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Bioinformatics Vol. 19 no. 13 2003
Pages 1650-1655
© 2003 Oxford University Press

Secondary structure prediction with support vector machines

J. J. Ward , L. J. McGuffin , B. F. Buxton and D. T. Jones *

Bioinformatics Group, Department of Computer Science, University College London, Gower Street, London, WC1E 6BT, UK

Received on November 25, 2002 ; revised on February 6, 2003 ; accepted on February 10, 2003

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.

Contact: dtj{at}cs.ucl.ac.uk

* To whom correspondence should be addressed.


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