Informative transcription factor selection using support vector machine-based generalized approximate cross validation criteria

Computational Statistics and Data Analysis

Volumn 53, Issue 5, 2009, Pages 1727-1735

  Sohn, Insuk ^a   Shim, Jooyong ^b   Hwang, Changha ^c   Kim, Sujong ^d   Lee, Jae Won ^a  

^a Korea University   (South Korea)

^b Catholic University of Daegu   (South Korea)

^c Dankook University   (South Korea)

^d AmorePacific Corporation   (South Korea)

Author keywords

[No Author keywords available]

Indexed keywords

BINDING ENERGY; BINDING SITES; BIOCHEMISTRY; DECISION THEORY; DECISION TREES; GENES; IMAGE RETRIEVAL; MULTILAYER NEURAL NETWORKS; PROBABILITY DENSITY FUNCTION; RECURSIVE FUNCTIONS; REGRESSION ANALYSIS; SUPPORT VECTOR MACHINES; TRANSCRIPTION FACTORS; TREES (MATHEMATICS);

BIOLOGICAL FUNCTIONS; BIOLOGICAL PROCESS; CO-REGULATED GENES; EXISTING METHODS; GENERALIZED APPROXIMATE CROSS VALIDATIONS; NEW APPROACHES; RECURSIVE FEATURE ELIMINATIONS; REGULATORY MECHANISMS; SIMULATED DATUM; SUPPORT VECTORS; TARGET GENES; TEST ERRORS; TRANSCRIPTION FACTOR BINDING SITES; VARIABLE SELECTION METHODS;

TRANSCRIPTION;

EID: 60349101755     PISSN: 01679473     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.csda.2008.05.001     Document Type: Article

References (22)

1. Gyuon I., Weston J., Barnhill S., and Vapnik V. Gene selection for cancer classification using support vector machines. Machine Learning 46 (2002) 389-422
2. Hastie T., Tibshirani R., and Friedman J. The Elements of Statistical Learning: Data Mining, Inference and Prediction (2002), Springer Verlag
3. Jin V.X., Leu Y.W., Liyanarachchi S., Sun H., Fan M., Nephew K.P., Huang T.H.M., and Davuluri R.V. Identifying estrogen receptor α target genes using integrated computational genomics and chromatin immunoprecipitation microarray. Nucleic Acids Research 32 (2004) 6627-6635
4. Jung S.H. Sample size for FDR-control in microarray data analysis. Bioinformatics 21 (2005) 3097-3104
5. Keles S., van der Laan M.J., and Vulpe C. Regulatory motif finding by logic regression. Bioinformatics 20 (2004) 2799-2811
6. Kondor, R.S., Lafferty, J., 2002. Diffusion kernels on graphs and other discrete structures. In: Proceedings of the ICML
7. Koo J.-Y., Sohn I., Kim S., and Lee J.W. Structured polychotomous machine diagnosis of multiple cancer types using gene expression. Bioinformatics 22 8 (2006) 950-990
8. Krivan W., and Wasserman W.W. A predictive model for regulatory sequences directing liver-specific transcription. Genome Research 278 (2001) 167-181
9. Lee Y., Kim Y., Lee S., and Koo J.-Y. Structured multicategory support vector machines with analysis of variance decomposition. Biometrika 93 3 (2006) 555-571
10. Liu R., McEachin R.C., and States D.J. Computationally identifying novel NF-kB-regulated immune genes in the humana genome. Genome Research 13 (2003) 654-661
11. Matys V., Fricke E., Geffers R., Gossling E., Haubrock M., Hehl R., Hornischer K., Karas D., Kel A.E., Kel-Margoulis O.V., et al. TRANSFAC: Transcriptional regulation, from patterns to profiles. Nucleic Acids Research 31 (2003) 374-378
12. Park C.Y., Koo J.-Y., Kim S., Sohn I., and Lee J.W. Classification of gene functions using SVM for time-course gene expression data. Computational Statistics and Data Analysis 52 (2008) 2578-2587
13. Perier R.C., Praz V., Junier T., Bonnard C., and Bucher P. The Eukaryotic Promoter Database (EPD). Nucleic Acids Res. 28 (2000) 302-303
14. Quandt K., Frech K., Karas H., Wingender E., and Werner T. MatInd and MatInspector: New fast and versatile tools for detection of consensus matches in nucleotide sequence data. Nucleic Acids Res. 23 (1995) 4878-4884
15. Rakotmamonjy A. Variable selection using SVM-based criteria. Journal of Machine Learning Research 3 (2003) 1357-1370
16. Ruczinski I., Kooperberg C., and Leblanc M. Logic regression. Journal of Computational and Graphical Statistics 12 (2003) 475-511
17. Scholkopf B., and Smola J. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond (2002), The MIT Press, Massachusetts
18. Vapnik V. The Nature of Statistical Learning Theory (1995), Springer Verlag, New York
19. Vapnik V. Statistical Learning Theory (1998), Wiley, New York
20. Wahba G., Lin X., Gao F., Klein R., and Klein B. The bias-variance tradeoff and the randomized GACV. Advances in Neural Information Processing Systems 11 (1998) 620-626
21. Wahba, G., Lin, Y., Zhang, H., 1999. Generalized approximate cross validation for support vector machines, or, another way to look at margin-like quantities. Technical Report 1006, University of Wisconsin
22. Wasserman W.W., and Fickett J.W. Identification of regulatory regions which confer muscle-specific gene expression. Journal of Molecular Biology 278 (1998) 167-181