title: Secondary structure prediction with support vector machines.
abstract: BACKGROUND
  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.
authors.affiliation: Department of Computer Science, University College London, Gower Street, London, WC1E 6BT, UK.
authors.forename: J J
authors.initials: JJ
authors.lastname: Ward
authors.forename: L J
authors.initials: LJ
authors.lastname: McGuffin
authors.forename: B F
authors.initials: BF
authors.lastname: Buxton
authors.forename: D T
authors.initials: DT
authors.lastname: Jones
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D019985:Benchmarking; D016000:Cluster Analysis; D003205:Computing Methodologies; D015233:Models, Statistical; D010363:Pattern Recognition, Automated; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry* / Q000145:classification; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D016415:Sequence Alignment / Q000379:methods*; D020539:Sequence Analysis, Protein / Q000379:methods*
chemical_list: D011506:Proteins