SVM-RFE with MRMR filter for gene selection

IEEE Transactions on Nanobioscience

Volumn 9, Issue 1, 2010, Pages 31-37

  Mundra, Piyushkumar A ^a   Rajapakse, Jagath C ^a,b  

^a NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

^b Massachusetts Institute of Technology Cambridge   (United States)

Author keywords

Cancer classification; Gene redundancy; Gene relevancy; Mutual information; Support vector machine recursive feature elimination (SVM RFE)

Indexed keywords

BENCHMARK DATASETS; BENIGN TISSUES; CANCER CLASSIFICATION; CANCER TISSUES; CLASS LABELS; DATA SETS; FILTER METHOD; FUNCTIONAL PROPERTIES; GENE ONTOLOGY; GENE SELECTION; MUTUAL INFORMATIONS; SUPPORT VECTOR MACHINE RECURSIVE FEATURE ELIMINATIONS; SVM-RFE; WRAPPER METHODS;

FEATURE EXTRACTION; GENES; HISTOLOGY; ONTOLOGY; QUALITY ASSURANCE; REDUNDANCY;

SUPPORT VECTOR MACHINES;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; BIOLOGICAL MODEL; BIOLOGY; CLASSIFICATION; CYTOCHEMISTRY; GENE; GENE EXPRESSION PROFILING; GENETIC DATABASE; GENETICS; HUMAN; METABOLISM; METHODOLOGY; NEOPLASM; STATISTICAL MODEL;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; COMPUTATIONAL BIOLOGY; DATABASES, GENETIC; GENE EXPRESSION PROFILING; GENES; HISTOCYTOCHEMISTRY; HUMANS; MODELS, GENETIC; MODELS, STATISTICAL; NEOPLASMS;

EID: 77950538864     PISSN: 15361241     EISSN: None     Source Type: Journal    
DOI: 10.1109/TNB.2009.2035284     Document Type: Article

References (38)

