Optimized between-group classification: A new jackknife-based gene selection procedure for genome-wide expression data

BMC Bioinformatics

Volumn 6, Issue , 2005, Pages

  Baty, Florent ^a   Bihl, Michel P ^a   Perrière, Guy ^b   Culhane, Aedín C ^c   Brutsche, Martin H ^a  

^a UNIVERSITY HOSPITAL BASEL   (Switzerland)

^b UNIVERSITÉ LYON 1   (France)

^c UNIVERSITY COLLEGE DUBLIN   (Ireland)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION ACCURACY; CLASSIFICATION METHODS; LEAVE-ONE-OUT CROSS VALIDATIONS; OPTIMIZATION PROCEDURES; OPTIMIZED SELECTION; PREDICTIVE ACCURACY; SUPERVISED CLASSIFICATION; VARIABLE SELECTION;

FORECASTING; GENE EXPRESSION; OPTIMIZATION; STATISTICAL METHODS;

CLASSIFICATION (OF INFORMATION);

ACCURACY; ANALYTIC METHOD; ARTICLE; CONTROLLED STUDY; DATA ANALYSIS; DISEASE CLASSIFICATION; GENE EXPRESSION; GENETIC SELECTION; HUMAN; MAJOR CLINICAL STUDY; PREDICTION; PROCESS OPTIMIZATION; VALIDATION PROCESS; ALGORITHM; DNA MICROARRAY; GENE EXPRESSION PROFILING; MONTE CARLO METHOD; PHYLOGENY; PROCEDURES; STATISTICAL ANALYSIS;

ALGORITHMS; DATA INTERPRETATION, STATISTICAL; GENE EXPRESSION PROFILING; MONTE CARLO METHOD; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS; PHYLOGENY;

EID: 27644475451     PISSN: 14712105     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/1471-2105-6-239     Document Type: Article

References (33)

