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Rough Set Theory Used for Gene Selection Based on Microarray Data

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Rough Set Theory is an effective mathematical tool for selecting informative genes from microarray data. The paper discusses using Rough Set Theory for gene selection, which is the process of choosing the most informative genes from a whole gene database. It describes the basics of Rough Set Theory and proposes using a Quick-Reduct based Genetic Algorithm for Rough Set attribute reduction to select informative genes from microarray data.

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International Journal of Computing Algorithm, Vol 1(2), December 2012 ISSN(Print):2278-2397 Website: www.ijcoa.com Gene Selection Based on Rough Set K.Anitha Department of Mathematics, S.A.Engineering College, Chennai, India E-mail: subramanianitha@yahoo.com Abstract Gene selection is a main procedure of discriminate analysis of microarray data which is the process of selecting most informative genes from the whole gene data base. This paper approaches a method for selecting informative genes by using Rough Set Theory. Rough Set Theory is an effective mathematical tool for selecting informative genes. This paper describes basics of Rough Set Theory and Rough Set attribute reduction by Quick –Reduct based Genetic Algorithm

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