EMD: an ensemble algorithm for discovering regulatory motifs in DNA sequences.
EMD: an ensemble algorithm for discovering regulatory motifs in DNA sequences.
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DOI：
10.1186/1471-2105-7-342
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发表时间：
2006-07-13
影响因子：
3
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了解基因调控网络已成为生物信息学的核心研究问题之一。在过去的三十年中，已经提出了三十多个算法来识别DNA调控位点。然而，这些算法的预测精度仍然相当低。在生物信息学中，通过利用多种算法的协同预测能力来提高预测精度，包围算法已经成为一种有效的策略。我们提出了一种新的基于聚类的集成算法命名为EMD从头模体发现结合多个预测从多个运行的一个或多个基本组件算法。集成方法被应用到模体发现问题的第一次。该算法在E. coli RegulonDB. EMD算法在核苷酸水平预测精度方面比最好的独立分量算法提高了22.4%。EMD算法的优点对于较短的输入序列更显着，但最重要的是，即使对于较长的序列，它也总是优于或至少保持在独立分量算法的相同性能水平。我们提出了一个集成的方法，利用大量的模体发现程序的可用性的模体发现问题。我们已经表明，集成方法是一种有效的策略，用于提高灵敏度和特异性，从而提高预测的准确性。EMD算法的优点是它的灵活性，在这个意义上，一个新的强大的算法可以很容易地添加到系统。
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.