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

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 al...

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Vydáno v:BMC bioinformatics Ročník 7; číslo 1; s. 342
Hlavní autoři: Hu, Jianjun, Yang, Yifeng D, Kihara, Daisuke
Médium: Journal Article
Jazyk:angličtina
Vydáno: England BioMed Central 13.07.2006
BMC
Témata:
Algorithms
Amino Acid Motifs
Cluster Analysis
Computational Biology - methods
DNA - chemistry
Escherichia coli
Escherichia coli - metabolism
Gene Expression Profiling
Pattern Recognition, Automated
Phylogeny
Sensitivity and Specificity
Sequence Alignment
Sequence Analysis, DNA - methods
Sequence Analysis, Protein - methods
ISSN:1471-2105, 1471-2105
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Shrnutí: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. 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. 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.
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ISSN:1471-2105
1471-2105
DOI:10.1186/1471-2105-7-342