Detecting the Maximum Similarity Bi-Clusters of Gene Expression Data with Evolutionary Computation

Bi-clustering of the gene expression data has become a special study in bioinformatics in recent years. In a gene expression data matrix a bi-cluster is a sub-matrix of genes and conditions that exhibits a high correlation of expression activity across both rows and columns. The difficulty of finding significant bi-clusters in gene expression data grows exponentially with the size of the dataset. This proposed approach is based on evolutionary algorithm, which goal is to extract maximum similarity bi-clusters. In addition, the algorithm works for a special case, where the bi-clusters are approximately squares. We then extend the algorithm to handle various kinds of other cases. Experimental results show the effectiveness of the proposed approach.