Application of SVM to predict membrane protein types

被引：150
作者：
Cai, YD
Ricardo, PW
Jen, CH
Chou, KC
机构：
[1] Chinese Acad Sci, Shanghai Res Ctr Biotechnol, Shanghai 200233, Peoples R China
[2] Roslin Inst, Roslin EH25 9PS, Midlothian, Scotland
[3] Univ Leeds, Bioinformat Grp, Sch Biochem & Mol Biol, Leeds LS2 9JT, W Yorkshire, England
[4] Gordon Life Sci Inst, San Diego, CA 92130 USA
[5] TIBDD, Tianjin, Peoples R China
来源：
JOURNAL OF THEORETICAL BIOLOGY | 2004年 / 226卷 / 04期
关键词：
type I membrane protein; type II membrane protein; multipass transmembrane proteins; lipid chain-anchored membrane proteins; GPI-anchored membrane proteins; chou's invariance theorem;
D O I：
10.1016/j.jtbi.2003.08.015
中图分类号：
Q [生物科学];
学科分类号：
07 ; 0710 ; 09 ;
摘要：
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. (C) 2003 Elsevier Ltd. All rights reserved.
引用
收藏
页码：373 / 376
页数：4
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