Biclustering analysis of gene expression data using multi-objective evolutionary algorithms

Abstract

Clustering is an unsupervised learning technique that groups data into clusters using the entire conditions. However, sometimes, data is similar only under a subset of conditions. Biclustering allows clustering of rows and columns of a dataset simultaneously. It extracts more accurate information from sparse datasets. In recent years, biclustering has found many useful applications in different fields and many biclustering algorithms have been proposed. Using both row and column information of data, biclustering requires the optimization of two conflicting objectives. In this study, a new multi-objective evolutionary biclustering framework using SPEA2 is proposed. A heuristic local search based on the gene and condition deletion and addition is added into SPEA2 and the best bicluster is selected using a new quantitative measure that considers both its coherence and size. The performance of our algorithm is evaluated using simulated and gene expression data and compared with several well-known biclustering methods. The experimental results demonstrate better performance with respect to the size and MSR of detected biclusters and significant enrichment of detected genes.