PFC: An Efficient Soft Graph Clustering Method for PPI Networks Based on Purifying and Filtering the Coupling Matrix

  • Ying LiuEmail author
  • Amir Foroushani
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9771)


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.


Protein-protein interaction networks Purifying and filtering the coupling matrix Overlapping clusters Functional modules 


  1. 1.
    Adamcsek, B.G., et al.: CFinder: locating cliques and overlapping modules in biological networks. Bioinformatics 22(8), 1021–1023 (2006)CrossRefGoogle Scholar
  2. 2.
    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)CrossRefGoogle Scholar
  3. 3.
    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)Google Scholar
  4. 4.
    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)Google Scholar
  5. 5.
    Derenyi, I., et al.: Clique percolation in random networks. Phys. Rev. Lett. 94(16), 160202 (2005)CrossRefGoogle Scholar
  6. 6.
    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]Google Scholar
  7. 7.
    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)CrossRefGoogle Scholar
  8. 8.
    Hartuv, E., Shamir, R.: A clustering algorithm based on graph connectivity. Inf. Process. Lett. 76(4–6), 175–181 (2000)MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    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)Google Scholar
  10. 10.
    MIPS. The functional catalogue (FunCat). Internet on-line. (2007). Accessed 10 Feb 2008
  11. 11.
    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]Google Scholar
  12. 12.
    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)Google Scholar
  13. 13.
    Spirin, V., Mirny, L.A.: Protein complexes and functional modules in molecular networks. Proc. Natl. Acad. Sci. 100(21), 12123–12128 (2003)CrossRefGoogle Scholar
  14. 14.
    Tatusov, R.L., Koonin, E.V., Lipman, D.J.: A genomic perspective on protein families. Science 278(5338), 631 (1997)CrossRefGoogle Scholar
  15. 15.
    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)CrossRefGoogle Scholar
  16. 16.
    Van Dongen, S.: A cluster algorithm for graphs. Rep.-Inf. Syst. 10, 1–40 (2000)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Division of Computer Science, Mathematics, and Science, College of Professional StudiesSt. John’s UniversityQueensUSA
  2. 2.Department of Computer ScienceTexas State UniversitySan MarcosUSA

Personalised recommendations