Support vector machine for predicting protein interactions using domain scores  

作　　者：彭新俊 王翼飞 

机构地区：[1]Department of Mathematics,College of Sciences,Shanghai University [2]Department of Mathematics,Shanghai Normal University

出　　处：《Journal of Shanghai University(English Edition)》2009年第3期207-212,共6页上海大学学报（英文版）

基　　金：supported by the National Natural Science Foundation of China (Grant No.30571059);the National High-Technology Research and Development Program of China (Grant No.2006AA02Z190);the Shanghai Leading Academic Discipline Project (Grant No.S30405)

摘　　要：Protein-protein interactions play a crucial role in the cellular processsuch as metabolic pathways and immunological recognition. This paper presents a new domain score-based support vector machine （SVM） to infer protein interactions, which can be used not only to explore all possible domain interactions by the kernel method, but also to reflect the evolutionary conservation of domains in proteins by using the domain scores of proteins. The experimental result on the Saccharomyces cerevisiae dataset demonstrates that this approach can predict protein-protein interactions with higher performances compared to the existing approaches.Protein-protein interactions play a crucial role in the cellular processsuch as metabolic pathways and immunological recognition. This paper presents a new domain score-based support vector machine （SVM） to infer protein interactions, which can be used not only to explore all possible domain interactions by the kernel method, but also to reflect the evolutionary conservation of domains in proteins by using the domain scores of proteins. The experimental result on the Saccharomyces cerevisiae dataset demonstrates that this approach can predict protein-protein interactions with higher performances compared to the existing approaches.

关 键 词：protein-protein interactions DOMAINS support vector machine （SVM） domain score 

分 类 号：Q51-3[生物学—生物化学]