Evolutionary fuzzy cluster analysis with Bayesian validation of gene expression profiles

Issue title: Evolutionary computation in bioinformatics

Article type: Research Article

Authors: Park, Han-Saem | Cho, Sung-Bae

Affiliations: Department of Computer Science, Yonsei University, 134 Shinchon-dong, Sudaemoon-ku, Seoul 120-749, Korea

Note: [] Corresponding author. Tel.: +82 2 2123 2720; Fax: +82 2 365 2579; E-mail: [email protected]

Abstract: Clustering analysis of the gene expression profiles has been used for identifying the functions of unknown genes. Fuzzy clustering method, which is one category of clustering, assigns one sample to multiple clusters as their degrees of membership. It is more appropriate for analyzing gene expression profiles because genes usually belong to multiple functional families. However, general clustering methods have problems that they are sensitive to initialization and can be trapped into local optima. In this paper, we propose an evolutionary fuzzy clustering method with Bayesian validation which uses a genetic algorithm for fuzzy clustering process of gene expression profiles and Bayesian validation method for the fitness evaluation process. We have conducted in-depth experiments to verify the usefulness of the proposed method with well-known gene expression profiles of SRBCT and Saccharomyces.

Keywords: Evolutionary clustering, fuzzy clustering, Bayesian validation, gene expression profiles

Journal: Journal of Intelligent & Fuzzy Systems, vol. 18, no. 6, pp. 543-559, 2007