New metaheuristics approaches for biclustering of gene expression data

Biclustering or simultaneous clustering of both genes and conditions have generated considerable interest over the past few decades, particularly related to the analysis of high-dimensional gene expression data in information retrieval, knowledge discovery, and data mining [1]. Given a gene expression data matrix, a bicluster is a submatrix of genes and conditions that exhibits a high correlation of expression activity across both rows and columns. The problem of locating the most significant bicluster has been shown to be NP-complete. Therefore, given the inner “intractability” of the problem from a computational point of view, to efficiently find good solutions in a reasonable running times we have designed and implemented several different metaheuristics based on a GRASP framework.