1. A. Blum and A. Langley, "Selection of relevant features and examples in machine learning," Artif. Intell., vol.97, pp. 245-271, 1997.
2. R. Kohavi and G. John, "Wrappers for feature subset selection," Artif. Intell., vol.97, pp. 273-324, 1997.
3. L. Yu and H. Liu, "Efficient feature selection via analysis of relevance and redundancy," J. Mach. Learn. Res., vol.5, pp. 1205-1224, 2004.
4. T. Golub, D. Slonim, P. Tamayo, C. Huard, M. Gaasenbeek, J. Mesirov, H. Coller, M. Loh, J. Downing, M. Caligiuri, C. Bloomfield, and E. Lander, "Molecular classification of cancer: Class discovery and class prediction by gene expression," Science, vol.286, pp. 531-537, 1999.
5. X. Liu, A. Krishnan, and A. Mondry, "An entropy-based gene selection method for cancer classification using microarray data" BMC Bioinformatics, vol.6, p. 76, 2005.
6. C. Ding and H. Peng, "Minimum redundancy feature selection from microarray gene expression data," J. Bioinformatics Comput. Biol., vol.3, pp. 185-205, 2005.
7. H. Peng, F. Long, and C. Ding, "Feature selection based on mutual information: Criteria of max-dependency, max-relevance, and minredundancy," IEEE Trans. Pattern Anal. Mach. Intell., vol.27, no.8, pp. 1226-1238, Aug. 2005.
8. C. Ooi, M. Chetty, and S. Teng, "Differential prioritization between relevance and redundancy in correlation-based feature selection techniques for multiclass gene expression data," BMC Bioinformatics, vol.7, pp. 320-339, 2006.
9. J. Zhang and H. Deng, "Gene selection for classification of microarray data based on Bayes error," BMC Bioinformatics, vol.8, p. 370, 2007.
10. M. Yousef, S. Jung, L. Showe, and M. Showe, "Recursive cluster elimination (rce) for classification and feature selection from gene expression data," BMC Bioinformatics, vol.8, p. 144, 2007.
11. E. Tang, P. Suganthan, and X. Yao, "Gene selection algorithms for microarray data based on least square support vector machine," BMC Bioinformatics, vol.7, p. 95, 2006.
12. S. Baker and S. Kramer, "Identifying genes that contributes most to good classification in microarray," BMC Bioinformatics, vol.7, p. 407, 2006.
13. Y. Zhang, C. Ding, and T. Li, "Gene selection algorithm by combining reliefF and mRMR," BMC Genomics, vol.9, no.2, p. S27, 2008.
14. I. Inza, P. Larranaga, R. Blanco, and A. Cerrolaza, "Filter versus wrapper gene selection approaches in DNA microarray domains," Artif. Intell. Med., vol.31, pp. 91-103, 2004.
15. I. Guyon and A. Elisseeff, "An introduction to variable and feature selection," J. Mach. Learn. Res., vol.3, pp. 1157-1182, 2003.
16. R. Battiti, "Using mutual information for selecting features in supervised neural net learning," IEEE Trans. Neural Netw., vol.5, no.4, pp. 537-550, Jul. 1994.
17. M.Wahde and Z. Szallasi, "A survey of methods for classification of geneexpression data using evolutionary algorithm," Expert Rev. Molecular Diagnostics, vol.6, no.1, pp. 101-110, 2006.
18. R. Diaz-Uriarte and S. Andres, "Gene selection and classification of microarray data using random forest," BMC Bioinformatics, vol.7, p. 3, 2006.
19. R. Ruiz, J. Riquelme, and J. Aguilar-Ruiz, "Incremental wrapper-based gene selection from microarray data for cancer classification," Pattern Recog., vol.39, pp. 2383-2392, 2006. (Pubitemid 44344733)
20. A. Rakotomamonjy, "Variable selection using SVM based criteria," J. Mach. Learn. Res., vol.3, pp. 1357-1370, 2003.
21. I. Guyon, J.Weston, S. Barhill, and V. Vapnik, "Gene selection for cancer classification using support vector machines," Mach. Learn., vol.46, pp. 389-422, 2002.
22. D. Kai-Bo, J. Rajapakse, H. Wang, and F. Azuaje, "Multiple SVM-RFE for gene selection in cancer classification with expression data," IEEE Trans. Nanobiosci., vol.4, no.3, pp. 228-234, Sep. 2005.
23. X. Zhou andK.Mao, "LS bound based gene selection forDNAmicroarray data," Bioinformatics, vol.21, no.8, pp. 1559-1564, 2005.
24. Y. Tang, Y.-Q. Zhang, and Z. Huang, "Development of two-stage SVMRFE gene selection strategy for microarray expression data analysis," IEEE Trans. Comput. Biol. Bioinformatics, vol.4, no.3, pp. 365-381, Jul.-Sep. 2007.
25. Y. Tang, Y.-Q. Zhang, Z. Huang, X. Hu, and Y. Zhao, "Recursive fuzzy granulation for gene subset extraction and cancer classification," IEEE Trans. Inf. Technol. Biomed., vol.12, no.6, pp. 723-730, Nov. 2008.
26. J. Rajapakse,D.Kai-Bo, andW.Yeo, "Proteomic cancer classificationwith mass spectrometry data," Amer. J. Pharmacogenom., vol.5, pp. 281-292, 2005.
27. Z.-X. Xie, Q.-H. Hu, and D.-R. Yu, "Improved feature selection algorithm based on SVM and correlation," in Int. Symp. Neural Netw., (LNBI Series 3971), J. Wang, Z. Yi, J. M. Zurada, B.-L. Lu, and Y. Hujun, Eds., 2006, pp. 1373-1380.
28. T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction. NewYork: Springer-Verlag, 2001.
29. H. Wang, F. Azuaze, O. Bodenreider, and J. Dopazo, "Gene expression correlation and gene ontology based similarity: An assessment of quantitative relationships," in Proc. IEEE Symp. Comput. Intell. Bioinformatics Comput. Biol., 2004, pp. 25-31.
30. P. Lord, R. Stevens, A. Brass, and A. Goble, "Investigating semantic similarity measures across the gene ontology: The relationship between sequence and annotation," Bioinformatics, vol.19, pp. 1275-1283, 2003.
31. J. Sevilla, V. Segura, A. Podhorski, E. Guruceaga, J. Mato, L. Martinez-Cruz, F. Corrales, and A. Rubio, "Correlation between gene expression and go semantic similarity," IEEE Trans. Comput. Biol. Bioinformatics, vol.2, no.4, pp. 330-338, Oct.-Dec. 2005.
32. D. Lin, "An information-theoretic definition of similarity," in Proc. 15th Int. Conf. Mach. Learn., 1998, pp. 296-304.
33. U. Alon, N. Barkai, D. Notterman, K. Gish, S. Ybarra, D. Mack, and A. Levine, "Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays," Proc. Nat. Acad. Sci. USA, vol.96, pp. 6745-6750, 1999.
34. N. Iizuka, M. Oka, H. Yamada Okabe, M. Nishida, Y. Maeda, N. Mori, T. Takao, T. Tamesa, A. Tangoku, H. Tabuchi, K. Hamada, H. Nakayama, K. Ishitsuka, T. Miyamoto, A. Hirabayashi, S. Uchimura, and Y. Hamamoto, "Oligonucleotide microarray for prediction of early intrahepatic recurrence of hepatocellular carcinoma after curative resection," Lancet, vol.361, pp. 923-929, 2003.
35. D. Singh, P. Febbo, K. Ross, D. Jackson, J. Manola, C. Ladd, P. Tamayo, A. Renshaw, A. D'Amico, J. Richie, E. Lander, M. Loda, P. Kantoff, T. Golub, and W. Sellers, "Gene expression correlates of clinical prostate cancer behavior," Cancer Cell, vol.1, pp. 203-209, 2002.
36. C. Lai, M. Reinders, L. Van't Veer, and L. Wessels, "A comparison of univariate and multivariate gene selection techniques for classification of cancer datasets," BMC Bioinformatics, vol.7, p. 235, 2006.
37. C. Chang and C. Lin. (2001). Libsvm: A library for support vector machines [Online]. Available: www.csie.ntu.edu.tw/ cjlin/libsvm.
38. H. Frohlich, N. Speer, A. Poustka, and T. Beibarth, "Gosim-An Rpackage for computation of information theoretic go similarities between terms and gene products," BMC Bioinformatics, vol.8, p. 166, 2007.