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reviewmicroarrays.bib
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@book{affymetrix_affymetrix_2001,
address = {Santa Clara, {CA}},
edition = {version 5},
title = {Affymetrix Microarray Suite User Guide},
author = {Affymetrix},
year = {2001}
},
@techreport{affymetrix_new_2001,
title = {New Statistical Algorithms for Monitoring Gene Expression on \${\textbackslash}{mboxGeneChip{\textasciicircum}{\textbackslash}mbox®\$} Probe Arrays},
institution = {Affymetrix},
author = {Affymetrix},
year = {2001}
},
@article{aharoni_dna_2002,
title = {{DNA} microarrays for functional plant genomics},
volume = {48},
issn = {0167-4412},
abstract = {{DNA} microarray technology is a key element in today's functional genomics toolbox. The power of the method lies in miniaturization, automation and parallelism permitting large-scale and genome-wide acquisition of quantitative biological information from multiple samples. {DNA} microarrays are currently fabricated and assayed by two main approaches involving either in situ synthesis of oligonucleotides ('oligonucleotide microarrays') or deposition of pre-synthesized {DNA} fragments ({'cDNA} microarrays') on solid surfaces. To date, the main applications of microarrays are in comprehensive, simultaneous gene expression monitoring and in {DNA} variation analyses for the identification and genotyping of mutations and polymorphisms. Already at a relatively early stage of its application in plant science, microarrays are being utilized to examine a range of biological issues including the circadian clock, plant defence, environmental stress responses, fruit ripening, phytochrome A signalling, seed development and nitrate assimilation. Novel insights are obtained into the molecular mechanisms co-ordinating metabolic pathways, regulatory and signalling networks. Exciting new information will be gained in the years to come not only from genome-wide expression analyses on a few model plant species, but also from extensive studies of less thoroughly studied species on a more limited scale. The value of microarray technology to our understanding of living processes will depend both on the amount of data to be generated and on its clever exploration and integration with other biological knowledge arising from complementary functional genomics tools for 'profiling' the genome, proteome, metabolome and phenome.},
language = {eng},
number = {1-2},
journal = {Plant molecular biology},
author = {Aharoni, Asaph and Vorst, Oscar},
month = jan,
year = {2002},
note = {{PMID:} 11860216},
keywords = {Gene Expression Profiling, Genes, Plant, Genomics, Oligonucleotide Array Sequence Analysis, Phylogeny, Plants},
pages = {99--118}
},
@article{alizadeh_distinct_2000,
title = {Distinct types of diffuse large B–cell lymphoma identified by gene expression profiling},
volume = {403},
journal = {Nature},
author = {Alizadeh, A. and Eisen, {M.B.} and Davis, E. and Ma, C. and Lossos, I. and Rosenwald, A. and Boldrick, J. and Sabet, H. and Tran, T. and Yu, X. and Powell, {J.I.} and Yang, L. and Marti, {G.E.} and Jr, J. Hudson and Lu, L. and Lewis, {D.B.} and Tibshirani, R. and Sherlock, G. and Chan, {W.C.} and Greiner, {T.C.} and Weisenburger, {D.D.} and Armitage, {J.O.} and Warnke, R. and Levy, R. and Wilson, W. and Grever, {M.R.} and Byrd, {J.C.} and Botstein, D. and Brown, {P.O.} and Staudt, {L.M.}},
month = feb,
year = {2000},
pages = {503–511}
},
@article{allison_microarray_2006,
title = {Microarray data analysis: from disarray to consolidation and consensus.},
volume = {7},
issn = {1471-0056},
number = {1},
journal = {Nat Rev Genet},
author = {Allison, David B and Cui, Xiangqin and Page, Grier P and Sabripour, Mahyar},
year = {2006},
pages = {55--65}
},
@book{allison_dna_2006,
title = {{DNA} Microarrays and Related Genomics Techniques: Design, Analysis, and Interpretation of Experiments},
publisher = {{CRC} Press},
author = {Allison, {D.B.}},
year = {2006}
},
@article{alon_broad_1999,
title = {Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays},
volume = {96},
journal = {Proceedings of the National Academy of Sciences},
author = {Alon, U. and Barkai, N. and Notterman, {D.A.} and Gish, K. and Ybarra, S. and Mack, D. and Levine, {A.J.}},
year = {1999},
pages = {6745–6750}
},
@article{alshahrour_fatigo:_2004,
