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Evolutionary computation for biclustering of gene expression

Microarray data are widely used in genomic research due to the enormous potential in gene expression profiling, facilitating the prognosis and the discovering of subtypes of diseases. The gene expression data are organized in matrices, where rows represent genes and columns represent experimental conditions. Each element in the matrix refers to the expression level of a particular gene under a specific condition. Biclustering of gene expression was first proposed by Cheng and Church [2], where the residue of an element in the bicluster and the mean squared residue of a submatrix were introduced. In addition, they adjusted that measure to reject trivial biclusters by means of the row variance. For details about these measures, we refer the reader to [2]. In this work, we address the biclustering problem with evolutionary computation (EC), which has been proven to have an excellent performance on highly complex optimization problems. In this paper we follow the model of biclustering proposed by [2]. As the algorithm, called SEBI, partially uses the squared mean residue, the results have been compared to those of Cheng and Church. In expression data analysis, the most important goal may not be finding the maximum bicluster or even finding a bicluster cover for the data matrix. It is more interesting to find a set of genes showing strikingly similar up–regulation and down– regulation under a set of conditions. A low mean squared residue score plus a large variation from the constant may be a good criterion for identifying these genes and conditions. Therefore, our goal is to find biclusters of maximum size, with mean squared residue lower than a given δ, with a relatively high row variance, and with a low level of overlapping among biclusters.