Bi-Phase Evolutionary Searching for Biclusters in Gene Expression Data

The analysis of gene expression data is useful for detecting the biological information of genes. Biclustering of microarray data has been proposed as a powerful computational tool to discover subsets of genes that exhibit consistent expression patterns along subsets of conditions. In this paper, we propose a novel biclustering algorithm called the bi-phase evolutionary biclustering algorithm. The first phase is for the evolution of rows and columns, and the other is for the evolution of biclusters. The interaction of the two phases ensures a reliable search direction and accelerates the convergence to good solutions. Furthermore, the population is initialized using a conventional hierarchical clustering strategy to discover bicluster seeds. We also developed a seed-based parallel implementation of evolutionary searching to search biclusters more comprehensively. The performance of the proposed algorithm is compared with several popular biclustering algorithms using synthetic datasets and real microarray datasets. The experimental results show that the algorithm demonstrates a significant improvement in discovering biclusters.