| Abstract | Motivation: In the interpretation of gene expression data from a group of microarray experiments that include samples from either different patients or conditions, special consideration must be given to the pleiotropic and epistatic roles of genes, as observed in the variation of gene co-expression patterns. Crisp clustering methods assign each gene to one cluster, thereby omitting information about the multiple roles of genes.Results: Here we present the application of a local search heuristic, Fuzzy J-Means, embedded into the Variable Neighborhood Search metaheuristic for the clustering of microarray gene expression data. We show that for all data sets studied this algorithm outperforms the standard Fuzzy C-Means heuristic. Different methods for the utilization of cluster membership information in determining gene co-regulation are presented. The clustering and data analyses were performed on simulated data sets as well as experimental cDNA microarray data for breast cancer and human blood from the Stanford Microarray Database. |
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