Bi-clustering of gene expression microarray using coarse grained Parallel Genetic Algorithm(CgPGA) with migration

Bi-clustering of gene expression microarray data deals with creating a sub-matrix that shows a high similarity across both genes and conditions. Bi-clustering aims at identifying several bi-clusters that reveal potential local patterns from a microarray matrix. In this paper, evolutionary algorithm is used to find bi-clusters of large size which have mean squared residue less than a given threshold, δ. Attention is also given to find bi-clusters with minimum overlapping among themselves by assigning weights to the elements of microarray matrix. Initially, Genetic Algorithm (GA) is implemented to derive bi-clusters from microarray matrix. From numerical simulations, it is observed that GA took too much time to converge so as to meet the stopping criteria. To further improve the performance of GA, Parallel GA (PGA) is implemented with an objective, so as to efficiently handle the problem of slow convergence encountered in traditional GA. A framework of Coarse grained Parallel Genetic Algorithm (CgPGA) for bi-clustering is implemented in this paper. The results obtained from CgPGA are quite encouraging as CgPGA took very less time to meet the stopping criteria. The bi-clusters derived by CgPGA are larger in size, which is one of the primary objective of bi-clustering problem. The experiment was performed on microarray dataset i.e. yeast Saccharomyces cerevisiae cell cycle.