wavelet.pdf},
file = {Zhang2004Wavelet.pdf:local/Zhang2004Wavelet.pdf:PDF},
url = {http://dx.doi.org/10.1109/TSMCB.2003.811113}
}
@article{Zhang2005Study,
author = {Lu-Da Zhang and Shi-Guang Su and Lai-Sheng Wang and Jun-Hui Li and
Li-Ming Yang},
title = {{S}tudy on application of {F}ourier transformation near-infrared
spectroscopy analysis with support vector machine ({SVM})},
journal = {Guang {P}u {X}ue {Y}u {G}uang {P}u {F}en {X}i},
year = {2005},
volume = {25},
pages = {33-5},
number = {1},
month = {Jan},
abstract = {Support {V}ector {M}achine ({SVM}) is a method for the research on
identifying two types of problem. {I}t is the latest branch in the
statistics study theories, and the identification model has a strict
mathematics foundation. {I}n this paper, the basic principle and
method of {SVM} are not only introduced, but also applied to chemometrics.
{O}ne hundred and three rhubarb samples were used as experimental
materials. {T}he identification models were established with near-infrared
spectroscopy and {SVM} training method with the intention of identifying
whether the rhubarb samples are true or false. {T}he thirty-three
samples in training set were identified by the identifying models
with the accurate rate of 100\%, while seventy estimate samples had
an accurate rate of 96.77\%. {T}he research result provided the method
of identifying the traditional {C}hinese medicine rhubarb quickly.
{S}o, it shows the feasibility of establishing the models with near-infrared
spectroscopy and {SVM} method to identify biological samples. {T}his
paper introduced the theme of {SVM} training method in order to beget
the attention of the research members who deal with chemometrics.}
}
@article{Zhang2010Detecting,
author = {Zhang, N. R. and Siegmund, D. O. and Ji, H. and Li, J.},
title = {Detecting Simultaneous Change-points in Multiple Sequences},
journal = {Biometrika},
year = {2010},
volume = {97},
pages = {631--645},
number = {3},
doi = {10.1093/biomet/asq025},
pdf = {../local/Zhang2010Detecting.pdf},
file = {Zhang2010Detecting.pdf:Zhang2010Detecting.pdf:PDF},
keywords = {segmentation},
owner = {jp},
timestamp = {2010.06.01},
url = {http://dx.doi.org/10.1093/biomet/asq025}
}
@article{Zhang2005Improved,
author = {Qidong Zhang and Sukjoon Yoon and William J Welsh},
title = {Improved method for predicting beta-turn using support vector machine.},
journal = {Bioinformatics},
year = {2005},
volume = {21},
pages = {2370-4},
number = {10},
month = {May},
abstract = {M{OTIVATION}: {N}umerous methods for predicting beta-turns in proteins
have been developed based on various computational schemes. {H}ere,
we introduce a new method of beta-turn prediction that uses the support
vector machine ({SVM}) algorithm together with predicted secondary
structure information. {V}arious parameters from the {SVM} have been
adjusted to achieve optimal prediction performance. {RESULTS}: {T}he
{SVM} method achieved excellent performance as measured by the {M}atthews
correlation coefficient ({MCC} = 0.45) using a 7-fold cross validation
on a database of 426 non-homologous protein chains. {T}o our best
knowledge, this {MCC} value is the highest achieved so far for predicting
beta-turn. {T}he overall prediction accuracy {Q}total was 77.3\%,
which is the best among the existing prediction methods. {A}mong
its unique attractive features, the present {SVM} method avoids overtraining
and compresses information and provides a predicted reliability index.},
doi = {10.1093/bioinformatics/bti358},
pdf = {../local/Zhang2005Improved.pdf},
file = {Zhang2005Improved.pdf:local/Zhang2005Improved.pdf:PDF},
keywords = {biosvm},
pii = {bti358},
url = {http://dx.doi.org/10.1093/bioinformatics/bti358}
}
@article{Zhang2003Classification,
author = {Zhang, S.-W. and Pan, Q. and Zhang, H.-C. and Zhang, Y-L. and Wang,
H.-Y.},
title = {Classification of protein quaternary structure with support vector
machine},
journal = {Bioinformatics},
year = {2003},
volume = {19},
pages = {2390-2396},
number = {18},
abstract = {Motivation: {S}ince the gap between sharply increasing known sequences
and slow accumulation of known structures is becoming large, an automatic
classification process based on the primary sequences and known three-dimensional
structure becomes indispensable. {T}he classification of protein
quaternary structure based on the primary sequences can provide some
useful information for the biologists. {S}o a fully automatic and
reliable classification system is needed. {T}his work tries to look
for the effective methods of extracting attribute and the algorithm
for classifying the quaternary structure from the primary sequences.
{R}esults: {B}oth of the support vector machine ({SVM}) and the covariant
discriminant algorithms have been first introduced to predict quaternary
structure properties from the protein primary sequences. {T}he amino
acid composition and the auto-correlation functions based on the
amino acid index profile of the primary sequence have been taken
into account in the algorithms. {W}e have analyzed 472 amino acid
indices and selected the four amino acid indices as the examples,
which have the best performance. {T}hus the five attribute parameter
data sets ({COMP}, {FASG}, {NISK}, {WOLS} and {KYTJ}) were established
from the protein primary sequences. {T}he {COMP} attribute data set
is composed of amino acid composition, and the {FASG}, {NISK}, {WOLS}
and {KYTJ} attribute data sets are composed of the amino acid composition
and the auto-correlation functions of the corresponding amino acid
residue index. {T}he overall accuracies of {SVM} are 78.5, 87.5,
83.2, 81.7 and 81.9%, respectively, for {COMP}, {FASG}, {NISK}, {WOLS}
and {KYTJ} data sets in jackknife test, which are 19.6, 7.8, 15.5,
13.1 and 15.8%, respectively, higher than that of the covariant discriminant
algorithm in the same test. {T}he results show that {SVM} may be
applied to discriminate between the primary sequences of homodimers
and non-homodimers and the two protein sequence descriptors can reflect
the quaternary structure information. {C}ompared with previous {R}obert
{G}arian's investigation, the performance of {SVM} is almost equal
to that of the {D}ecision tree models, and the methods of extracting
feature vector from the primary sequences are superior to {R}obert's
binning function method. {A}vailability: {P}rograms are available
on request from the authors.},
pdf = {../local/Zhang2003Classification.pdf},
file = {Zhang2003Classification.pdf:local/Zhang2003Classification.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://bioinformatics.oupjournals.org/cgi/content/abstract/19/18/2390}
}
@article{Zhang2004Statistical,
author = {Zhang, T.},
title = {Statistical behavior and consistency of classification methods based
on convex risk minimization},
journal = {Ann. {S}tat.},
year = {2004},
volume = {32},
pages = {56-134},
abstract = {We study how closely the optimal {B}ayes error rate can be approximately
reached using a classification algorithm that computes a classifier
by minimizing a convex upper bound of the classification error function.
{T}he measurement of closeness is characterized by the loss function
used in the estimation. {W}e show that such a classification scheme
can be generally regarded as a (nonmaximum-likelihood) conditional
in-class probability estimate, and we use this analysis to compare
various convex loss functions that have appeared in the literature.
