A novel evolutionary algorithm for bi-clustering of gene expression data based on the Order Preserving Sub-Matrix (OPSM) constraint

Biclustering is a popular method which can reveal unknown genetic pathways. However, even though many algorithms have been suggested, no overwhelming algorithm has been suggested, due to its significant search space, until now. In this respect, several evolutionary algorithms tried to address this problem utilizing the powerful search capability of Evolutionary Computation (EC). However, most algorithms focused on exploiting the Mean Square Residue (MSR) measure which was proposed by Cheng and Church. The Order Preserving Sub-Matrix (OPSM) constraint was rarely considered even though it promises more biologically relevant biclusters than the MSR measure. The goal of this paper is to design an EC algorithm which ensures biologically significant biclusters by using the OPSM constraint and better biclusters than the original OPSM algorithm. We designed a novel encoding method and evolutionary operators suitable for the OPSM constraint. To efficiently explore the search space, we modulized our evolutionary algorithm and applied the co-evolution concept. Through a set of experiments, it was confirmed that our algorithm outperformed a representative EC biclustering algorithm based on CC and the original OPSM algorithm.