Selection of Discriminative Genes from Microarray Data

This chapter establishes the effectiveness of the fuzzy equivalence partition matrix (FEPM) for the problem of gene selection from microarray data and compares its performance with some existing methods on a set of microarray gene expression data sets. It first briefly introduces various evaluation criteria used for computing both the relevance and redundancy of the genes. To measure both gene-class relevance and gene-gene redundancy using information theoretic measures such as entropy, mutual information, and f-information measures, the true density functions of continuous-valued genes have to be approximated. The chapter presents several approaches to approximate the true probability density function for continuous-valued gene expression data. It then describes the problem of gene selection from microarray data sets using information theoretic approaches. Finally, the chapter reports a few case studies and a comparison among different approximation methods. fuzzy logic; numerical analysis; probability density function

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