A Meta-Heuristic Approach for Discovering Local Pattern in Gene Expression Data

Abstract

The biclustering of gene expression data is an important technology for biologists. Biclustering is used to discover groups of genes that are co-expressed over a subset of conditions in microarray gene expression data. There has been a lot of research in biclustering involving statistical and graph-theoretic approach. This entails that an exhaustive search of the space of solutions may be infeasible and problem is NP-hard. Hence metaheuristic approach, the Harmony Search (HS) method finds the coherent biclusters in large expression data. The experiment results are demonstrated on two benchmark datasets. Furthermore, we also test the ability of HS method to locate biologically verifiable biclusters within an annotated set of genes.