Abstract
One of the most pressing problems of the post genomic era is identifying protein functions. Clustering Protein-Protein-Interaction networks is a systems biological approach to this problem. Traditional Graph Clustering Methods are crisp, and allow only membership of each node in at most one cluster. However, most real world networks contain overlapping clusters. Recently the need for scalable, accurate and efficient overlapping graph clustering methods has been recognized and various soft (overlapping) graph clustering methods have been proposed. In this paper, an efficient, novel, and fast overlapping clustering method is proposed based on purifying and filtering the coupling matrix (PFC). PFC is tested on PPI networks. The experimental results show that PFC method outperforms many existing methods by a few orders of magnitude in terms of average statistical (hypergeometrical) confidence regarding biological enrichment of the identified clusters.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Adamcsek, B.G., et al.: CFinder: locating cliques and overlapping modules in biological networks. Bioinformatics 22(8), 1021–1023 (2006)
Altschul, S.F., et al.: Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 25(17), 3389 (1997)
Biemann, C.: Chinese whispers-an efficient graph clustering algorithm and its application to natural language processing problems. In: Proceedings of the HLT-NAACL-06 Workshop on Textgraphs-06, New York, USA (2006)
Chua, H.N., Sung, W.K., Wong, L.: Exploiting indirect neighbours and topological weight to predict protein function from protein-protein interactions. Bioinformatics 22(13), 1623–1630, 1 July 2006. PMID: 16632496; btl145 [pii] (Oxford, England)
Derenyi, I., et al.: Clique percolation in random networks. Phys. Rev. Lett. 94(16), 160202 (2005)
Girvan, M., Newman, M. E.: Community structure in social and biological networks. In: Proceedings of the National Academy of Sciences of the United States of America, vol. 99, no. 12, pp. 7821–7826, 11 June 2002. PMID: 12060727; 99/12/7821 [pii]
Gregory, S.: An algorithm to find overlapping community structure in networks. In: Kok, J.N., Koronacki, J., Lopez de Mantaras, R., Matwin, S., Mladenič, D., Skowron, A. (eds.) PKDD 2007. LNCS (LNAI), vol. 4702, pp. 91–102. Springer, Heidelberg (2007)
Hartuv, E., Shamir, R.: A clustering algorithm based on graph connectivity. Inf. Process. Lett. 76(4–6), 175–181 (2000)
King, A.D., Przulj, N., Jurisica, I.: Protein complex prediction via cost-based clustering. Bioinformatics 20(17), 3013–3020, 22 November 2004. PMID: 15180928; bth351 [pii] (Oxford, England)
MIPS. The functional catalogue (FunCat). Internet on-line. http://mips.gsf.de/projects/funcat (2007). Accessed 10 Feb 2008
Palla, G., Derenyi, I., Farkas, I., Vicsek, T.: Uncovering the overlapping community structure of complex networks in nature and society. Nature 435(7043), 814–818, 9 June 2005. PMID: 15944704; nature03607 [pii]
Pinney, J.W., Westhead, D.R.: Betweenness-based decomposition methods for social and biological networks. In: Barber, S., Baxter, P.D., Mardia, K.V., Walls, R.E. (eds.) Interdisciplinary Statistics and Bioinformatics. Leeds University Press, Leeds (2006)
Spirin, V., Mirny, L.A.: Protein complexes and functional modules in molecular networks. Proc. Natl. Acad. Sci. 100(21), 12123–12128 (2003)
Tatusov, R.L., Koonin, E.V., Lipman, D.J.: A genomic perspective on protein families. Science 278(5338), 631 (1997)
Thompson, J.D., Higgins, D.G., Gibson, T.J.: CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Res. 22(22), 4673–4680 (1994)
Van Dongen, S.: A cluster algorithm for graphs. Rep.-Inf. Syst. 10, 1–40 (2000)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Liu, Y., Foroushani, A. (2016). PFC: An Efficient Soft Graph Clustering Method for PPI Networks Based on Purifying and Filtering the Coupling Matrix. In: Huang, DS., Bevilacqua, V., Premaratne, P. (eds) Intelligent Computing Theories and Application. ICIC 2016. Lecture Notes in Computer Science(), vol 9771. Springer, Cham. https://doi.org/10.1007/978-3-319-42291-6_24
Download citation
DOI: https://doi.org/10.1007/978-3-319-42291-6_24
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-42290-9
Online ISBN: 978-3-319-42291-6
eBook Packages: Computer ScienceComputer Science (R0)