Predicting Protein Complexes in Weighted Dynamic PPI Networks Based on ICSC

被引:10
|
作者
Zhao, Jie [1 ]
Lei, Xiujuan [1 ]
Wu, Fang-Xiang [2 ,3 ]
机构
[1] Shaanxi Normal Univ, Sch Comp Sci, Xian 710119, Shaanxi, Peoples R China
[2] Nankai Univ, Sch Math Sci, Tianjin 300071, Peoples R China
[3] Univ Saskatchewan, Div Biomed Engn, Saskatoon, SK S7N 5A9, Canada
基金
中国国家自然科学基金;
关键词
GENE-EXPRESSION DATA; SACCHAROMYCES-CEREVISIAE; FUNCTIONAL MODULES; MASS-SPECTROMETRY; TIME-COURSE; YEAST; IDENTIFICATION; ALGORITHM;
D O I
10.1155/2017/4120506
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
摘要
Protein complexes play a critical role in understanding the biological processes and the functions of cellular mechanisms. Most existing protein complex detection algorithms cannot reflect dynamics of protein complexes. In this paper, a novel algorithm named Improved Cuckoo Search Clustering (ICSC) algorithm is proposed to detect protein complexes in weighted dynamic protein-protein interaction (PPI) networks. First, we constructed weighted dynamic PPI networks and detected protein complex cores in each dynamic subnetwork. Then, ICSC algorithm was used to cluster the protein attachments to the cores. The experimental results on both DIP dataset and Krogan dataset demonstrated that ICSC algorithm is more effective in identifying protein complexes than other competing methods.
引用
收藏
页码:1 / 11
页数:11
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