Parallel biclustering detection using strength Pareto front evolutionary algorithm

Abstract

Biclustering has become a popular technique to analyse gene expression datasets and extract valuable information by clustering rows and columns of a dataset simultaneously. Using a good merit function together with a suitable local search can lead to the detection of interesting biclusters. In this paper, a multi-objective evolutionary algorithm with local search is proposed to search for multiple biclusters concurrently in a single run of the evolutionary algorithm. We call our method PBD-SPEA2 (Parallel Biclustering Detection using Strength Pareto front Evolutionary Algorithm 2). In our algorithm, a new dynamic encoding scheme is used to encode multiple biclusters in each individual. Our multi-objective function consists of three objectives that simultaneously optimizes the homogeneity of the elements in the bicluster, the size of the bicluster, and the variance of the column in the bicluster with respect to the entire dataset. Crossover is done by selecting and combining the best biclusters among the encoded biclusters from both parents through a strategy of exploration and exploitation. Finally, a sequential selection procedure is used to select the final set of biclusters from individuals that constitute the Pareto front. Experimental results are presented to compare the performance and biological enrichment of detected biclusters with several existing algorithms.