NDLI: Using Global Sequence Similarity to Enhance Biological Sequence Labeling

Content Provider	IEEE Xplore Digital Library
Author	Caragea, C. Sinapov, J. Dobbs, D. Honavar, V.
Copyright Year	2008
Description	Author affiliation: Comput. Sci. Dept., Iowa State Univ., Ames, IA (Caragea, C.; Sinapov, J.; Honavar, V.) \|\| Dept. of Genetics & Cell Biol., Iowa State Univ., Ames, IA (Dobbs, D.)
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.
Starting Page	104
Ending Page	111
File Size	649132
Page Count	8
File Format	PDF
ISBN	9780769534527
DOI	10.1109/BIBM.2008.54
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-11-03
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Sequences Biological system modeling Predictive models Biology Biological information theory global similarity Proteins Computer science Supervised learning biological sequence labeling Labeling Bioinformatics mixture of experts
Content Type	Text
Resource Type	Article

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