GOGA: GO-driven Genetic Algorithm-based fuzzy clustering of gene expression data

摘要

In this article, a Genetic Algorithm-based fuzzy clustering method (GOGA), which incorporates Gene Ontology (GO) knowledge in the clustering process, has been proposed for clustering microarray gene expression data. The proposed technique combines the expression-based and GO-based gene dissimilarity measures for this purpose. Both expression-based and GO-based clustering objectives have been incorporated in the fitness function. The performance of the proposed technique has been demonstrated on real-life Yeast Cell Cycle data set. KEGG pathway based enrichment studies have been conducted for validating the clustering results.