{F}urthermore, the theoretical insight allows us to design good loss
functions with desirable properties. {A}nother aspect of our analysis
is to demonstrate the consistency of certain classification methods
using convex risk minimization. {T}his study sheds light on the good
performance of some recently proposed linear classification methods
including boosting and support vector machines. {I}t also shows their
limitations and suggests possible improvements.},
doi = {10.1214/aos/1079120130},
pdf = {../local/Zhang2004Statistical.pdf},
file = {Zhang2004Statistical.pdf:local/Zhang2004Statistical.pdf:PDF},
owner = {jeanphilippevert},
url = {http://dx.doi.org/10.1214/aos/1079120130}
}
@article{Zhang2003Sequence,
author = {Zhang, X. H-F. and Heller, K. A. and Hefter, I. and Leslie, C. S.
and Chasin, L. A.},
title = {Sequence {I}nformation for the {S}plicing of {H}uman {P}re-m{RNA}
{I}dentified by {S}upport {V}ector {M}achine {C}lassification},
journal = {Genome {R}es.},
year = {2003},
volume = {13},
pages = {2637-2650},
number = {12},
abstract = {Vertebrate pre-m{RNA} transcripts contain many sequences that resemble
splice sites on the basis of agreement to the consensus, yet these
more numerous false splice sites are usually completely ignored by
the cellular splicing machinery. {E}ven at the level of exon definition,
pseudo exons defined by such false splices sites outnumber real exons
by an order of magnitude. {W}e used a support vector machine to discover
sequence information that could be used to distinguish real exons
from pseudo exons. {T}his machine learning tool led to the definition
of potential branch points, an extended polypyrimidine tract, and
{C}-rich and {TG}-rich motifs in a region limited to 50 nt upstream
of constitutively spliced exons. {C}-rich sequences were also found
in a region extending to 80 nt downstream of exons, along with {G}-triplet
motifs. {I}n addition, it was shown that combinations of three bases
within the splice donor consensus sequence were more effective than
consensus values in distinguishing real from pseudo splice sites;
two-way base combinations were optimal for distinguishing 3' splice
sites. {T}hese data also suggest that interactions between two or
more of these elements may contribute to exon recognition, and provide
candidate sequences for assessment as intronic splicing enhancers.},
doi = {10.1101/gr.1679003},
pdf = {../local/Zhang2003Sequence.pdf},
file = {Zhang2003Sequence.pdf:local/Zhang2003Sequence.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://www.genome.org/cgi/content/abstract/13/12/2637}
}
@article{Zhang2008Progress,
author = {Zhang, Yang},
title = {Progress and challenges in protein structure prediction},
journal = {Curr. Opin. Struct. Biol.},
year = {2008},
volume = {18},
pages = {342--348},
number = {3},
month = {June},
abstract = {Depending on whether similar structures are found in the PDB library,
the protein structure prediction can be categorized into template-based
modeling and free modeling. Although threading is an efficient tool
to detect the structural analogs, the advancements in methodology
development have come to a steady state. Encouraging progress is
observed in structure refinement which aims at drawing template structures
closer to the native; this has been mainly driven by the use of multiple
structure templates and the development of hybrid knowledge-based
and physics-based force fields. For free modeling, exciting examples
have been witnessed in folding small proteins to atomic resolutions.
However, predicting structures for proteins larger than 150 residues
still remains a challenge, with bottlenecks from both force field
and conformational search.},
booktitle = {Nucleic acids / Sequences and topology},
doi = {10.1016/j.sbi.2008.02.004},
keywords = {casp8, modeling, zhang},
url = {http://dx.doi.org/10.1016/j.sbi.2008.02.004}
}
@article{Zhang2009Copy,
author = {Zhang, Y. and Martens, J. W. M. and Yu, J. X. and Jiang, J. and Sieuwerts,
A. M. and Smid, M. and Klijn, J. G. M. and Wang, Y. and Foekens,
J. A.},
title = {Copy number alterations that predict metastatic capability of human
breast cancer.},
journal = {Cancer Res},
year = {2009},
volume = {69},
pages = {3795--3801},
number = {9},
month = {May},
abstract = {We have analyzed the DNA copy numbers for over 100,000 single-nucleotide
polymorphism loci across the human genome in genomic DNA from 313
lymph node-negative primary breast tumors for which genome-wide gene
expression data were also available. Combining these two data sets
allowed us to identify the genomic loci and their mapped genes, having
high correlation with distant metastasis. An estimation of the likely
response based on published predictive signatures was performed in
the identified prognostic subgroups defined by gene expression and
DNA copy number data. In the training set of 200 patients, we constructed
an 81-gene prognostic copy number signature (CNS) that identified
a subgroup of patients with increased probability of distant metastasis
in the independent validation set of 113 patients [hazard ratio (HR),
2.8; 95\% confidence interval (95\% CI), 1.4-5.6] and in an external
data set of 116 patients (HR, 3.7; 95\% CI, 1.3-10.6). These high-risk
patients constituted a subset of the high-risk patients predicted
by our previously established 76-gene gene expression signature (GES).
This very poor prognostic group identified by CNS and GES was putatively
more resistant to preoperative paclitaxel and 5-fluorouracil-doxorubicin-cyclophosphamide
combination chemotherapy (P = 0.0048), particularly against the doxorubicin
compound, while potentially benefiting from etoposide. Our study
shows the feasibility of using copy number alterations to predict
patient prognostic outcome. When combined with gene expression-based
signatures for prognosis, the CNS refines risk classification and
can help identify those breast cancer patients who have a significantly
worse outlook in prognosis and a potential differential response
to chemotherapeutic drugs.},
doi = {10.1158/0008-5472.CAN-08-4596},
pdf = {../local/Zhang2009Copy.pdf},
file = {Zhang2009Copy.pdf:Zhang2009Copy.pdf:PDF},
institution = { Johnson Company, San Diego, California, USA.},
language = {eng},
medline-pst = {ppublish},
owner = {philippe},
pii = {0008-5472.CAN-08-4596},
pmid = {19336569},
timestamp = {2011.05.13},
url = {http://dx.doi.org/10.1158/0008-5472.CAN-08-4596}
}
@article{Zhang2012Spatial,
author = {Zhang, Y. and McCord, R. A. and Ho, Y.-J. and Lajoie, B. R. and Hildebrand,
D. G. and Simon, A. C. and Becker, M. S. and Alt, F. W. and Dekker,
J.},
title = {Spatial Organization of the Mouse Genome and Its Role in Recurrent
Chromosomal Translocations},
journal = {Cell},
year = {2012},
volume = {148},
pages = {908 - 921},
number = {5},
abstract = {Summary The extent to which the three-dimensional organization of
the genome contributes to chromosomal translocations is an important
question in cancer genomics. We generated a high-resolution Hi-C
spatial organization map of the G1-arrested mouse pro-B cell genome
and used high-throughput genome-wide translocation sequencing to
map translocations from target DNA double-strand breaks (DSBs) within
it. RAG endonuclease-cleaved antigen-receptor loci are dominant translocation
partners for target DSBs regardless of genomic position, reflecting
high-frequency DSBs at these loci and their colocalization in a fraction
of cells. To directly assess spatial proximity contributions, we
normalized genomic DSBs via ionizing radiation. Under these conditions,
translocations were highly enriched in cis along single chromosomes
containing target DSBs and within other chromosomes and subchromosomal
domains in a manner directly related to pre-existing spatial proximity.
By combining two high-throughput genomic methods in a genetically
tractable system, we provide a new lens for viewing cancer genomes.},
doi = {10.1016/j.cell.2012.02.002},
pdf = {../local/Zhang2012Spatial.pdf},
file = {Zhang2012Spatial.pdf:Zhang2012Spatial.pdf:PDF},
issn = {0092-8674},
keywords = {hic, ngs},
owner = {nelle},
url = {http://www.sciencedirect.com/science/article/pii/S0092867412001584}
}
@book{Zhang2008Machine,
title = {Machine learning in bioinformatics},
publisher = {Wiley},
year = {2008},
author = {Zhang, Y. and Rajapakse, J.C.},
volume = {4}
}
@techreport{Zhang1992Iterative,
author = {Z. Zhang},
title = {Iterative Point Matching for Registration of Free-form Curves},
institution = {Institut National de Recherche en Informatique et en Automatique
(INRIA)},
year = {1992},
publisher = {Institut National de Recherche en Informatique et en Automatique
(INRIA)}
}
@article{Zhang2006Similarity,
author = {Zhang, Z. and Grigorov, M.G.},
title = {Similarity networks of protein binding sites.},
journal = {Proteins},
year = {2006},
volume = {62},
pages = {470--478},
number = {2},
month = {Feb},
abstract = {An increasing attention has been dedicated to the characterization
of complex networks within the protein world. This work is reporting
how we uncovered networked structures that reflected the structural
similarities among protein binding sites. First, a 211 binding sites
dataset has been compiled by removing the redundant proteins in the
Protein Ligand Database (PLD) (http://www-mitchell.ch.cam.ac.uk/pld/).
