Biclustering of gene expression data using Particle Swarm Optimization integrated with pattern-driven local search

Biclustering is of great significance in the analysis of gene expression data and is proven to be a NP-hard problem. Among the existing intelligent optimization algorithms used in the gene expression data analysis, most concentrate on the global search ability but ignore the inherent trajectory information of gene expression data, so the search efficiency is low. In this paper, a pattern-driven local search operator is incorporated in the binary Particle Swarm Optimization (PSO) algorithm in order to improve the search efficiency. Experiments show that our approach is valid.