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@inproceedings{Aliferis2002Machine,
author = {Aliferis, C.F. and Hardin, D.P. and Massion, P.},
title = {Machine {L}earning {M}odels {F}or {L}ung {C}ancer {C}lassification
{U}sing {A}rray {C}omparative {G}enomic {H}ybridization},
booktitle = {Proceedings of the 2002 {A}merican {M}edical {I}nformatics {A}ssociation
({AMIA}) {A}nnual {S}ymposium},
year = {2002},
pages = {7-11},
abstract = {Array {CGH} is a recently introduced technology that measures changes
in the gene copy number of hundreds of genes in a single experiment.
{T}he primary goal of this study was to develop machine learning
models that classify non-small {L}ung {C}ancers according to histopathology
types and to compare several machine learning methods in this learning
task. {DNA} from tumors of 37 patients (21 squamous carcinomas, and
16 adenocarcinomas) were extracted and hybridized onto a 452 {BAC}
clone array. {T}he following algorithms were used: {KNN}, {D}ecision
{T}ree {I}nduction, {S}upport {V}ector {M}achines and {F}eed-{F}orward
{N}eural {N}etworks. {P}erformance was measured via leave-one-out
classification accuracy. {T}he best multi-gene model found had a
leave-one-out accuracy of 89.2\%. {D}ecision {T}rees performed poorer
than the other methods in this learning task and dataset. {W}e conclude
that gene copy numbers as measured by array {CGH} are, collectively,
an excellent indicator of histological subtype. {S}everal interesting
research directions are discussed.},
pdf = {../local/Aliferis2002Machine.pdf},
file = {Aliferis2002Machine.pdf:local/Aliferis2002Machine.pdf:PDF},
keywords = {biosvm microarray, cgh},
owner = {jeanphilippevert}
}
@article{Bao2002Identifying,
author = {Bao, L. and Sun, Z.},
title = {Identifying genes related to drug anticancer mechanisms using support
vector machine},
journal = {F{EBS} {L}ett.},
year = {2002},
volume = {521},
pages = {109--114},
abstract = {In an effort to identify genes related to the cell line chemosensitivity
and to evaluate the functional relationships between genes and anticancer
drugs acting by the same mechanism, a supervised machine learning
approach called support vector machine was used to label genes into
any of the five predefined anticancer drug mechanistic categories.
{A}mong dozens of unequivocally categorized genes, many were known
to be causally related to the drug mechanisms. {F}or example, a few
genes were found to be involved in the biological process triggered
by the drugs (e.g. {DNA} polymerase epsilon was the direct target
for the drugs from {DNA} antimetabolites category). {DNA} repair-related
genes were found to be enriched for about eight-fold in the resulting
gene set relative to the entire gene set. {S}ome uncharacterized
transcripts might be of interest in future studies. {T}his method
of correlating the drugs and genes provides a strategy for finding
novel biologically significant relationships for molecular pharmacology.},
pdf = {../local/bao02.pdf},
file = {bao02.pdf:local/bao02.pdf:PDF},
keywords = {biosvm microarray},
subject = {biokernel},
url = {http://www.elsevier.com/febs/402/19/42/article.html}
}
@article{Ben-Dor2000Tissue,
author = {Ben-Dor, A. and Bruhn, L. and Friedman, N. and Nachman, I. and Schummer,
M. and Yakhini, Z.},
title = {Tissue Classification with Gene Expression Profiles},
journal = {J. Comput. Biol.},
year = {2000},
volume = {7},
pages = {559-583},
number = {3-4},
abstract = {Constantly improving gene expression profiling technologies are expected
to provide understanding and insight into cancer-related cellular
processes. {G}ene expression data is also expected to significantly
aid in the development of efficient cancer diagnosis and classification
platforms. {I}n this work we examine three sets of gene expression
data measured across sets of tumor(s) and normal clinical samples:
{T}he first set consists of 2,000 genes, measured in 62 epithelial
colon samples ({A}lon et al., 1999). {T}he second consists of approximately
equal to 100,000 clones, measured in 32 ovarian samples (unpublished
extension of data set described in {S}chummer et al. (1999)). {T}he
third set consists of approximately equal to 7,100 genes, measured
in 72 bone marrow and peripheral blood samples ({G}olub et al, 1999).
{W}e examine the use of scoring methods, measuring separation of
tissue type (e.g., tumors from normals) using individual gene expression
levels. {T}hese are then coupled with high-dimensional classification
methods to assess the classification power of complete expression
profiles. {W}e present results of performing leave-one-out cross
validation ({LOOCV}) experiments on the three data sets, employing
nearest neighbor classifier, {SVM} ({C}ortes and {V}apnik, 1995),
{A}da{B}oost ({F}reund and {S}chapire, 1997) and a novel clustering-based
classification technique. {A}s tumor samples can differ from normal
samples in their cell-type composition, we also perform {LOOCV} experiments
using appropriately modified sets of genes, attempting to eliminate
the resulting bias. {W}e demonstrate success rate of at least 90%
in tumor versus normal classification, using sets of selected genes,
with, as well as without, cellular-contamination-related members.
{T}hese results are insensitive to the exact selection mechanism,
over a certain range.},
pdf = {../local/Ben-Dor2000Tissue.pdf},
file = {Ben-Dor2000Tissue.pdf:local/Ben-Dor2000Tissue.pdf:PDF},
keywords = {biosvm microarray},
owner = {jeanphilippevert},
url = {http://www.liebertonline.com/doi/abs/10.1089/106652700750050943}
}
@article{Bozdech2004Antioxidant,
author = {Bozdech, Z. and Ginsburg, H.},
title = {Antioxidant defense in {P}lasmodium falciparum - data mining of the
transcriptome},
journal = {Malaria {J}ournal},
year = {2004},
volume = {3},
pages = {23},
number = {1},
abstract = {The intraerythrocytic malaria parasite is under constant oxidative
stress originating both from endogenous and exogenous processes.
{T}he parasite is endowed with a complete network of enzymes and
proteins that protect it from those threats, but also uses redox
activities to regulate enzyme activities. {I}n the present analysis,
the transcription of the genes coding for the antioxidant defense
elements are viewed in the time-frame of the intraerythrocytic cycle.
{T}ime-dependent transcription data were taken from the transcriptome
of the human malaria parasite {P}lasmodium falciparum. {W}hereas
for several processes the transcription of the many participating
genes is coordinated, in the present case there are some outstanding
deviations where gene products that utilize glutathione or thioredoxin
are transcribed before the genes coding for elements that control
the levels of those substrates are transcribed. {S}uch insights may
hint to novel, non-classical pathways that necessitate further investigations.},
doi = {10.1186/1475-2875-3-23},
pdf = {../local/Bozdech2004Antioxidant.pdf},
file = {Bozdech2004Antioxidant.pdf:local/Bozdech2004Antioxidant.pdf:PDF},
keywords = {microarray plasmodium},
owner = {vert},
url = {http://www.malariajournal.com/content/3/1/23}
}
@article{Bozdech2003Transcriptome,
author = {Bozdech, Z. and Llinas, M. and Pulliam, B. L. and Wong, E. D. and
Zhu, J. and DeRisi, J. L.},
title = {The {T}ranscriptome of the {I}ntraerythrocytic {D}evelopmental {C}ycle
of {P}lasmodium falciparum },
journal = {P{L}o{S} {B}iology},
year = {2003},
volume = {1},
pages = {e5},
number = {1},
abstract = {Plasmodium falciparum is the causative agent of the most burdensome
form of human malaria, affecting 200-300 million individuals per
year worldwide. {T}he recently sequenced genome of {P}. falciparum
revealed over 5,400 genes, of which 60{percnt} encode proteins of
unknown function. {I}nsights into the biochemical function and regulation
of these genes will provide the foundation for future drug and vaccine
development efforts toward eradication of this disease. {B}y analyzing
the complete asexual intraerythrocytic developmental cycle ({IDC})
transcriptome of the {HB}3 strain of {P}. falciparum, we demonstrate
that at least 60{percnt} of the genome is transcriptionally active
during this stage. {O}ur data demonstrate that this parasite has
evolved an extremely specialized mode of transcriptional regulation
that produces a continuous cascade of gene expression, beginning
with genes corresponding to general cellular processes, such as protein
synthesis, and ending with {P}lasmodium-specific functionalities,
such as genes involved in erythrocyte invasion. {T}he data reveal
that genes contiguous along the chromosomes are rarely coregulated,
while transcription from the plastid genome is highly coregulated
and likely polycistronic. {C}omparative genomic hybridization between
{HB}3 and the reference genome strain (3{D}7) was used to distinguish
between genes not expressed during the {IDC} and genes not detected
because of possible sequence variations. {G}enomic differences between
these strains were found almost exclusively in the highly antigenic
subtelomeric regions of chromosomes. {T}he simple cascade of gene
regulation that directs the asexual development of {P}. falciparum
is unprecedented in eukaryotic biology. {T}he transcriptome of the
{IDC} resembles a "just-in-time" manufacturing process whereby induction
of any given gene occurs once per cycle and only at a time when it
is required. {T}hese data provide to our knowledge the first comprehensive
view of the timing of transcription throughout the intraerythrocytic
development of {P}. falciparum and provide a resource for the identification
of new chemotherapeutic and vaccine candidates.},
comment = {(JP Vert) The paper that monitors the 48h cell cycle of P. falciparum},
doi = {10.1371/journal.pbio.0000005},
pdf = {../local/Bozdech2003Transcriptome.pdf},
file = {Bozdech2003Transcriptome.pdf:local/Bozdech2003Transcriptome.pdf:PDF},
keywords = {microarray plasmodium},
owner = {vert},
url = {http://dx.doi.org/10.1371/journal.pbio.0000005 }
}
@article{Bozdech2003Expression,
author = {Bozdech, Z. and Zhu, J. and Joachimiak, M. and Cohen, F. and Pulliam,
B. and DeRisi, J.},
title = {Expression profiling of the schizont and trophozoite stages of {P}lasmodium
falciparum with a long-oligonucleotide microarray},
journal = {Genome {B}iology},
year = {2003},
volume = {4},
pages = {R9},
number = {2},
abstract = {B{ACKGROUND}:{T}he worldwide persistence of drug-resistant {P}lasmodium
falciparum, the most lethal variety of human malaria, is a global
health concern. {T}he {P}. falciparum sequencing project has brought
new opportunities for identifying molecular targets for antimalarial
drug and vaccine development.{RESULTS}:{W}e developed a software
package, {A}rray{O}ligo{S}elector, to design an open reading frame
({ORF})-specific {DNA} microarray using the publicly available {P}.
falciparum genome sequence. {E}ach gene was represented by one or
more long 70 mer oligonucleotides selected on the basis of uniqueness
within the genome, exclusion of low-complexity sequence, balanced
base composition and proximity to the 3' end. {A} first-generation
microarray representing approximately 6,000 {ORF}s of the {P}. falciparum
genome was constructed. {A}rray performance was evaluated through
the use of control oligonucleotide sets with increasing levels of
introduced mutations, as well as traditional northern blotting. {U}sing
this array, we extensively characterized the gene-expression profile
of the intraerythrocytic trophozoite and schizont stages of {P}.
falciparum. {T}he results revealed extensive transcriptional regulation
of genes specialized for processes specific to these two stages.{CONCLUSIONS}:{DNA}
microarrays based on long oligonucleotides are powerful tools for
the functional annotation and exploration of the {P}. falciparum
genome. {E}xpression profiling of trophozoites and schizonts revealed
genes associated with stage-specific processes and may serve as the
basis for future drug targets and vaccine development.},
doi = {10.1186/gb-2003-4-2-r9},
pdf = {../local/Bozdech2003Expression.pdf},
file = {Bozdech2003Expression.pdf:local/Bozdech2003Expression.pdf:PDF},
keywords = {microarray plasmodium},
owner = {vert},
url = {http://genomebiology.com/2003/4/2/R9}
}
@article{Brown2000Knowledge-based,
author = {Brown, M. P. and Grundy, W. N. and Lin, D. and Cristianini, N. and
Sugnet, C. W. and Furey, T. S. and Ares, M. and Haussler, D.},
title = {Knowledge-based analysis of microarray gene expression data by using
support vector machines.},
journal = {Proc. {N}atl. {A}cad. {S}ci. {USA}},
year = {2000},
volume = {97},
pages = {262-7},
number = {1},
month = {Jan},
abstract = {We introduce a method of functionally classifying genes by using gene
expression data from {DNA} microarray hybridization experiments.
