CEMD: A Cluster-based Ensemble Motif Discovery Tool

Motif discovery is a challenging problem in bioinformatics. It is an essential step towards understanding gene regulation. Although numerous algorithms and tools have been proposed in the literature, the accuracy of motif finding is still low. In this paper, we tackle the motif discovery problem using ensemble methods. A review and classification of current ensemble motif discovery tools is presented. We then propose our Clusterbased Ensemble Motif Discovery Tool (CEMD) which is based on kmedoids clustering of stateofart standalone motif finding tools. We evaluate the performance of CEMD on benchmark datasets and compare the results to both standalone and similar ensemble tools. Experimental results indicate that CEMD has better sensitivity than stateofart standalone tools when dealing with human datasets. CEMD also obtains better values of sensitivity when motifs are implanted in real promoter sequences. As for the comparison of CEMD with ensemble motif discovery tools, results indicate that CEMD achieves better results than MEMEChIP on all evaluation measures. CEMD shows comparable performance to RSAT peakmotifs and MODSIDE.