Using a clique detection algorithm we have performed all-against-all
binding site comparisons among the 211 available ones. Within the
set of nodes representing each binding site an edge was added whenever
a pair of binding sites had a similarity higher than a threshold
value. The generated similarity networks revealed that many nodes
had few links and only few were highly connected, but due to the
limited data available it was not possible to definitively prove
a scale-free architecture. Within the same dataset, the binding site
similarity networks were compared with the networks of sequence and
fold similarity networks. In the protein world, indications were
found that structure is better conserved than sequence, but on its
own, sequence was better conserved than the subset of functional
residues forming the binding site. Because a binding site is strongly
linked with protein function, the identification of protein binding
site similarity networks could accelerate the functional annotation
of newly identified genes. In view of this we have discussed several
potential applications of binding site similarity networks, such
as the construction of novel binding site classification databases,
as well as the implications for protein molecular design in general
and computational chemogenomics in particular.},
keywords = {complex network, binding site, small world, scale free, sequence,
fold, drug design},
owner = {vero},
pmid = {16299776},
timestamp = {2009.02.04}
}
@article{Zhang2005Descriptor-based,
author = {Zhang, Z. and Kochhar, S. and Grigorov, M. G.},
title = {Descriptor-based protein remote homology identification.},
journal = {Protein {S}ci.},
year = {2005},
volume = {42},
pages = {431-444},
number = {2},
abstract = {Here, we report a novel protein sequence descriptor-based remote homology
identification method, able to infer fold relationships without the
explicit knowledge of structure. {I}n a first phase, we have individually
benchmarked 13 different descriptor types in fold identification
experiments in a highly diverse set of protein sequences. {T}he relevant
descriptors were related to the fold class membership by using simple
similarity measures in the descriptor spaces, such as the cosine
angle. {O}ur results revealed that the three best-performing sets
of descriptors were the sequence-alignment-based descriptor using
{PSI}-{BLAST} e-values, the descriptors based on the alignment of
secondary structural elements ({SSEA}), and the descriptors based
on the occurrence of {PROSITE} functional motifs. {I}n a second phase,
the three top-performing descriptors were combined to obtain a final
method with improved performance, which we named {D}esc{F}old. {C}lass
membership was predicted by {S}upport {V}ector {M}achine ({SVM})
learning. {I}n comparison with the individual {PSI}-{BLAST}-based
descriptor, the rate of remote homology identification increased
from 33.7% to 46.3%. {W}e found out that the composite set of descriptors
was able to identify the true remote homolog for nearly every sixth
sequence at the 95% confidence level, or some 10% more than a single
{PSI}-{BLAST} search. {W}e have benchmarked the {D}esc{F}old method
against several other state-of-the-art fold recognition algorithms
for the 172 {L}ive{B}ench-8 targets, and we concluded that it was
able to add value to the existing techniques by providing a confident
hit for at least 10% of the sequences not identifiable by the previously
known methods.},
doi = {10.1110/ps.041035505},
pdf = {../local/Zhang2005Descriptor-based.pdf},
file = {Zhang2005Descriptor-based.pdf:local/Zhang2005Descriptor-based.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://dx.doi.org/10.1110/ps.041035505}
}
@article{Zhao2004Diagnosing,
author = {C. Y. Zhao and R. S. Zhang and H. X. Liu and C. X. Xue and S. G.
Zhao and X. F. Zhou and M. C. Liu and B. T. Fan},
title = {Diagnosing anorexia based on partial least squares, back propagation
neural network, and support vector machines.},
journal = {J {C}hem {I}nf {C}omput {S}ci},
year = {2004},
volume = {44},
pages = {2040-6},
number = {6},
abstract = {Support vector machine ({SVM}), as a novel type of learning machine,
for the first time, was used to develop a predictive model for early
diagnosis of anorexia. {I}t was based on the concentration of six
elements ({Z}n, {F}e, {M}g, {C}u, {C}a, and {M}n) and the age extracted
from 90 cases. {C}ompared with the results obtained from two other
classifiers, partial least squares ({PLS}) and back-propagation neural
network ({BPNN}), the {SVM} method exhibited the best whole performance.
{T}he accuracies for the test set by {PLS}, {BPNN}, and {SVM} methods
were 52\%, 65\%, and 87\%, respectively. {M}oreover, the models we
proposed could also provide some insight into what factors were related
to anorexia.},
doi = {10.1021/ci049877y},
pdf = {../local/Zhao2004Diagnosing.pdf},
file = {Zhao2004Diagnosing.pdf:local/Zhao2004Diagnosing.pdf:PDF},
url = {http://dx.doi.org/10.1021/ci049877y}
}
@article{Zhao2008Grouped,
author = {Zhao, P. and Rocha, G. and Yu, B.},
title = {Grouped and hierarchical model selection through composite absolute
penalties},
journal = {Ann. Stat.},
year = {2009},
volume = {37},
pages = {3468--3497},
number = {6A},
owner = {jp}
}
@article{Zhao2009composite,
author = {Zhao, P. and Rocha, G. and Yu, B.},
title = {The composite absolute penalties family for grouped and hierarchical
variable selection},
journal = {The Annals of Statistics},
year = {2009},
volume = {37},
pages = {3468--3497},
number = {6A},
publisher = {Institute of Mathematical Statistics}
}
@article{Zhao2006model,
author = {Zhao, P. and Yu, B.},
title = {On model selection consistency of lasso},
journal = {J. Mach. Learn. Res.},
year = {2006},
volume = {7},
pages = {2541},
abstract = {Sparsity or parsimony of statistical models is crucial for their proper
interpretations, as in sciences and social sciences. Model selection
is a commonly used method to find such models, but usually involves
a computationally heavy combinatorial search. Lasso (Tibshirani,
1996) is now being used as a computationally feasible alternative
to model selection. Therefore it is important to study Lasso for
model selection purposes.
In this paper, we prove that a single condition, which we call the
Irrepresentable Condition, is almost necessary and sufficient for
Lasso to select the true model both in the classical fixed p setting
and in the large p setting as the sample size n gets large. Based
on these results, sufficient conditions that are verifiable in practice
are given to relate to previous works and help applications of Lasso
for feature selection and sparse representation.
This Irrepresentable Condition, which depends mainly on the covariance
of the predictor variables, states that Lasso selects the true model
consistently if and (almost) only if the predictors that are not
in the true model are "irrepresentable" (in a sense to be clarified)
by predictors that are in the true model. Furthermore, simulations
are carried out to provide insights and understanding of this result.},
pdf = {../local/Zhao2006model.pdf},
file = {Zhao2006model.pdf:local/Zhao2006model.pdf:PDF},
keywords = {lasso},
owner = {jp},
timestamp = {2008.12.21},
url = {http://jmlr.csail.mit.edu/papers/v7/zhao06a.html}
}
@article{Zhao2003Application,
author = {Zhao, Y. and Pinilla, C. and Valmori, D. and Martin, R. and Simon,
R.},
title = {Application of support vector machines for {T}-cell epitopes prediction},
journal = {Bioinformatics},
year = {2003},
volume = {19},
pages = {1978-1984},
number = {15},
abstract = {Motivation: {T}he {T}-cell receptor, a major histocompatibility complex
({MHC}) molecule, and a bound antigenic peptide, play major roles
in the process of antigen-specific {T}-cell activation. {T}-cell
recognition was long considered exquisitely specific. {R}ecent data
also indicate that it is highly flexible, and one receptor may recognize
thousands of different peptides. {D}eciphering the patterns of peptides
that elicit a {MHC} restricted {T}-cell response is critical for
vaccine development. {R}esults: {F}or the first time we develop a
support vector machine ({SVM}) for {T}-cell epitope prediction with
an {MHC} type {I} restricted {T}-cell clone. {U}sing cross-validation,
we demonstrate that {SVM}s can be trained on relatively small data
sets to provide prediction more accurate than those based on previously
published methods or on {MHC} binding. {S}upplementary information:
{D}ata for 203 synthesized peptides is available at http://linus.nci.nih.gov/{D}ata/{LAU}203_{P}eptide.pdf},
pdf = {../local/Zhao2003Application.pdf},
file = {Zhao2003Application.pdf:local/Zhao2003Application.pdf:PDF},
keywords = {biosvm immunoinformatics},
owner = {jeanphilippevert},
url = {http://bioinformatics.oupjournals.org/cgi/content/abstract/19/15/1978}
}
@article{Zhao2003Applicationa,
author = {Zhao, Y. and Pinilla, C. and Valmori, D. and Martin, R. and Simon,
R.},
title = {{A}pplication of support vector machines for {T}-cell epitopes prediction.},
journal = {Bioinformatics},
year = {2003},
volume = {19},
pages = {1978--1984},
number = {15},
month = {Oct},
abstract = {MOTIVATION: The T-cell receptor, a major histocompatibility complex
(MHC) molecule, and a bound antigenic peptide, play major roles in
the process of antigen-specific T-cell activation. T-cell recognition
was long considered exquisitely specific. Recent data also indicate
that it is highly flexible, and one receptor may recognize thousands
of different peptides. Deciphering the patterns of peptides that
elicit a MHC restricted T-cell response is critical for vaccine development.
