Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06) (2006)
Hong Kong, China
Dec. 18, 2006 to Dec. 22, 2006
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICDMW.2006.84
Yuanchen He , Georgia State University, Atlanta, GA
Yuchun Tang , CipherTrust Inc., Alpharetta, GA
Yan-Qing Zhang , Georgia State University, Atlanta, GA
Rajshekhar Sunderraman , Georgia State University, Atlanta, GA
Selecting informative and discriminative genes from huge microarray gene expression data is an important and challenging bioinformatics research topic. This paper proposes a fuzzy-granular method for the gene selection task. Firstly, genes are grouped into different function granules with the Fuzzy C-Means algorithm (FCM). And then informative genes in each cluster are selected with the Signal to Noise metric (S2N). With fuzzy granulation, information loss in the process of gene selection is decreased. As a result, more informative genes for cancer classification are selected and more accurate classifiers can be modeled. The simulation results on two publicly available microarray expression datasets show that the proposed method is more accurate than traditional algorithms for cancer classification. And hence we expect that genes being selected can be more helpful for further biological studies.
Y. He, R. Sunderraman, Y. Zhang and Y. Tang, "Fuzzy-Granular Gene Selection from Microarray Expression Data," Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06)(ICDMW), Hong Kong, China, 2006, pp. 153-157.