Support vector machines for predicting membrane protein types by using functional domain composition
Support vector machines for predicting membrane protein types by using functional domain composition
复制标题
DOI：
10.1016/s0006-3495(03)70050-2
复制
发表时间：
2003-05-01
影响因子：
3.4
通讯作者：
Chou, KC
中科院分区：
文献类型：
--
作者：
Cai, YD;Zhou, GP;Chou, KC
膜蛋白通常分为以下五种类型：1）I型膜蛋白; 2）II型膜蛋白; 3）多通道跨膜蛋白; 4）脂质链锚定的膜蛋白;和5）GPI锚定的膜蛋白。本文基于用功能域组成来定义蛋白质的概念，提出了一种用于预测膜蛋白类型的支持向量机算法。通过自洽检验和折刀检验获得了高成功率。目前的方法，补充了强大的协变判别算法的基础上的伪氨基酸组成，已纳入准序列顺序效应，最近提出的K。C. Chou（2001），可能成为生物信息学和蛋白质组学领域中非常有用的高通量工具。
Membrane proteins are generally classified into the following five types: 1), type I membrane protein; 2), type 11 membrane protein; 3), multipass transmembrane proteins; 4), lipid chain-anchored membrane proteins; and 5), GPI-anchored membrane proteins. In this article, based on the concept of using the functional domain composition to define a protein, the Support Vector Machine algorithm is developed for predicting the membrane protein type. High success rates are obtained by both the self-consistency and jackknife tests. The current approach, complemented with the powerful covariant discriminant algorithm based on the pseudo-amino acid composition that has incorporated quasi-sequence-order effect as recently proposed by K. C. Chou (2001), may become a very useful high-throughput tool in the area of bioinformatics and proteomics.