title = {{FatiGO:} a web tool for finding significant associations of Gene Ontology terms with groups of genes},
volume = {20},
journal = {Bioinformatics},
author = {{Al–Shahrour}, F. Díaz-Uriarte R. and Dopazo, J.},
year = {2004},
pages = {578–580}
},
@book{alwine_method_1977,
title = {Method for detection of specific {RNAs} in agarose gels by transfer to diazobenzyloxymethyl-paper and hybridization with {DNA} probes.},
volume = {74},
number = {12},
author = {Alwine, J C and Kemp, D J and Stark, G R},
year = {1977}
},
@article{analysis_multiple_2001,
title = {Multiple transcription-factor genes are early targets of phytochrome A signaling},
volume = {98},
number = {16},
journal = {Proceedings of the National Academy of Sciences},
author = {analysis, Cluster and patterns, display of genomewide expression},
year = {2001},
pages = {9437--9442}
},
@article{authors_chipping_1999,
title = {The Chipping Forecast},
volume = {21},
journal = {Nature Genetics},
author = {authors, Several},
year = {1999},
keywords = {bibtex-import},
pages = {all}
},
@book{azuaje_data_2005,
title = {Data Analysis and Visualization in Genomics and Proteomics},
isbn = {0470094397},
publisher = {John Wiley \& Sons},
author = {Azuaje, Francisco and Dopazo, Joaquin},
year = {2005}
},
@article{barrier_[gene_2007,
title = {[Gene expression profiling in colon cancer]},
volume = {191},
abstract = {Identification of new prognostic factors for colon cancer with no lymph node involvement may improve the selection of patients for adjuvant chemotherapy. The aim of this study was to assess the possibility of using gene expression profiling for this purpose. Fifty patients operated on for stage {II} colon cancer were included. Twenty-five of these patients relapsed, while the other 25 remained disease-free for at least 5 years. {MRNA} was extracted from fresh-frozen biopsies and hybridized to the Affymetrix {GeneChip} {HGU133A.} One thousand six hundred random splits of the 50 patients into a training set and a validation set were considered. For each split, a prognostic combination was derived from the training set (selection of the 30 genes most differentially expressed between patients who recurred and those who did not, by linear discriminant analysis), and its prognostic performance was assessed with the validation set. On average, accuracy was 76\%, sensitivity 85\%, and specificity 68\%. A total of 6,124 genes were included in at least one of the 1,600 predictive combinations, and 55 genes were included in more than 100 combinations. This study supports the possibility of predicting the prognosis of non-metastatic colon cancer by tumor gene expression profiling. It also shows the highly variable gene composition of predictive combinations.},
number = {6},
journal = {Bull Acad Natl Med},
author = {Barrier, Alain and Boelle, Pierre-Yves and Lemoine, Antoinette and Flahault, Antoine and Dudoit, Sandrine and Huguier, Michel},
month = jun,
year = {2007},
pages = {1091–101; discussion 1102–3}
},
@article{beisvag_genetoolsapplication_2006,
title = {{GeneTools–application} for functional annotation and statistical hypothesis testing.},
volume = {7},
issn = {1471-2105},
journal = {{BMC} Bioinformatics},
author = {Beisvag, Vidar and Jünge, Frode K R and Bergum, Hallgeir and {JU00F8lsum}, Lars and Lydersen, Stian and Günther, Clara-Cecilie and Ramampiaro, Heri and Langaas, Mette and Sandvik, Arne K and Laegreid, Astrid},
year = {2006},
pages = {470}
},
@book{berrar_practical_2003,
title = {A Practical Approach to Microarray Data Analysis},
publisher = {Kluwer Academic Publishers},
author = {Berrar, {D.P.} and Dubitzky, W. and Granzow, M.},
year = {2003}
},
@article{biotechnology_microarray_2006,
title = {The {MicroArray} Quality Control ({MAQC)} project shows inter-and intraplatform reproducibility of gene expression measurements},
volume = {24},
journal = {Nature Biotechnology},
author = {Biotechnology, N.},
year = {2006},
pages = {1151–1161}
},
@book{bittner_molecular_2000,
title = {Molecular classification of cutaneous malignant melanoma by gene expression profiling.},
volume = {406},
number = {6795},
author = {Bittner, M. and Meltzer, P. and Chen, Y. and Jiang, Y. and Seftor, E. and Hendrix, M. and Radmacher, M. and Simon, R. and Yakhini, Z. and Ben-Dor, A. and Sampas, N. and Dougherty, E. and Wang, E. and Marincola, F. and Gooden, C. and Lueders, J. and Glatfelter, A. and Pollock, P. and Carpten, J. and Gillanders, E. and Leja, D. and Dietrich, K. and Beaudry, C. and Berens, M. and Alberts, D. and Sondak, V.},