{T}he method is based on the theory of support vector machines ({SVM}s).
{SVM}s are considered a supervised computer learning method because
they exploit prior knowledge of gene function to identify unknown
genes of similar function from expression data. {SVM}s avoid several
problems associated with unsupervised clustering methods, such as
hierarchical clustering and self-organizing maps. {SVM}s have many
mathematical features that make them attractive for gene expression
analysis, including their flexibility in choosing a similarity function,
sparseness of solution when dealing with large data sets, the ability
to handle large feature spaces, and the ability to identify outliers.
{W}e test several {SVM}s that use different similarity metrics, as
well as some other supervised learning methods, and find that the
{SVM}s best identify sets of genes with a common function using expression
data. {F}inally, we use {SVM}s to predict functional roles for uncharacterized
yeast {ORF}s based on their expression data.},
pdf = {../local/Brown2000Knowledge-based.pdf},
file = {Brown2000Knowledge-based.pdf:local/Brown2000Knowledge-based.pdf:PDF},
keywords = {biosvm microarray},
url = {http://www.pnas.org/cgi/content/abstract/97/1/262}
}
@article{Brown2000Exploring,
author = {P.O. Brown and D. Botstein},
title = {Exploring the new world of the genome with {DNA} microarrays},
journal = {Nat. {G}enet.},
year = {2000},
volume = {21},
pages = {33--37},
pdf = {../local/brow00b.pdf},
file = {brow00b.pdf:local/brow00b.pdf:PDF},
subject = {microarray},
url = {http://www.nature.com/ng/journal/v21/n1s/abs/ng0199supp_33.html}
}
@article{Burckin2005Exploring,
author = {Burckin, T. and Nagel, R. and Mandel-Gutfreund, Y. and Shiue, L.
and Clark, T. A. and Chong, J.-L. and Chang, T.-H. and Squazzo, S.
and Hartzog, G. and Ares, M.},
title = {Exploring functional relationships between components of the gene
expression machinery.},
journal = {Nat. {S}truct. {M}ol. {B}iol.},
year = {2005},
volume = {12},
pages = {175-82},
number = {2},
month = {Feb},
abstract = {Eukaryotic gene expression requires the coordinated activity of many
macromolecular machines including transcription factors and {RNA}
polymerase, the spliceosome, m{RNA} export factors, the nuclear pore,
the ribosome and decay machineries. {Y}east carrying mutations in
genes encoding components of these machineries were examined using
microarrays to measure changes in both pre-m{RNA} and m{RNA} levels.
{W}e used these measurements as a quantitative phenotype to ask how
steps in the gene expression pathway are functionally connected.
{A} multiclass support vector machine was trained to recognize the
gene expression phenotypes caused by these mutations. {I}n several
cases, unexpected phenotype assignments by the computer revealed
functional roles for specific factors at multiple steps in the gene
expression pathway. {T}he ability to resolve gene expression pathway
phenotypes provides insight into how the major machineries of gene
expression communicate with each other.},
doi = {10.1038/nsmb891},
pdf = {../local/Burckin2005Exploring.pdf},
file = {Burckin2005Exploring.pdf:local/Burckin2005Exploring.pdf:PDF},
keywords = {biosvm microarray},
pii = {nsmb891},
url = {http://dx.doi.org/10.1038/nsmb891}
}
@article{Bussemaker2001Regulatory,
author = {Bussemaker, H. J. and Li, H. and Siggia, E. D.},
title = {Regulatory element detection using correlation with expression},
journal = {Nat. {G}enet.},
year = {2001},
volume = {27},
pages = {167--174},
pdf = {../local/buss01.pdf},
file = {buss01.pdf:local/buss01.pdf:PDF},
subject = {microarray},
url = {http://www.nature.com/cgi-taf/DynaPage.taf?file=/ng/journal/v27/n2/full/ng0201_167.html&filetype=pdf}
}
@article{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{Chiang2001Visualizing,
author = {Chiang, D. Y. and Brown, P. O. and Eisen, M. B.},
title = {Visualizing associations between genome sequences and gene expression
data using genome-mean expression profiles},
journal = {Bioinformatics},
year = {2001},
volume = {17},
pages = {49S--55S},
pdf = {../local/chia01.pdf},
file = {chia01.pdf:local/chia01.pdf:PDF},
subject = {microarray},
url = {http://bioinformatics.oupjournals.org/cgi/reprint/17/suppl_1/S49.pdf}
}
@article{Chu1998Transcriptional,
author = {S. Chu and J. DeRisi and M. Eisen and J. Mulholland and D. Botstein
and P.O. Brown and I. Herskowitz},
title = {The {T}ranscriptional {P}rogram of {S}porulation in {B}udding {Y}east},
journal = {Science},
year = {1998},
volume = {282},
pages = {699--705},
pdf = {../local/chu98.pdf},
file = {chu98.pdf:local/chu98.pdf:PDF},
owner = {phupe},
subject = {microarray},
timestamp = {2009.10.15},
url = {http://www.sciencemag.org/cgi/reprint/282/5389/699.pdf}
}
@article{DeRisi1997Exploring,
author = {DeRisi, J. L. and Iyer, V. R. and Brown, P. O.},
title = {Exploring the metabolic and genetic control of gene expression on
a genomic scale},
journal = {Science},
year = {1997},
volume = {278},
pages = {680--686},
number = {5338},
pdf = {../local/deri97.pdf},
file = {deri97.pdf:local/deri97.pdf:PDF},
subject = {microarray},
url = {http://www.sciencemag.org/cgi/reprint/278/5338/680.pdf}
}
@article{Dong2005Fast,
author = {Jian-xiong Dong and Adam Krzyzak and Ching Y Suen},
title = {Fast {SVM} training algorithm with decomposition on very large data
sets.},
journal = {I{EEE} {T}rans {P}attern {A}nal {M}ach {I}ntell},
year = {2005},
volume = {27},
pages = {603-18},
number = {4},
month = {Apr},
abstract = {Training a support vector machine on a data set of huge size with
thousands of classes is a challenging problem. {T}his paper proposes
an efficient algorithm to solve this problem. {T}he key idea is to
introduce a parallel optimization step to quickly remove most of
the nonsupport vectors, where block diagonal matrices are used to
approximate the original kernel matrix so that the original problem
can be split into hundreds of subproblems which can be solved more
efficiently. {I}n addition, some effective strategies such as kernel
caching and efficient computation of kernel matrix are integrated
to speed up the training process. {O}ur analysis of the proposed
algorithm shows that its time complexity grows linearly with the
number of classes and size of the data set. {I}n the experiments,
many appealing properties of the proposed algorithm have been investigated
and the results show that the proposed algorithm has a much better
scaling capability than {L}ibsvm, {SVM}light, and {SVMT}orch. {M}oreover,
the good generalization performances on several large databases have
also been achieved.},
keywords = {Algorithms, Animals, Antibiotics, Antineoplastic, Artificial Intelligence,
Automated, Automatic Data Processing, Butadienes, Chloroplasts, Comparative
Study, Computer Simulation, Computer-Assisted, Database Management
Systems, Databases, Diagnosis, Disinfectants, Dose-Response Relationship,
Drug, Drug Toxicity, Electrodes, Electroencephalography, Ethylamines,
Expert Systems, Factual, Feedback, Fungicides, Gene Expression Profiling,
Genes, Genetic Markers, Humans, Image Enhancement, Image Interpretation,
Implanted, Industrial, Information Storage and Retrieval, Kidney,
Kidney Tubules, MEDLINE, Male, Mercuric Chloride, Microarray Analysis,
Molecular Biology, Motor Cortex, Movement, Natural Language Processing,
Neural Networks (Computer), Non-P.H.S., Non-U.S. Gov't, Numerical
Analysis, Pattern Recognition, Plant Proteins, Predictive Value of
Tests, Proteins, Proteome, Proximal, Puromycin Aminonucleoside, Rats,
Reproducibility of Results, Research Support, Sensitivity and Specificity,
Signal Processing, Sprague-Dawley, Subcellular Fractions, Terminology,
Therapy, Time Factors, Toxicogenetics, U.S. Gov't, User-Computer
Interface, 15794164}
}
@article{Ein-Dor2005Outcome,
author = {Ein-Dor, L. and Kela, I. and Getz, G. and Givol, D. and Domany, E.},
title = {Outcome signature genes in breast cancer: is there a unique set?},
journal = {Bioinformatics},
year = {2005},
volume = {21},
pages = {171--178},
number = {2},
month = {Jan},
abstract = {MOTIVATION: Predicting the metastatic potential of primary malignant
tissues has direct bearing on the choice of therapy. Several microarray
studies yielded gene sets whose expression profiles successfully
predicted survival. Nevertheless, the overlap between these gene
sets is almost zero. Such small overlaps were observed also in other
complex diseases, and the variables that could account for the differences
had evoked a wide interest. One of the main open questions in this
context is whether the disparity can be attributed only to trivial
reasons such as different technologies, different patients and different
types of analyses. RESULTS: To answer this question, we concentrated
on a single breast cancer dataset, and analyzed it by a single method,
the one which was used by van't Veer et al. to produce a set of outcome-predictive
genes. We showed that, in fact, the resulting set of genes is not
unique; it is strongly influenced by the subset of patients used
for gene selection. Many equally predictive lists could have been
produced from the same analysis. Three main properties of the data
explain this sensitivity: (1) many genes are correlated with survival;
(2) the differences between these correlations are small; (3) the
correlations fluctuate strongly when measured over different subsets
of patients. A possible biological explanation for these properties
is discussed. CONTACT: eytan.domany@weizmann.ac.il SUPPLEMENTARY
INFORMATION: http://www.weizmann.ac.il/physics/complex/compphys/downloads/liate/},
doi = {10.1093/bioinformatics/bth469},
pdf = {../local/Ein-Dor2005Outcome.pdf},
file = {Ein-Dor2005Outcome.pdf:Ein-Dor2005Outcome.pdf:PDF},
institution = {Department of Physics of Complex Systems, Weizmann Institute of Science
Rehovot 76100, Israel.},
keywords = {breastcancer, microarray, featureselection},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {bth469},
pmid = {15308542},
timestamp = {2010.10.12},
url = {http://dx.doi.org/10.1093/bioinformatics/bth469}
}
@article{Eisen1998Cluster,
author = {Eisen, M. B. and Spellman, P. T. and Brown, P. O. and Botstein, D.},
title = {Cluster analysis and display of genome-wide expression patterns},
journal = {Proc. Natl. Acad. Sci. USA},
year = {1998},
volume = {95},
pages = {14863--14868},
month = {Dec},
pdf = {../local/Eisen1998Cluster.pdf},
file = {Eisen1998Cluster.pdf:Eisen1998Cluster.pdf:PDF},
subject = {microarray},
url = {http://www.pnas.org/cgi/reprint/95/25/14863.pdf}
}
@article{Fan2006Concordance,
author = {Fan, C. and Oh, D.S. and Wessels, L. and Weigelt, B. and Nuyten,
D.S.A. and Nobel, A.B. and van't Veer, L.J. and Perou, C.M.},
title = {Concordance among gene-expression-based predictors for breast cancer},
journal = {N. Engl. J. Med.},
year = {2006},
volume = {355},
pages = {560},
number = {6},
doi = {10.1056/NEJMoa052933},
pdf = {../local/Fan2006Concordance.pdf},
file = {Fan2006Concordance.pdf:Fan2006Concordance.pdf:PDF},
keywords = {breastcancer, microarray},
owner = {jp},
publisher = {Mass Med Soc},
timestamp = {2011.01.13},
url = {http://dx.doi.org/10.1056/NEJMoa052933}
}
@article{Ferea1999Systematic,
author = {Ferea, T. L. and Botstein, D. and Brown, P. O. and Rosenzweig, R.
