EMD: an ensemble algorithm for discovering regulatory motifs in DNA sequences

Background: Understanding gene regulatory networks has become one of the central research problems in bioinformatics. More than thirty algorithms have been proposed to identify DNA regulatory sites during the past thirty years. However, the prediction accuracy of these algorithms is still quite low. Ensemble algorithms have emerged as an effective strategy in bioinformatics for improving the prediction accuracy by exploiting the synergetic prediction capability of multiple algorithms. Results: We proposed a novel clustering-based ensemble algorithm named EMD for de novo motif discovery by combining multiple predictions from multiple runs of one or more base component algorithms. The ensemble approach is applied to the motif discovery problem for the first time. The algorithm is tested on a benchmark dataset generated from E. coli RegulonDB. The EMD algorithm has achieved 22.4% improvement in terms of the nucleotide level prediction accuracy over the best stand-alone component algorith...

• Type: Journal
• Year: 2006
• Where: BMCBI
• Authors: Jianjun Hu, Yifeng D. Yang, Daisuke Kihara
• Keywords: Algorithm, BMCBI 2006, Motif Discovery, Prediction