Journal
JOURNAL OF COMPUTATIONAL BIOLOGY
Volume 10, Issue 6, Pages 857-868Publisher
MARY ANN LIEBERT, INC
DOI: 10.1089/106652703322756113
Keywords
pairwise sequence comparison; homology; detection; support vector machines.
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Funding
- NCRR NIH HHS [P41 RR08605-07] Funding Source: Medline
- NATIONAL CENTER FOR RESEARCH RESOURCES [P41RR008605] Funding Source: NIH RePORTER
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One key element in understanding the molecular machinery of the cell is to understand the structure and function of each protein encoded in the genome. A very successful means of inferring the structure or function of a previously unannotated protein is via sequence similarity with one or more proteins whose structure or function is already known. Toward this end, we propose a means of representing proteins using pairwise sequence similarity scores. This representation, combined with a discriminative classification algorithm known as the support vector machine (SVM), provides a powerful means of detecting subtle structural and evolutionary relationships among proteins. The algorithm, called SVM-pairwise, when tested on its ability to recognize previously unseen families from the SCOP database, yields significantly better performance than SVM-Fisher, profile HMMs, and PSI-BLAST.
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