year = {2000}
},
@article{bolshakova_cluster_2003,
title = {Cluster validation techniques for genome expression data},
volume = {83},
number = {4},
journal = {Signal Processing},
author = {Bolshakova, N. and Azuaje, F.},
year = {2003},
pages = {825–833}
},
@book{bolstad_comparison_2003,
title = {A comparison of normalization methods for high density oligonucleotide array data based on variance and bias},
volume = {19},
number = {2},
publisher = {Oxford Univ Press},
author = {Bolstad, {BM} and Irizarry, {RA} and Astrand, M. and Speed, {TP}},
year = {2003}
},
@article{br_gominer:_2003,
title = {{GoMiner:} a resource for biological interpretation of genomic and proteomic data},
volume = {4},
journal = {Genome Biology},
author = {{BR}, Feng W Wang G Wang {MD} Fojo {AT} Sunshine M Narasimhan S Kane {DW} Reinhold {WC} Lababidi S Bussey {KJ} Riss J Barrett {JC} Zeeberg and {JN}, Weinstein},
year = {2003},
pages = {R28}
},
@book{brazma_quick_????,
title = {A quick introduction to elements of biology - cells, molecules, genes, functional genomics, microarrays},
url = {http://www.ebi.ac.uk/microarray/biology_intro.html},
author = {Brazma, Helen Parkinson Thomas Schlitt Mohammadreza Shojatalab Alvis}
},
@article{breiman_bagging_1996,
title = {Bagging Predictors},
volume = {24},
number = {2},
journal = {Machine Learning},
author = {Breiman, L.},
year = {1996},
pages = {123–140}
},
@book{breiman_classification_1998,
title = {Classification and Regression Trees},
publisher = {Chapman \& {Hall/CRC}},
author = {Breiman, L.},
year = {1998}
},
@article{callow_microarray_2000,
title = {Microarray Expression Profiling Identifies Genes with Altered Expression in {HDL-Deficient} Mice},
journal = {Genome Research},
author = {Callow, {M.J.} and Dudoit, S. and Gong, {E.L.} and Speed, {T.P.} and Rubin, {E.M.}},
year = {2000}
},
@article{carey_ontology_2004,
title = {Ontology concepts and tools for statistical genomics},
volume = {90},
author = {Carey, {V.J.}},
year = {2004},
pages = {213--228}
},
@article{carlin_tutorial_2000,
title = {Tutorial in biostatistics. Meta-analysis: formulating, evaluating, combining, and reporting},
volume = {19},
issn = {0277-6715},
number = {5},
journal = {Stat Med},
author = {Carlin, {J.B.} and Normand, T.},
month = mar,
year = {2000},
pages = {753–9}
},
@book{chambers_programming_1998,
title = {Programming with data: a guide to the S language},
isbn = {9780387985039},
shorttitle = {Programming with data},
abstract = {Here is a thorough and authoritative guide to the latest version of the S language and to its programming environment, the premier software platform for computing with data. Programming with Data describes a new and greatly extended version of S, and is written by the chief designer of the language. The book is a guide to the complete programming process, starting from simple, interactive use and continuing through ambitious software {projects.S} is designed for computing with data - for any project in which organizing, visualizing, summarizing, or modeling data is a central concern. Its focus is on the needs of the programmer/user, and its goal is "to turn ideas into software, quickly and faithfully." S is a functional, object-based language with a huge library of functions for all aspects of computing with data. Its long and enthusiastic use in statistics and applied fields has also led to many valuable libraries of user-written {functions.The} new version of S provides a powerful class/method structure, new techniques to deal with large objects, extended interfaces to other languages and files, object-based documentation compatible with {HTML}, and powerful new interactive programming techniques. This version of S underlies the S-Plus system, versions 5.0 and {higher.John} Chambers has been a member of the technical staff in research at Bell Laboratories since 1966. In 1977, he became the first statistician to be named a Bell Labs Fellow, cited for "pioneering contributions to the field of statistical computing." His research has touched on nearly all aspects of computing with data, but he is best known for the design of the S language. He is the author or co-author of seven books on S, on computational methods, and on graphical methods; and he is a Fellow of the American Statistical Association and the American Association for the Advancement of Science.},
