Data mining of gene expression data by fuzzy and hybrid fuzzy methods.
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 in which 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.
Department of Computer Science, Loughborough University, Loughborough LE11 3TU, UK. g.schaefer@lboro.ac.uk
Gerald
G
Schaefer
Tomoharu
T
Nakashima
D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D057225:Data Mining; D030541:Databases, Genetic; D017143:Fuzzy Logic; D015870:Gene Expression; D020869:Gene Expression Profiling; D020411:Oligonucleotide Array Sequence Analysis; D015203:Reproducibility of Results