Data mining of gene expression data by fuzzy and hybrid fuzzy methods

Authors:
Gerald Schaefer;Tomoharu Nakashima
Affiliations:
Department of Computer Science, Loughborough University, Loughborough, UK;Department of Computer Science and Intelligent Systems, Osaka Prefecture University, Osaka, Japan
Venue:
IEEE Transactions on Information Technology in Biomedicine
Year:
2010

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Abstract

Microarray studies and gene expression analysis have received tremendous attention over the last few years and provide many promising avenues toward the understanding of fundamental questions in biology and medicine. Data mining of these vasts amount of data is crucial in gaining this understanding. In this paper, we present a fuzzy rule-based classification system that allows for effective analysis of gene expression data. The applied classifier consists of a set of fuzzy if-then rules that enable accurate nonlinear classification of input patterns.We further present a hybrid fuzzy classification scheme inwhich a small number of fuzzy if-then rules are selected through means of a genetic algorithm, leading to a compact classifier for gene expression analysis. Extensive experimental results on various well-known gene expression datasets confirm the efficacy of our approaches.