Dynamical systems for discovering protein complexes and functional modules from biological networks

Li, Wenyuan, Liu, Ying, Huang, Hung-Chung, Peng, Yanxiong, Lin, Yongjing, Ng, Wee-Keong and Ong, Kok-Leong 2007, Dynamical systems for discovering protein complexes and functional modules from biological networks, IEEE-ACM transactions on computational biology and bioinformatics, vol. 4, no. 2, pp. 233-250, doi: 10.1109/TCBB.2007.070210.

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Title Dynamical systems for discovering protein complexes and functional modules from biological networks
Author(s) Li, Wenyuan
Liu, Ying
Huang, Hung-Chung
Peng, Yanxiong
Lin, Yongjing
Ng, Wee-Keong
Ong, Kok-Leong
Journal name IEEE-ACM transactions on computational biology and bioinformatics
Volume number 4
Issue number 2
Start page 233
End page 250
Publisher IEEE Computer Society
Place of publication New York, N.Y.
Publication date 2007-04
ISSN 1545-5963
Keyword(s) graph algorithms
neural nets
evolutionary computing
bioinformatics databases
Summary Recent advances in high throughput experiments and annotations via published literature have provided a wealth of interaction maps of several biomolecular networks, including metabolic, protein-protein, and protein-DNA interaction networks. The architecture of these molecular networks reveals important principles of cellular organization and molecular functions. Analyzing such networks, i.e., discovering dense regions in the network, is an important way to identify protein complexes and functional modules. This task has been formulated as the problem of finding heavy subgraphs, the Heaviest k-Subgraph Problem (k-HSP), which itself is NPhard. However, any method based on the k-HSP requires the parameter k and an exact solution of k-HSP may still end up as a “spurious” heavy subgraph, thus reducing its practicability in analyzing large scale biological networks. We proposed a new formulation, called the rank-HSP, and two dynamical systems to approximate its results. In addition, a novel metric, called the Standard deviation and Mean Ratio (SMR), is proposed for use in “spurious” heavy subgraphs to automate the discovery by setting a fixed threshold. Empirical results on both the simulated graphs and biological networks have demonstrated the efficiency and effectiveness of our proposal.
Language eng
DOI 10.1109/TCBB.2007.070210
Field of Research 080610 Information Systems Organisation
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2007, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30007068

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