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Using Global Sequence Similarity to Enhance Biological Sequence Labeling
Genetics, Development and Cell Biology Presentations, Posters and Proceedings
  • Cornelia Caragea, Iowa State University
  • Jivko Sinapov, Iowa State University
  • Drena Dobbs, Iowa State University
  • Vasant Honavar, Iowa State University
Document Type
Conference Proceeding
2008 IEEE International Conference on Bioinformatics and Biomedicine
Publication Version
Accepted Manuscript
Publication Date
Conference Title
2008 IEEE International Conference on Bioinformatics and Biomedicine
Conference Date
November 3-5, 2008
(39.9525839, -75.16522150000003)

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.


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

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Citation Information
Cornelia Caragea, Jivko Sinapov, Drena Dobbs and Vasant Honavar. "Using Global Sequence Similarity to Enhance Biological Sequence Labeling" Philadelphia, Pennsylvania(2008) p. 104 - 111
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