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Secondary structure prediction with support vector machines

Ward, JJ; McGuffin, LJ; Buxton, BF; Jones, DT; (2003) Secondary structure prediction with support vector machines. BIOINFORMATICS , 19 (13) 1650 - 1655. 10.1093/bioinformatics/btg223.

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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 (Q(3)) 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.

Type: Article
Title: Secondary structure prediction with support vector machines
DOI: 10.1093/bioinformatics/btg223
URI: http://discovery.ucl.ac.uk/id/eprint/155651
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