RESULTS: For the first time we develop a support vector machine (SVM)
for T-cell epitope prediction with an MHC type I restricted T-cell
clone. Using cross-validation, we demonstrate that SVMs can be trained
on relatively small data sets to provide prediction more accurate
than those based on previously published methods or on MHC binding.
SUPPLEMENTARY INFORMATION: Data for 203 synthesized peptides is available
at http://linus.nci.nih.gov/Data/LAU203_Peptide.pdf},
keywords = {Algorithms, Amino Acid Sequence, Antigen, Antigen Presentation, Antigen-Antibody
Complex, Artificial Intelligence, Autoimmune Diseases, Autoimmunity,
Bacterial Proteins, CD4-Positive T-Lymphocytes, Cell Proliferation,
Cells, Clone Cells, Cluster Analysis, Conserved Sequence, Cross Reactions,
Cultured, Cytokines, Databases, Epitope Mapping, Epitopes, Gene Products,
Genetic, HIV-1, HLA-DQ Antigens, HLA-DR2 Antigen, Haplotypes, Helper-Inducer,
Hemagglutination, Histocompatibility Antigens Class I, Humans, K562
Cells, Molecular Mimicry, Molecular Sequence Data, Multiple Sclerosis,
Myelin Proteins, Neural Networks (Computer), Orthomyxoviridae, Peptide
Library, Peptides, Protein, Protein Binding, Protein Interaction
Mapping, ROC Curve, Receptors, Relapsing-Remitting, Reproducibility
of Results, Reverse Transcriptase Polymerase Chain Reaction, Sensitivity
and Specificity, Sequence Analysis, Structure-Activity Relationship,
T-Cell, T-Lymphocyte, T-Lymphocytes, Torque teno virus, Viral, Viral
Proteins, gag, 14555632},
pmid = {14555632},
timestamp = {2007.01.25}
}
@article{Zheng2005Foley-Sammon,
author = {Wenming Zheng and Li Zhao and Cairong Zou},
title = {Foley-{S}ammon optimal discriminant vectors using kernel approach.},
journal = {I{EEE} {T}rans {N}eural {N}etw},
year = {2005},
volume = {16},
pages = {1-9},
number = {1},
month = {Jan},
abstract = {A new nonlinear feature extraction method called kernel {F}oley-{S}ammon
optimal discriminant vectors ({KFSODV}s) is presented in this paper.
{T}his new method extends the well-known {F}oley-{S}ammon optimal
discriminant vectors ({FSODV}s) from linear domain to a nonlinear
domain via the kernel trick that has been used in support vector
machine ({SVM}) and other commonly used kernel-based learning algorithms.
{T}he proposed method also provides an effective technique to solve
the so-called small sample size ({SSS}) problem which exists in many
classification problems such as face recognition. {W}e give the derivation
of {KFSODV} and conduct experiments on both simulated and real data
sets to confirm that the {KFSODV} method is superior to the previous
commonly used kernel-based learning algorithms in terms of the performance
of discrimination.}
}
@article{Zhou2002covering,
author = {Zhou, D.},
title = {The covering number in learning theory},
journal = {J. {C}omplexity},
year = {2002},
volume = {18},
pages = {739-767},
abstract = {The covering number of a ball of a reproducing kernel {H}ilbert space
as a subset of the continuous function space plays an important role
in {L}earning {T}heory. {W}e give estimates for this covering number
by means of the regularity of the {M}ercer kernel {K}. {F}or convolution
type kernels {K}(x, t) = k(x - t) on [0, 1]n, we provide estimates
depending on the decay of k, the {F}ourier transform of k. {I}n particular,
when k decays exponentially, our estimate for this covering number
is better than all the previous results and covers many important
{M}ercer kernels. {A} counter example is presented to show that the
eigenfunctions of the {H}ilbert-{S}chmidt operator {LK} associated
with a {M}ercer kernel {K} may not be uniformly bounded. {H}ence
some previous methods used for estimating the covering number in
{L}earning {T}heory are not valid. {W}e also provide an example of
a {M}ercer kernel to show that {LK}½ may not be generated by a {M}ercer
kernel.},
doi = {10.1006/jcom.2002.0635},
pdf = {../local/Zhou2002covering.pdf},
file = {Zhou2002covering.pdf:local/Zhou2002covering.pdf:PDF},
owner = {jeanphilippevert},
url = {http://dx.doi.org/10.1006/jcom.2002.0635}
}
@article{Zhou2005Recognition,
author = {GuoDong Zhou and Dan Shen and Jie Zhang and Jian Su and SoonHeng
Tan},
title = {Recognition of protein/gene names from text using an ensemble of
classifiers.},
journal = {B{MC} {B}ioinformatics},
year = {2005},
volume = {6 Suppl 1},
pages = {S7},
abstract = {This paper proposes an ensemble of classifiers for biomedical name
recognition in which three classifiers, one {S}upport {V}ector {M}achine
and two discriminative {H}idden {M}arkov {M}odels, are combined effectively
using a simple majority voting strategy. {I}n addition, we incorporate
three post-processing modules, including an abbreviation resolution
module, a protein/gene name refinement module and a simple dictionary
matching module, into the system to further improve the performance.
{E}valuation shows that our system achieves the best performance
from among 10 systems with a balanced {F}-measure of 82.58 on the
closed evaluation of the {B}io{C}reative protein/gene name recognition
task ({T}ask 1{A}).},
doi = {10.1186/1471-2105-6-S1-S7},
pdf = {../local/Zhou2005Recognition.pdf},
file = {Zhou2005Recognition.pdf:local/Zhou2005Recognition.pdf:PDF},
keywords = {biosvm nlp},
pii = {1471-2105-6-S1-S7},
url = {http://dx.doi.org/10.1186/1471-2105-6-S1-S7}
}
@article{Zhou2004Recognizing,
author = {GuoDong Zhou and Jie Zhang and Jian Su and Dan Shen and ChewLim Tan},
title = {Recognizing names in biomedical texts: a machine learning approach.},
journal = {Bioinformatics},
year = {2004},
volume = {20},
pages = {1178-90},
number = {7},
month = {May},
abstract = {M{OTIVATION}: {W}ith an overwhelming amount of textual information
in molecular biology and biomedicine, there is a need for effective
and efficient literature mining and knowledge discovery that can
help biologists to gather and make use of the knowledge encoded in
text documents. {I}n order to make organized and structured information
available, automatically recognizing biomedical entity names becomes
critical and is important for information retrieval, information
extraction and automated knowledge acquisition. {RESULTS}: {I}n this
paper, we present a named entity recognition system in the biomedical
domain, called {P}ower{B}io{NE}. {I}n order to deal with the special
phenomena of naming conventions in the biomedical domain, we propose
various evidential features: (1) word formation pattern; (2) morphological
pattern, such as prefix and suffix; (3) part-of-speech; (4) head
noun trigger; (5) special verb trigger and (6) name alias feature.
{A}ll the features are integrated effectively and efficiently through
a hidden {M}arkov model ({HMM}) and a {HMM}-based named entity recognizer.
