Secondary structure prediction with support vector machines.

J J Ward; L J McGuffin; B F Buxton; D T Jones

doi:10.1093/bioinformatics/btg223

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Secondary structure prediction with support vector machines.

J J Ward·L J McGuffin·B F Buxton·D T Jones

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Abstract

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.

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Algorithms Artificial Intelligence Benchmarking Cluster Analysis Computing Methodologies Models, Statistical Pattern Recognition, Automated Protein Structure, Secondary Proteins Reproducibility of Results Sensitivity and Specificity Sequence Alignment Sequence Analysis, Protein Comparative Study Evaluation Study Journal Article Research Support, Non-U.S. Gov't Validation Study

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J J Ward, L J McGuffin, B F Buxton, D T Jones. 2003-09-01. Secondary structure prediction with support vector machines.. https://doi.org/10.1093/bioinformatics%2Fbtg223

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