Triclustering on temporary microarray data using the TriGen algorithm

Abstract

The analysis of microarray data is a computational challenge due to the characteristics of these data. Clustering techniques are widely applied to create groups of genes that exhibit a similar behavior under the conditions tested. Biclustering emerges as an improvement of classical clustering since it relaxes the constraints for grouping allowing genes to be evaluated only under a subset of the conditions and not under all of them. However, this technique is not appropriate for the analysis of temporal microarray data in which the genes are evaluated under certain conditions at several time points. In this paper, we propose the TriGen algorithm, which finds triclusters that take into account the experimental conditions and the time points, using evolutionary computation, in particular genetic algorithms, enabling the evaluation of the gene's behavior under subsets of conditions and of time points.