Campus Units

Genetics, Development and Cell Biology, Bioinformatics and Computational Biology, Computer Science

Document Type

Conference Proceeding

Conference

IEEE International Conference on Bioinformatics and Biomedicine (BIBM) 2008

Publication Version

Published Version

Publication Date

2009

Journal or Book Title

BMC Bioinformatics

Volume

10

Issue

Suppl 4

First Page

S4

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

City

Philadelphia, PA, USA

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

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