Parallel prediction of protein-protein interactions using proximal SVM

被引：0
|
作者：
Chung, YJ [1 ]
Cho, SY
Shin, SY
机构：
[1] Hankuk Univ Foreign Studies, Comp Sci & Informat Commun Engn Div, Yongin, South Korea
[2] S Dakota State Univ, Dept Comp Sci, Brookings, SD 57007 USA
来源：
ROUGH SETS, FUZZY SETS, DATA MINING, AND GRANULAR COMPUTING, PT 2, PROCEEDINGS | 2005年 / 3642卷
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In general, the interactions between proteins are fundamental to a broad area of biological functions. In this paper, we try to predict protein-protein interactions in parallel on a 12-node PC-cluster using domains of a protein. For this, we use a hydrophobicity among protein's amino acid's physicochemical feature and a support vector machine (SVM) among machine learning techniques. According to the experiments, we get approximately 60% average accuracy with 5 trials and we obtained an average speed-up of 5.11 with a 12-node cluster using a proximal SVM.
引用
收藏
页码：430 / 437
页数：8
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