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Article
Mixture of experts models to exploit global sequence similarity on biomolecular sequence labeling
BMC Bioinformatics
  • Cornelia Caragea, Iowa State University
  • Jivko Sinapov, Iowa State University
  • Drena Dobbs, Iowa State University
  • Vasant Honavar, Iowa State University
Document Type
Conference Proceeding
Conference
IEEE International Conference on Bioinformatics and Biomedicine (BIBM) 2008
Publication Version
Published Version
Publication Date
1-1-2009
DOI
10.1186/1471-2105-10-S4-S4
Conference Title
IEEE International Conference on Bioinformatics and Biomedicine (BIBM) 2008
Conference Date
November 3-5, 2008
Geolocation
(39.9525839, -75.16522150000003)
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.

Comments

This is a proceeding from IEEE International Conference on Bioinformatics and Biomedicine (BIBM) 10 (2009): S4, doi: 10.1186/1471-2105-10-S4-S4. Posted with permission.

Rights
This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright Owner
Caragea et al
Language
en
File Format
application/pdf
Citation Information
Cornelia Caragea, Jivko Sinapov, Drena Dobbs and Vasant Honavar. "Mixture of experts models to exploit global sequence similarity on biomolecular sequence labeling" Philadelphia, PA, USABMC Bioinformatics Vol. 10 Iss. Suppl 4 (2009) p. S4
Available at: http://works.bepress.com/drena-dobbs/45/