A novel bicluster method with mixed optimal search algorithm

Abstract

A novel biclustering algorithm is proposed in this paper, which can be used to cluster gene expression data. One of the contributions of this paper is a novel and effective residue function of the biclustering algorithm. Furthermore, a new optimal algorithm which is mixed by the parallel genetic algorithm and the particle swarm optimal algorithm is firstly used to the algorithm of the biclustering for gene expression data. This method can avoid local convergence in the optimal algorithm mostly. Two tests were used in the algorithm. One is the simulation data test. The other is the real data test. Among of them, the Yeast Saccharomyces cerevisiae cell cycle gene expression profiles from the Spellman’s data to bicluster are the real data and used to test the performance of new algorithm. And we compared our algorithm with traditional genetic algorithm in biclustering. The results reveal that novel proposed algorithms could discover the interesting patterns in the gene expression profiles.