Lee JW, Lee JB, Park M, Song SH: An extensive evaluation of recent classification tools applied to microarray data. Computation Statistics and Data Analysis 2005, 48: 869–885.
Article
Google Scholar
Yeung KY, Bumgarner RE, Raftery AE: Bayesian model averaging: development of an improved multi-class, gene selection and classification tool for microarray data. Bioinformatics 2005, 21: 2394–2402.
Article
CAS
PubMed
Google Scholar
Jirapech-Umpai T, Aitken S: Feature selection and classification for microarray data analysis: Evolutionary methods for identifying predictive genes. BMC Bioinformatics 2005, 6: 148.
Article
PubMed Central
PubMed
Google Scholar
Hua J, Xiong Z, Lowey J, Suh E, Dougherty ER: Optimal number of features as a function of sample size for various classification rules. Bioinformatics 2005, 21: 1509–1515.
Article
CAS
PubMed
Google Scholar
Li Y, Campbell C, Tipping M: Bayesian automatic relevance determination algorithms for classifying gene expression data. Bioinformatics 2002, 18: 1332–1339.
Article
CAS
PubMed
Google Scholar
Díaz-Uriarte R: Supervised methods with genomic data: a review and cautionary view. In Data analysis and visualization in genomics and proteomics. Edited by: Azuaje F, Dopazo J. New York: Wiley; 2005:193–214.
Chapter
Google Scholar
Dudoit S, Fridlyand J, Speed TP: Comparison of discrimination methods for the classification of tumors suing gene expression data. J Am Stat Assoc 2002, 97(457):77–87.
Article
CAS
Google Scholar
Li T, Zhang C, Ogihara M: A comparative study of feature selection and multiclass classification methods for tissue classification based on gene expression. Bioinformatics 2004, 20: 2429–2437.
Article
CAS
PubMed
Google Scholar
van't Veer LJ, Dai H, van de Vijver MJ, He YD, Hart AAM, Mao M, Peterse HL, van der Kooy K, Marton MJ, Witteveen AT, Schreiber GJ, Kerkhoven RM, Roberts C, Linsley PS, Bernards R, Friend SH: Gene expression profiling predicts clinical outcome of breast cancer. Nature 2002, 415: 530–536.
Article
Google Scholar
Roepman P, Wessels LF, Kettelarij N, Kemmeren P, Miles AJ, Lijnzaad P, Tilanus MG, Koole R, Hordijk GJ, van der Vliet PC, Reinders MJ, Slootweg PJ, Holstege FC: An expression profile for diagnosis of lymph node metastases from primary head and neck squamous cell carcinomas. Nat Genet 2005, 37: 182–186.
Article
CAS
PubMed
Google Scholar
Furlanello C, Serafini M, Merler S, Jurman G: An accelerated procedure for recursive feature ranking on microarray data. Neural Netw 2003, 16: 641–648.
Article
CAS
PubMed
Google Scholar
Bø TH, Jonassen I: New feature subset selection procedures for classification of expression profiles. Genome Biology 2002, 3(4):0017.1–0017.11.
Article
Google Scholar
Breiman L: Random forests. Machine Learning 2001, 45: 5–32.
Article
Google Scholar
Breiman L, Friedman J, Olshen R, Stone C: Classification and regression trees. New York: Chapman & Hall; 1984.
Google Scholar
Ripley BD: Pattern recognition and neural networks. Cambridge: Cambridge University Press; 1996.
Book
Google Scholar
Hastie T, Tibshirani R, Friedman J: The elements of statistical learning. New York: Springer; 2001.
Book
Google Scholar
Breiman L: Bagging predictors. Machine Learning 1996, 24: 123–140.
Google Scholar
Alvarez S, Diaz-Uriarte R, Osorio A, Barroso A, Melchor L, Paz MF, Honrado E, Rodriguez R, Urioste M, Valle L, Diez O, Cigudosa JC, Dopazo J, Esteller M, Benitez J: A Predictor Based on the Somatic Genomic Changes of the BRCA1/BRCA2 Breast Cancer Tumors Identifies the Non-BRCAl/BRCA2 Tumors with BRCA1 Promoter Hypermethylation. Clin Cancer Res 2005, 11: 1146–1153.
