A new regularized least squares support vector regression for gene selection

BMC Bioinformatics

Volumn 10, Issue , 2009, Pages

  Chen, Pei Chun ^a   Huang, Su Yun ^b   Chen, Wei J ^a   Hsiao, Chuhsing K ^a  

^a NATIONAL TAIWAN UNIVERSITY   (Taiwan)

^b INSTITUTE OF STATISTICAL SCIENCE   (Taiwan)

Author keywords

[No Author keywords available]

Indexed keywords

CUMULATIVE SUMS; CURSE OF DIMENSIONALITY; EXPRESSION LEVELS; KERNEL FISHER DISCRIMINANT ANALYSIS; MICROARRAY DATA; MULTI-CLASS CLASSIFICATION; MULTI-CLASS PROBLEMS; REGULARIZED LEAST SQUARES;

DISEASES;

GENE EXPRESSION;

DNA;

ACUTE LEUKEMIA; ARTICLE; BREAST CANCER; CHILDHOOD CANCER; COLON CANCER; DISCRIMINANT ANALYSIS; DNA MICROARRAY; GENE EXPRESSION; GENETIC ALGORITHM; GENETIC ANALYSIS; GENETIC SELECTION; HUMAN; INTERMETHOD COMPARISON; KERNEL METHOD; LUNG CANCER; SUPPORT VECTOR MACHINE; ALGORITHM; ARTIFICIAL INTELLIGENCE; BIOLOGY; CLUSTER ANALYSIS; GENE EXPRESSION PROFILING; GENETICS; METHODOLOGY; NEOPLASM; REGRESSION ANALYSIS; TUMOR GENE;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; CLUSTER ANALYSIS; COMPUTATIONAL BIOLOGY; GENE EXPRESSION PROFILING; GENES, NEOPLASM; HUMANS; LEAST-SQUARES ANALYSIS; NEOPLASMS; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS;

EID: 65249163979     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/1471-2105-10-44     Document Type: Article

References (33)