F.},
title = {Systematic changes in gene expression patterns following adaptive
evolution in yeast},
journal = {Proc. {N}atl. {A}cad. {S}ci. {USA}},
year = {1999},
volume = {96},
pages = {9721--9726},
number = {17},
pdf = {../local/fere99.pdf},
file = {fere99.pdf:local/fere99.pdf:PDF},
subject = {microarray},
url = {http://www.pnas.org/cgi/reprint/96/17/9721.pdf}
}
@article{Friedman2000Using,
author = {Friedman, N. and Linial, M. and Nachman, I. and Pe'er, D.},
title = {Using {B}ayesian Networks to Analyze Expression Data},
journal = {J. Comput. Biol.},
year = {2000},
volume = {7},
pages = {601--620},
number = {3-4},
abstract = {D{NA} hybridization arrays simultaneously measure the expression level
for thousands of genes. {T}hese measurements provide a "snapshot"
of transcription levels within the cell. {A} major challenge in computational
biology is to uncover, from such measurements, gene/protein interactions
and key biological features of cellular systems. {I}n this paper,
we propose a new framework for discovering interactions between genes
based on multiple expression measurements. {T}his framework builds
on the use of {B}ayesian networks for representing statistical dependencies.
{A} {B}ayesian network is a graph-based model of joint multivariate
probability distributions that captures properties of conditional
independence between variables. {S}uch models are attractive for
their ability to describe complex stochastic processes and because
they provide a clear methodology for learning from (noisy) observations.
{W}e start by showing how {B}ayesian networks can describe interactions
between genes. {W}e then describe a method for recovering gene interactions
from microarray data using tools for learning {B}ayesian networks.
{F}inally, we demonstrate this method on the {S}. cerevisiae cell-cycle
measurements of {S}pellman et al. (1998).},
doi = {10.1089/106652700750050961},
pdf = {../local/Friedman2000Using.pdf},
file = {Friedman2000Using.pdf:local/Friedman2000Using.pdf:PDF},
keywords = {biogm},
subject = {microarray},
url = {http://dx.doi.org/10.1089/106652700750050961}
}
@article{Garnis2006High,
author = {C. Garnis and W. W. Lockwood and E. Vucic and Y. Ge and L. Girard
and J. D. Minna and A. F. Gazdar and S. Lam and C. MacAulay and W.
L. Lam},
title = {High resolution analysis of non-small cell lung cancer cell lines
by whole genome tiling path array {CGH}.},
journal = {Int. J. Cancer},
year = {2006},
volume = {118},
pages = {1556--1564},
number = {6},
abstract = {Chromosomal regions harboring tumor suppressors and oncogenes are
often deleted or amplified. Array comparative genomic hybridization
detects segmental DNA copy number alterations in tumor DNA relative
to a normal control. The recent development of a bacterial artificial
chromosome array, which spans the human genome in a tiling path manner
with >32,000 clones, has facilitated whole genome profiling at an
unprecedented resolution. Using this technology, we comprehensively
describe and compare the genomes of 28 commonly used non-small cell
lung carcinoma (NSCLC) cell models, derived from 18 adenocarcinomas
(AC), 9 squamous cell carcinomas and 1 large cell carcinoma. Analysis
at such resolution not only provided a detailed genomic alteration
template for each of these model cell lines, but revealed novel regions
of frequent duplication and deletion. Significantly, a detailed analysis
of chromosome 7 identified 6 distinct regions of alterations across
this chromosome, implicating the presence of multiple novel oncogene
loci on this chromosome. As well, a comparison between the squamous
and AC cells revealed alterations common to both subtypes, such as
the loss of 3p and gain of 5p, in addition to multiple hotspots more
frequently associated with only 1 subtype. Interestingly, chromosome
3q, which is known to be amplified in both subtypes, showed 2 distinct
regions of alteration, 1 frequently altered in squamous and 1 more
frequently altered in AC. In summary, our data demonstrate the unique
information generated by high resolution analysis of NSCLC genomes
and uncover the presence of genetic alterations prevalent in the
different NSCLC subtypes.},
doi = {10.1002/ijc.21491},
institution = {British Columbia Cancer Research Centre, Vancouver, BC, Canada. cgarnis@bccrc.ca},
keywords = {Carcinoma, Non-Small-Cell Lung, genetics/pathology; Cell Line, Tumor;
Chromosomes, Artificial, Bacterial, genetics; Gene Amplification;
Gene Dosage; Gene Expression Profiling; Genome, Human, genetics;
Humans; Loss of Heterozygosity; Lung Neoplasms, genetics/pathology;
Microarray Analysis, methods; Nucleic Acid Hybridization, methods},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pmid = {16187286},
timestamp = {2010.01.08},
url = {http://dx.doi.org/10.1002/ijc.21491}
}
@article{Gasch2001Genomic,
author = {A.P. Gasch and M. Huang and S. Metzner and D. Botstein and S.J. Elledge
and P.O. Brown},
title = {Genomic expression responses to {DNA}-damaging agents and the regulatory
role of the yeast {ATR} homolog {M}ec1p},
journal = {Mol. {B}iol. {C}ell},
year = {2001},
volume = {12},
pages = {2987--3003},
number = {10},
pdf = {../local/gasc01.pdf},
file = {gasc01.pdf:local/gasc01.pdf:PDF},
subject = {microarray},
url = {http://www.molbiolcell.org/cgi/content/full/12/10/2987}
}
@article{Gasch2000Genomic,
author = {Gasch, A. P. and Spellman, P. T. and Kao, C. M. and Carmel-Harel,
O. and Eisen, M. B. and Storz, G. and Botstein, D. and Brown, P.
O.},
title = {Genomic {E}xpression {P}rograms in the {R}esponse of {Y}east {C}ells
to {E}nvironmental {C}hanges},
journal = {Mol. {B}iol. {C}ell},
year = {2000},
volume = {11},
pages = {4241--4257},
month = {Dec},
pdf = {../local/gasc00.pdf},
file = {gasc00.pdf:local/gasc00.pdf:PDF},
subject = {microarray},
url = {http://www.molbiolcell.org/cgi/reprint/11/12/4241.pdf}
}
@article{Golub1999Molecular,
author = {Golub, T. R. and Slonim, D. K. and Tamayo, P. and Huard, C. and Gaasenbeek,
M. and Mesirov, J. P. and Coller, H. and Loh, M. L. and Downing,
J. R. and Caligiuri, M. A. and Bloomfield, C. D. and Lander, E. S.},
title = {Molecular classification of cancer: class discovery and class prediction
by gene expression monitoring},
journal = {Science},
year = {1999},
volume = {286},
pages = {531--537},
abstract = {Although cancer classification has improved over the past 30 years,
there has been no general approach for identifying new cancer classes
(class discovery) or for assigning tumors to known classes (class
prediction). Here, a generic approach to cancer classification based
on gene expression monitoring by DNA microarrays is described and
applied to human acute leukemias as a test case. A class discovery
procedure automatically discovered the distinction between acute
myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL) without
previous knowledge of these classes. An automatically derived class
predictor was able to determine the class of new leukemia cases.
The results demonstrate the feasibility of cancer classification
based solely on gene expression moni- toring and suggest a general
strategy for discovering and predicting cancer classes for other
types of cancer, independent of previous biological knowledge.},
doi = {10.1126/science.286.5439.531},
pdf = {../local/Golub1999Molecular.pdf},
file = {Golub1999Molecular.pdf:Golub1999Molecular.pdf:PDF},
keywords = {csbcbook, csbcbook-ch3, csbcbook-ch4},
subject = {microarray},
url = {http://dx.doi.org/10.1126/science.286.5439.531}
}
@article{Gross2000Identification,
author = {C. Gross and M. Kelleher and V.R. Iyer and P.O. Brown and D.R. Winge},
title = {Identification of the copper regulon in {S}accharomyces cerevisiae
by {DNA} microarrays},
journal = {J. {B}iol. {C}hem.},
year = {2000},
volume = {275},
pages = {32310--32316},
number = {41},
pdf = {../local/gros00.pdf},
file = {gros00.pdf:local/gros00.pdf:PDF},
subject = {microarray},
url = {http://www.jbc.org/cgi/content/full/275/41/32310}
}
@article{Haasdonk2005Feature,
author = {Bernard Haasdonk},
title = {Feature space interpretation of {SVM}s with indefinite kernels.},
journal = {I{EEE} {T}rans {P}attern {A}nal {M}ach {I}ntell},
year = {2005},
volume = {27},
pages = {482-92},
number = {4},
month = {Apr},
abstract = {Kernel methods are becoming increasingly popular for various kinds
of machine learning tasks, the most famous being the support vector
machine ({SVM}) for classification. {T}he {SVM} is well understood
when using conditionally positive definite (cpd) kernel functions.
{H}owever, in practice, non-cpd kernels arise and demand application
in {SVM}s. {T}he procedure of "plugging" these indefinite kernels
in {SVM}s often yields good empirical classification results. {H}owever,
they are hard to interpret due to missing geometrical and theoretical
understanding. {I}n this paper, we provide a step toward the comprehension
of {SVM} classifiers in these situations. {W}e give a geometric interpretation
of {SVM}s with indefinite kernel functions. {W}e show that such {SVM}s
are optimal hyperplane classifiers not by margin maximization, but
by minimization of distances between convex hulls in pseudo-{E}uclidean
spaces. {B}y this, we obtain a sound framework and motivation for
indefinite {SVM}s. {T}his interpretation is the basis for further
theoretical analysis, e.g., investigating uniqueness, and for the
derivation of practical guidelines like characterizing the suitability
of indefinite {SVM}s.},
doi = {10.1109/TPAMI.2005.78},
pdf = {../local/Haasdonk2005Feature.pdf},
file = {Haasdonk2005Feature.pdf:local/Haasdonk2005Feature.pdf:PDF},
keywords = {Algorithms, Animals, Antibiotics, Antineoplastic, Artificial Intelligence,
Automated, Automatic Data Processing, Butadienes, Chloroplasts, Cluster
Analysis, Comparative Study, Computer Simulation, Computer-Assisted,
Computing Methodologies, Database Management Systems, Databases,
Diagnosis, Disinfectants, Dose-Response Relationship, Drug, Drug
Toxicity, Electrodes, Electroencephalography, Ethylamines, Expert
Systems, Factual, Feedback, Fungicides, Gene Expression Profiling,
Genes, Genetic Markers, Humans, Image Enhancement, Image Interpretation,
Implanted, Industrial, Information Storage and Retrieval, Kidney,
Kidney Tubules, MEDLINE, Male, Mercuric Chloride, Microarray Analysis,
Molecular Biology, Motor Cortex, Movement, Natural Language Processing,
Neural Networks (Computer), Non-P.H.S., Non-U.S. Gov't, Numerical
Analysis, Pattern Recognition, Plant Proteins, Predictive Value of
Tests, Proteins, Proteome, Proximal, Puromycin Aminonucleoside, Rats,
Reproducibility of Results, Research Support, Sensitivity and Specificity,
Signal Processing, Sprague-Dawley, Subcellular Fractions, Terminology,
Therapy, Time Factors, Toxicogenetics, U.S. Gov't, User-Computer
Interface, 15794155},
url = {http://dx.doi.org/10.1109/TPAMI.2005.78}
}
@article{Haferlach2005AML,
author = {Torsten Haferlach and Alexander Kohlmann and Susanne Schnittger and
Martin Dugas and Wolfgang Hiddemann and Wolfgang Kern and Claudia
Schoch},
title = {A{ML} {M}3 and {AML} {M}3 variant each have a distinct gene expression
signature but also share patterns different from other genetically
defined {AML} subtypes.},
journal = {Genes {C}hromosomes {C}ancer},
year = {2005},
volume = {43},
pages = {113-27},
number = {2},
month = {Jun},
abstract = {Acute promyelocytic leukemia ({APL}) with t(15;17) appears in two
phenotypes: {AML} {M}3, with abnormal promyelocytes showing heavy
granulation and bundles of {A}uer rods, and {AML} {M}3 variant ({M}3v),
with non- or hypogranular cytoplasm and a bilobed nucleus. {W}e investigated
the global gene expression profiles of 35 {APL} patients (19 {AML}
{M}3, 16 {AML} {M}3v) by using high-density {DNA}-oligonucleotide
microarrays. {F}irst, an unsupervised approach clearly separated
{APL} samples from other {AML}s characterized genetically as t(8;21)
(n = 35), inv(16) (n = 35), or t(11q23)/{MLL} (n = 35) or as having
a normal karyotype (n = 50). {S}econd, we found genes with functional
relevance for blood coagulation that were differentially expressed
between {APL} and other {AML}s. {F}urthermore, a supervised pairwise
comparison between {M}3 and {M}3v revealed differential expression
of genes that encode for biological functions and pathways such as
granulation and maturation of hematologic cells, explaining morphologic
and clinical differences. {D}iscrimination between {M}3 and {M}3v
based on gene signatures showed a median classification accuracy
of 90\% by use of 10-fold {CV} and support vector machines. {A}dditional
molecular mutations such as {FLT}3-{LM}, which were significantly
more frequent in {M}3v than in {M}3 ({P} < 0.0001), may partly contribute
to the different phenotypes. {H}owever, linear regression analysis
demonstrated that genes differentially expressed between {M}3 and
{M}3v did not correlate with {FLT}3-{LM}.},
doi = {10.1002/gcc.20175},
pdf = {../local/Haferlach2005AML.pdf},
file = {Haferlach2005AML.pdf:local/Haferlach2005AML.pdf:PDF},
keywords = {biosvm microarray},
url = {http://dx.doi.org/10.1002/gcc.20175}
}
@article{Haferlach2005global,
author = {Torsten Haferlach and Alexander Kohlmann and Susanne Schnittger and
Martin Dugas and Wolfgang Hiddemann and Wolfgang Kern and Claudia
Schoch},
title = {A global approach to the diagnosis of leukemia using gene expression
profiling.},
journal = {Blood},
year = {2005},
volume = {106},
pages = {1189-1198},
number = {4},
month = {Aug},
abstract = {Accurate diagnosis and classification of leukemias are the bases for
the appropriate management of patients. {T}he diagnostic accuracy
and efficiency of present methods may be improved by the use of microarrays
for gene expression profiling. {W}e analyzed gene expression profiles
in bone marrow and peripheral blood samples from 937 patients with
all clinically relevant leukemia subtypes (n=892) and non-leukemic
controls (n=45) by {U}133{A} and {B} {G}ene{C}hips ({A}ffymetrix).
