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@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{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{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{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{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}
}
@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{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{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{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}
}
@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}
}
@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{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{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{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{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{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{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}
}
@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}
}
@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{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{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{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{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}
}
@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{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{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{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{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}
}
@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{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{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{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{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{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{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}
}
@book{Lauritzen1996Graphical,
title = {Graphical {M}odels},
publisher = {Oxford},
year = {1996},
author = {S. Lauritzen},
subject = {stat}
}
@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{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}
}
@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{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{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{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{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{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{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{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{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{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{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{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{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}
}
@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{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{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{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}
}
@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}
}
@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{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}
}
@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{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{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{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}
}
@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{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{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{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{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{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{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}
}
@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{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{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{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{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{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{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{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}
}
@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{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{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}
}
@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{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{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{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}
}
@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{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{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}
}
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ochim. Biophys. Acta;Bioinformatics;Biometrika;BMC Bioinformatics;Br.
J. Pharmacol.;Breast Cancer Res.;Cell;Cell. Signal.;Chem. Res. Toxicol
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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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