title: Application of SVM to predict membrane protein types.
abstract: As a continuous effort to develop automated methods for predicting membrane protein types that was initiated by Chou and Elrod (PROTEINS: Structure, Function, and Genetics, 1999, 34, 137-153), the support vector machine (SVM) is introduced. Results obtained through re-substitution, jackknife, and independent data set tests, respectively, have indicated that the SVM approach is quite a promising one, suggesting that the covariant discriminant algorithm (Chou and Elrod, Protein Eng. 12 (1999) 107) and SVM, if effectively complemented with each other, will become a powerful tool for predicting membrane protein types and the other protein attributes as well.
authors.affiliation: Shanghai Research Centre of Biotechnology, Chinese Academy of Sciences, Shanghai 200233, China. y.cai@umist.ac.uk
authors.forename: Yu-Dong
authors.initials: YD
authors.lastname: Cai
authors.forename: Pong-Wong
authors.initials: PW
authors.lastname: Ricardo
authors.forename: Chih-Hung
authors.initials: CH
authors.lastname: Jen
authors.forename: Kuo-Chen
authors.initials: KC
authors.lastname: Chou
mesh_terms: D000465:Algorithms; D000596:Amino Acids / Q000032:analysis; D008565:Membrane Proteins / Q000737:chemistry* / Q000145:classification
chemical_list: D000596:Amino Acids; D008565:Membrane Proteins