Campus Units

Genetics, Development and Cell Biology, Computer Science

Document Type

Conference Proceeding

Conference

2008 IEEE International Conference on Bioinformatics and Biomedicine

Publication Version

Accepted Manuscript

Publication Date

2008

First Page

104

Last Page

111

DOI

10.1109/BIBM.2008.54

Conference Title

2008 IEEE International Conference on Bioinformatics and Biomedicine

Conference Date

November 3-5, 2008

City

Philadelphia, Pennsylvania

Abstract

Identifying functionally important sites from biological sequences, formulated as a biological sequence labeling problem, has broad applications ranging from rational drug design to the analysis of metabolic and signal transduction networks. In this paper, we present an approach to biological sequence labeling that takes into account the global similarity between biological 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 approaches to combine the predictions of the experts. We evaluate our approach on two important biological 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 biological sequence data.

Comments

This is a proceeding from IEEE International Conference on Bioinformatics and Biomedicine (2008): 104, doi: 10.1109/BIBM.2008.54. Posted with permission.

Rights

© 2008 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Copyright Owner

IEEE

Language

en

File Format

application/pdf

Share

Article Location

 
COinS