A two-stage gene selection algorithm by combining ReliefF and mRMR

Proceedings of the 7th IEEE International Conference on Bioinformatics and Bioengineering, BIBE

Volumn , Issue , 2007, Pages 164-171

  Zhang, Yi ^a   Ding, Chris ^b   Li, Tao ^a  

^a FLORIDA INTERNATIONAL UNIVERSITY   (United States)

^b University of Texas at Arlington   (United States)

Author keywords

Gene selection algorithms; mRMR; mRMR reliefF; ReliefF

Indexed keywords

BIOACTIVITY; BIOINFORMATICS; GENE EXPRESSION; GENES;

BIOLOGICAL SAMPLES; CANDIDATE GENES; CANDIDATE SETS; DATA SETS; FEATURE SELECTION METHODS; FEATURE SELECTIONS; GENE EXPRESSION DATUMS; GENE SELECTION ALGORITHMS; GENE SUBSETS; MRMR; MRMR-RELIEFF; NAIVE BAYES; RELIEFF; SELECTION ALGORITHMS;

FEATURE EXTRACTION;

EID: 47649103157     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/BIBE.2007.4375560     Document Type: Conference Paper

References (26)

1. A. A. Alizadeh, M. B. Eisen, R. E. David, C. Ma, I. S. Lossos, A. R. osenwald, H. C. Boldrick, H. Sabet, T. Tran, X. Yu, J. I. Powell, L. Yang, G. E. Martu, T. Moore, J. Hudson, L. Lu, D. B. Lewis, R. Tibshirani, G. Sherlock, W. C. Chan, T. C. Greiner, D. D. Weisenburger, G. P. Armitage, R. Warnke, R. Levy, W. Wilson, M. R. Grever, J. C. Byrd, D. Botsten, P. O. Brown, and L. M. Staudt. Distinct types of diffuse large B-cell lymphoma identified by gene expression pr ofiling. Nature, 403:503-511, 2000.
2. http://www.stjuderesearch.org/data/ALL1/.
3. http://www.ics.uci.edu/mlearn/MLSummary.html.
4. M. Chee, R. Yang, E. Hubbell, A. Berno, X. Huang, D. Stern, J. Winkler, D. Lockhart, M. Morris, and S. Fodor. Accessing genetic information with high density DNA arrays. Science, 274:610-614, 1996.
5. T. Cover. The best two independent measurements are not the two best. IEEE Trans. Systems, and Cybernetics, 4:116-117, 1974.
6. C. Ding, H. Peng. Minimum redundancy feature selection from microarray gene expression data. CSB 03, 2003.
7. S. Dudoit, J. Fridlyand, and T. P. Speed. Comparison of discrimination methods for the classification of tumors using gene expression data. Journal of the American Statistical Association, 97(457):77-87, 2002.
8. S. Fodor, J. Read, M. Pirrung, L. Stryer, A. Lu, and D. Solas. Light-directed, spatioally addressable parallel chemical synthesis. Science, 251:767-783, 1991.
9. http://www.columbia.edu/~xy56/project.htm.
10. I. Hedenfalk, D. Duggan, Y. C. Y, M. Radmacher, M. Bittner, R. Simon, P. Meltzer, B. Gusterson, M. Esteller, O. P. Kallioniemi, B. W. B, A. Borg, and J. Trent. Gene-expression profiles in hereditary breast cancer. The New England Journal of Medcine, 344(8):539-548, 2001.
11. R. Kohavi and G. H. John. Wrappers for feature subset selection. Artificial Intelligence, 97(1-2):273-324, 1997.
12. P. Langley. Selection of relevant features in machine learning. In AAAI Fall Symposium on Relevance, pages 140-144, 1994.
13. T. Li, C. Zhang, and M. Ogihara. A comparative study of feature selection and multiclass classification methods for tissue classification based on gene expression. Bioinformatics, 20(15):2429-2437, 2004.
14. http://genome-www.stanford.edu/lymphoma.
15. R. S. Marko and K. Igor. Theoretical and empirical analysis of relieff and rrelieff. Machine Learning Journal, 53:23-69, 2003.
16. http://research.dfci.harvard.edu/korsmeyer/MLL.htm.
17. http://genome-www.stanford.edu/nci60/.
18. C. Ooi and P. Tan. Genetic algorithms applied to multi-class prediction for the analysis of gene expression data. Bioinformatics, 19(1):37-44, 2003.
19. H. Peng, F. Long, and C. Ding. Feature selection based on mutual information: Criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans. Pattern Analysis and Machine Intelligence, 27, 2005.
20. S. Ramaswamy, P. Tamayo, R. Rifkin, S. Mukherjee, C.-H. Yeang, M. Angelo, C. Ladd, M. Reich, E. Latulippe, J. P. Mesirov, T. Poggio, W. Gerald, M. Loda, E. S. Lander, and T. R.Golub. Multiclass cancer diagnosis using tumor gene expression signatures. 98(26):15149-15154, 2001.
21. D. T. Ross, U. Scherf, M. B. Eisen, C. M. Perou, C. Rees, P. Spellmand, V. Iyer, S. S. Jeffrey, M. Van de Rijn, M. Waltham, A. Pergamenschikov, J. C. F. Lee, D. Lashkari, D. Shalon, T. G. Myers, J. N. Weinstein, D. Botstein, and M. P. O. Brown. Systematic variation in gene expression patterns in human cancer cell lines. Nature Genetics, 24:227-235, 2000.
22. Y. Su, T. M. Murali, V. Pavlovic, and S. Kasif. Rankgene: Identification of diagnostic genes based on expression data. Bioinformatics, 2003. The program can be downloaded from http://genomics10.bu.edu/yangsu/rankgene/.
23. Y. Sun and J. Li. Iterative RELIEF for feature weighting. In Proceedings of the 23rd International Conference on Machine Learning, 2006.
24. E. P. Xing, M. I. Jordan, and R. M. Karp. Feature selection for high-dimensional genomic microarray data. In Proc. 18th International Conf. on Machine Learning, pages 601-608. Morgan Kaufmann, San Francisco, CA, 2001.
25. E.-J. Yeoh, M. E. Ross, S. A. Shurtleff, W. K. williams, D. Patel, R. Mahrouz, F. G. Behm, S. C. Raimondi, M. V. Relling, A. Patel, C. Cheng, D. Campana, D. Wilkins, X. Zhou, J. Li, H. Liu, C.-H. Pui, W. E. Evans, C. Naeve, L. Wong, and J. R. Downing. Classification, subtype discovery, and prediction of outcome in pediatric lymphoblastic leukemia by gene expression profiling. Cancer Cell, 1(2): 133-143, 2002.
26. G. Zheng. Statistical analysis of biomedical data with emphasis on data integration. Dissertation in Florida International University, 2006.