Filtering for improved gene selection on microarray data

Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics

Volumn , Issue , 2010, Pages 3250-3257

  Canul Reich, Juana ^a   Hall, Lawrence O ^a   Goldgof, Dmitry ^a   Eschricht, Steven A ^b  

^a University of South Florida   (United States)

^b H LEE MOFFITT CANCER CENTER AND RESEARCH INSTITUTE   (United States)

Author keywords

Feature selection; IFP; Information gain; Microarray data; ReliefF; SVM RFE; T test

Indexed keywords

FEATURE SELECTION; IFP; INFORMATION GAIN; MICROARRAY DATA; RELIEFF; SVM-RFE; T-TEST;

CLASSIFICATION (OF INFORMATION); CYBERNETICS; DATA REDUCTION; GENES; TESTING;

FEATURE EXTRACTION;

EID: 78751539324     PISSN: 1062922X     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICSMC.2010.5642302     Document Type: Conference Paper

References (19)

1. S. Mukkamala, Q. Liu, R. Veeraghattam, and A. Sung, "Feature Selection and Ranking of Key Genes for Tumor Classification: Using Microarray Gene Expression Data," LECTURE NOTES IN COMPUTER SCIENCE, vol. 4029, p. 951, 2006.
2. I. Guyon and A. Elisseeff, "An introduction to variable and feature selection," The Journal of Machine Learning Research, vol. 3, pp. 1157-1182, 2003.
3. A. Hobson and B. Cheng, "A comparison of the Shannon and Kullback information measures," Journal of Statistical Physics, vol. 7, no. 4, pp. 301-310, 1973.
4. I. Kononenko, E. Šimec, and M. Robnik-Šikonja, "Overcoming the myopia of inductive learning algorithms with RELIEFF," Applied Intelligence, vol. 7, no. 1, pp. 39-55, 1997.
5. J. Canul-Reich, L. Hall, D. Goldgof, and S. Eschrich, "Iterative feature perturbation method as a gene selector for microarray data," University of South Florida, Department of Computer Science and Engineering, Tech. Rep. ISL-09-01, 2009. [Online]. Available: http://morden.csee.usf.edu/ reports/
6. I. Guyon, J. Weston, S. Barnhill, and V. Vapnik, "Gene selection for cancer classification using support vector machines," Machine learning, vol. 46, no. 1, pp. 389-422, 2002.
7. L. Chen, D. Goldgof, L. Hall, and S. Eschrich, "Noise-based feature perturbation as a selection method for microarray data," Lecture Notes in Computer Science, vol. 4463, p. 237, 2007.
8. B. Schölkopf, C. Burges, and V. Vapnik, "Extracting support data for a given task," in Knowledge Discovery and Data Mining, 1995, pp. 252-257. [Online]. Available: http://www.aaai.org/Papers/KDD/1995/KDD95-030.pdf
9. I. H. Witten and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann Pub, 2005.
10. J. Canul-Reich, L. Hall, D. Goldgof, and S. Eschrich, "Feature selection for microarray data by auc analysis," in 2008 IEEE International Conference on Systems, Man, and Cybernetics, Singapore, October 12-15, Proceedings, 2008, pp. 768-773.
11. DADiSP/SE 2002: The ultimate engineering spreadsheet, version 6.0. [Online]. Available: http://www.dadisp.com
12. D. Pyle, Data preparation for data mining. Morgan Kaufmann Pub, 1999.
13. 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," Proceedings of the National Academy of Sciences, vol. 96, no. 12, pp. 6745-6750, 1999.
14. T. Golub, D. Slonim, P. Tamayo, C. Huard, M. Gaasenbeek, J. Mesirov, H. Coller, M. Loh, J. Downing, M. Caligiuri et al., "Molecular classification of cancer: class discovery and class prediction by gene expression monitoring," Science, vol. 286, no. 5439, p. 531, 1999.
15. S. Eschrich, I. Yang, G. Bloom, K. Kwong, D. Boulware, A. Cantor, D. Coppola, M. Kruhoffer, L. Aaltonen, T. Orntoft et al., "Molecular staging for survival prediction of colorectal cancer patients," Journal of Clinical Oncology, vol. 23, no. 15, p. 3526, 2005.
16. K. Shedden, J. Taylor, S. Enkemann, M. Tsao, T. Yeatman, W. Gerald, S. Eschrich, I. Jurisica, T. Giordano, D. Misek et al., "Gene expression-based survival prediction in lung adenocarcinoma: a multi-site, blinded validation study," Nature Medicine, vol. 14, no. 8, pp. 822-827, 2008.
17. C.-C. Chang and C.-J. Lin, LIBSVM: a library for support vector machines, 2001. [Online]. Available: http://www.csie.ntu.edu.tw/~cjlin/libsvm
18. M. Friedman, "The use of ranks to avoid the assumption of normality implicit in the analysis of variance," Journal of the American Statistical Association, vol. 32, no. 200, pp. 675-701, 1937.
19. J. Demšar, "Statistical comparisons of classifiers over multiple data sets," The Journal of Machine Learning Research, vol. 7, pp. 1-30, 2006.