  Microarray techniques are widely used in Gene expression analysis. These
techniques are based on discovering submatrices of genes that share similar
expression patterns across a set of experimental conditions with coherence
constraint. Actually, these submatrices are called biclusters and the
extraction process is called biclustering. In this paper we present a novel
binary particle swarm optimization model for the gene expression biclustering
problem. Hence, we apply the binary particle swarm optimization algorithm with
a proposed measure, called Discretized Column-based Measure (DCM) as a novel
cost function for evaluating biclusters where biological relevance, MSR and the
size of the bicluster are considered as evaluation metrics for our results.
Results are compared to the existing algorithms and they show the validity of
our proposed approach.
