Abstract
Most biclustering algorithms for microarrays data analysis focus on positive correlations of genes. However, recent studies demonstrate that groups of biologically significant genes can show negative correlations as well. So, discovering negatively correlated patterns from microarrays data represents a real need. In this paper, we propose a Memetic Biclustering Algorithm (MBA) which is able to detect negatively correlated biclusters. The performance of the method is evaluated based on two well-known microarray datasets ( Yeast cell cycle and Saccharomyces cerevisiae ), showing that MBA is able to obtain statistically and biologically significant biclusters.
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