{F}or each subgroup differentially expressed genes were calculated.
{C}lass prediction was performed using support vector machines. {P}rediction
accuracies were estimated by 10-fold cross validation and assessed
for robustness in a 100-fold resampling approach using randomly chosen
test-sets consisting of 1/3 of the samples. {A}pplying the top 100
genes of each subgroup an overall prediction accuracy of 95.1\% was
achieved which was confirmed by resampling (median, 93.8\%; 95\%
confidence interval, 91.4\%-95.8\%). {I}n particular, {AML} with
t(15;17), t(8;21), or inv(16), {CLL}, and {P}ro-{B}-{ALL} with t(11q23)
were classified with 100\% sensitivity and 100\% specificity. {A}ccordingly,
cluster analysis completely separated all of the 13 subgroups analyzed.
{G}ene expression profiling can predict all clinically relevant subentities
of leukemia with high accuracy.},
doi = {10.1182/blood-2004-12-4938},
pdf = {../local/Haferlach2005global.pdf},
file = {Haferlach2005global.pdf:local/Haferlach2005global.pdf:PDF},
keywords = {biosvm microarray},
pii = {2004-12-4938},
url = {http://dx.doi.org/10.1182/blood-2004-12-4938}
}
@article{Hanisch2002Co-clustering,
author = {D. Hanisch and A. Zien and R. Zimmer and T. Lengauer},
title = {Co-clustering of biological networks and gene expression data},
journal = {Bioinformatics},
year = {2002},
annote = {To appear},
subject = {microarraybionet},
url = {http://cartan.gmd.de/~hanisch/paper/CoClustering.pdf}
}
@article{Ioannidis2005Microarrays,
author = {Ioannidis, J. P. A.},
title = {Microarrays and molecular research: noise discovery?},
journal = {Lancet},
year = {2005},
volume = {365},
pages = {454},
number = {9458},
pdf = {../local/Ioannidis2005Microarrays.pdf},
file = {Ioannidis2005Microarrays.pdf:Ioannidis2005Microarrays.pdf:PDF},
keywords = {microarray},
owner = {jp},
timestamp = {2011.01.12}
}
@article{Ishkanian2004tiling,
author = {Ishkanian, A. S. and Malloff, C. A. and Watson, S. K. and DeLeeuw,
R. J. and Chi, B. and Coe, B. P. and Snijders, A. and Albertson,
D. G. and Pinkel, D. and Marra, M. A. and Ling, V. and MacAulay,
C. and Lam, W. L.},
title = {A tiling resolution {DNA} microarray with complete coverage of the
human genome},
journal = {Nat. Genet.},
year = {2004},
volume = {36},
pages = {299--303},
number = {3},
month = {Mar},
abstract = {We constructed a tiling resolution array consisting of 32,433 overlapping
BAC clones covering the entire human genome. This increases our ability
to identify genetic alterations and their boundaries throughout the
genome in a single comparative genomic hybridization (CGH) experiment.
At this tiling resolution, we identified minute DNA alterations not
previously reported. These alterations include microamplifications
and deletions containing oncogenes, tumor-suppressor genes and new
genes that may be associated with multiple tumor types. Our findings
show the need to move beyond conventional marker-based genome comparison
approaches, that rely on inference of continuity between interval
markers. Our submegabase resolution tiling set for array CGH (SMRT
array) allows comprehensive assessment of genomic integrity and thereby
the identification of new genes associated with disease.},
doi = {10.1038/ng1307},
pdf = {../local/Ishkanian2004tiling.pdf},
file = {Ishkanian2004tiling.pdf:Ishkanian2004tiling.pdf:PDF},
institution = {British Columbia Cancer Research Centre, 601 West 10th Avenue, Vancouver,
British Columbia V5Z 1L3, Canada.},
keywords = {csbcbook, microarray},
owner = {jp},
pii = {ng1307},
pmid = {14981516},
timestamp = {2009.10.08},
url = {http://dx.doi.org/10.1038/ng1307}
}
@article{Kuhn2001Global,
author = {K. M. Kuhn and J. L. DeRisi and P. O. Brown and P. Sarnow},
title = {Global and specific translational regulation in the genomic response
of {S}accharomyces cerevisiae to a rapid transfer from a fermentable
to a nonfermentable carbon source},
journal = {Mol. {C}ell. {B}iol.},
year = {2001},
volume = {21},
pages = {916--927},
number = {3},
pdf = {../local/kuhn01.pdf},
file = {kuhn01.pdf:local/kuhn01.pdf:PDF},
subject = {microarray},
url = {http://mcb.asm.org/cgi/content/full/21/3/916?view=full&pmid=11154278}
}
@article{LeRoch2003Discovery,
author = {Le Roch, K. G. and Zhou, Y. and Blair, P. L. and Grainger, M. and
Moch, J. K. and Haynes, J. D. and De la Vega, P. and Holder, A. A.
and Batalov, S. and Carucci, D. J. and Winzeler, E. A.},
title = {Discovery of Gene Function by Expression Profiling of the Malaria
Parasite Life Cycle},
journal = {Science},
year = {2003},
volume = {301},
pages = {1503-1508},
number = {5639},
abstract = {The completion of the genome sequence for {P}lasmodium falciparum,
the species responsible for most malaria human deaths, has the potential
to reveal hundreds of new drug targets and proteins involved in pathogenesis.
{H}owever, only approximately 35% of the genes code for proteins
with an identifiable function. {T}he absence of routine genetic tools
for studying {P}lasmodium parasites suggests that this number is
unlikely to change quickly if conventional serial methods are used
to characterize encoded proteins. {H}ere, we use a high-density oligonucleotide
array to generate expression profiles of human and mosquito stages
of the malaria parasite's life cycle. {G}enes with highly correlated
levels and temporal patterns of expression were often involved in
similar functions or cellular processes.},
doi = {10.1126/science.1087025},
pdf = {../local/LeRoch2003Discovery.pdf},
file = {LeRoch2003Discovery.pdf:LeRoch2003Discovery.pdf:PDF},
keywords = {microarray plasmodium},
owner = {vert},
url = {http://www.sciencemag.org/cgi/content/full/301/5639/1503}
}
@article{Levy2007Diploid,
author = {Samuel Levy and Granger Sutton and Pauline C Ng and Lars Feuk and
Aaron L Halpern and Brian P Walenz and Nelson Axelrod and Jiaqi Huang
and Ewen F Kirkness and Gennady Denisov and Yuan Lin and Jeffrey
R MacDonald and Andy Wing Chun Pang and Mary Shago and Timothy B
Stockwell and Alexia Tsiamouri and Vineet Bafna and Vikas Bansal
and Saul A Kravitz and Dana A Busam and Karen Y Beeson and Tina C
McIntosh and Karin A Remington and Josep F Abril and John Gill and
Jon Borman and Yu-Hui Rogers and Marvin E Frazier and Stephen W Scherer
and Robert L Strausberg and J. Craig Venter},
title = {The diploid genome sequence of an individual human.},
journal = {PLoS Biol},
year = {2007},
volume = {5},
pages = {e254},
number = {10},
month = {Sep},
abstract = {Presented here is a genome sequence of an individual human. It was
produced from approximately 32 million random DNA fragments, sequenced
by Sanger dideoxy technology and assembled into 4,528 scaffolds,
comprising 2,810 million bases (Mb) of contiguous sequence with approximately
7.5-fold coverage for any given region. We developed a modified version
of the Celera assembler to facilitate the identification and comparison
of alternate alleles within this individual diploid genome. Comparison
of this genome and the National Center for Biotechnology Information
human reference assembly revealed more than 4.1 million DNA variants,
encompassing 12.3 Mb. These variants (of which 1,288,319 were novel)
included 3,213,401 single nucleotide polymorphisms (SNPs), 53,823
block substitutions (2-206 bp), 292,102 heterozygous insertion/deletion
events (indels)(1-571 bp), 559,473 homozygous indels (1-82,711 bp),
90 inversions, as well as numerous segmental duplications and copy
number variation regions. Non-SNP DNA variation accounts for 22\%
of all events identified in the donor, however they involve 74\%
of all variant bases. This suggests an important role for non-SNP
genetic alterations in defining the diploid genome structure. Moreover,
44\% of genes were heterozygous for one or more variants. Using a
novel haplotype assembly strategy, we were able to span 1.5 Gb of
genome sequence in segments >200 kb, providing further precision
to the diploid nature of the genome. These data depict a definitive
molecular portrait of a diploid human genome that provides a starting
point for future genome comparisons and enables an era of individualized
genomic information.},
doi = {10.1371/journal.pbio.0050254},
institution = {J. Craig Venter Institute, Rockville, Maryland, USA. slevy@jcvi.org},
keywords = {Base Sequence; Chromosome Mapping, instrumentation/methods; Chromosomes,
Human; Chromosomes, Human, Y, genetics; Diploidy; Gene Dosage; Genome,
Human; Genotype; Haplotypes; Human Genome Project; Humans; INDEL
Mutation; In Situ Hybridization, Fluorescence; Male; Microarray Analysis;
Middle Aged; Molecular Sequence Data; Pedigree; Phenotype; Polymorphism,
Single Nucleotide; Reproducibility of Results; Sequence Analysis,
DNA, instrumentation/methods},
language = {eng},
medline-pst = {ppublish},
owner = {philippe},
pii = {07-PLBI-RA-1258},
pmid = {17803354},
timestamp = {2010.07.28},
url = {http://dx.doi.org/10.1371/journal.pbio.0050254}
}
@article{Mavroforakis2005Significance,
author = {Michael Mavroforakis and Harris Georgiou and Nikos Dimitropoulos
and Dionisis Cavouras and Sergios Theodoridis},
title = {Significance analysis of qualitative mammographic features, using
linear classifiers, neural networks and support vector machines.},
journal = {Eur {J} {R}adiol},
year = {2005},
volume = {54},
pages = {80-9},
number = {1},
month = {Apr},
abstract = {Advances in modern technologies and computers have enabled digital
image processing to become a vital tool in conventional clinical
practice, including mammography. {H}owever, the core problem of the
clinical evaluation of mammographic tumors remains a highly demanding
cognitive task. {I}n order for these automated diagnostic systems
to perform in levels of sensitivity and specificity similar to that
of human experts, it is essential that a robust framework on problem-specific
design parameters is formulated. {T}his study is focused on identifying
a robust set of clinical features that can be used as the base for
designing the input of any computer-aided diagnosis system for automatic
mammographic tumor evaluation. {A} thorough list of clinical features
was constructed and the diagnostic value of each feature was verified
against current clinical practices by an expert physician. {T}hese
features were directly or indirectly related to the overall morphological
properties of the mammographic tumor or the texture of the fine-scale
tissue structures as they appear in the digitized image, while others
contained external clinical data of outmost importance, like the
patient's age. {T}he entire feature set was used as an annotation
list for describing the clinical properties of mammographic tumor
cases in a quantitative way, such that subsequent objective analyses
were possible. {F}or the purposes of this study, a mammographic image
database was created, with complete clinical evaluation descriptions
and positive histological verification for each case. {A}ll tumors
contained in the database were characterized according to the identified
clinical features' set and the resulting dataset was used as input
for discrimination and diagnostic value analysis for each one of
these features. {S}pecifically, several standard methodologies of
statistical significance analysis were employed to create feature
rankings according to their discriminating power. {M}oreover, three
different classification models, namely linear classifiers, neural
networks and support vector machines, were employed to investigate
the true efficiency of each one of them, as well as the overall complexity
of the diagnostic task of mammographic tumor characterization. {B}oth
the statistical and the classification results have proven the explicit
correlation of all the selected features with the final diagnosis,
qualifying them as an adequate input base for any type of similar
automated diagnosis system. {T}he underlying complexity of the diagnostic
task has justified the high value of sophisticated pattern recognition
architectures.},
doi = {10.1016/j.ejrad.2004.12.015},
pdf = {../local/Mavroforakis2005Significance.pdf},
file = {Mavroforakis2005Significance.pdf:local/Mavroforakis2005Significance.pdf:PDF},
keywords = {Algorithms, Animals, Antibiotics, Antineoplastic, Artificial Intelligence,
