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Open Access Research article

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

Jianjun Hu1, Yifeng D Yang2 and Daisuke Kihara1234*

Author Affiliations

1 Department of Computer Science, Purdue University, West Lafayette, IN, 47907, USA

2 Department of Biological Sciences, Purdue University, West Lafayette, IN, 47907, USA

3 Markey Center for Structural Biology, Purdue University, West Lafayette, IN, 47907, USA

4 The Bindley Bioscience Center, Discovery Park, Purdue University, West Lafayette, IN, 47907, USA

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BMC Bioinformatics 2006, 7:342  doi:10.1186/1471-2105-7-342

Published: 13 July 2006

Abstract

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 algorithm. The advantage of the EMD algorithm is more significant for shorter input sequences, but most importantly, it always outperforms or at least stays at the same performance level of the stand-alone component algorithms even for longer sequences.

Conclusion

We proposed an ensemble approach for the motif discovery problem by taking advantage of the availability of a large number of motif discovery programs. We have shown that the ensemble approach is an effective strategy for improving both sensitivity and specificity, thus the accuracy of the prediction. The advantage of the EMD algorithm is its flexibility in the sense that a new powerful algorithm can be easily added to the system.