Mixture of experts models to exploit global sequence similarity on biomolecular sequence labeling

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Article discussing models for increasing the reliability of computational methods for identifying functionally important sites from biomolecular sequences.

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14 p.: ill.

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Caragea, Cornelia; Sinapov, Jivko; Dobbs, Drena & Honavar, Vasant Creation Date: Unknown.

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This article is part of the collection entitled: UNT Scholarly Works and was provided by UNT College of Engineering to Digital Library, a digital repository hosted by the UNT Libraries. It has been viewed 97 times . More information about this article can be viewed below.

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The UNT College of Engineering promotes intellectual and scholarly pursuits in the areas of computer science and engineering, preparing innovative leaders in a variety of disciplines. The UNT College of Engineering encourages faculty and students to pursue interdisciplinary research among numerous subjects of study including databases, numerical analysis, game programming, and computer systems architecture.

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Article discussing models for increasing the reliability of computational methods for identifying functionally important sites from biomolecular sequences.

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14 p.: ill.

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Abstract: Background: Identification of functionally important sites in biomolecular sequences has broad applications ranging from rational drug design to the analysis of metabolic and signal transduction networks. Experimental determination of such sites lags far behind the number of known biomolecular sequences. Hence, there is a need to develop reliable computational methods for identifying functionally important sites from biomolecular sequences. Results: We present a mixture of experts approach to biomolecular sequence labeling that takes into account the global similarity between biomolecular sequences. Our approach combines unsupervised and supervised learning techniques. Given a set of sequences and a similarity measure defined on pairs of sequences, we learn a mixture of experts model by using spectral clustering to learn the hierarchical structure of the model and by using bayesian techniques to combine the predictions of the experts. We evaluate our approach on two biomolecular sequence labeling problems: RNA-protein and DNA-protein interface prediction problems. The results of our experiments show that global sequence similarity can be exploited to improve the performance of classifiers trained to label biomolecular sequence data. Conclusion: The mixture of experts model helps improve the performance of machine learning methods for identifying functionally important sites in biomolecular sequences.

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  • BMC Bioinformatics, 2009, London: BioMed Central Ltd.

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  • Publication Title: BMC Bioinformatics
  • Volume: 10
  • Issue: Suppl 4
  • Peer Reviewed: Yes

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UNT Scholarly Works

Materials from the UNT community's research, creative, and scholarly activities and UNT's Open Access Repository. Access to some items in this collection may be restricted.

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  • Unknown

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  • April 29, 2009

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  • Sept. 6, 2013, 3:22 p.m.

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Caragea, Cornelia; Sinapov, Jivko; Dobbs, Drena & Honavar, Vasant. Mixture of experts models to exploit global sequence similarity on biomolecular sequence labeling, article, Date Unknown; [London, United Kingdom]. (digital.library.unt.edu/ark:/67531/metadc180946/: accessed August 23, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT College of Engineering.