Identification of Protein Complexes Using Weighted PageRank-Nibble Algorithm and Core-Attachment Structure

Protein complexes play a significant role in understanding the underlying mechanism of most cellular functions. Recently, many researchers have explored computational methods to identify protein complexes from protein-protein interaction (PPI) networks. One group of researchers focus on detecting lo...

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Bibliographic Details
Published in:IEEE/ACM transactions on computational biology and bioinformatics 2015-01, Vol.12 (1), p.179-192
Main Authors: Peng, Wei, Wang, Jianxin, Zhao, Bihai, Wang, Lusheng
Format: Article
Language:English
Subjects:
Algorithms
Bioinformatics
Clustering algorithms
Computational biology
Computational Biology - methods
Databases, Protein
Methods
Partitioning algorithms
Protein Interaction Mapping - methods
Proteins
Proteins - chemistry
Proteins - metabolism
Software
Yeast
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Summary:Protein complexes play a significant role in understanding the underlying mechanism of most cellular functions. Recently, many researchers have explored computational methods to identify protein complexes from protein-protein interaction (PPI) networks. One group of researchers focus on detecting local dense subgraphs which correspond to protein complexes by considering local neighbors. The drawback of this kind of approach is that the global information of the networks is ignored. Some methods such as Markov Clustering algorithm (MCL), PageRank-Nibble are proposed to find protein complexes based on random walk technique which can exploit the global structure of networks. However, these methods ignore the inherent core-attachment structure of protein complexes and treat adjacent node equally. In this paper, we design a weighted PageRank-Nibble algorithm which assigns each adjacent node with different probability, and propose a novel method named WPNCA to detect protein complex from PPI networks by using weighted PageRank-Nibble algorithm and core-attachment structure. Firstly, WPNCA partitions the PPI networks into multiple dense clusters by using weighted PageRank-Nibble algorithm. Then the cores of these clusters are detected and the rest of proteins in the clusters will be selected as attachments to form the final predicted protein complexes. The experiments on yeast data show that WPNCA outperforms the existing methods in terms of both accuracy and p-value.The software for WPNCA is available at "http://netlab.csu.edu.cn/bioinfomatics/weipeng/WPNCA/download.html".
ISSN:1545-5963
1557-9964
DOI:10.1109/TCBB.2014.2343954