language = {en},
publisher = {Springer},
author = {Chambers, John M.},
year = {1998},
keywords = {Business \& Economics / Statistics, Computers / Enterprise Applications / General, Computers / Programming Languages / General, Computers / Security / General, Estatistica./ larpcal, Mathematics / Probability \& Statistics / General, S (Computer program language), Statistics, Statistics - Data processing, Statistics/ Data processing}
},
@book{chambers_programming_1998-1,
title = {Programming with data: a guide to the S language},
isbn = {9780387985039},
shorttitle = {Programming with data},
abstract = {Here is a thorough and authoritative guide to the latest version of the S language and to its programming environment, the premier software platform for computing with data. Programming with Data describes a new and greatly extended version of S, and is written by the chief designer of the language. The book is a guide to the complete programming process, starting from simple, interactive use and continuing through ambitious software {projects.S} is designed for computing with data - for any project in which organizing, visualizing, summarizing, or modeling data is a central concern. Its focus is on the needs of the programmer/user, and its goal is "to turn ideas into software, quickly and faithfully." S is a functional, object-based language with a huge library of functions for all aspects of computing with data. Its long and enthusiastic use in statistics and applied fields has also led to many valuable libraries of user-written {functions.The} new version of S provides a powerful class/method structure, new techniques to deal with large objects, extended interfaces to other languages and files, object-based documentation compatible with {HTML}, and powerful new interactive programming techniques. This version of S underlies the S-Plus system, versions 5.0 and {higher.John} Chambers has been a member of the technical staff in research at Bell Laboratories since 1966. In 1977, he became the first statistician to be named a Bell Labs Fellow, cited for "pioneering contributions to the field of statistical computing." His research has touched on nearly all aspects of computing with data, but he is best known for the design of the S language. He is the author or co-author of seven books on S, on computational methods, and on graphical methods; and he is a Fellow of the American Statistical Association and the American Association for the Advancement of Science.},
language = {en},
publisher = {Springer},
author = {Chambers, John M.},
year = {1998},
keywords = {Business \& Economics / Statistics, Computers / Enterprise Applications / General, Computers / Programming Languages / General, Computers / Security / General, Estatistica./ larpcal, Mathematics / Probability \& Statistics / General, S (Computer program language), Statistics, Statistics - Data processing, Statistics/ Data processing}
},
@article{chelvarajan_molecular_2006,
title = {Molecular basis of age-associated cytokine dysregulation in {LPS-stimulated} macrophages},
volume = {79},
number = {6},
journal = {Journal of Leukocyte Biology},
author = {Chelvarajan, {R.L.} and Liu, Y. and Popa, D. and Getchell, {M.L.} and Getchell, {T.V.} and Stromberg, {A.J.} and Bondada, S.},
year = {2006},
pages = {1314}
},
@article{conesa_masigpro:_2006,
title = {{maSigPro:} a method to identify significantly differential expression profiles in time-course microarray experiments},
volume = {22},
number = {9},
journal = {Bioinformatics},
author = {Conesa, A. Nueda {M.J.} Ferrer A. and Talon, M.},
year = {2006},
pages = {1096–1102}
},
@article{consortium_gene_2000,
title = {Gene Ontology: tool for the unification of biology},
volume = {25},
journal = {Nature Genetics},
author = {Consortium, The Gene Ontology},
year = {2000},
pages = {25--29}
},
@article{consortium_creating_2001,
title = {Creating the gene ontology resource: design and implementation},
volume = {11},
journal = {Genome Research},
author = {Consortium, The Gene Ontology},
year = {2001},
pages = {1425–1433}
},
