A multi-objective genetic algorithm with side effect machines for motif discovery

Understanding the machinery of gene regulation to control gene expression has been one of the main focuses of bioinformaticians for years. We use a multi-objective genetic algorithm to evolve a specialized version of side effect machines for degenerate motif discovery. We compare some suggested objectives for the motifs they find and report preliminary results on a synthetic dataset and some biological benchmarking suites. We obtain results that are comparable to the best motif discovery algorithms available. We conclude that since our approach finds multiple degenerate motifs in one run it could benefit from using some post processing technique to cluster the output, allowing it to be tested on larger datasets and to obtain more accurate performance feedback.