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This article is part of the supplement: Ninth International Conference on Bioinformatics (InCoB2010): Computational Biology

Open Access Proceedings

Exploring hierarchical and overlapping modular structure in the yeast protein interaction network

Changning Liu*, Jing Li and Yi Zhao*

Author Affiliations

Bioinformatics Group, Key Laboratory of Intelligent Information Processing, Center for Advanced Computing Research, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, PR China

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BMC Genomics 2010, 11(Suppl 4):S17  doi:10.1186/1471-2164-11-S4-S17

Published: 2 December 2010

Abstract

Background

Developing effective strategies to reveal modular structures in protein interaction networks is crucial for better understanding of molecular mechanisms of underlying biological processes. In this paper, we propose a new density-based algorithm (ADHOC) for clustering vertices of a protein interaction network using a novel subgraph density measurement.

Results

By statistically evaluating several independent criteria, we found that ADHOC could significantly improve the outcome as compared with five previously reported density-dependent methods. We further applied ADHOC to investigate the hierarchical and overlapping modular structure in the yeast PPI network. Our method could effectively detect both protein modules and the overlaps between them, and thus greatly promote the precise prediction of protein functions. Moreover, by further assaying the intermodule layer of the yeast PPI network, we classified hubs into two types, module hubs and inter-module hubs. Each type presents distinct characteristics both in network topology and biological functions, which could conduce to the better understanding of relationship between network architecture and biological implications.

Conclusions

Our proposed algorithm based on the novel subgraph density measurement makes it possible to more precisely detect hierarchical and overlapping modular structures in protein interaction networks. In addition, our method also shows a strong robustness against the noise in network, which is quite critical for analyzing such a high noise network.