CAS
PubMed
Google Scholar
Izmirlian G: Application of the random forest classification algorithm to a SELDI-TOF proteomics study in the setting of a cancer prevention trial. Ann NY Acad Sci 2004, 1020: 154–174.
Article
CAS
PubMed
Google Scholar
Wu B, Abbott T, Fishman D, McMurray W, Mor G, Stone K, Ward D, Williams K, Zhao H: Comparison of statistical methods for classification of ovarian cancer using mass spectrometry data. Bioinformatics 2003, 19: 1636–1643.
Article
CAS
PubMed
Google Scholar
Gunther EC, Stone DJ, Gerwien RW, Bento P, Heyes MP: Prediction of clinical drug efficacy by classification of drug-induced genomic expression profiles in vitro. Proc Natl Acad Sci USA 2003, 100: 9608–9613.
Article
PubMed Central
CAS
PubMed
Google Scholar
Man MZ, Dyson G, Johnson K, Liao B: Evaluating methods for classifying expression data. J Biopharm Statist 2004, 14: 1065–1084.
Article
Google Scholar
Schwender H, Zucknick M, Ickstadt K, Bolt HM: A pilot study on the application of statistical classification procedures to molecular epidemiological data. Toxicol Lett 2004, 151: 291–299.
Article
CAS
PubMed
Google Scholar
Liaw A, Wiener M: Classification and regression by randomForest. Rnews 2002, 2: 18–22.
Google Scholar
Dudoit S, Fridlyand J: Classification in microarray experiments. In Statistical analysis of gene expression microarray data. Edited by: Speed T. New York: Chapman & Hall; 2003:93–158.
Google Scholar
Svetnik V, Liaw A, Tong C, Wang T: Application of Breiman's random forest to modeling structure-activity relationships of pharmaceutical molecules. Multiple Classier Systems, Fifth International Workshop, MCS 2004, Proceedings, 9–11 June 2004, Cagliari, Italy. Lecture Notes in Computer Science, Springer 2004, 3077: 334–343.
Google Scholar
Somorjai RL, Dolenko B, Baumgartner R: Class prediction and discovery using gene microarray and proteomics mass spectroscopy data: curses, caveats, cautions. Bioinformatics 2003, 19: 1484–1491.
Article
CAS
PubMed
Google Scholar
Pan KH, Lih CJ, Cohen SN: Effects of threshold choice on biological conclusions reached during analysis of gene expression by DNA microarrays. Proc Natl Acad Sci USA 2005, 102: 8961–8965.
Article
PubMed Central
CAS
PubMed
Google Scholar
Ein-Dor L, Kela I, Getz G, Givol D, Domany E: Outcome signature genes in breast cancer: is there a unique set? Bioinformatics 2005, 21: 171–178.
Article
CAS
PubMed
Google Scholar
Michiels S, Koscielny S, Hill C: Prediction of cancer outcome with microarrays: a multiple random validation strategy. Lancet 2005, 365: 488–492.
Article
CAS
PubMed
Google Scholar
Romualdi C, Campanaro S, Campagna D, Celegato B, Cannata N, Toppo S, Valle G, Lanfranchi G: Pattern recognition in gene expression profiling using DNA array: a comparative study of different statistical methods applied to cancer classification. Hum Mol Genet 2003, 12(8):823–836.
Article
CAS
PubMed
Google Scholar
Dettling M: BagBoosting for tumor classification with gene expression data. Bioinformatics 2004, 20: 3583–593.
Article
CAS
PubMed
Google Scholar
Tibshirani R, Hastie T, Narasimhan B, Chu G: Diagnosis of multiple cancer types by shrunken centroids of gene expression. Proc Natl Acad Sci USA 2002, 99(10):6567–6572.
Article
PubMed Central
CAS
PubMed
Google Scholar
Ambroise C, McLachlan GJ: Selection bias in gene extraction on the basis of microarray gene-expression data. Proc Natl Acad Sci USA 2002, 99(10):6562–6566.
Article
PubMed Central
CAS
PubMed
Google Scholar
Efron B, Tibshirani RJ: Improvements on cross-validation: the .632+ bootstrap method. J American Statistical Association 1997, 92: 548–560.