Butadienes, Chloroplasts, Comparative Study, Computer Simulation,
Computer-Assisted, Diagnosis, Disinfectants, Dose-Response Relationship,
Drug, Drug Toxicity, Electrodes, Electroencephalography, Ethylamines,
Expert Systems, Feedback, Fungicides, Gene Expression Profiling,
Genes, Genetic Markers, Humans, Implanted, Industrial, Information
Storage and Retrieval, Kidney, Kidney Tubules, MEDLINE, Male, Mercuric
Chloride, Microarray Analysis, Molecular Biology, Motor Cortex, Movement,
Natural Language Processing, Neural Networks (Computer), Non-P.H.S.,
Non-U.S. Gov't, Plant Proteins, Predictive Value of Tests, Proteins,
Proteome, Proximal, Puromycin Aminonucleoside, Rats, Reproducibility
of Results, Research Support, Sprague-Dawley, Subcellular Fractions,
Terminology, Therapy, Time Factors, Toxicogenetics, U.S. Gov't, User-Computer
Interface, 15797296},
pii = {S0720-048X(05)00023-9},
url = {http://dx.doi.org/10.1016/j.ejrad.2004.12.015}
}
@article{Meireles2003Differentially,
author = {Meireles, S.I. and Carvalho, A.F. and Hirata, R. and Montagnini,
A.L. and Martins, W.K. and Runza, F.B. and Stolf, B.S. and Termini,
L. and Neto, C.E. and Silva, R.L. and Soares, F.A. and Neves, E.J.
and Reis, L.F.},
title = {Differentially expressed genes in gastric tumors identified by c{DNA}
array.},
journal = {Cancer {L}ett.},
year = {2003},
volume = {190},
pages = {199-211},
number = {2},
month = {Feb},
abstract = {Using c{DNA} fragments from the {FAPESP}/l{ICR} {C}ancer {G}enome
{P}roject, we constructed a c{DNA} array having 4512 elements and
determined gene expression in six normal and six tumor gastric tissues.
{U}sing t-statistics, we identified 80 c{DNA}s whose expression in
normal and tumor samples differed more than 3.5 sample standard deviations.
{U}sing {S}elf-{O}rganizing {M}ap, the expression profile of these
c{DNA}s allowed perfect separation of malignant and non-malignant
samples. {U}sing the supervised learning procedure {S}upport {V}ector
{M}achine, we identified trios of c{DNA}s that could be used to classify
samples as normal or tumor, based on single-array analysis. {F}inally,
we identified genes with altered linear correlation when their expression
in normal and tumor samples were compared. {F}urther investigation
concerning the function of these genes could contribute to the understanding
of gastric carcinogenesis and may prove useful in molecular diagnostics.},
doi = {10.1016/S0304-3835(02)00587},
pdf = {../local/Meireles2003Differentially.pdf},
file = {Meireles2003Differentially.pdf:local/Meireles2003Differentially.pdf:PDF},
keywords = {biosvm microarray},
owner = {jeanphilippevert},
url = {http://dx.doi.org/10.1016/S0304-3835(02)00587-6}
}
@article{Michiels2005Prediction,
author = {Michiels, S. and Koscielny, S. and Hill, C.},
title = {Prediction of cancer outcome with microarrays: a multiple random
validation strategy},
journal = {Lancet},
year = {2005},
volume = {365},
pages = {488--492},
number = {9458},
abstract = {BACKGROUND: General studies of microarray gene-expression profiling
have been undertaken to predict cancer outcome. Knowledge of this
gene-expression profile or molecular signature should improve treatment
of patients by allowing treatment to be tailored to the severity
of the disease. We reanalysed data from the seven largest published
studies that have attempted to predict prognosis of cancer patients
on the basis of DNA microarray analysis. METHODS: The standard strategy
is to identify a molecular signature (ie, the subset of genes most
differentially expressed in patients with different outcomes) in
a training set of patients and to estimate the proportion of misclassifications
with this signature on an independent validation set of patients.
We expanded this strategy (based on unique training and validation
sets) by using multiple random sets, to study the stability of the
molecular signature and the proportion of misclassifications. FINDINGS:
The list of genes identified as predictors of prognosis was highly
unstable; molecular signatures strongly depended on the selection
of patients in the training sets. For all but one study, the proportion
misclassified decreased as the number of patients in the training
set increased. Because of inadequate validation, our chosen studies
published overoptimistic results compared with those from our own
analyses. Five of the seven studies did not classify patients better
than chance. INTERPRETATION: The prognostic value of published microarray
results in cancer studies should be considered with caution. We advocate
the use of validation by repeated random sampling.},
doi = {10.1016/S0140-6736(05)17866-0},
institution = {Biostatistics and Epidemiology Unit, Institut Gustave Roussy, Villejuif,
France.},
keywords = {featureselection, breastcancer, microarray},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {S0140-6736(05)17866-0},
pmid = {15705458},
timestamp = {2010.10.12},
url = {http://dx.doi.org/10.1016/S0140-6736(05)17866-0}
}
@techreport{Mukherjee1998Support,
author = {S. Mukherjee and P. Tamayo and J. P. Mesirov and D. Slonim and A.
Verri and T. Poggio},
title = {Support vector machine classification of microarray data},
institution = {C.B.L.C.},
year = {1998},
number = {182},
note = {A.I. Memo 1677},
pdf = {../local/Mukherjee1998Support.pdf},
file = {Mukherjee1998Support.pdf:local/Mukherjee1998Support.pdf:PDF},
keywords = {biosvm microarray},
subject = {biokernel},
url = {http://citeseer.nj.nec.com/437379.html}
}
@article{ODonnell2005Gene,
author = {Rebekah K O'Donnell and Michael Kupferman and S. Jack Wei and Sunil
Singhal and Randal Weber and Bert O'Malley and Yi Cheng and Mary
Putt and Michael Feldman and Barry Ziober and Ruth J Muschel},
title = {Gene expression signature predicts lymphatic metastasis in squamous
cell carcinoma of the oral cavity.},
journal = {Oncogene},
year = {2005},
volume = {24},
pages = {1244-51},
number = {7},
month = {Feb},
abstract = {Metastasis via the lymphatics is a major risk factor in squamous cell
carcinoma of the oral cavity ({OSCC}). {W}e sought to determine whether
the presence of metastasis in the regional lymph node could be predicted
by a gene expression signature of the primary tumor. {A} total of
18 {OSCC}s were characterized for gene expression by hybridizing
{RNA} to {A}ffymetrix {U}133{A} gene chips. {G}enes with differential
expression were identified using a permutation technique and verified
by quantitative {RT}-{PCR} and immunohistochemistry. {A} predictive
rule was built using a support vector machine, and the accuracy of
the rule was evaluated using crossvalidation on the original data
set and prediction of an independent set of four patients. {M}etastatic
primary tumors could be differentiated from nonmetastatic primary
tumors by a signature gene set of 116 genes. {T}his signature gene
set correctly predicted the four independent patients as well as
associating five lymph node metastases from the original patient
set with the metastatic primary tumor group. {W}e concluded that
lymph node metastasis could be predicted by gene expression profiles
of primary oral cavity squamous cell carcinomas. {T}he presence of
a gene expression signature for lymph node metastasis indicates that
clinical testing to assess risk for lymph node metastasis should
be possible.},
doi = {10.1038/sj.onc.1208285},
pdf = {../local/O'Donnell2005Gene.pdf},
file = {O'Donnell2005Gene.pdf:local/O'Donnell2005Gene.pdf:PDF},
keywords = {biosvm microarray},
pii = {1208285},
url = {http://dx.doi.org/10.1038/sj.onc.1208285}
}
@article{Ogawa2000New,
author = {Nobuo Ogawa and Joseph DeRisi and Patrick O. Brown},
title = {New {C}omponents of a {S}ystem for {P}hosphate {A}ccumulation and
{P}olyphosphate {M}etabolism in {S}accharomyces cerevisiae {R}evealed
by {G}enomic {E}xpression {A}nalysis},
journal = {Mol. {B}iol. {C}ell},
year = {2000},
volume = {11},
pages = {4309--4321},
month = {Dec},
pdf = {../local/ogaw00.pdf},
file = {ogaw00.pdf:local/ogaw00.pdf:PDF},
subject = {microarray},
url = {http://www.molbiolcell.org/cgi/reprint/11/12/4309.pdf}
}
@article{Pavey2004Microarray,
author = {Pavey, S. and Johansson, P. and Packer, L. and Taylor, J. and Stark,
M. and Pollock, P.M. and Walker, G.J. and Boyle, G.M. and Harper,
U. and Cozzi, S.J. and Hansen, K. and Yudt, L. and Schmidt, C. and
Hersey, P. and Ellem, K.A. and O'Rourke, M.G. and Parsons, P.G. and
Meltzer, P. and Ringner, M. and Hayward, N.K.},
title = {Microarray expression profiling in melanoma reveals a {BRAF} mutation
signature},
journal = {Oncogene},
year = {2004},
volume = {23},
pages = {4060-4067},
number = {23},
month = {May},
abstract = {We have used microarray gene expression profiling and machine learning
to predict the presence of {BRAF} mutations in a panel of 61 melanoma
cell lines. {T}he {BRAF} gene was found to be mutated in 42 samples
(69%) and intragenic mutations of the {NRAS} gene were detected in
seven samples (11%). {N}o cell line carried mutations of both genes.
{U}sing support vector machines, we have built a classifier that
differentiates between melanoma cell lines based on {BRAF} mutation
status. {A}s few as 83 genes are able to discriminate between {BRAF}
mutant and {BRAF} wild-type samples with clear separation observed
using hierarchical clustering. {M}ultidimensional scaling was used
to visualize the relationship between a {BRAF} mutation signature
and that of a generalized mitogen-activated protein kinase ({MAPK})
activation (either {BRAF} or {NRAS} mutation) in the context of the
discriminating gene list. {W}e observed that samples carrying {NRAS}
mutations lie somewhere between those with or without {BRAF} mutations.
