Clustering gene expression data with memetic algorithms based on minimum spanning trees

With the invention of microarray technology, researchers are capable of measuring the expression levels of ten thousands of genes in parallel at various time points of the biological process. During the investigation of gene regulatory networks and general cellular mechanisms, biologists are attempting to group genes based on the time-depending pattern of the obtained expression levels. In this paper, we propose a new memetic algorithm - a genetic algorithm combined with local search-based on a tree representation of the data - a minimum spanning tree minus; for clustering gene expression data. The combination of both concepts is shown to find near-optimal solutions quickly. Due to the minimum spanning tree representation of the data, our algorithm is capable of finding clusters of different shapes. We show that our approach is superior in solution quality compared to classical clustering methods.