Google Scholar
Bureau A, Dupuis J, Hayward B, Falls K, Van Eerdewegh P: Mapping complex traits using Random Forests. BMC Genet 2003, 4(Suppl 1):S64.
Article
PubMed Central
PubMed
Google Scholar
Simon R, Radmacher MD, Dobbin K, McShane LM: Pitfalls in the use of DNA microarray data for diagnostic and prognostic classification. Journal of the National Cancer Institute 2003, 95: 14–18.
Article
CAS
PubMed
Google Scholar
Braga-Neto U, Hashimoto R, Dougherty ER, Nguyen DV, Carroll RJ: Is cross-validation better than resubstitution for ranking genes? Bioinformatics 2004, 20: 253–258.
Article
CAS
PubMed
Google Scholar
Faraway J: On the cost of data analysis. Journal of Computational and Graphical Statistics 1992, 1: 251–231.
Google Scholar
Harrell JFE: Regression modeling strategies. New York: Springer; 2001.
Book
Google Scholar
Efron B, Gong G: A leisurely look at the bootstrap, the jacknife, and cross-validation. Am Stat 1983, 37: 36–48.
Google Scholar
Deutsch JM: Evolutionary algorithms for finding optimal gene sets in microarray prediction. Bioinformatics 2003, 19: 45–52.
Article
CAS
PubMed
Google Scholar
Zhou X, Mao KZ: LS Bound based gene selection for DNA microarray data. Bioinformatics 2005, 21: 1559–1564.
Article
CAS
PubMed
Google Scholar
Golub TR, Slonim DK, Tamayo P, Huard C, Gaasenbeek M, Mesirov JP, Coller H, Loh ML, Downing JR, Caligiuri MA, Bloomfield CD, Lander ES: Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 1999, 286: 531–537.
Article
CAS
PubMed
Google Scholar
Jiang H, Deng Y, Chen H, Tao L, Sha Q, Chen J, Tsai C, Zhang S: Joint analysis of two microarray gene-expression data sets to select lung adenocarcinoma marker genes. BMC Bioinformatics 2004, 5: 81.
Article
PubMed Central
PubMed
Google Scholar
Yeung KY, Bumgarner RE: Multiclass classification of microarray data with repeated measurements: application to cancer. Genome Biol 2003, 4: R83.
Article
PubMed Central
PubMed
Google Scholar
Breiman L: Statistical modeling: the two cultures (with discussion). Statistical Science 2001, 16: 199–231.
Article
Google Scholar
Dettling M, Bühlmann P: Finding predictive gene groups from microarray data. J Multivariate Anal 2004, 90: 106–131.
Article
Google Scholar
Simon RM, Korn EL, McShane LM, Radmacher MD, Wright GW, Zhao Y: Design and analysis of DNA microarray investigations. New York: Springer; 2003.
Google Scholar
Yu H: Rmpi: Interface (Wrapper) to MPI (Message-Passing Interface).Tech. rep., Department of Statistics, University of Western Ontario; 2004. [http://www.stats.uwo.ca/faculty/yu/Rmpi]
Google Scholar
Tierney L, Rossini AJ, Li N, Sevcikova H: SNOW: Simple Network of Workstations. Tech. rep 2004. [http://www.stat.uiowa.edu/~luke/R/cluster/cluster.html]
Google Scholar
McLachlan GJ: Discriminant analysis and statistical pattern recognition. New York: Wiley; 1992.
Book
Google Scholar
Furey TS, Cristianini N, Duffy N, Bednarski DW, Schummer M, Haussler D: Support vector machine classification and validation of cancer tissue samples using microarray expression data. Bioinformatics 2000, 16(10):906–914.
Article
CAS
PubMed
Google Scholar
Lee Y, Lee CK: Classification of multiple cancer types by multicategory support vector machines using gene expression data. Bioinformatics 2003, 19(9):1132–1139.
Article
CAS
PubMed
Google Scholar
Ramaswamy S, Tamayo P, Rifkin R, Mukherjee S, Yeang C, Angelo M, Ladd C, Reich M, Latulippe E, Mesirov J, Poggio T, Gerald W, Loda M, Lander E, Golub T: Multiclass cancer diagnosis using tumor gene expression signatures. Proc Natl Acad Sci USA 2001, 98(26):15149–15154.
