New Entropy-Based Method for Gene Selection

References

• W. Pan, “A comparative review of statistical methods for discovering differentially expressed genes in replicated microarray experiments,” Bioinformatics, Vol. 8, No. 4, pp. 546–54, 2002.
   Google Scholar
• O. Troyanskaya, M. Garber, P. Brown, D. Botstein, and R. Altman, “Nonparametric methods for identifying differentially expressed genes in microarray data,” Bioinformatics, Vol. 18, No. 11, pp. 1454–61, 2002.
   Google Scholar
• W. Pan, J. Lin, and C. Le, “How many replicates of arrays are required to detect gene expression changes in microarray experiments? A mixture model approach,” Genome biol. Vol. 3 No. 5, pp. 0022.1–22.10, 2002.
   Google Scholar
• J. Li, H. Liu, J. Downing, A. Yeoh, and L. Wong, “Simple rules underlying gene expression profiles of more than six subtypes of acute lymphoblastic leukemia (ALL) patients,” Bioinformatics, Vol. 19, No. 1, pp. 71–8, 2003.
   Google Scholar
• W. Li, and Y. Yang, “Zipf’s law in importance of genes for cancer classification using microarray data,” Theor Biol , 219, No. 4, pp. 539–51, 2002.
   Google Scholar
• O. Alter, P. Brown, and D. Botstein, “Singular value decomposition for genome-wide expression data processing and modeling,” Proc. Natl. Acad. Sci. USA, Vol. 97, No. 18, pp. 10101–06, 2000.
   Google Scholar
• M. Blazadonakis, M. Zervakis, M. Kounelakis, E. Biganzoli, and N. Lama, “Support Vector Machines and Neural Networks as Marker Selectors for Cancer Gene Analysis” in Proceeding of 3rd International IEEE Conference on Intelligent Systems, 2006, pp. 626–30.
   Google Scholar
• Y. Liu, “Cancer Identification Based on DNA Microarray Data,” LNAI 4819, pp. 153–161, 2007.
   Google Scholar
• S. Li, and X. Wu, and M. Tan, “Gene selection using hybrid particle swarm optimization and genetic algorithm,” Soft Computing. 12:1039–48, 2008.
   Google Scholar
• L. Yu, and H. Liu, “Redundancy based feature selection for microarray data” in Proceeding off the Tenth ACM SIGKDD conferences on Knowledge Discovery and Data Minin, 2004, Pages 737–42.ACM.
   Google Scholar
• R. Cai, Z. Hao, X. Yang, and W. Wen, “An efficient gene selection algorithm based on mutual information,” Neurocomputing journal, Vol. 72, pp. 991–9, 2009.
   Google Scholar
• X. Liu, A. Krishnan, and A. Mondry, “An Entropy-based gene selection method for cancer classification using microarray data,” BMC Bioinformatics, Vol.6:76, 2005.
   Google Scholar
• C. Ding, and H. Peng, “Minimum Redundancy feature selection from microarray gene expression data,” Computational Systems Bioinformatics, 2003.
   Google Scholar
• Y. Guo, T. Hastie, and R. Tibshirani, “Regularized linear discriminant analysis and its application in microarrays,” Biostatistics, Vol. 8, No. 1, pp. 86–100, 2007.
   Google Scholar
• I. Guyon, J. Weston, S. Barnhill, and V. Vapnik, “Gene selection for cancer classification using support vector machines,” Machine Learning, 46(1–3):389–422, 2002.
   Google Scholar
• I. Frank, and J. Friedman, “A statistical view of some chemometric regression tools,” Technometrics 35, 109–48, 1993.
   Google Scholar
• M. Park, and T. Hastie, “Penalized logistic regression for detecting gene interactions,” Biostatistics, Vol.9, pp. 30–50, 2007.
   Google Scholar
• L. van’t Veer, and H. Dai, M. Van de Vijver, and Y. He, et al., “Gene expression profiling predicts clinical outcome of breast cancer” Letters to Nature, 415, 2002, pp. 530–36, 2002.
   Google Scholar
• M. Blazadonkis, A. Peroglou, and M. Zervakis, “Using a single neuron as a marker selector-A breast cancer case study” in Proceeding of the 29th Annual international conference of the IEEE Embs, Lyon, France, 2007, pp. 4219–22.
   Google Scholar
• R. Shen, D. Ghosh, A. Chinnaiyan, and M. Zhaoling, “Eigengene-based linear discriminant model for tumor classification using gene expression microarray data,” Bioinformatics, vol. 22, pp. 2635–42, 2006.
   Google Scholar
• M. Takahashi, S. Shichijo, M. Noguchi, M. Hirohata, and K. Itoh, “Identification of MAGE-1 and MAGE-4 proteins in spermatogonia and primary spermatocytes of testis,” Cancer Res. 15, 55(16), pp.3478–82, 1995.
   Google Scholar
• R. Boidot, F. Vegran, D. Jacob, S. Chevrier, N. Gangneux, J. Taboureau, C. Oudin, V. Rainville, L. Mercier, and S. Lizard-Nacol, “The expression of BIRC5 is correlated with loss of specific chromosomal regions in breast carcinomas,” Genes Chromosomes Cancer. 47(4), pp. 299–308, 2008.
   Google Scholar
• D. Stav, I. BarI, and J. Sandbank, “Usefulness of CDK5RAP3, CCNB2, and RAGE genes for the diagnosis of lung adenocarcinoma,” Int J Biol Markers., 22(2):pp. 108–13, 2007.
   Google Scholar
• S. Sotiriou, S. Neo, L. McShane, E. Korn, P. Long , A. Jazaeri, P. Martiat, S. Fox, A. Harris, and E. Liu, “Breast cancer classification and prognosis based on gene expression profiles from a population-based study,” PNAS, Vol. 100, No.18, pp. 10393–98, 2003.
   Google Scholar
• T. Golub, D. Slonim, P. Tamayo, C. Huard, M. Gaasenbeek, J. Mesirov, H. Coller, M. Loh, J. Downing, M. Caligiuri, C. BloomÞeld, and E. Lander, “Molecular Classification of Cancer: Class Discovery and Class Prediction by Gene Expression Monitoring,” Science, Vol. 286, pp. 531–37, 1999.
   Google Scholar
• W. Xiong, Z. Cai, and J. Ma, “A DSRPCL-SVM approach to informative gene analysis,” Geno, prot, Bioinfo. Vol6, No.2, 2008.
   Google Scholar
• S. Li, X. Wu, and X. Hu, “Gene selection using genetic algorithm and support vector machines,” Soft computing, Vol. 12, pp. 693–98, 2008.
   Google Scholar
• U. Alon, N. Baraki, 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. Natl. Acad. Sci. Vol. 96, pp. 6745–50, 1999.
   Google Scholar
• G. Gordon, R. Jensen, L. Hsiao, S. Gullans, J. Blumenstock, S. Ramaswamy, W. Richards, D. Sugarbaker, and R. Bueno, “Translation of Microarray Data into Clinically Relevant Cancer Diagnostic Tests Using Gene Expression Ratios in Lung Cancer and Mesothelioma,” Cancer research, Vol. 62, 4963–67, 2002.
   Google Scholar