Dynamic Biclustering of Microarray Data with MOPSO

Abstract

Most of optimization problems have more than one objective function. As a heuristic search technique, particle swarm optimization (PSO) simulates the movements of a flock of birds which aim to find food. The success of PSO has motivated researchers to extend the use of population-based technique to multi-objective optimization. Rapid development of the DNA microarray technology make it very possible to study the transcriptional response of a complete genome to different experimental conditions. Biclustering technique has successfully used to analysis those gene expression data. During biclustering several objectives in conflict with each other have to be optimized simultaneously, so multi-objective modeling is suitable for solving biclustering problem. Based on dynamic population, this paper proposes a novel dynamic multi-objective particle swarm optimization biclustering (DMOPSOB) algorithm to mine coherent patterns from microarray data. Experimental results on real datasets show that our approach can effectively find significant biclusters of high quality.