1. Li L, Pedersen LG, Darden TA, Weinberg CR: Class prediction and discovery based on gene expression data. Genome Information Systems and Technology 2001.
2. Yeung KY, Bumgarner RE: Multiclass classification of microarray data with repeated measurements: application to cancer. Genome Biol 2003, 4(12):R83.
3. Li W, Sun F, Grosse I: Extreme value distribution based gene selection criteria for discriminant microarray data analysis using logistic regression. J Comput Biol 2004, 11(2-3):215-226.
4. Tan Y, Shi L, Tong W, Wang C: Multi-class cancer classification by total principal component regression (TPCR) using microarray gene expression data. Nucleic Acids Res 2005, 33:56-65.
5. Xiong M, Jin L, Li W, Boerwinkle E: Computational methods for gene expression-based tumor classification. Biotechniques 2000, 29(6):1264-8. 1270
6. Li L, Weinberg C, Darden T, Pedersen L: Gene selection for sample classification based on gene expression data: study of sensitivity to choice of parameters of the GA/KNN method. Bioinformatics 2001, 17(12):1131-42.
7. Lyons-Weiler J, Patel S, Bhattacharya S: A classification-based machine learning approach for the analysis of genome-wide expression data. Genome Res 2003, 13(3):503-512.
8. Brown M, Grundy W, Lin D, Cristianini N, Sugnet C, Furey T, Ares M, Haussler D: Knowledge-based analysis of microarray gene expression data by using support vector machines. Proc Natl Acad Sci USA 2000, 97:262-7.
9. Furey T, Cristianini N, Duffy N, Bednarski D, Schummer M, Haussler D: Support vector machine classification and validation of cancer tissue samples using microarray expression data. Bioinformatics 2000, 16(10):906-14.
10. 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(106567-72 [http://dx.doi.org/10.1073/pnas.082099299].
11. Dudoit S, Fridlyand J, Speed TP: Comparison of discrimination methods for the classification of tumors using gene expression data. J Amer Stat Assoc 2002, 97(457):77-87.
12. Li W, Yang Y: How many genes are needed for a discriminant microarray data analysis? In Methods of microarray data analysis Edited by: Lin S, KF Johnson E. Kluwer Academic; 2002:137-150.
13. 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(2):171-178.
14. Doledec S, Chessel D: Rhytmer sarsmiers et composantes stationnelles en milieu aquatique. l - Description d'un plan d'observation complet par projection de variables. Acta Oecologica Oecologia Generalis 1987, 8:403-426.
15. Culhane AC, Perrière G, Considine EC, Cotter TG, Higgins DG, Between-group analysis of microarray data. Bioinformatics 2002, 18(12):1600-1608.
16. Zhang H, Yu CY, Singer B: Cell and tumor classification using gene expression data: construction of forests. Proc Natl Acad Sci USA 2003, 100(7):4168-4172.
17. Antonov AV, Tetko IV, Mader MT, Budczies J, Mewes HW: Optimization models for cancer classification: extracting gene interaction information from microarray expression data. Bioinformatics 2004, 20(5):644-652.
18. Rutherford RM, Staedtler F, Kehren J, Chibout SD, Joos L, Tamm M, Gilmartin JJ, Brutsche MH: Functional genomics and prognosis in sarcoidosis-the critical role of antigen presentation. Sarcoidosis Vasc Diffuse Lung Dis 2004, 21:10-18.
19. Gene Expression Omnibus [http://www.ncbi.nlm-nih.gov/geo/]
20. 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(6):673-679.
21. Small round blue cell tumours dataset [http://research.nhgri.nih.gov/microarray/Supplement]
22. 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(12):6745-6750.
23. Colon cancer dataset http://www.bioconductor.org/packages/data/experiment/stable/src/contrib/ html/colonCA.html
24. 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(5439):531-537.
25. Leukemia dataset http://www.bioconductor.org/packages/data/experiment/stable/src/contrib/ html/golubEsets.html
26. R Development Core Team: R: A language and environment for statistical computing. 2004 [http://www.R-project.org]. R Foundation for Statistical Computing, Vienna Austria [ISBN 3-900051-00-3].
27. Gentleman RC, Carey VJ, Bates DM, Bolstad B, Dettling M, Dudoit S, Ellis B, Gautier L, Ge Y, Gentry J, Hornik K, Hothorn T, Huber W, Iacus S, Irizarry R, Leisch F, Li C, Maechler M, Rossini AJ, Sawitzki G, Smith C, Smyth G, Tierney L, Yang JY, Zhang J: Bioconductor: open software development for computational biology and bloinformatics. Genome Biol 2004, 5(10):R80.
28. Chessel D, Dufour AB, Thioulouse J: The ade4 package - 1: One-table methods. R News 2004, 4:5-10 [http://cran.R-project.org/doc/Rnews/].
29. Culhane AC, Thioulouse J, Perrière G, Higgins DG: MADE4: an R package for multivariate analysis of gene expression data. Bioinformatics 2005, 21(11):2789-2790.
30. Huber W, von Heydebreck A, Sultmann H, Poustka A, Vingron M: Variance stabilization applied to microarray data calibration and to the quantification of differential expression. Bioinformatics 2002, 18(Suppl 1):S96-104.
31. Fellenberg K, Hauser NC, Brors B, Neutzner A, Hoheisel JD, Vingron M: Correspondence analysis applied to microarray data. Proc Natl Acad Sci USA 2001, 98(19):10781-10786.
32. Wouters L, Gohlmann HW, Bijnens L, Kass SU, Molenberghs G, Lewi PJ: Graphical exploration of gene expression data: a comparative study of three multivariate methods. Biometrics 2003, 59(4):1131-1139.
33. Perrière G, Thioulouse J: Use of correspondence discriminant analysis to predict the subcellular location of bacterial proteins. Comput Methods Programs Biomed 2003, 70(2):99-105.