Automatic generation of biclusters from gene expression data using multi-objective simulated annealing approach

Abstract

The invention of microarray technology aids in the successful monitoring of the gene expression patterns. Biclustering is a method in which a number of co-regulated genes are identified over subset of conditions. Our aim is to detect all the non trivial biclusters having low mean squared residue(MSR) and high row variance. In this paper, we have proposed a multi-objective simulated annealing based solution framework to solve the biclustering problem from gene expression data sets. Two objective functions MSR and row-variance capturing two important properties of biclusters are optimized in parallel using the search capability of multi-objective simulated annealing based optimization technique, AMOSA. A new encoding strategy and several different search operators are defined for fast convergence of the algorithm. We have done experiment on two real-life data sets and obtained results are quantified by using several cluster validity indices. We have compared our obtained results with some state-of-the-art biclustering techniques.