Secondary structure prediction with support vector machines

J. J. Ward, L. J. McGuffin, B. F. Buxton, D. T. Jones
2003 Bioinformatics  
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:
more » ... 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.
doi:10.1093/bioinformatics/btg223 pmid:12967961 fatcat:5odmyhfxqbamtdw5pj6pcyiqem