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European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics

EvoBIO 2008: Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics pp 153–164Cite as

Detection of Protein Complexes in Protein Interaction Networks Using n-Clubs

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4973))

Abstract

Protein complexes, identified as functional modules in protein interaction networks, are cellular entities that perform certain biological functions. Revealing these modular structures is significant in understanding how cells function. Protein interaction networks can be constructed by representing nodes as proteins and edges as interactions between proteins. In this paper, we use a graph based distance measure, n-clubs, to detect protein complexes in these interaction networks. The quality of clustering protein interaction networks using n-clubs is comparable to that obtained by best known clustering algorithms applied to various protein networks. Moreover, n-clubs approach is driven by a single parameter n in contrast to other clustering algorithms which have numerous parameters to tune for best results.

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References

  1. Bader, G., Hogue, C.: An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinformatics 4(1), 2 (2003)

    Article  Google Scholar 

  2. Blatt, M., Wiseman, S., Domany, E.: Superparamagnetic clustering of data. Phys Rev Lett 76, 3251–3254 (1996)

    Article  Google Scholar 

  3. Boginski, V., Butenko, S., Pardalos, P.M.: Mining market data: A network approach. Comput. Oper. Res. 33(11), 3171–3184 (2006)

    Article  MATH  Google Scholar 

  4. Brohee, S., van Helden, J.: Evaluation of clustering algorithms for protein-protein interaction networks. BMC Bioinformatics 7(488) (2006)

    Google Scholar 

  5. Butenko, S., Wilhelm, W.: Clique-detection models in computational biochemistry and genomics. European Journal of Operational Research 173, 1–17 (2006)

    Article  MATH  MathSciNet  Google Scholar 

  6. Deane, C.M., Salwinski, L., Xenarios, I., Eisenberg, D.: Protein interactions: two methods for assessment of the reliability of high throughput observations. Molecular and Cellular Proteomics 1, 349–356 (2002)

    Article  Google Scholar 

  7. Enright, A.J., Van Dongen, S., Ouzounis, C.A.: An efficient algorithm for large-scale detection of protein families. Nucleic Acids Res 30, 1575–1584 (2002)

    Article  Google Scholar 

  8. Tong, A.H.Y., et al.: A combined experimental and computational strategy to define protein interaction networks for peptide recognition modules. Science 295(5553), 321–324 (2002)

    Article  Google Scholar 

  9. Stark, C., et al.: Biogrid: a general repository for interaction datasets. Nucl. Acids Res. 539, D535–539 (2006)

    Google Scholar 

  10. Anne-Claude, G., et al.: Functional organization of the yeast proteome by systematic analysis of protein complexes. Nature 415, 141–147 (2002)

    Article  Google Scholar 

  11. Anne-Claude, G., et al.: Proteome survey reveals modularity of the yeast cell machinery. Nature 440, 631–636 (2006)

    Article  Google Scholar 

  12. Mewes, H.W., Frishman, D., et al.: MIPS: A database for genomes and protein sequences. Nucliec Acids Research 28, 74–78 (2000)

    Article  Google Scholar 

  13. Ho, Y., Gruhler, A., et al.: Systematic identification of protein complexes in saccharomyces cerevisiae by mass spectrometry. Nature 415, 180–183 (2002)

    Article  Google Scholar 

  14. Krogan, N.J., et al.: Global landscape of protein complexes in the yeast saccharomyces cerevisiae. Nature 440, 637–643 (2006)

    Article  Google Scholar 

  15. Ito, T., et al.: A comprehensive two-hybrid analysis to explore the yeast protein interactome. Proc. National Academy of Sciences, USA 98(8), 4569–4574 (2001)

    Article  Google Scholar 

  16. Uetz, et al.: A comprehensive analysis of protein-protein interactions in saccharomyces cerevisiae. Nature 203, 623–627 (2000)

    Google Scholar 

  17. Hishgak, H., Nakai, K., Ono, T., Tanigami, T., Takagi, T.: Assessment of prediction accuracy of protein function from protein-protein interaction data. Yeast 18, 523–531 (2001)

    Article  Google Scholar 

  18. Hwang, W., Cho, Y.-R., Zhang, A., Ramanathan, M.: A novel functional module detection algorithm for protein-protein interaction networks. Algorithms for Molecular Biology 1(24) (2006)

    Google Scholar 

  19. King, A.D., Przulj, N., Jurisica, I.: Protein complex prediction via cost-based clustering. Bioinformatics 20(17), 3013–3020 (2004)

    Article  Google Scholar 

  20. Palla, G., Derenyi, I., Farkas, I., Vicsek, T.: Uncovering the overlapping community structure of complex networks in nature and society. Nature 435, 814–818 (2005)

    Article  Google Scholar 

  21. Pereira-Leal, J.B., Enright, A.J., Ouzounis, C.A.: Detection of functional modules from protein interaction networks. Proteins 54, 49–57 (2004)

    Article  Google Scholar 

  22. Spirin, V., Mirny, L.A.: Protein complexes and functional modules in molecular networks. PNAS 100(21), 12123–12128 (2003)

    Article  Google Scholar 

Download references

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Authors and Affiliations

  1. ArsLogica IT Laboratories, Mezzolombardo (Trento), Italy

    Srinivas Pasupuleti

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  1. Srinivas Pasupuleti
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Elena Marchiori Jason H. Moore

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© 2008 Springer-Verlag Berlin Heidelberg

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Pasupuleti, S. (2008). Detection of Protein Complexes in Protein Interaction Networks Using n-Clubs. In: Marchiori, E., Moore, J.H. (eds) Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics. EvoBIO 2008. Lecture Notes in Computer Science, vol 4973. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78757-0_14

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  • DOI: https://doi.org/10.1007/978-3-540-78757-0_14

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-78756-3

  • Online ISBN: 978-3-540-78757-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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