Application of SVM to predict membrane protein types.
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.
Cai
Yu-Dong
YD
Shanghai Research Centre of Biotechnology, Chinese Academy of Sciences, Shanghai 200233, China. y.cai@umist.ac.uk
Ricardo
Pong-Wong
PW
Jen
Chih-Hung
CH
Chou
Kuo-Chen
KC
D000465:Algorithms; D000596:Amino Acids / Q000032:analysis; D008565:Membrane Proteins / Q000737:chemistry* / Q000145:classification
D000596:Amino Acids; D008565:Membrane Proteins