A hybrid clustering algorithm for identifying modules in Protein-Protein Interaction networks

被引：12
|
作者：
Yu, Liang [1 ]
Gao, Lin [1 ]
Sun, Peng Gang [1 ]
机构：
[1] Xidian Univ, Sch Comp Sci & Technol, Xian 710071, Peoples R China
来源：
INTERNATIONAL JOURNAL OF DATA MINING AND BIOINFORMATICS | 2010年 / 4卷 / 05期
基金：
中国国家自然科学基金;
关键词：
protein-protein interaction networks; graph clustering; functional modules; protein complexes; FUNCTIONAL MODULES; ORGANIZATION; PREDICTION; COMPLEXES;
D O I：
10.1504/IJDMB.2010.035903
中图分类号：
Q [生物科学];
学科分类号：
07 ; 0710 ; 09 ;
摘要：
Identifying modules in Protein-Protein Interaction (PPI) networks is Important to understand the organisation of the cellular processes In this paper, we present a novel algorithm combining Molecular Complex Detection (MCODE) with Girvan-Newman (GN) to identify modules in PPI networks Our algorithm can accurately discover denser modules in large-scale protein interaction networks We applied it to S cerevisiae PPI networks and obtained high matching rate between the predicted modules and the known protein complexes in Munich Information Center for Protein Sequences (MIPS) The simulation results show that our algorithm provides an effective, reliable and scalable method of identifying modules in PPI networks
引用
收藏
页码：600 / 615
页数：16
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