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Investigating Power and Limitations of Ensemble Motif Finders Using Metapredictor CE3

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Ensemble methods represent a relatively new approach to motif discovery that combines the results returned by "third-party" finders with the aim of achieving a better accuracy than that obtained by the single tools. Besides the choice of the external finders, another crucial element for the success of an ensemble method is the particular strategy adopted to combine the finders' results, a.k.a. learning function.

Results appeared in the literature seem to suggest that ensemble methods can provide noticeable improvements over the quality of the most popular tools available for motif discovery.

With the goal of better understanding potentials and limitations of ensemble methods, we developed a general software architecture whose major feature is the flexibility with respect to the crucial aspects of ensemble methods mentioned above. The architecture provides facilities for the easy addition of virtually any third-party tool for motif discovery whose code is publicly available, and for the definition of new learning functions. We present a prototype implementation of our architecture, called CE3 (Customizable and Easily Extensible Ensemble).

Using CE3, and available ensemble methods, we performed experiments with three well-known datasets. The results presented here are varied. On the one hand, they confirm that ensemble methods cannot be just considered as the universal remedy for "in-silico" motif discovery. On the other hand, we found some encouraging regularities that may help to find a general set up for CE3 (and other ensemble methods as well) able to guarantee substantial improvements over single finders in a systematic way.
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Keywords: DNA binding; XML; ensemble methods; motif discovery; software tool; transcription factor

Document Type: Research Article

Publication date: April 1, 2015

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  • Current Bioinformatics aims to publish all the latest and outstanding developments in bioinformatics. Each issue contains a series of timely, in-depth reviews written by leaders in the field, covering a wide range of the integration of biology with computer and information science.

    The journal focuses on reviews on advances in computational molecular/structural biology, encompassing areas such as computing in biomedicine and genomics, computational proteomics and systems biology, and metabolic pathway engineering. Developments in these fields have direct implications on key issues related to health care, medicine, genetic disorders, development of agricultural products, renewable energy, environmental protection, etc.

    Current Bioinformatics is an essential journal for all academic and industrial researchers who want expert knowledge on all major advances in bioinformatics.
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