{T}hese observations suggest that there are gene-specific mutation
signals in addition to a common {MAPK} activation that result from
the pleiotropic effects of either {BRAF} or {NRAS} on other signaling
pathways, leading to measurably different transcriptional changes.},
doi = {10.1038/sj.onc.1207563},
pdf = {../local/Pavey2004Microarray.pdf},
file = {Pavey2004Microarray.pdf:local/Pavey2004Microarray.pdf:PDF},
keywords = {biosvm microarray},
owner = {jeanphilippevert},
url = {http://dx.doi.org/10.1038/sj.onc.1207563}
}
@article{Peng2003Molecular,
author = {Peng, S. and Xu, Q. and Ling, X.B. and Peng, X. and Du, W. and Chen,
L.},
title = {Molecular classification of cancer types from microarray data using
the combination of genetic algorithms and support vector machines.},
journal = {F{EBS} {L}ett.},
year = {2003},
volume = {555},
pages = {358-362},
number = {2},
abstract = {Simultaneous multiclass classification of tumor types is essential
for future clinical implementations of microarray-based cancer diagnosis.
{I}n this study, we have combined genetic algorithms ({GA}s) and
all paired support vector machines ({SVM}s) for multiclass cancer
identification. {T}he predictive features have been selected through
iterative {SVM}s/{GA}s, and recursive feature elimination post-processing
steps, leading to a very compact cancer-related predictive gene set.
{L}eave-one-out cross-validations yielded accuracies of 87.93% for
the eight-class and 85.19% for the fourteen-class cancer classifications,
outperforming the results derived from previously published methods.},
doi = {10.1016/S0014-5793(03)01275-4},
pdf = {../local/Peng2003Molecular.pdf},
file = {Peng2003Molecular.pdf:local/Peng2003Molecular.pdf:PDF},
keywords = {biosvm microarray},
owner = {jeanphilippevert},
url = {http://dx.doi.org/10.1016/S0014-5793(03)01275-4}
}
@article{Pilpel2001Identifying,
author = {Pilpel, Y. and Sudarsanam, P. and Church, G. M.},
title = {Identifying regulatory networks by combinatorial analysis of promoter
elements},
journal = {Nature},
year = {2001},
volume = {29},
pages = {153--159},
pdf = {../local/pilp01.pdf},
file = {pilp01.pdf:local/pilp01.pdf:PDF},
subject = {microarray},
url = {http://www.nature.com/cgi-taf/DynaPage.taf?file=/ng/journal/v29/n2/full/ng724.html&filetype=PDF}
}
@article{Pochet2004Systematic,
author = {Pochet, N. and De Smet, F. and Suykens, J. A. K. and De Moor, B.
L. R.},
title = {Systematic benchmarking of microarray data classification: assessing
the role of non-linearity and dimensionality reduction},
journal = {Bioinformatics},
year = {2004},
volume = {20},
pages = {3185-3195},
number = {17},
month = {Nov},
abstract = {Motivation: {M}icroarrays are capable of determining the expression
levels of thousands of genes simultaneously. {I}n combination with
classification methods, this technology can be useful to support
clinical management decisions for individual patients, e.g. in oncology.
{T}he aim of this paper is to systematically benchmark the role of
non-linear versus linear techniques and dimensionality reduction
methods. {R}esults: {A} systematic benchmarking study is performed
by comparing linear versions of standard classification and dimensionality
reduction techniques with their non-linear versions based on non-linear
kernel functions with a radial basis function ({RBF}) kernel. {A}
total of 9 binary cancer classification problems, derived from 7
publicly available microarray datasets, and 20 randomizations of
each problem are examined. {C}onclusions: {T}hree main conclusions
can be formulated based on the performances on independent test sets.
(1) {W}hen performing classification with least squares support vector
machines ({LS}-{SVM}s) (without dimensionality reduction), {RBF}
kernels can be used without risking too much overfitting. {T}he results
obtained with well-tuned {RBF} kernels are never worse and sometimes
even statistically significantly better compared to results obtained
with a linear kernel in terms of test set receiver operating characteristic
and test set accuracy performances. (2) {E}ven for classification
with linear classifiers like {LS}-{SVM} with linear kernel, using
regularization is very important. (3) {W}hen performing kernel principal
component analysis (kernel {PCA}) before classification, using an
{RBF} kernel for kernel {PCA} tends to result in overfitting, especially
when using supervised feature selection. {I}t has been observed that
an optimal selection of a large number of features is often an indication
for overfitting. {K}ernel {PCA} with linear kernel gives better results.
{A}vailability: {M}atlab scripts are available on request. {S}upplementary
information: http://www.esat.kuleuven.ac.be/~npochet/{B}ioinformatics/},
doi = {10.1093/bioinformatics/bth383},
pdf = {../local/Pochet2004Systematic.pdf},
file = {Pochet2004Systematic.pdf:local/Pochet2004Systematic.pdf:PDF},
keywords = {biosvm microarray},
owner = {jeanphilippevert},
url = {http://dx.doi.org/10.1093/bioinformatics/bth383}
}
@article{Ramaswamy2001Multiclass,
author = {Ramaswamy, S. and Tamayo, P. and Rifkin, R. and Mukherjee, S. and
Yeang, C.H. and Angelo, M. and Ladd, C. and Reich, M. and Latulippe,
E. and Mesirov, J.P. and Poggio, T. and Gerald, W. and Loda, M. and
Lander, E.S. and Golub, T.R.},
title = {Multiclass cancer diagnosis using tumor gene expression signatures},
journal = {Proc. {N}atl. {A}cad. {S}ci. {USA}},
year = {2001},
volume = {98},
pages = {15149-15154},
number = {26},
month = {Dec},
abstract = {The optimal treatment of patients with cancer depends on establishing
accurate diagnoses by using a complex combination of clinical and
histopathological data. {I}n some instances, this task is difficult
or impossible because of atypical clinical presentation or histopathology.
{T}o determine whether the diagnosis of multiple common adult malignancies
could be achieved purely by molecular classification, we subjected
218 tumor samples, spanning 14 common tumor types, and 90 normal
tissue samples to oligonucleotide microarray gene expression analysis.
{T}he expression levels of 16,063 genes and expressed sequence tags
were used to evaluate the accuracy of a multiclass classifier based
on a support vector machine algorithm. {O}verall classification accuracy
was 78%, far exceeding the accuracy of random classification (9%).
{P}oorly differentiated cancers resulted in low-confidence predictions
and could not be accurately classified according to their tissue
of origin, indicating that they are molecularly distinct entities
with dramatically different gene expression patterns compared with
their well differentiated counterparts. {T}aken together, these results
demonstrate the feasibility of accurate, multiclass molecular cancer
classification and suggest a strategy for future clinical implementation
of molecular cancer diagnostics.},
doi = {10.1073/pnas.211566398},
pdf = {../local/Ramaswamy2001Multiclass.pdf},
file = {Ramaswamy2001Multiclass.pdf:local/Ramaswamy2001Multiclass.pdf:PDF},
keywords = {biosvm microarray},
owner = {vert},
url = {http://dx.doi.org/10.1073/pnas.211566398}
}
@phdthesis{Reyal2009Analyse,
author = {Reyal, F.},
title = {Analyse du profil d'expression par la technique des puces {\`a} ADN.
Application \`a la caract\'erisation mol\'eculaire et \`a la d\'etermination
du pronostic des cancers canalaires infiltrants du sein.},
school = {Universit\'e Paris 11},
year = {2009},
keywords = {breastcancer, microarray},
owner = {jp},
timestamp = {2009.10.31}
}
@article{Schena1995Quantitative,
author = {M. Schena and D. Shalon and R. W. Davis and P. O. Brown},
title = {Quantitative monitoring of gene expression patterns with a complementary
DNA microarray.},
journal = {Science},
year = {1995},
volume = {270},
pages = {467--470},
number = {5235},
month = {Oct},
abstract = {A high-capacity system was developed to monitor the expression of
many genes in parallel. Microarrays prepared by high-speed robotic
printing of complementary DNAs on glass were used for quantitative
expression measurements of the corresponding genes. Because of the
small format and high density of the arrays, hybridization volumes
of 2 microliters could be used that enabled detection of rare transcripts
in probe mixtures derived from 2 micrograms of total cellular messenger
RNA. Differential expression measurements of 45 Arabidopsis genes
were made by means of simultaneous, two-color fluorescence hybridization.},
doi = {10.1126/science.270.5235.467},
pdf = {../local/Schena1995Quantitative.pdf},
file = {Schena1995Quantitative.pdf:Schena1995Quantitative.pdf:PDF},
institution = {Department of Biochemistry, Beckman Center, Stanford University Medical
Center, CA 94305, USA.},
keywords = {microarray},
owner = {jp},
pmid = {7569999},
timestamp = {2009.02.08},
url = {http://dx.doi.org/10.1126/science.270.5235.467}
}
@article{Segal2005From,
author = {Segal, E. and Friedman, N. and Kaminski, N. and Regev, A. and Koller,
D.},
title = {From signatures to models: understanding cancer using microarrays},
journal = {Nat {G}enet},
year = {2005},
volume = {37},
pages = {S38-45},
number = {6 Suppl},
abstract = {Genomics has the potential to revolutionize the diagnosis and management
of cancer by offering an unprecedented comprehensive view of the
molecular underpinnings of pathology. {C}omputational analysis is
essential to transform the masses of generated data into a mechanistic
understanding of disease. {H}ere we review current research aimed
at uncovering the modular organization and function of transcriptional
networks and responses in cancer. {W}e first describe how methods
that analyze biological processes in terms of higher-level modules
can identify robust signatures of disease mechanisms. {W}e then discuss
methods that aim to identify the regulatory mechanisms underlying
these modules and processes. {F}inally, we show how comparative analysis,
combining human data with model organisms, can lead to more robust
findings. {W}e conclude by discussing the challenges of generalizing
these methods from cells to tissues and the opportunities they offer
to improve cancer diagnosis and management.},
doi = {10.1038/ng1561},
pdf = {../local/Segal2005From.pdf},
file = {Segal2005From.pdf:Segal2005From.pdf:PDF},
keywords = {microarray},
url = {http://dx.doi.org/10.1038/ng1561}
}
@article{Selinger2000RNA,
author = {Douglas W. Selinger and Kevin J. Cheung and Rui Mei and Erik M. Johansson
and Craig S. Richmond and Frederick R. Blattner and David J. Lockhart
and George M. Church},
title = {R{NA} expression analysis using a 30 base pair resolution {E}scherichia
coli genome array},
journal = {Nat. {B}iotechnol.},
year = {2000},
volume = {18},
pages = {1262--1268},
pdf = {../local/seli00.pdf},
file = {seli00.pdf:local/seli00.pdf:PDF},
subject = {microarray},
url = {http://www.nature.com/cgi-taf/DynaPage.taf?file=/nbt/journal/v18/n12/full/nbt1200_1262.html&filetype=PDF}
}
@article{Sherlock2001Stanford,
author = {G. Sherlock and T. Hernandez-Boussard and A. Kasarskis and G. Binkley
and J.C. Matese and S.S. Dwight and M. Kaloper and S. Weng and H.
Jin and C.A. Ball and M.B. Eisen and P.T. Spellman},
title = {The {S}tanford {M}icroarray {D}atabase},
journal = {Nucleic {A}cids {R}es.},
year = {2001},
volume = {29},
pages = {152--155},
number = {1},
month = {Jan},
pdf = {../local/sher01.pdf},
file = {sher01.pdf:local/sher01.pdf:PDF},
subject = {microarray},
url = {http://genome-www5.Stanford.EDU/MicroArray/SMD/SMD.pdf}
}
@article{Spellman1998Comprehensive,
author = {Spellman, P.T. and Sherlock, G. and Zhang, M.Q. and Iyer, V.R. and
Anders, K. and Eisen, M.B. and Brown, P.O. and Botstein, D. and Futcher,
B.},
title = {Comprehensive {I}dentification of {C}ell {C}ycle-regulated {G}enes
of the {Y}east {S}accharomyces cerevisiae by {M}icroarray {H}ybridization},
journal = {Mol. {B}iol. {C}ell},
year = {1998},
volume = {9},
pages = {3273--3297},
pdf = {../local/spel98.pdf},
file = {spel98.pdf:local/spel98.pdf:PDF},
subject = {microarray},
url = {http://www.molbiolcell.org/cgi/reprint/9/12/3273.pdf}
}
@article{Statnikov2005comprehensive,
author = {Statnikov, A. and Aliferis, C. F. and Tsamardinos, I. and Hardin,
D. and Levy, S.},
title = {A comprehensive evaluation of multicategory classification methods
for microarray gene expression cancer diagnosis},
journal = {Bioinformatics},
year = {2005},
note = {To appear},
abstract = {Motivation: {C}ancer diagnosis is one of the most important emerging
clinical applications of gene expression microarray technology. {W}e
are seeking to develop a computer system for powerful and reliable
cancer diagnostic model creation based on microarray data. {T}o keep
a realistic perspective on clinical applications we focus on multicategory
diagnosis. {I}n order to equip the system with the optimum combination
of classifier, gene selection and cross-validation methods, we performed
a systematic and comprehensive evaluation of several major algorithms
for multicategory classification, several gene selection methods,
multiple ensemble classifier methods, and two cross validation designs
using 11 datasets spanning 74 diagnostic categories and 41 cancer
types and 12 normal tissue types.{R}esults: {M}ulticategory {S}upport
{V}ector {M}achines ({MC}-{SVM}s) are the most effective classifiers
in performing accurate cancer diagnosis from gene expression data.
