Gene selection for cancer classification through ensemble of methods

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5495 LNCS, Issue , 2009, Pages 507-516

  Wilinski, Artur ^a   Osowski, Stanislaw ^b,c   Siwek, Krzysztof ^c  

^a WARSAW UNIVERSITY OF LIFE SCIENCES   (Poland)

^b MILITARY UNIVERSITY OF TECHNOLOGY   (Poland)

^c WARSAW UNIVERSITY OF TECHNOLOGY   (Poland)

Author keywords

Feature selection; Gene expression array; SVM classification

Indexed keywords

CANCER CLASSIFICATION; FEATURE SELECTION; GENE EXPRESSION MICROARRAY; GENE SELECTION; GENE SEQUENCES; NUMERICAL EXPERIMENTS; SVM CLASSIFICATION; SVM CLASSIFIERS; TWO STAGE;

ALGORITHMS; FEATURE EXTRACTION;

GENE EXPRESSION;

EID: 78650754570     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-04921-7_52     Document Type: Conference Paper

References (13)

1. Conover, W.J.: Practical Nonparametric Statistics. Wiley, New York (1980)
2. Golub, T., et al.: Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286, 531-537 (1999)
3. Guyon, I., Weston, A.J.,, Barnhill, S., Vapnik, V.: Gene selection for cancer classification using SVM. Machine Learning 46, 389-422 (2002)
4. Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. Journal of Machine Learning Research 3, 1158-1182 (2003)
5. Huang, T.M., Kecman, V.: Gene extraction for cancer diagnosis by support vector machines - An improvement. Artificial Intelligence in Medicine 35, 185-194 (2005)
6. Huang, X., Pan, W.: Linear regression and two-class classification with gene expression data. Bioinformatics 19, 2072-2078 (2003) (Pubitemid 37408158)
7. Jirapech-Umpai, T., Aitken, S.: Feature selection and classification for microarray data analysis. BMC Bioinformatics 149, 1-11 (2005)
8. Schölkopf, B., Smola, A.: Learning with Kernels. MIT Press, Cambridge (2002)
9. Schurmann, J.: Pattern Classification, a Unified View of Statistical and Neural Approaches. Wiley, New York (1996)
10. Vert, J.P.: Kernel methods in genomics and computational biology. In: Camps-Vals, G., et al. (eds.) Kernel Methods in Bioengineering, pp. 42-64. Idea Group, London (2007)
11. Yusef, M., Jung, S., Showe, L., Showe, M.: Recursive cluster elimination for classification and feature selection from gene expression. BMC Bioinformatics 8(144), 1-12 (2007)
12. Matlab user manual - Statistics toolbox, MathWorks, Natick (1999)
13. Genes database: http://discover1.mc.vanderbilt.edu/discover/public/mcsvm