Secondary structure prediction with support vector machines

Ward, J. J., McGuffin, L. J. ORCID: https://orcid.org/0000-0003-4501-4767, Buxton, B. F. and Jones, D. T. (2003) Secondary structure prediction with support vector machines. Bioinformatics, 19 (13). pp. 1650-1655. ISSN 1460-2059 doi: 10.1093/bioinformatics/btg223

Abstract/Summary

Motivation: A new method that uses support vector machines (SVMs) to predict protein secondary structure is described and evaluated. The study is designed to develop a reliable prediction method using an alternative technique and to investigate the applicability of SVMs to this type of bioinformatics problem. Methods: Binary SVMs are trained to discriminate between two structural classes. The binary classifiers are combined in several ways to predict multi-class secondary structure. Results: The average three-state prediction accuracy per protein (Q3) is estimated by cross-validation to be 77.07 ± 0.26% with a segment overlap (Sov) score of 73.32 ± 0.39%. The SVM performs similarly to the 'state-of-the-art' PSIPRED prediction method on a non-homologous test set of 121 proteins despite being trained on substantially fewer examples. A simple consensus of the SVM, PSIPRED and PROFsec achieves significantly higher prediction accuracy than the individual methods. Availability: The SVM classifier is available from the authors. Work is in progress to make the method available on-line and to integrate the SVM predictions into the PSIPRED server.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/27435
Item Type	Article
Refereed	Yes
Divisions	Life Sciences > School of Biological Sciences > Biomedical Sciences No Reading authors. Back catalogue items
Publisher	Oxford University Press
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