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abstract = {Molecular diagnostics is a rapidly advancing field in which insights into disease mechanisms are being elucidated by use of new gene-based biomarkers. Until recently, diagnostic and prognostic assessment of diseased tissues and tumors relied heavily on indirect indicators that permitted only general classifications into broad histologic or morphologic subtypes and did not take into account the alterations in individual gene expression. Global expression analysis using microarrays now allows for simultaneous interrogation of the expression of thousands of genes in a high-throughput fashion and offers unprecedented opportunities to obtain molecular signatures of the state of activity of diseased cells and patient samples. Microarray analysis may provide invaluable information on disease pathology, progression, resistance to treatment, and response to cellular microenvironments and ultimately may lead to improved early diagnosis and innovative therapeutic approaches for cancer.},
language = {en},
number = {8},
urldate = {2013-07-08},
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year = {2002},
note = {{PMID:} 12142369},
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journal = {Nature Genet.},
author = {Quackenbush, J.},
year = {2002},
pages = {496–--501}
},
@article{rhodes_meta-analysis_2002,
title = {Meta-analysis of microarrays: interstudy validation of gene expression profiles reveals pathway dysregulation in prostate cancer.},
volume = {62},
issn = {0008-5472},
number = {15},
journal = {Cancer Res},
author = {Rhodes, Daniel R and Barrette, Terrence R and Rubin, Mark A and Ghosh, Debashis and Chinnaiyan, Arul M},
year = {2002},
pages = {4427–33}
},
@book{ripley_pattern_1996,
title = {Pattern Recognition and Neural Networks},
publisher = {Cambridge University Press},
author = {Ripley, Brian D.},
year = {1996}
},
@article{ritchie_comparison_2007,
title = {A comparison of background correction methods for two-colour microarrays.},
volume = {23},
issn = {1460-2059},
abstract = {{MOTIVATION:} Microarray data must be background corrected to remove the effects of non-specific binding or spatial heterogeneity across the array, but this practice typically causes other problems such as negative corrected intensities and high variability of low intensity log-ratios. Different estimators of background, and various model-based processing methods, are compared in this study in search of the best option for differential expression analyses of small microarray experiments. {RESULTS:} Using data where some independent truth in gene expression is known, eight different background correction alternatives are compared, in terms of precision and bias of the resulting gene expression measures, and in terms of their ability to detect differentially expressed genes as judged by two popular algorithms, {SAM} and limma {eBayes.} A new background processing method (normexp) is introduced which is based on a convolution model. The model-based correction methods are shown to be markedly superior to the usual practice of subtracting local background estimates. Methods which stabilize the variances of the log-ratios along the intensity range perform the best. The normexp+offset method is found to give the lowest false discovery rate overall, followed by morph and vsn. Like vsn, normexp is applicable to most types of two-colour microarray data. {AVAILABILITY:} The background correction methods compared in this article are available in the R package limma (Smyth, 2005) from http://www.bioconductor.org. {SUPPLEMENTARY} {INFORMATION:} Supplementary data are available from {http://bioinf.wehi.edu.au/resources/webReferences.html.}},
number = {20},
journal = {Bioinformatics},
author = {Ritchie, Matthew E and Silver, Jeremy and Oshlack, Alicia and Holmes, Melissa and Diyagama, Dileepa and Holloway, Andrew and Smyth, Gordon K},
month = oct,
year = {2007},
pages = {2700–7}
},
@article{rousseeuw_silhouette-based_1989,
title = {Some silhouette-based graphics for clustering interpretation},
volume = {29},
number = {3},
journal = {Belgian Journal of Operations Research, Statistics and Computer Science},
author = {Rousseeuw, P. and Trauwaert, E. and Kaufman, L.},
year = {1989},
pages = {35–55}
},
@incollection{sanchez-pla_quest_2007,
title = {The Quest for Biological Significance},
booktitle = {Progress in Industrial Mathematics at {ECMI} 2006},
publisher = {Springer, New York},
author = {Sánchez-Pla, A. and Mosquera, {J.L}},
editor = {Bonilla, {L.L.} and Moscoso, M. and Platero, G. and Vega, {J.M.}},
year = {2007}
},