Commenced in January 2007
Paper Count: 31100
Community Detection-based Analysis of the Human Interactome Network
Abstract:The study of proteomics reached unexpected levels of interest, as a direct consequence of its discovered influence over some complex biological phenomena, such as problematic diseases like cancer. This paper presents a new technique that allows for an accurate analysis of the human interactome network. It is basically a two-step analysis process that involves, at first, the detection of each protein-s absolute importance through the betweenness centrality computation. Then, the second step determines the functionallyrelated communities of proteins. For this purpose, we use a community detection technique that is based on the edge betweenness calculation. The new technique was thoroughly tested on real biological data and the results prove some interesting properties of those proteins that are involved in the carcinogenesis process. Apart from its experimental usefulness, the novel technique is also computationally effective in terms of execution times. Based on the analysis- results, some topological features of cancer mutated proteins are presented and a possible optimization solution for cancer drugs design is suggested.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1074401Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1007
 R. Dunn et al., The use of node-clustering to investigate biological function in protein interaction networks. BMC Bioinformatics, 2004.
 D. Bader et al., Approximating betweenness centrality. Georgia Institute of Technology, 2007.
 D. Meunier and H. Paugam-Moisy, Cluster detection algorithm in neural networks. Institute for cognitive science, BRON, France, 2006.
 J. Yoon, A. Blumer and K. Lee, An algorithm for modularity analysis of directed and weighted biological networks based on edge-betweenness centrality. Bioinformatics, 2006.
 M.E.J. Newman, Shortest paths, weighted networks, and centrality. Physical review, volume 64, 2001.
 M. Girvan and M.E.J. Newman, Community structure in social and biological networks. State University of New Jersey, 2002.
 P. Holme et al., Subnetwork hierarchies of biochemical pathways. Bioinformatics, 2003.
 D. Ucar et al., Improving functional fodularity in protein-protein interactions graphs using hub-induced subgraphs. Ohio State University, 2007.
 K. Lehmann and M. Kaufmann, Decentralized algorithms for evaluating centrality in complex networks. IEEE, 2002.
 J. Griebsch et al., A fast algorithm for the iterative calculation of betweenness centrality. Technical University of Munchen, 2004.
 G.H. Traver et al., How complete are current yeast and human proteininteraction networks?. Genome biology, 2006.
 R. Bunescu et al., Consolidating the set of known human proteinprotein interactions in preparation for large-scale mapping of the human interactome. Genome biology, 2005.
 U. Brandes, A faster algorithm for betweenness centrality. University of Konstanz, 2001.
 B. Preiss, Data structures and algorithms with object-oriented design patterns in C++. John Wiley and sons, 1998.
 EMBL-EBI, The IntAct protein interactions database. URL: http://www.ebi.ac.uk/intact/site/index.jsf, 2009.
 C. Demetrescu et al., The Leonardo Library. URL: http://www.leonardovm. org/, 2003.
 University of California, The DIP protein interactions database. URL: http://dip.doe-mbi.ucla.edu/, 2009.
 Johns Hopkins University, The HPRD protein interactions database. URL: http://www.hprd.org/, 2009.
 R. Bocu and S. Tabirca, Betweenness Centrality Computation - A New Way for Analyzing the Biological Systems. Proceedings of the BSB 2009 conference, Leipzig, Germany, 2009.
 L.C. Freeman, A set of measures of centrality based on betweenness. Sociometry, Vol. 40, 35-41, 1977.
 P.F. Jonsson and P.A. Bates, Global topological features of cancer proteins in the human interactome. Bioinformatics Advance Access, 2006.
 Wellcome Trust Sanger Institute, The Pfam protein families database. URL: http://pfam.sanger.ac.uk/, 2009.