{T}he {MC}-{SVM} techniques by {C}rammer and {S}inger, {W}eston and
{W}atkins, and one-versus-rest were found to be the best methods
in this domain. {MC}-{SVM}s outperform other popular machine learning
algorithms such as {K}-{N}earest {N}eighbors, {B}ackpropagation and
{P}robabilistic {N}eural {N}etworks, often to a remarkable degree.
{G}ene selection techniques can significantly improve classification
performance of both {MC}-{SVM}s and other non-{SVM} learning algorithms.
{E}nsemble classifiers do not generally improve performance of the
best non-ensemble models. {T}hese results guided the construction
of a software system {GEMS} ({G}ene {E}xpression {M}odel {S}elector)
that automates high-quality model construction and enforces sound
optimization and performance estimation procedures. {T}his is the
first such system to be informed by a rigorous comparative analysis
of the available algorithms and datasets.{A}vailability: {T}he software
system {GEMS} is available for download from http://www.gems-system.org
for non-commercial use.},
pdf = {../local/Statnikov2005comprehensive.pdf},
file = {Statnikov2005comprehensive.pdf:local/Statnikov2005comprehensive.pdf:PDF},
keywords = {biosvm microarray},
owner = {jeanphilippevert},
url = {http://bioinformatics.oupjournals.org/cgi/content/abstract/bti033v1}
}
@article{Tavazoie1999Systematic,
author = {Tavazoie, S. and Hughes, J. D. and Campbell, M. J. and Cho, R. J.
and Church, G. M.},
title = {Systematic determination of genetic network architecture},
journal = {Nat. Genet.},
year = {1999},
volume = {22},
pages = {281--285},
doi = {doi:10.1038/10343},
pdf = {../local/Tavazoie1999Systematic.pdf},
file = {Tavazoie1999Systematic.pdf:local/Tavazoie1999Systematic.pdf:PDF},
subject = {microarray},
url = {http://dx.doi.org/10.1038/10343}
}
@article{Thukral2005Prediction,
author = {Sushil K Thukral and Paul J Nordone and Rong Hu and Leah Sullivan
and Eric Galambos and Vincent D Fitzpatrick and Laura Healy and Michael
B Bass and Mary E Cosenza and Cynthia A Afshari},
title = {Prediction of nephrotoxicant action and identification of candidate
toxicity-related biomarkers.},
journal = {Toxicol {P}athol},
year = {2005},
volume = {33},
pages = {343-55},
number = {3},
abstract = {A vast majority of pharmacological compounds and their metabolites
are excreted via the urine, and within the complex structure of the
kidney,the proximal tubules are a main target site of nephrotoxic
compounds. {W}e used the model nephrotoxicants mercuric chloride,
2-bromoethylamine hydrobromide, hexachlorobutadiene, mitomycin, amphotericin,
and puromycin to elucidate time- and dose-dependent global gene expression
changes associated with proximal tubular toxicity. {M}ale {S}prague-{D}awley
rats were dosed via intraperitoneal injection once daily for mercuric
chloride and amphotericin (up to 7 doses), while a single dose was
given for all other compounds. {A}nimals were exposed to 2 different
doses of these compounds and kidney tissues were collected on day
1, 3, and 7 postdosing. {G}ene expression profiles were generated
from kidney {RNA} using 17{K} rat c{DNA} dual dye microarray and
analyzed in conjunction with histopathology. {A}nalysis of gene expression
profiles showed that the profiles clustered based on similarities
in the severity and type of pathology of individual animals. {F}urther,
the expression changes were indicative of tubular toxicity showing
hallmarks of tubular degeneration/regeneration and necrosis. {U}se
of gene expression data in predicting the type of nephrotoxicity
was then tested with a support vector machine ({SVM})-based approach.
{A} {SVM} prediction module was trained using 120 profiles of total
profiles divided into four classes based on the severity of pathology
and clustering. {A}lthough mitomycin {C} and amphotericin {B} treatments
did not cause toxicity, their expression profiles were included in
the {SVM} prediction module to increase the sample size. {U}sing
this classifier, the {SVM} predicted the type of pathology of 28
test profiles with 100\% selectivity and 82\% sensitivity. {T}hese
data indicate that valid predictions could be made based on gene
expression changes from a small set of expression profiles. {A} set
of potential biomarkers showing a time- and dose-response with respect
to the progression of proximal tubular toxicity were identified.
{T}hese include several transporters ({S}lc21a2, {S}lc15, {S}lc34a2),
{K}im 1, {IGF}bp-1, osteopontin, alpha-fibrinogen, and {G}stalpha.},
doi = {10.1080/01926230590927230},
keywords = {Algorithms, Animals, Antibiotics, Antineoplastic, Artificial Intelligence,
Butadienes, Chloroplasts, Comparative Study, Computer Simulation,
Computer-Assisted, Diagnosis, Disinfectants, Dose-Response Relationship,
Drug, Drug Toxicity, Electrodes, Electroencephalography, Ethylamines,
Expert Systems, Feedback, Fungicides, Gene Expression Profiling,
Genes, Genetic Markers, Humans, Implanted, Industrial, Information
Storage and Retrieval, Kidney, Kidney Tubules, MEDLINE, Male, Mercuric
Chloride, Microarray Analysis, Molecular Biology, Motor Cortex, Movement,
Natural Language Processing, Neural Networks (Computer), Non-P.H.S.,
Non-U.S. Gov't, Plant Proteins, Predictive Value of Tests, Proteins,
Proteome, Proximal, Puromycin Aminonucleoside, Rats, Reproducibility
of Results, Research Support, Sprague-Dawley, Subcellular Fractions,
Terminology, Therapy, Time Factors, Toxicogenetics, U.S. Gov't, User-Computer
Interface, 15805072},
pii = {X3U2206L2747H31G},
url = {http://dx.doi.org/10.1080/01926230590927230}
}
@article{Tothill2005expression-based,
author = {Richard W Tothill and Adam Kowalczyk and Danny Rischin and Alex Bousioutas
and Izhak Haviv and Ryan K van Laar and Paul M Waring and John Zalcberg
and Robyn Ward and Andrew V Biankin and Robert L Sutherland and Susan
M Henshall and Kwun Fong and Jonathan R Pollack and David D L Bowtell
and Andrew J Holloway},
title = {An expression-based site of origin diagnostic method designed for
clinical application to cancer of unknown origin.},
journal = {Cancer {R}es.},
year = {2005},
volume = {65},
pages = {4031-40},
number = {10},
month = {May},
abstract = {Gene expression profiling offers a promising new technique for the
diagnosis and prognosis of cancer. {W}e have applied this technology
to build a clinically robust site of origin classifier with the ultimate
aim of applying it to determine the origin of cancer of unknown primary
({CUP}). {A} single c{DNA} microarray platform was used to profile
229 primary and metastatic tumors representing 14 tumor types and
multiple histologic subtypes. {T}his data set was subsequently used
for training and validation of a support vector machine ({SVM}) classifier,
demonstrating 89\% accuracy using a 13-class model. {F}urther, we
show the translation of a five-class classifier to a quantitative
{PCR}-based platform. {S}electing 79 optimal gene markers, we generated
a quantitative-{PCR} low-density array, allowing the assay of both
fresh-frozen and formalin-fixed paraffin-embedded ({FFPE}) tissue.
{D}ata generated using both quantitative {PCR} and microarray were
subsequently used to train and validate a cross-platform {SVM} model
with high prediction accuracy. {F}inally, we applied our {SVM} classifiers
to 13 cases of {CUP}. {W}e show that the microarray {SVM} classifier
was capable of making high confidence predictions in 11 of 13 cases.
{T}hese predictions were supported by comprehensive review of the
patients' clinical histories.},
doi = {10.1158/0008-5472.CAN-04-3617},
pdf = {../local/Tothill2005expression-based.pdf},
file = {Tothill2005expression-based.pdf:Tothill2005expression-based.pdf:PDF},
keywords = {biosvm microarray},
pii = {65/10/4031},
url = {http://dx.doi.org/10.1158/0008-5472.CAN-04-3617}
}
@article{Tsai2004Gene,
author = {Tsai, C.A. and Chen, C.H. and Lee, T.C. and Ho, I.C. and Yang, U.C.
and Chen, J.J.},
title = {Gene selection for sample classifications in microarray experiments.},
journal = {D{NA} {C}ell {B}iol.},
year = {2004},
volume = {23},
pages = {607-614},
number = {10},
abstract = {D{NA} microarray technology provides useful tools for profiling global
gene expression patterns in different cell/tissue samples. {O}ne
major challenge is the large number of genes relative to the number
of samples. {T}he use of all genes can suppress or reduce the performance
of a classification rule due to the noise of nondiscriminatory genes.
{S}election of an optimal subset from the original gene set becomes
an important prestep in sample classification. {I}n this study, we
propose a family-wise error ({FWE}) rate approach to selection of
discriminatory genes for two-sample or multiple-sample classification.
{T}he {FWE} approach controls the probability of the number of one
or more false positives at a prespecified level. {A} public colon
cancer data set is used to evaluate the performance of the proposed
approach for the two classification methods: k nearest neighbors
(k-{NN}) and support vector machine ({SVM}). {T}he selected gene
sets from the proposed procedure appears to perform better than or
comparable to several results reported in the literature using the
univariate analysis without performing multivariate search. {I}n
addition, we apply the {FWE} approach to a toxicogenomic data set
with nine treatments (a control and eight metals, {A}s, {C}d, {N}i,
{C}r, {S}b, {P}b, {C}u, and {A}s{V}) for a total of 55 samples for
a multisample classification. {T}wo gene sets are considered: the
gene set omega{F} formed by the {ANOVA} {F}-test, and a gene set
omega{T} formed by the union of one-versus-all t-tests. {T}he predicted
accuracies are evaluated using the internal and external crossvalidation.