1. 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 10521349 10.1126/ science.286.5439.531
2. Brown MPS Grundy WN Lin D Cristianini N Sugnet CW Furey TS Manuel Ares J Haussler D Knowledge-based analysis of microarray gene expression data by using support vector machines Proc Natl Acad Sci U S A 2000, 97(1):262-267 10618406 26651 10.1073/pnas.97.1.262
3. Hastie T Tibshirani R Friedman J The Elements of Statistical Learning. Springer Series in Statistics New York, Springer-Verlag 2001
4. Nguyen DV Rocke DM Tumor classification by partial least squares using microarray gene expression data Bioinformatics 2002, 18(1);39-50 11836210 10.1093/bioinformatics/18.1.39
5. Dudoit S Fridlyand J Speed TP Comparison of discrimination methods for the classification of tumors using gene expression data Journal of the American Statistical Association 2002, 97:77-87 10.1198/ 016214502753479248
6. Liu X Krishnan A Mondry A An entropy-based gene selection method for cancer classification using microarray data BMC Bioinformatics 2005, 6:76 15790388 1087831 10.1186/1471-2105-6-76
7. Yang K Cai Z Li J Lin G A stable gene selection in microarray data analysis BMC Bioinformatics 2006, 7:228 16643657 1524991 10.1186/ 1471-2105-7-228
8. Saeys Y Inza I Larranaga P A review of feature selection techniques in bioinformatics Bioinformatics 2007, 23(19):2507-2517 17720704 10.1093/ bioinformatics/btm344
9. Koller D Sahami M Toward optimal feature selection Proceedings of the Thirteenth International Conference on Machine Learning 1996, 96:284-292
10. Xing EP Jordan MI Karp RM Feature selection for high dimensional genomic microarray data Proceedings of Eighteenth International Conference on Machine Learning 2001, 601-608
11. Mamitsuka H Selecting features in microarray classification using ROC curves Pattern Recognition 2006, 39:2393-2404 10.1016/ j.patcog.2006.07.010
12. Yu L Liu H Efficient feature selection via analysis of relevance and redundancy Journal of Machine Learning Research 2004, 5:1205-1224
13. Schölkopf B Smola A Learning with Kernels Cambridge, MA, MIT Press 2002
14. Bi J Bennett K Embrechts M Breneman C Song M Dimensionality reduction via sparse support vector machines Journal of Machine Learning Research 2003, 3:1229-1243 10.1162/153244303322753643
15. Guyon I Weston J Barnhill S Vapnik V Gene selection for cancer classification using support vector machines Machine Learning 2002, 46:389-422 10.1023/A:1012487302797
16. Lee YJ Chang CC Chao CH Incremental forward feature selection with application to microarray gene expression data J Biopharm Stat 2008, 18(5):827-840 18781519 10.1080/10543400802277868
17. Jirapech-Umpai T Aitken S Feature selection and classification for microarray data analysis: Evolutionary methods for identifying predictive genes BMC Bioinformatics 2005, 6:148 15958165 1181625 10.1186/ 1471-2105-6-148
18. Tang EK Suganthan P Yao X Gene selection algorithms for microarray data based on least squares support vector machine BMC Bioinformatics 2006, 7:95 16504159 1409801 10.1186/1471-2105-7-95
19. Lee KE Sha N Dougherty ER Vannucci M Mallick BK Gene selection: A Bayesian variable selection approach Bioinformatics 2003, 19(1):90-97 12499298 10.1093/bioinformatics/19.1.90
20. Sha N Vannucci M Tadesse MG Brown PJ Dragoni I Davies N Roberts TC Contestabile A Salmon M Buckley C Falciani F Bayesian variable selection in multinomial probit models to identify molecular signatures of disease stage Biometrics 2004, 60(3);812-819 15339306 10.1111/ j.0006-341X.2004.00233.x
21. Yeung KY Bumgarner RE Raftery AE Bayesian model average: Development of an improved multi-class, gene selection and classification tool for microarray data Bioinformatics 2005, 21(10):2394-2402 10.1093/ bioinformatics/bti319 15713736
22. Zhou X Wang X Dougherty ER Multi-class cancer classification using multinomial probit regression with Bayesian gene selection Syst Biol (Stevenage) 2006, 153(2):70-78 16986255
23. Suykens JA Gestel TV Brabanter JD Moor BD Vandewalle J Least Squares Support Vector Machines New Jersey, World Scientific 2002
24. Anderson TW An Introduction to Multivariate Statistical Analysis New York, Wiley 2003
25. Hastie T Tibshirani R Buja A Flexible discriminant analysis by optimal scoring Journal of the American Statistical Association 1994, 89:1255-1270 10.2307/2290989
26. Mika S Rätsch G Weston J Schölkopf B Mullers KR Fisher discriminant analysis with kernels Neural Networks for Signal Processing 1999, IX:41-48
27. Chapelle O Training a support vector machine in the primal Neural Comput 2007, 19(5):1155-1178 17381263 10.1162/neco.2007.19.5.1155
28. Huang CM Lee YJ Lin D Huang SY Model selection for support vector machine via uniform design Computational Statistics and Data Analysis 2007, 52:335-346 10.1016/j.csda.2007.02.013
29. 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 U S A 1999, 96(12):6745-6750 10359783 21986 10.1073/pnas.96.12.6745
30. Khan J Wei JS Ringér 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 11385503 1282521 10.1038/89044
31. Hedenfalk I Duggan D Chen Y Radmacher M Bittner M Simon R Meltzer P Gusterson B Esteller M Olli-PKallioniemi Wilfond B Borg A Trent J Gene-expression profiles in hereditary breast cancer N Engl J Med 2001, 344(8):539-548 11207349 10.1056/NEJM200102223440801
32. Bhattacharjee A Richards WG Staunton J Li C Monti S Vasa P Ladd C Beheshti J Bueno R Gillette M Loda M Weber G Mark EJ Lander ES Wong W Johnson BE Golub TR Sugarbaker DJ Meyerson M Classification of human lung carcinomas by mRNA expression profiling reveals distinct adenocarcinoma subclasses Proc Natl Acad Sci 2001, 98(24):13790-13795 11707567 61120 10.1073/pnas.191502998
33. Lee YJ Mangasarian OL SSVM: A smooth support vector machine for classification Computational Optimization and Applications 2001, 20:5-22 10.1023/A:1011215321374