{I}n addition, a k-{N}earest {N}eighbor (k-{NN}) algorithm is proposed
to resolve the data sparseness problem in our system. {F}inally,
we present a pattern-based post-processing to automatically extract
rules from the training data to deal with the cascaded entity name
phenomenon. {F}rom our best knowledge, {P}ower{B}io{NE} is the first
system which deals with the cascaded entity name phenomenon. {E}valuation
shows that our system achieves the {F}-measure of 66.6 and 62.2 on
the 23 classes of {GENIA} {V}3.0 and {V}1.1, respectively. {I}n particular,
our system achieves the {F}-measure of 75.8 on the "protein" class
of {GENIA} {V}3.0. {F}or comparison, our system outperforms the best
published result by 7.8 on {GENIA} {V}1.1, without help of any dictionaries.
{I}t also shows that our {HMM} and the k-{NN} algorithm outperform
other models, such as back-off {HMM}, linear interpolated {HMM},
support vector machines, {C}4.5, {C}4.5 rules and {RIPPER}, by effectively
capturing the local context dependency and resolving the data sparseness
problem. {M}oreover, evaluation on {GENIA} {V}3.0 shows that the
post-processing for the cascaded entity name phenomenon improves
the {F}-measure by 3.9. {F}inally, error analysis shows that about
half of the errors are caused by the strict annotation scheme and
the annotation inconsistency in the {GENIA} corpus. {T}his suggests
that our system achieves an acceptable {F}-measure of 83.6 on the
23 classes of {GENIA} {V}3.0 and in particular 86.2 on the "protein"
class, without help of any dictionaries. {W}e think that a {F}-measure
of 90 on the 23 classes of {GENIA} {V}3.0 and in particular 92 on
the "protein" class, can be achieved through refining of the annotation
scheme in the {GENIA} corpus, such as flexible annotation scheme
and annotation consistency, and inclusion of a reasonable biomedical
dictionary. {AVAILABILITY}: {A} demo system is available at http://textmining.i2r.a-star.edu.sg/{NLS}/demo.htm.
{T}echnology license is available upon the bilateral agreement.},
doi = {10.1093/bioinformatics/bth060},
pdf = {../local/Zhou2004Recognizing.pdf},
file = {Zhou2004Recognizing.pdf:Zhou2004Recognizing.pdf:PDF},
pii = {bth060},
url = {http://dx.doi.org/10.1093/bioinformatics/bth060}
}
@article{Zhou2005LS,
author = {Xin Zhou and K. Z. Mao},
title = {L{S} {B}ound based gene selection for {DNA} microarray data.},
journal = {Bioinformatics},
year = {2005},
volume = {21},
pages = {1559-64},
number = {8},
month = {Apr},
abstract = {M{OTIVATION}: {O}ne problem with discriminant analysis of {DNA} microarray
data is that each sample is represented by quite a large number of
genes, and many of them are irrelevant, insignificant or redundant
to the discriminant problem at hand. {M}ethods for selecting important
genes are, therefore, of much significance in microarray data analysis.
{I}n the present study, a new criterion, called {LS} {B}ound measure,
is proposed to address the gene selection problem. {T}he {LS} {B}ound
measure is derived from leave-one-out procedure of {LS}-{SVM}s (least
squares support vector machines), and as the upper bound for leave-one-out
classification results it reflects to some extent the generalization
performance of gene subsets. {RESULTS}: {W}e applied this {LS} {B}ound
measure for gene selection on two benchmark microarray datasets:
colon cancer and leukemia. {W}e also compared the {LS} {B}ound measure
with other evaluation criteria, including the well-known {F}isher's
ratio and {M}ahalanobis class separability measure, and other published
gene selection algorithms, including {W}eighting factor and {SVM}
{R}ecursive {F}eature {E}limination. {T}he strength of the {LS} {B}ound
measure is that it provides gene subsets leading to more accurate
classification results than the filter method while its computational
complexity is at the level of the filter method. {AVAILABILITY}:
{A} companion website can be accessed at http://www.ntu.edu.sg/home5/pg02776030/lsbound/.
{T}he website contains: (1) the source code of the gene selection
algorithm; (2) the complete set of tables and figures regarding the
experimental study; (3) proof of the inequality (9). {CONTACT}: ekzmao@ntu.edu.sg.},
doi = {10.1093/bioinformatics/bti216},
pdf = {../local/Zhou2005LS.pdf},
file = {Zhou2005LS.pdf:local/Zhou2005LS.pdf:PDF},
keywords = {biosvm featureselection microarray},
pii = {bti216},
url = {http://dx.doi.org/10.1093/bioinformatics/bti216}
}
@article{Zhu2000Two,
author = {Zhu, G. and Spellman, P. T. and Volpe, T. and Brown, P. O. and Botstein,
D. and Davis, T. N. and Futcher, B.},
title = {Two yeast forkhead genes regulate the cell cycle and pseudohyphal
growth},
journal = {Nature},
year = {2000},
volume = {406},
pages = {90--94},
pdf = {../local/zhu00.pdf},
file = {zhu00.pdf:local/zhu00.pdf:PDF},
subject = {microarray},
url = {http://www.nature.com/cgi-taf/DynaPage.taf?file=/nature/journal/v406/n6791/full/406090a0_fs.html&content_filetype=pdf}
}
@article{Zhu2001Global,
author = {H. Zhu and M. Bilgin and R. Bangham and D. Hall and A. Casamayor
and P. Bertone and N. Lan and R. Jansen and S. Bidlingmaier and T.
Houfek and T. Mitchell and P. Miller and R. A. Dean and M. Gerstein
and M. Snyder},
title = {Global analysis of protein activities using proteome chips.},
journal = {Science},
year = {2001},
volume = {293},
pages = {2101-5},
number = {5537},
month = {Sep},
abstract = {To facilitate studies of the yeast proteome, we cloned 5800 open reading
frames and overexpressed and purified their corresponding proteins.
{T}he proteins were printed onto slides at high spatial density to
form a yeast proteome microarray and screened for their ability to
interact with proteins and phospholipids. {W}e identified many new
calmodulin- and phospholipid-interacting proteins; a common potential
binding motif was identified for many of the calmodulin-binding proteins.
{T}hus, microarrays of an entire eukaryotic proteome can be prepared
and screened for diverse biochemical activities. {T}he microarrays
can also be used to screen protein-drug interactions and to detect
posttranslational modifications.},
doi = {10.1126/science.1062191},
pdf = {../local/zhu01.pdf},
file = {zhu01.pdf:local/zhu01.pdf:PDF},
keywords = {Amino Acid Motifs, Amino Acid Sequence, Calmodulin, Calmodulin-Binding
Proteins, Cell Membrane, Cloning, Fungal Proteins, Glucose, Liposomes,
Membrane Proteins, Molecular, Molecular Sequence Data, Non-U.S. Gov't,
Open Reading Frames, P.H.S., Peptide Library, Phosphatidylcholines,
Phosphatidylinositols, Phospholipids, Protein Binding, Proteome,
Recombinant Fusion Proteins, Research Support, Saccharomyces cerevisiae,
Signal Transduction, Streptavidin, U.S. Gov't, 11474067},
pii = {1062191},
url = {http://dx.doi.org/10.1126/science.1062191}
}
@article{Zhu2005Kernel,
author = {Zhu, J. and Hastie, T.},
title = {Kernel {L}ogistic {R}egression and the {I}mport {V}ector {M}achine},
journal = {Journal of {C}omputational \& {G}raphical {S}tatistics},
year = {2005},
volume = {14},
pages = {185-205},
number = {1},
month = {Mar},
abstract = {The support vector machine ({SVM}) is known for its good performance
in two-class classification, but its extension to multiclass classification
is still an ongoing research issue. {I}n this article, we propose
a new approach for classification, called the import vector machine
({IVM}), which is built on kernel logistic regression ({KLR}). {W}e
show that the {IVM} not only performs as well as the {SVM} in two-class
classification, but also can naturally be generalized to the multiclass
case. {F}urthermore, the {IVM} provides an estimate of the underlying
probability. {S}imilar to the support points of the {SVM}, the {IVM}
model uses only a fraction of the training data to index kernel basis
functions, typically a much smaller fraction than the {SVM}. {T}his
gives the {IVM} a potential computational advantage over the {SVM}.},
doi = {10.1198/106186005X25619},
pdf = {../local/Zhu2005Kernel.pdf},
file = {Zhu2005Kernel.pdf:local/Zhu2005Kernel.pdf:PDF},
url = {http://dx.doi.org/10.1198/106186005X25619}
}
@article{Zhu2004Classification,
author = {Zhu, J. and Hastie, T.},
title = {Classification of gene microarrays by penalized logistic regression},
journal = {Biostatistics},
year = {2004},
volume = {5},
pages = {427-43},
number = {3},
month = {Jul},
abstract = {Classification of patient samples is an important aspect of cancer
diagnosis and treatment. {T}he support vector machine ({SVM}) has
been successfully applied to microarray cancer diagnosis problems.