{U}sing the {SVM} classification, the overall accuracies to predict
55 samples into one of the nine treatments are above 80% for internal
crossvalidation. {O}mega{F} has slightly higher accuracy rates than
omega{T}. {T}he overall predicted accuracies are above 70% for the
external crossvalidation; the two gene sets omega{T} and omega{F}
performed equally well.},
doi = {10.1089/1044549042476947},
pdf = {../local/Tsai2004Gene.pdf},
file = {Tsai2004Gene.pdf:local/Tsai2004Gene.pdf:PDF},
keywords = {biosvm microarray},
owner = {jeanphilippevert},
url = {http://dx.doi.org/10.1089/1044549042476947}
}
@article{Wang2005Gene-expression,
author = {Wang, Y. and Klijn, J.G.M. and Zhang, Y. and Sieuwerts, A.M. and
Look, M.P. and Yang, F. and Talantov, D. and Timmermans, M. and Meijer-van
Gelder, M.E. and Yu, J. and Jatkoe, T. and Berns, E.M.J.J. and Atkins,
D. and Foekens, J.A.},
title = {Gene-expression profiles to predict distant metastasis of lymph-node-negative
primary breast cancers},
journal = {Lancet},
year = {2005},
volume = {365},
pages = {671--679},
number = {9460},
abstract = {BACKGROUND: Genome-wide measures of gene expression can identify patterns
of gene activity that subclassify tumours and might provide a better
means than is currently available for individual risk assessment
in patients with lymph-node-negative breast cancer. METHODS: We analysed,
with Affymetrix Human U133a GeneChips, the expression of 22000 transcripts
from total RNA of frozen tumour samples from 286 lymph-node-negative
patients who had not received adjuvant systemic treatment. FINDINGS:
In a training set of 115 tumours, we identified a 76-gene signature
consisting of 60 genes for patients positive for oestrogen receptors
(ER) and 16 genes for ER-negative patients. This signature showed
93\% sensitivity and 48\% specificity in a subsequent independent
testing set of 171 lymph-node-negative patients. The gene profile
was highly informative in identifying patients who developed distant
metastases within 5 years (hazard ratio 5.67 [95\% CI 2.59-12.4]),
even when corrected for traditional prognostic factors in multivariate
analysis (5.55 [2.46-12.5]). The 76-gene profile also represented
a strong prognostic factor for the development of metastasis in the
subgroups of 84 premenopausal patients (9.60 [2.28-40.5]), 87 postmenopausal
patients (4.04 [1.57-10.4]), and 79 patients with tumours of 10-20
mm (14.1 [3.34-59.2]), a group of patients for whom prediction of
prognosis is especially difficult. INTERPRETATION: The identified
signature provides a powerful tool for identification of patients
at high risk of distant recurrence. The ability to identify patients
who have a favourable prognosis could, after independent confirmation,
allow clinicians to avoid adjuvant systemic therapy or to choose
less aggressive therapeutic options.},
doi = {10.1016/S0140-6736(05)17947-1},
pdf = {../local/Wang2005Gene-expression.pdf},
file = {Wang2005Gene-expression.pdf:local/Wang2005Gene-expression.pdf:PDF},
keywords = {microarray, breastcancer},
owner = {jp},
pii = {S0140673605179471},
pmid = {15894094},
timestamp = {2006.07.06},
url = {http://dx.doi.org/10.1016/S0140-6736(05)17947-1}
}
@article{Wang2005Gene,
author = {Yu Wang and Igor V Tetko and Mark A Hall and Eibe Frank and Axel
Facius and Klaus F X Mayer and Hans W Mewes},
title = {Gene selection from microarray data for cancer classification--a
machine learning approach.},
journal = {Comput. {B}iol. {C}hem.},
year = {2005},
volume = {29},
pages = {37-46},
number = {1},
month = {Feb},
abstract = {A {DNA} microarray can track the expression levels of thousands of
genes simultaneously. {P}revious research has demonstrated that this
technology can be useful in the classification of cancers. {C}ancer
microarray data normally contains a small number of samples which
have a large number of gene expression levels as features. {T}o select
relevant genes involved in different types of cancer remains a challenge.
{I}n order to extract useful gene information from cancer microarray
data and reduce dimensionality, feature selection algorithms were
systematically investigated in this study. {U}sing a correlation-based
feature selector combined with machine learning algorithms such as
decision trees, naïve {B}ayes and support vector machines, we show
that classification performance at least as good as published results
can be obtained on acute leukemia and diffuse large {B}-cell lymphoma
microarray data sets. {W}e also demonstrate that a combined use of
different classification and feature selection approaches makes it
possible to select relevant genes with high confidence. {T}his is
also the first paper which discusses both computational and biological
evidence for the involvement of zyxin in leukaemogenesis.},
doi = {10.1016/j.compbiolchem.2004.11.001},
pdf = {../local/Wang2005Gene.pdf},
file = {Wang2005Gene.pdf:local/Wang2005Gene.pdf:PDF},
keywords = {biosvm microarray},
pii = {S1476-9271(04)00108-2},
url = {http://dx.doi.org/10.1016/j.compbiolchem.2004.11.001}
}
@article{Wirapati2008Meta-analysis,
author = {Wirapati, P. and Sotiriou, C. and Kunkel, S. and Farmer, P. and Pradervand,
S. and Haibe-Kains, B. and Desmedt, C. and Ignatiadis, M. and Sengstag,
T. and Sch\"utz, F. and Goldstein, D. R. and Piccart, M. and Delorenzi,
M.},
title = {Meta-analysis of gene expression profiles in breast cancer: toward
a unified understanding of breast cancer subtyping and prognosis
signatures.},
journal = {Breast Cancer Res.},
year = {2008},
volume = {10},
pages = {R65},
number = {4},
abstract = {INTRODUCTION: Breast cancer subtyping and prognosis have been studied
extensively by gene expression profiling, resulting in disparate
signatures with little overlap in their constituent genes. Although
a previous study demonstrated a prognostic concordance among gene
expression signatures, it was limited to only one dataset and did
not fully elucidate how the different genes were related to one another
nor did it examine the contribution of well-known biological processes
of breast cancer tumorigenesis to their prognostic performance. METHOD:
To address the above issues and to further validate these initial
findings, we performed the largest meta-analysis of publicly available
breast cancer gene expression and clinical data, which are comprised
of 2,833 breast tumors. Gene coexpression modules of three key biological
processes in breast cancer (namely, proliferation, estrogen receptor
[ER], and HER2 signaling) were used to dissect the role of constituent
genes of nine prognostic signatures. RESULTS: Using a meta-analytical
approach, we consolidated the signatures associated with ER signaling,
ERBB2 amplification, and proliferation. Previously published expression-based
nomenclature of breast cancer 'intrinsic' subtypes can be mapped
to the three modules, namely, the ER-/HER2- (basal-like), the HER2+
(HER2-like), and the low- and high-proliferation ER+/HER2- subtypes
(luminal A and B). We showed that all nine prognostic signatures
exhibited a similar prognostic performance in the entire dataset.
Their prognostic abilities are due mostly to the detection of proliferation
activity. Although ER- status (basal-like) and ERBB2+ expression
status correspond to bad outcome, they seem to act through elevated
expression of proliferation genes and thus contain only indirect
information about prognosis. Clinical variables measuring the extent
of tumor progression, such as tumor size and nodal status, still
add independent prognostic information to proliferation genes. CONCLUSION:
This meta-analysis unifies various results of previous gene expression
studies in breast cancer. It reveals connections between traditional
prognostic factors, expression-based subtyping, and prognostic signatures,
highlighting the important role of proliferation in breast cancer
prognosis.},
doi = {10.1186/bcr2124},
pdf = {../local/Wirapati2008Meta-analysis.pdf},
file = {Wirapati2008Meta-analysis.pdf:Wirapati2008Meta-analysis.pdf:PDF},
institution = {Swiss Institute of Bioinformatics, 'Batiment Genopode', University
of Lausanne, 1015 Lausanne, Switzerland. Pratyaksha.Wirapati@isb-sib.ch},
keywords = {microarray, breastcancer},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {bcr2124},
pmid = {18662380},
timestamp = {2010.10.13},
url = {http://dx.doi.org/10.1186/bcr2124}
}
@article{Zhou2005LS,
author = {Xin Zhou and K. Z. Mao},
title = {L{S} {B}ound based gene selection for {DNA} microarray data.},
journal = {Bioinformatics},
year = {2005},
volume = {21},
pages = {1559-64},
number = {8},
month = {Apr},
abstract = {M{OTIVATION}: {O}ne problem with discriminant analysis of {DNA} microarray
data is that each sample is represented by quite a large number of
genes, and many of them are irrelevant, insignificant or redundant
to the discriminant problem at hand. {M}ethods for selecting important
genes are, therefore, of much significance in microarray data analysis.
{I}n the present study, a new criterion, called {LS} {B}ound measure,
is proposed to address the gene selection problem. {T}he {LS} {B}ound
measure is derived from leave-one-out procedure of {LS}-{SVM}s (least
squares support vector machines), and as the upper bound for leave-one-out
classification results it reflects to some extent the generalization
performance of gene subsets. {RESULTS}: {W}e applied this {LS} {B}ound
measure for gene selection on two benchmark microarray datasets:
colon cancer and leukemia. {W}e also compared the {LS} {B}ound measure
with other evaluation criteria, including the well-known {F}isher's
ratio and {M}ahalanobis class separability measure, and other published
gene selection algorithms, including {W}eighting factor and {SVM}
{R}ecursive {F}eature {E}limination. {T}he strength of the {LS} {B}ound
measure is that it provides gene subsets leading to more accurate
classification results than the filter method while its computational
complexity is at the level of the filter method. {AVAILABILITY}:
{A} companion website can be accessed at http://www.ntu.edu.sg/home5/pg02776030/lsbound/.
{T}he website contains: (1) the source code of the gene selection
algorithm; (2) the complete set of tables and figures regarding the
experimental study; (3) proof of the inequality (9). {CONTACT}: ekzmao@ntu.edu.sg.},
doi = {10.1093/bioinformatics/bti216},
pdf = {../local/Zhou2005LS.pdf},
file = {Zhou2005LS.pdf:local/Zhou2005LS.pdf:PDF},
keywords = {biosvm featureselection microarray},
pii = {bti216},
url = {http://dx.doi.org/10.1093/bioinformatics/bti216}
}
@article{Zhu2000Two,
author = {Zhu, G. and Spellman, P. T. and Volpe, T. and Brown, P. O. and Botstein,
D. and Davis, T. N. and Futcher, B.},
title = {Two yeast forkhead genes regulate the cell cycle and pseudohyphal
growth},
journal = {Nature},
year = {2000},
volume = {406},
pages = {90--94},
pdf = {../local/zhu00.pdf},
file = {zhu00.pdf:local/zhu00.pdf:PDF},
subject = {microarray},
url = {http://www.nature.com/cgi-taf/DynaPage.taf?file=/nature/journal/v406/n6791/full/406090a0_fs.html&content_filetype=pdf}
}
@comment{{jabref-meta: selector_author:}}
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ochim. Biophys. Acta;Bioinformatics;Biometrika;BMC Bioinformatics;Br.
J. Pharmacol.;Breast Cancer Res.;Cell;Cell. Signal.;Chem. Res. Toxicol
.;Clin. Cancer Res.;Combinator. Probab. Comput.;Comm. Pure Appl. Math.
;Comput. Chem.;Comput. Comm. Rev.;Comput. Stat. Data An.;Curr. Genom.;
Curr. Opin. Chem. Biol.;Curr. Opin. Drug Discov. Devel.;Data Min. Know
l. Discov.;Electron. J. Statist.;Eur. J. Hum. Genet.;FEBS Lett.;Found.
Comput. Math.;Genome Biol.;IEEE T. Neural Networ.;IEEE T. Pattern. An
al.;IEEE T. Signal. Proces.;IEEE Trans. Inform. Theory;IEEE Trans. Kno
wl. Data Eng.;IEEE/ACM Trans. Comput. Biol. Bioinf.;Int. J. Comput. Vi
sion;Int. J. Data Min. Bioinform.;Int. J. Qantum Chem.;J Biol Syst;J.
ACM;J. Am. Soc. Inf. Sci. Technol.;J. Am. Stat. Assoc.;J. Bioinform. C
omput. Biol.;J. Biol. Chem.;J. Biomed. Inform.;J. Cell. Biochem.;J. Ch
em. Inf. Comput. Sci.;J. Chem. Inf. Model.;J. Clin. Oncol.;J. Comput.
Biol.;J. Comput. Graph. Stat.;J. Eur. Math. Soc.;J. Intell. Inform. Sy
st.;J. Mach. Learn. Res.;J. Med. Chem.;J. Mol. BIol.;J. R. Stat. Soc.
Ser. B;Journal of Statistical Planning and Inference;Mach. Learn.;Math
. Program.;Meth. Enzymol.;Mol. Biol. Cell;Mol. Biol. Evol.;Mol. Cell.
Biol.;Mol. Syst. Biol.;N. Engl. J. Med.;Nat. Biotechnol.;Nat. Genet.;N
at. Med.;Nat. Methods;Nat. Rev. Cancer;Nat. Rev. Drug Discov.;Nat. Rev
. Genet.;Nature;Neural Comput.;Neural Network.;Neurocomputing;Nucleic
Acids Res.;Pattern Anal. Appl.;Pattern Recognit.;Phys. Rev. E;Phys. Re
v. Lett.;PLoS Biology;PLoS Comput. Biol.;Probab. Theory Relat. Fields;
Proc. IEEE;Proc. Natl. Acad. Sci. USA;Protein Eng.;Protein Eng. Des. S
el.;Protein Sci.;Protein. Struct. Funct. Genet.;Random Struct. Algorit
hm.;Rev. Mod. Phys.;Science;Stat. Probab. Lett.;Statistica Sinica;Theo
r. Comput. Sci.;Trans. Am. Math. Soc.;Trends Genet.;}}
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