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Application of SVM to predict membrane protein types
Authors:Cai Yu-Dong  Ricardo Pong-Wong  Jen Chih-Hung  Chou Kuo-Chen
Affiliation:Shanghai Research Centre of Biotechnology, Chinese Academy of Sciences, Shanghai 200233, China. y.cai@umist.ac.uk
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.
Keywords:Type I membrane protein   Type II membrane protein   Multipass transmembrane proteins   Lipid chain-anchored membrane proteins   GPI-anchored membrane proteins   Chou's invariance theorem
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