Analysis of Functional Modules in Protein Networks Using Graph Clustering Method

Juan Mei; Ji Zhao; Yi Fu

doi:10.4028/www.scientific.net/AMR.482-484.612

Paper Titles

Trigonometric Coons Patch with Shape Parameters
p.595

Optimize Track Design and its Simulation Based on Simulated Annealing Algorithm
p.599

The Fire Identification of Electric Heating Cable to Solar Energy Water Heater
p.603

Application of Deinking Selectivity in Evaluating the Deinking Performance of Flotation Equipments
p.607

Analysis of Functional Modules in Protein Networks Using Graph Clustering Method
p.612

Fast Prediction of Temperature by FEM in Hot Strip Rolling
p.616

Numerical Simulation of Marine Recycled Concrete Specimen Based on ABAQUS
p.621

Kinetics Study of Aluminum Deposition on Inner Wall of Pipes by Atomic Layer Deposition
p.627

Based on the Interconnection of Gas Monitoring System Research and Design
p.633

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 482-484Analysis of Functional Modules in Protein Networks...

Analysis of Functional Modules in Protein Networks Using Graph Clustering Method

Abstract:

Interaction detection methods have led to the discovery of thousands of interactions between proteins, and discerning relevance within large-scale data sets is important to present-day biology. As an important means for knowledge discovery, graph clustering attracts much attention in analysis of protein-protein interaction networks. Here, a modularity-based method was used to find communities of protein-protein interaction networks. Using this method, 177 communities were detected from a network involving 11,855 interactions among 2617 proteins in yeast and annotated according to MIPS hierarchical functional categories. We validated that these communities are indeed densely connected subgraphs.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Advanced Materials Research (Volumes 482-484)

Pages:

612-615

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.482-484.612

Citation:

Cite this paper

Online since:

February 2012

Authors:

Juan Mei, Ji Zhao, Yi Fu

Keywords:

Functional Modules, Graph Clustering, Protein Interaction Networks

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] F. Luo, Y.F. Yang, C.F. Chen, R. Chang, J,Z. Zhou and R.H. Scheuermann: Bioinformatics Vol. 23 (2007), p.207

Google Scholar

[2] A.L. Barabasi and Z.N. Oltvai: Nat. Rev. Genet. Vol. 5 (2004), p.101

Google Scholar

[3] C.C. Friedel, J. Krumsiek, and R. Zimmer: J. Comput. Biol. Vol. 16 (2009), p.971

Google Scholar

[4] V. Spirin and L.A. Mirny: Proc. Natl. Acad. Sci. USA Vol. 100 (2003), p.12123

Google Scholar

[5] D.B. Bu, Y. Zhao, L. Cai, et al: Nucleic Acids Res. Vol. 31 (2003), p.2443

Google Scholar

[6] G.D. Bader, and C.W. Hogue: BMC Bioinformatics Vol. 4 (2003), p.2

Google Scholar

[7] C. Brun, F. Chevenet, D. Martin, J. Wojcik, A. Guenoche, and B. Jacq: Genome Biol. Vol. 5 (2003), p. R6

DOI: 10.1186/gb-2003-5-1-r6

Google Scholar

[8] A.D. King, N. Przulj, and I. Jurisica: Bioinformatics Vol. 20 (2004), p.3013

Google Scholar

[9] E. Hartuv and R.A. Shamir: Information Processing Letters Vol. 76 (2000), p.175

Google Scholar

[10] A.J. Enright, S. van Dongen, and C.A. Ouzounis: Nucleic Acids Res. Vol. 30 (2002), p.1575

Google Scholar

[11] J. Mei, S. He, G.Y. Shi, Z.X. Wang and W. J. Li: New Journal of Physics Vol. 11 (2009), p.043025.

Google Scholar