{H}owever, one weakness of the {SVM} is that given a tumor sample,
it only predicts a cancer class label but does not provide any estimate
of the underlying probability. {W}e propose penalized logistic regression
({PLR}) as an alternative to the {SVM} for the microarray cancer
diagnosis problem. {W}e show that when using the same set of genes,
{PLR} and the {SVM} perform similarly in cancer classification, but
{PLR} has the advantage of additionally providing an estimate of
the underlying probability. {O}ften a primary goal in microarray
cancer diagnosis is to identify the genes responsible for the classification,
rather than class prediction. {W}e consider two gene selection methods
in this paper, univariate ranking ({UR}) and recursive feature elimination
({RFE}). {E}mpirical results indicate that {PLR} combined with {RFE}
tends to select fewer genes than other methods and also performs
well in both cross-validation and test samples. {A} fast algorithm
for solving {PLR} is also described.},
doi = {10.1093/biostatistics/5.3.427},
pdf = {../local/Zhu2004Classification.pdf},
file = {Zhu2004Classification.pdf:Zhu2004Classification.pdf:PDF},
pii = {5/3/427},
url = {http://dx.doi.org/10.1093/biostatistics/5.3.427}
}
@inproceedings{Zhu20041norm,
author = {Zhu, J. and Rosset, S. and Hastie, T. and Tibshirani, R.},
title = {1-norm Support Vector Machines},
booktitle = {Adv. Neural. Inform. Process Syst.},
year = {2004},
editor = {Thrun, S. and Saul, L. and {Sch\"{o}lkopf}, B.},
volume = {16},
address = {Cambridge, MA},
publisher = {MIT Press},
timestamp = {2008.01.17}
}
@article{Zhu2003Introduction,
author = {Lingyun Zhu and Baoming Wu and Changxiu Cao},
title = {Introduction to medical data mining},
journal = {Sheng {W}u {Y}i {X}ue {G}ong {C}heng {X}ue {Z}a {Z}hi},
year = {2003},
volume = {20},
pages = {559-62},
number = {3},
month = {Sep},
abstract = {Modern medicine generates a great deal of information stored in the
medical database. {E}xtracting useful knowledge and providing scientific
decision-making for the diagnosis and treatment of disease from the
database increasingly becomes necessary. {D}ata mining in medicine
can deal with this problem. {I}t can also improve the management
level of hospital information and promote the development of telemedicine
and community medicine. {B}ecause the medical information is characteristic
of redundancy, multi-attribution, incompletion and closely related
with time, medical data mining differs from other one. {I}n this
paper we have discussed the key techniques of medical data mining
involving pretreatment of medical data, fusion of different pattern
and resource, fast and robust mining algorithms and reliability of
mining results. {T}he methods and applications of medical data mining
based on computation intelligence such as artificial neural network,
fuzzy system, evolutionary algorithms, rough set, and support vector
machine have been introduced. {T}he features and problems in data
mining are summarized in the last section.},
keywords = {Algorithms, Anion Exchange Resins, Automatic Data Processing, Chemical,
Chromatography, Computational Biology, Computer-Assisted, Data Interpretation,
Databases, Decision Making, Decision Trees, English Abstract, Factual,
Fuzzy Logic, Humans, Indicators and Reagents, Information Storage
and Retrieval, Ion Exchange, Models, Neural Networks (Computer),
Non-P.H.S., Non-U.S. Gov't, Nucleic Acid Conformation, P.H.S., Proteins,
Quantitative Structure-Activity Relationship, RNA, ROC Curve, Research
Support, Sequence Analysis, Statistical, Transfer, U.S. Gov't, 14565039}
}
@article{Zhu2006Improving,
author = {Shanfeng Zhu and Keiko Udaka and John Sidney and Alessandro Sette
and Kiyoko F. Aoki-Kinoshita and Hiroshi Mamitsuka},
title = {Improving {MHC} binding peptide prediction by incorporating binding
data of auxiliary {MHC} molecules.},
journal = {Bioinformatics},
year = {2006},
volume = {22},
pages = {1648-1655},
number = {13},
bibsource = {DBLP, http://dblp.uni-trier.de},
ee = {http://dx.doi.org/10.1093/bioinformatics/btl141}
}
@article{Zhu1998FRAME:,
author = {Zhu, S. C. and Wu, Y. and Mumford, D.},
title = {F{RAME}: {F}ilters, {R}andom field {A}nd {M}aximum {E}ntropy: ---
{T}owards a {U}nified {T}heory for {T}exture {M}odeling },
journal = {Int'l {J}ournal of {C}omputer {V}ision},
year = {1998},
volume = {27},
pages = {1--20},
number = {2},
pdf = {../local/zhu98.pdf},
file = {zhu98.pdf:local/zhu98.pdf:PDF},
subject = {stat},
url = {http://www.cis.ohio-state.edu/~szhu/frame_ijcv.ps.gz}
}
@article{Zhu1997Minimax,
author = {Zhu, S. C. and Wu, Z. N. and Mumford, D.},
title = {Minimax {E}ntropy {P}rinciple and {I}ts {A}pplication to {T}exture
{M}odeling},
journal = {Neural {C}omput.},
year = {1997},
volume = {9},
pages = {1627-1660},
number = {8},
pdf = {../local/zhu97.pdf},
file = {zhu97.pdf:local/zhu97.pdf:PDF},
subject = {stat},
url = {http://www.cis.ohio-state.edu/~szhu/frame_neuro.ps.gz}
}
@incollection{Zien2007Multiclass,
author = {A. Zien and C. Ong},
title = {Multiclass multiple kernel learning},
booktitle = {Proceedings of the 24th Annual International Conference on Machine
Learning (ICML 2007)},
publisher = {Omnipress},
year = {2007},
editor = {Zoubin Ghahramani},
pages = {1191--1198},
location = {Corvallis, OR}
}
@article{Zien2000Engineering,
author = {Zien, A. and R{\"a}tsch, G. and Mika, S. and Sch{\"o}lkopf, B. and
Lengauer, T. and M{\"u}ller, K.-R.},
title = {Engineering support vector machine kernels that recognize translation
initiation sites},
journal = {Bioinformatics},
year = {2000},
volume = {16},
pages = {799-807},
number = {9},
abstract = {Motivation: {I}n order to extract protein sequences from nucleotide
sequences, it is an important step to recognize points at which regions
start that code for proteins. {T}hese points are called translation
initiation sites ({TIS}). {R}esults: {T}he task of finding {TIS}
can be modeled as a classification problem. {W}e demonstrate the
applicability of support vector machines for this task, and show
how to incorporate prior biological knowledge by engineering an appropriate
kernel function. {W}ith the described techniques the recognition
performance can be improved by 26% over leading existing approaches.
{W}e provide evidence that existing related methods (e.g. {ESTS}can)
could profit from advanced {TIS} recognition.},
pdf = {../local/Zien2000Engineering.pdf},
file = {Zien2000Engineering.pdf:local/Zien2000Engineering.pdf:PDF},
keywords = {biosvm},
owner = {jeanphilippevert},
url = {http://bioinformatics.oupjournals.org/cgi/content/abstract/16/9/799}
}
@article{Zinovyev2008Bioinformatics,
author = {Zinovyev, A. and Viara, E. and Calzone, L. and Barillot, E.},
title = {{BiNoM}: a {C}ytoscape plugin for manipulating and analyzing biological
networks},
journal = {Bioinformatics},
year = {2008},
volume = {24},
pages = {876-877},
number = {6},
abstract = {BiNoM (Biological Network Manager) is a new bioinformatics software
that significantly facilitates the usage and the analysis of biological
networks in standard systems biology formats (SBML, SBGN, BioPAX).
