An efficient gene selection algorithm based on mutual information

Neurocomputing

Volumn 72, Issue 4-6, 2009, Pages 991-999

  Cai, Ruichu ^a   Hao, Zhifeng ^a   Yang, Xiaowei ^a   Wen, Wen ^a  

^a SOUTH CHINA UNIVERSITY OF TECHNOLOGY   (China)

Author keywords

Gene expression; Gene selection; Microarray data; Mutual information; Parzen window density estimation

Indexed keywords

COMPUTATIONAL COSTS; GENE EXPRESSION DATASETS; GENE SELECTION; GENE SELECTION ALGORITHMS; GENE SETS; INFORMATIVE GENES; MICROARRAY DATA; MUTUAL INFORMATION; PARZEN WINDOW DENSITY ESTIMATION; PRE-PROCESSING; PRUNING STRATEGIES; SELECTION PROCEDURES;

BIOACTIVITY; DISCRIMINANT ANALYSIS; GENE EXPRESSION; GENES; PROBABILITY DENSITY FUNCTION; WINDOWS;

FEATURE EXTRACTION;

ACCURACY; ALGORITHM; ARTICLE; BREAST CANCER; COLON CANCER; COMPUTER ANALYSIS; DISCRIMINANT ANALYSIS; DNA MICROARRAY; ENTROPY; GENE EXPRESSION REGULATION; GENE IDENTIFICATION; GENOME ANALYSIS; LEUKEMIA; MATHEMATICAL COMPUTING; MICROARRAY ANALYSIS; NUCLEOTIDE SEQUENCE; PRIORITY JOURNAL;

EID: 58149465401     PISSN: 09252312     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neucom.2008.04.005     Document Type: Article

References (33)

1. Ambroise C., and McLachlan G.J. Selection bias in gene extraction on the basis of microarray gene-expression data. Proc. Natl. Acad. Sci. USA 99 10 (2002) 6562-6566
2. Berg B.A. Markov Chain Monte Carlo Simulations and Their Statistical Analysis (2004), World Scientific, Singapore
3. Chang C.F., Wai K.M., and Patterton H.G. Calculating the statistical significance of physical clusters of co-regulated genes in the genome: the role of chromatin in domain-wide gene regulation. Nucl. Acids Res. 32 5 (2004) 1798-1807
4. Chow T.W.S., and Huang D. Estimating optimal feature subsets using efficient estimation of high-dimensional mutual information. IEEE Trans. Neural Networks 16 1 (2005) 213-224
5. Cover T.M., and Thomas J.A. Elements of Information Theory (2005), Wiley, New Jersey
6. de Souza R.M.C.R., de Carvalho F.A.T., and Tenorio C.P. Two partitional methods for interval-valued data using mahalanobis distances. Adv. Artif. Intell.-Iberamia 2004 3315 (2004) 454-463
7. Debernardi S., et al. Genome-wide analysis of acute myeloid leukemia with normal karyotype reveals a unique pattern of homeobox gene expression distinct from those with translocation-mediated fusion events. Genes Chromosomes Cancer 37 2 (2003) 149-158
8. Ding Y.Y., and Wilkins D. Improving the performance of SVM-RFE to select genes in microarray data. BMC Bioinformatics 7 (2006)
9. Dudoit S., Fridlyand J., and Speed T.P. Comparison of discrimination methods for the classification of tumors using gene expression data. J. Am. Stat. Assoc. 97 457 (2002) 77-87
10. Fleuret F. Fast binary feature selection with conditional mutual information. J. Mach. Learning Res. 5 (2004) 1531-1555
11. Furlanello C., et al. Entropy-based gene ranking without selection bias for the predictive classification of microarray data. BMC Bioinformatics 4 (2003)
12. Golub T.R., et al. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286 5439 (1999) 531-537
13. Guyon I., and Elisseeff A. An introduction to variable and feature selection. J. Mach. Learning Res. 3 (2003) 1157-1182
14. Guyon I., et al. Gene selection for cancer classification using support vector machines. Mach. Learning 46 1-3 (2002) 389-422
15. Han J., and Kamber M. Data Mining: Concepts and Techniques (2000), Morgan Kaufmann, San Francisco, CA
16. Kelly L., Clark J., and Gilliland D.G. Comprehensive genotypic analysis of leukemia: clinical and therapeutic implications. Curr. Opin. Oncol. 14 1 (2002) 10-18
17. Kohavi R., and John G.H. Wrappers for feature subset selection. Artif. Intell. 97 1-2 (1997) 273-324
18. Kwak N., and Choi C.H. Input feature selection for classification problems. IEEE Trans. Neural Networks 13 1 (2002) 143-159
19. Kwak N., and Choi C.H. Input feature selection by mutual information based on Parzen window. IEEE Trans. Pattern Anal. Mach. Intell. 24 12 (2002) 1667-1671
20. Lai C., Reinders M.J.T., and Wessels L. Random subspace method for multivariate feature selection. Pattern Recogn. Lett. 27 10 (2006) 1067-1076
21. T.W. LeBien, R.T. McCormack, The common acute lymphoblastic leukemia antigen (CD10)-emancipation from a functional enigma, 1989, pp. 625-635.
22. Li L.B., et al. A robust hybrid between genetic algorithm and support vector machine for extracting an optimal feature gene subset. Genomics 85 1 (2005) 16-23
23. Nutt C.L., et al. Gene expression-based classification of malignant gliomas correlates better with survival than histological classification. Cancer Res. 63 7 (2003) 1602-1607
24. I. Priness, O. Maimon, I. Ben-Gal, Evaluation of gene-expression clustering via mutual information distance measure, 2007, p. 111.
25. Raaijmakers M. ATP-binding-cassette transporters in hematopoietic stem cells and their utility as therapeutical targets in acute and chronic myeloid leukemia. Leukemia 21 10 (2007) 2094-2102
26. Reiner A., Yekutieli D., and Benjamini Y. Identifying differentially expressed genes using false discovery rate controlling procedures. Bioinformatics 19 3 (2003) 368-375
27. Ruichu Cai, Zhifeng Hao, W. Wen, A novel gene ranking algorithm based on random subspace method, in: Neural Networks, 2007, IJCNN 2007, International Joint Conference on, 2007, Orlando, FL.
28. P. Secchiero, et al., Potential pathogenetic implications of cyclooxygenase-2 overexpression in B chronic lymphoid leukemia cells, 2005, pp. 1599-1607.
29. Tang E.K., Suganthan P.N., and Yao X. Gene selection algorithms for microarray data based on least squares support vector machine. BMC Bioinformatics 7 (2006)
30. N. Tishby, F.C. Pereira, W. Bialek, The information bottleneck method, in: The 37th Annual Allerton Conference on Communication, Control and Computing, 1999.
31. Wong E.T.L., et al. Changes in chromatin organization at the neutrophil elastase locus associated with myeloid cell differentiation. Blood 94 11 (1999) 3730
32. Yang J.J., and Yang M.C.K. An improved procedure for gene selection from microarray experiments using false discovery rate criterion. BMC Bioinformatics 7 (2006)
33. Zhou X., and Mao K.Z. LS bound based gene selection for DNA microarray data. Bioinformatics 21 8 (2005) 1559-1564