Article
PubMed Central
CAS
PubMed
Google Scholar
Chang CC, Lin CJ: LIBSVM: a library for Support Vector Machines.Tech. rep., Department of Computer Science, National Taiwan University; 2003. [http://www.csie.ntu.edu.tw/~cjlin/libsvm]
Google Scholar
Burgues CJC: A tutorial on support vector machines for pattern recognition. Knowledge Discovery and Data Mining 1998, 2: 121–167.
Article
Google Scholar
Vaquerizas JM, Conde L, Yankilevich P, Cabezon A, Minguez P, Diaz-Uriarte R, Al-Shahrour F, Herrero J, Dopazo J: GEPAS, an experiment-oriented pipeline for the analysis of microarray gene expression data. Nucleic Acids Res 2005, 33: W616–20.
Article
PubMed Central
CAS
PubMed
Google Scholar
R Development Core Team: R: A language and environment for statistical computing.R Foundation for Statistical Computing, Vienna, Austria; 2004. [http://www.R-project.org]
Google Scholar
[http://ligarto.org/rdiaz/Papers/rfVS/randomForestVarSel.html]
Ross DT, Scherf U, Eisen MB, Perou CM, Rees C, Spellman P, Iyer V, Jeffrey SS, de Rijn MV, Waltham M, Pergamenschikov A, Lee JC, Lashkari D, Shalon D, Myers TG, Weinstein JN, Botstein D, Brown PO: Systematic variation in gene expression patterns in human cancer cell lines. Nature Genetics 2000, 24(3):227–235.
Article
CAS
PubMed
Google Scholar
Ramaswamy S, Ross KN, Lander ES, Golub TR: A molecular signature of metastasis in primary solid tumors. Nature Genetics 2003, 33: 49–54.
Article
CAS
PubMed
Google Scholar
Pomeroy SL, Tamayo P, Gaasenbeek M, Sturla LM, Angelo M, McLaughlin ME, Kim JY, Goumnerova LC, Black PM, Lau C, Allen JC, Zagzag D, Olson JM, Curran T, Wetmore C, Biegel JA, Poggio T, Mukherjee S, Rifkin R, Califano A, Stolovitzky G, Louis DN, Mesirov JP, Lander ES, Golub TR: Prediction of central nervous system embryonal tumour outcome based on gene expression. Nature 2002, 415: 436–442.
Article
CAS
PubMed
Google Scholar
Alon U, Barkai N, Notterman DA, Gish K, Ybarra S, Mack D, Levine AJ: Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. Proc Natl Acad Sci USA 1999, 96: 6745–6750.
Article
PubMed Central
CAS
PubMed
Google Scholar
Alizadeh AA, Eisen MB, Davis RE, Ma C, Losses IS, Rosenwald A, Boldrick JC, Sabet H, Tran T, Yu X, Powell JI, Yang L, Marti GE, Moore T, Hudson J Jr, Lu L, Lewis DB, Tibshirani R, Sherlock G, Chan WC, Greiner TC, Weisenburger DD, Armitage JO, Warnke R, Levy R, Wilson W, Grever MR, Byrd JC, Botstein D, Brown PO, Staudt LM: Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature 2000, 403: 503–511.
Article
CAS
PubMed
Google Scholar
Singh D, Febbo PG, Ross K, Jackson DG, Manola J, Ladd C, Tamayo P, Renshaw AA, D'Amico AV, Richie JP, Lander ES, Loda M, Kantoff PW, Golub TR, Sellers WR: Gene expression correlates of clinical prostate cancer behavior. Cancer Cell 2002, 1: 203–209.
Article
CAS
PubMed
Google Scholar
Khan J, Wei JS, Ringner M, Saal LH, Ladanyi M, Westermann F, Berthold F, Schwab M, Antonescu CR, Peterson C, Meltzer PS: Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks. Nat Med 2001, 7: 673–679.
Article
PubMed Central
CAS
PubMed
Google Scholar
[http://cran.r-project.org/src/contrib/PACKAGES.html]