BiNoM implements a full-featured BioPAX editor and a method of interfaces'
for accessing BioPAX content. BiNoM is able to work with huge BioPAX
files such as whole pathway databases. In addition, BiNoM allows
the analysis of networks created with CellDesigner software and their
conversion into BioPAX format. BiNoM comes as a library and as a
Cytoscape plugin which adds a rich set of operations to Cytoscape
such as path and cycle analysis, clustering sub-networks, decomposition
of network into modules, clipboard operations and others. Availability:
Last version of BiNoM distributed under the LGPL licence together
with documentation, source code and API are available at http://bioinfo.curie.fr/projects/binom
Contact: andrei.zinovyev@curie.fr},
doi = {10.1093/bioinformatics/btm553},
eprint = {http://bioinformatics.oxfordjournals.org/cgi/reprint/24/6/876.pdf},
keywords = {csbcbook},
url = {http://bioinformatics.oxfordjournals.org/cgi/content/abstract/24/6/876}
}
@article{Ziv1978Compression,
author = {Ziv, J. and Lempel, A.},
title = {Compression of individual sequences via variable-rate coding},
journal = {I{EEE} {T}rans. {I}nform. {T}heory},
year = {1978},
volume = {24},
pages = {530-536},
number = {5},
month = {Sep},
pdf = {../local/Ziv1978Compression.pdf},
file = {Ziv1978Compression.pdf:local/Ziv1978Compression.pdf:PDF},
owner = {vert}
}
@article{Zomer2004Toxicological,
author = {Simeone Zomer and Christelle Guillo and Richard G Brereton and Melissa
Hanna-Brown},
title = {Toxicological classification of urine samples using pattern recognition
techniques and capillary electrophoresis.},
journal = {Anal {B}ioanal {C}hem},
year = {2004},
volume = {378},
pages = {2008-20},
number = {8},
month = {Apr},
abstract = {In toxicology, hazardous substances detected in organisms may often
lead to different pathological conditions depending on the type of
exposure and level of dosage; hence, further analysis on this can
suggest the best cure. {U}rine profiling may serve the purpose because
samples typically contain hundreds of compounds representing an effective
metabolic fingerprint. {T}his paper proposes a pattern recognition
procedure for determining the type of cadmium dosage, acute or chronic,
administrated to laboratory rats, where urinary profiles are detected
using capillary electrophoresis. {T}he procedure is based on the
composition of a sample data matrix consisting of areas of common
peaks, with appropriate pre-processing aimed at reducing the lack
of reproducibility and enhancing the potential contribution of low-level
metabolites in discrimination. {T}he matrix is then used for pattern
recognition including principal components analysis, cluster analysis,
discriminant analysis and support vector machines. {A}ttention is
particularly focussed on the last of these techniques, because of
its novelty and some attractive features such as its suitability
to work with datasets that are small and/or have low samples/variable
ratios. {T}he type of cadmium administration is detected as a relevant
feature that contributes to the structure of the sample matrix, and
samples are classified according to the class membership, with discriminant
analysis and support vector machines performing complementarily on
a training and on a test set.},
doi = {10.1007/s00216-004-2518-0},
pdf = {../local/Zomer2004Toxicological.pdf},
file = {Zomer2004Toxicological.pdf:local/Zomer2004Toxicological.pdf:PDF},
keywords = {Algorithms, Ambergris, Animals, Automated, Cadmium, Candida, Candida
albicans, Capillary, Cluster Analysis, Combinatorial Chemistry Techniques,
Electrophoresis, Eye Enucleation, Humans, Magnetic Resonance Spectroscopy,
Melanoma, Models, Molecular, Molecular Conformation, Non-U.S. Gov't,
Odors, P.H.S., Pattern Recognition, Perfume, Predictive Value of
Tests, Prognosis, Prospective Studies, Quantitative Structure-Activity
Relationship, Rats, Research Support, U.S. Gov't, Uveal Neoplasms,
15007590},
url = {http://dx.doi.org/10.1007/s00216-004-2518-0}
}
@article{Zoppoli2010TimeDelay,
author = {Zoppoli, P. and Morganella, S. and Ceccarelli, M.},
title = {TimeDelay-ARACNE: Reverse engineering of gene networks from time-course
data by an information theoretic approach},
journal = {Bmc Bioinformatics},
year = {2010},
volume = {11},
pages = {154},
number = {1},
publisher = {BioMed Central Ltd}
}
@article{Zou2006adaptive,
author = {Zou, Hui},
title = {The Adaptive Lasso and Its Oracle Properties},
journal = {J. Am. Stat. Assoc.},
year = {2006},
volume = {101},
pages = {1418-1429},
month = {December},
pdf = {../local/Zou2006adaptive.pdf},
file = {Zou2006adaptive.pdf:Zou2006adaptive.pdf:PDF},
url = {http://ideas.repec.org/a/bes/jnlasa/v101y2006p1418-1429.html}
}
@article{Zou2005Regularization,
author = {Zou, H. and Hastie, T.},
title = {Regularization and variable selection via the {E}lastic {N}et},
journal = {J. R. Stat. Soc. Ser. B},
year = {2005},
volume = {67},
pages = {301--320},
abstract = {Summary. We propose the elastic net, a new regularization and variable
selection method. Real world data and a simulation study show that
the elastic net often outperforms the lasso, while enjoying a similar
sparsity of representation. In addition, the elastic net encourages
a grouping effect, where strongly correlated predictors tend to be
in or out of the model together.The elastic net is particularly useful
when the number of predictors (p) is much bigger than the number
of observations (n). By contrast, the lasso is not a very satisfactory
variable selection method in the p n case. An algorithm called LARS-EN
is proposed for computing elastic net regularization paths efficiently,
much like algorithm LARS does for the lasso.},
keywords = {elastic-net, feature-selection, lars, lasso},
url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.89.1596}
}
@article{Zou2006Sparse,
author = {Zou, H. and Hastie, T. and Tibshirani, R.},
title = {Sparse Principal Component Analysis},
journal = {J. Comput. Graph. Stat.},
year = {2006},
volume = {15},
pages = {265--286},
number = {2},
abstract = {Principal component analysis (PCA) is widely used in data processing
and dimensionality reduction. However, PCA suffers from the fact
that each principal component is a linear combination of all the
original variables, thus it is often difficult to interpret the results.
We introduce a new method called sparse principal component analysis
(SPCA) using the lasso (elastic net) to produce modified principal
components with sparse loadings. We first show that PCA can be formulated
as a regression-type optimization problem; sparse loadings are then
obtained by imposing the lasso (elastic net) constraint on the regression
coefficients. Efficient algorithms are proposed to fit our SPCA models
for both regular multivariate data and gene expression arrays. We
also give a new formula to compute the total variance of modified
principal components. As illustrations, SPCA is applied to real and
simulated data with encouraging results.},
doi = {10.1198/106186006X113430},
owner = {jp},
timestamp = {2011.12.30},
url = {http://dx.doi.org/10.1198/106186006X113430}
}
@article{Zucknick2008Comparing,
author = {Zucknick, M. and Richardson, S. and Stronach, E.A. and others},
title = {Comparing the characteristics of gene expression profiles derived
by univariate and multivariate classification methods},
journal = {Stat Appl Genet Mol Biol},
year = {2008},
volume = {7},
pages = {7},
number = {1}
}
@article{Zuker1989finding,
author = {Zuker, M.},
title = {{O}n finding all suboptimal foldings of an {RNA} molecule.},
journal = {Science},
year = {1989},
volume = {244},
pages = {48--52},
number = {4900},
month = {Apr},
abstract = {An algorithm and a computer program have been prepared for determining
RNA secondary structures within any prescribed increment of the computed
global minimum free energy. The mathematical problem of determining
how well defined a minimum energy folding is can now be solved. All
predicted base pairs that can participate in suboptimal structures
may be displayed and analyzed graphically. Representative suboptimal
foldings are generated by selecting these base pairs one at a time
and computing the best foldings that contain them. A distance criterion
that ensures that no two structures are "too close" is used to avoid
multiple generation of similar structures. Thermodynamic parameters,
including free-energy increments for single-base stacking at the
ends of helices and for terminal mismatched pairs in interior and
hairpin loops, are incorporated into the underlying folding model
of the above algorithm.},
keywords = {sirna},
owner = {vert},
pmid = {2468181},
timestamp = {2006.04.27}
}
@book{Zupan1999Neural,
title = {Neural Networks in Chemistry and Drug Design},
publisher = {Wiley-VCH},
year = {1999},
author = {J. Zupan and J. Gasteiger},
owner = {mahe},
timestamp = {2006.09.06}
}
@book{Bottou2007Large-scale,
title = {Large-scale kernel machines},
publisher = {MIT Press},
year = {2007},
editor = {Bottou, L. and Chapelle, O. and DeCoste, D. and Weston, J.},
timestamp = {2007.10.25}
}
@book{Callison-Burch08Proceedings,
title = {Proceedings of the Third Workshop on SMT},
publisher = {ACL},
year = {2008},
editor = {C. Callison-Burch and P. Koehn and C. Monz and J. Schroeder and C.
