Gene association study with SVM, MLP and cross-validation for the diagnosis of diseases

Progress in Natural Science

Volumn 18, Issue 6, 2008, Pages 741-750

  Zhang, Junying ^a   Liu, Shenling ^a   Wang, Yue ^b  

^a XIDIAN UNIVERSITY   (China)

^b VIRGINIA POLYTECHNIC INSTITUTE AND STATE UNIVERSITY   (United States)

Author keywords

Cross validation; Curse of dimensionality; Diagnostic genes; DNA microarray data; Gene selection; MLP; SVM

Indexed keywords

BIOCHIPS; DIAGNOSIS; DNA; GENE EXPRESSION; STATISTICAL METHODS;

CROSS VALIDATION; CURSE OF DIMENSIONALITY; DIAGNOSTIC GENE; DNA MICROARRAY DATA; GENE ASSOCIATIONS; GENE SELECTION; LEAVE ONE OUT; LINEAR SUPPORT VECTOR MACHINES; MULTILAYERS PERCEPTRONS; SUPPORT VECTORS MACHINE;

SUPPORT VECTOR MACHINES;

EID: 46449111788     PISSN: 10020071     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.pnsc.2007.11.022     Document Type: Article

References (21)

1. E. Mjolsness D. DeCoste Machine learning for science: state of the art and future prospects Science 293 14 2001 2051 2055
2. G.V. Trunk A problem of dimensionality: a simple example IEEE Trans Pattern Anal Mach Intell 1 3 1995 306 307
3. A.K. Jain B. Chandrasekaran Handbook of Statistics 1982 North-Holland Amsterdam
4. S. Haykin Neural networks: a comprehensive foundation 2nd ed. 1999 Prentice-Hall Inc.
5. B.D. Ripley Pattern recognition and neural networks 1996 Cambridge University Press Cambridge
6. P.M. Narendra K. Fukunaga A branch and bound algorithm for feature subset selection IEEE Trans Comput 26 9 1977 917 922
7. Y. Hamamoto S. Uchimura Y. Matsunra Evaluation of the branch and bound algorithm for feature selection Pattern Recognit Lett 11 1990 453 456
8. J. Herrero A. Valencia J. Dopazo A hierarchical unsupervised growing neural network for clustering gene expression patterns Bioinformatics 17 2 2001 126 136
9. Ramaswarmy S, Tamayo P, Rifkin R, et al. Multiclass cancer diagnosis using tumor gene expression signatures. PNAS 2001(26):15149–54.
10. M.P.S. Brown W.N. Grundy D. Lin Knowledge-based analysis of microarray gene expression data by using support vector machines PNAS 97 1 2000 262 267
11. M.M. Xiong X.Z. Fang J.Y. Zhao Biomarker identification by feature wrappers Genome Res 11 2001 188 1878
12. Dudoit S, Fridlyand J, Speed TP. Comparison of discrimination methods for the classification of tumors using gene expression data. Technical Report 576. Berkeley: University of California; 2000 [June].
13. J.Y. Zhang Y. Wang J. Khan Gene selection in class space for molecular classification of cancer Sci China (Ser F) 47 3 2004 301 314
14. J. Khan J.S. Wei M. Ringner Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks Nat Med 7 6 2001 673 679
15. I. Guyon J. Weston S. Barnhill Gene selection for cancer classification using support vector machines Mach Learn 46 3 2002 389 422
16. R. Kohavi G.H. John Wrappers for feature subset selection Artif Intell 97 1–2 1997 273 324
17. A. Jain F. Zongker Feature selection: evaluation, application, and small sample performance IEEE Trans Pattern Anal Mach Intell 19 2 1997 153 158
18. J.C. Christopher A tutorial on support vector machines for pattern recognition Data Mining Knowledge Discov 2 1998 121 167
19. M. Loog R.P.W. Duin Multiclass linear dimension reduction by weighted pairwise fisher criteria IEEE Trans Pattern Anal Mach Intell 23 7 2001 762 766
20. K. Anil P.R. Robert J.C. Mar Statistical pattern recognition: a review IEEE Trans Pattern Anal Mach Intell 22 1 2000 4 37
21. K. Hao X.B. Wang T.H. Niu A candidate gene association study on preterm delivery: application of high-throughput genotyping technology and advanced statistical methods Human Molecular Genetics 13 7 2004 683 691