S. Fordyce},
address = {Columbus, Ohio},
month = {June},
url = {http://www.aclweb.org/anthology/W/W08/W08-03}
}
@proceedings{Cohen2008Machine,
title = {Machine Learning, Proceedings of the Twenty-Fifth International Conference
(ICML 2008), Helsinki, Finland, June 5-9, 2008},
year = {2008},
editor = {William W. Cohen and Andrew McCallum and Sam T. Roweis},
volume = {307},
series = {ACM International Conference Proceeding Series},
publisher = {ACM},
bibsource = {DBLP, http://dblp.uni-trier.de},
booktitle = {ICML},
isbn = {978-1-60558-205-4}
}
@book{Gasteiger2003Chemoinformatics,
title = {Chemoinformatics : a {T}extbook},
publisher = {Wiley},
year = {2003},
editor = {Gasteiger, J. and Engel, T.},
address = {New York, NY, USA},
keywords = {chemoinformatics},
location = {Sheffield, United Kingdom},
owner = {mahe},
timestamp = {2006.08.09}
}
@book{Guyon2006Feature,
title = {Feature Extraction, Foundations and Applications},
publisher = {Springer},
year = {2006},
editor = {Guyon, I. and Gunn, S. and Nikravesh, M. and Zadeh, L.},
owner = {jp},
timestamp = {2008.11.27}
}
@book{Jacoby2006Chemogenomics,
title = {Chemogenomics: Knowledge-based Approaches to Drug Discovery},
publisher = {Imperial College Press},
year = {2006},
editor = {Jacoby, E.},
owner = {vert},
timestamp = {2007.08.02}
}
@book{2006Chemical,
title = {Chemical Genomics: Small Molecule Probes to Study Cellular Function},
publisher = {Springer},
year = {2006},
editor = {Jaroch, S. E. and Weinmann, H.},
series = {Ernst Schering Research Foundation Workshop},
address = {Berlin},
keywords = {chemogenomics},
owner = {vert},
timestamp = {2007.08.02}
}
@book{Johnson1990Concepts,
title = {Concepts and Applications of Molecular Similarity},
publisher = {Wiley},
year = {1990},
editor = {M. A. Johnson and G. M. Maggiora},
owner = {mahe},
timestamp = {2006.09.01}
}
@book{Jordan2001Learning,
title = {Learning in Graphical Models},
publisher = {The MIT Press},
year = {2001},
editor = {M. Jordan},
owner = {michael},
timestamp = {2008.10.02}
}
@book{Kubinyi2004Chemogenomics,
title = {Chemogenomics in Drug Discovery: A Medicinal Chemistry Perspective},
publisher = {Wiley-VCH},
year = {2004},
editor = {Kubinyi, H. and M{\"u}ller, G. and Mannhold, R. and Folkers, G.},
series = {Methods and Principles in Medicinal Chemistry},
address = {New York},
owner = {vert},
timestamp = {2007.08.02}
}
@book{Scherer2009Batch,
title = {Batch Effects and Noise in Microarray Experiments: Sources and Solutions},
publisher = {John Wiley and Sons},
year = {2009},
editor = {Scherer, A.},
owner = {jp},
timestamp = {2012.02.29}
}
@book{Thrun1998Learning,
title = {Learning to learn},
publisher = {Kluwer Academic Publishers},
year = {1998},
editor = {Thrun, Sebastian and Pratt, Lorien},
address = {Norwell, MA, USA},
isbn = {0-7923-8047-9}
}
@comment{{jabref-meta: selector_author:}}
@comment{{jabref-meta: selector_journal:Adv. Drug Deliv. Rev.;Am. J. Hu
m. Genet.;Am. J. Pathol.;Ann. Appl. Stat.;Ann. Math. Statist.;Ann. N.
Y. Acad. Sci.;Ann. Probab.;Ann. Stat.;Artif. Intell. Med.;Bernoulli;Bi
ochim. Biophys. Acta;Bioinformatics;Biometrika;BMC Bioinformatics;Br.
J. Pharmacol.;Breast Cancer Res.;Cell;Cell. Signal.;Chem. Res. Toxicol
.;Clin. Cancer Res.;Combinator. Probab. Comput.;Comm. Pure Appl. Math.
;Comput. Chem.;Comput. Comm. Rev.;Comput. Stat. Data An.;Curr. Genom.;
Curr. Opin. Chem. Biol.;Curr. Opin. Drug Discov. Devel.;Data Min. Know
l. Discov.;Electron. J. Statist.;Eur. J. Hum. Genet.;FEBS Lett.;Found.
Comput. Math.;Genome Biol.;IEEE T. Neural Networ.;IEEE T. Pattern. An
al.;IEEE T. Signal. Proces.;IEEE Trans. Inform. Theory;IEEE Trans. Kno
wl. Data Eng.;IEEE/ACM Trans. Comput. Biol. Bioinf.;Int. J. Comput. Vi
sion;Int. J. Data Min. Bioinform.;Int. J. Qantum Chem.;J Biol Syst;J.
ACM;J. Am. Soc. Inf. Sci. Technol.;J. Am. Stat. Assoc.;J. Bioinform. C
omput. Biol.;J. Biol. Chem.;J. Biomed. Inform.;J. Cell. Biochem.;J. Ch
em. Inf. Comput. Sci.;J. Chem. Inf. Model.;J. Clin. Oncol.;J. Comput.
Biol.;J. Comput. Graph. Stat.;J. Eur. Math. Soc.;J. Intell. Inform. Sy
st.;J. Mach. Learn. Res.;J. Med. Chem.;J. Mol. BIol.;J. R. Stat. Soc.
Ser. B;Journal of Statistical Planning and Inference;Mach. Learn.;Math
. Program.;Meth. Enzymol.;Mol. Biol. Cell;Mol. Biol. Evol.;Mol. Cell.
Biol.;Mol. Syst. Biol.;N. Engl. J. Med.;Nat. Biotechnol.;Nat. Genet.;N
at. Med.;Nat. Methods;Nat. Rev. Cancer;Nat. Rev. Drug Discov.;Nat. Rev
. Genet.;Nature;Neural Comput.;Neural Network.;Neurocomputing;Nucleic
Acids Res.;Pattern Anal. Appl.;Pattern Recognit.;Phys. Rev. E;Phys. Re
v. Lett.;PLoS Biology;PLoS Comput. Biol.;Probab. Theory Relat. Fields;
Proc. IEEE;Proc. Natl. Acad. Sci. USA;Protein Eng.;Protein Eng. Des. S
el.;Protein Sci.;Protein. Struct. Funct. Genet.;Random Struct. Algorit
hm.;Rev. Mod. Phys.;Science;Stat. Probab. Lett.;Statistica Sinica;Theo
r. Comput. Sci.;Trans. Am. Math. Soc.;Trends Genet.;}}
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