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Protein Complex Discovery from Protein Interaction Network with High False-Positive Rate

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Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics (EvoBIO 2011)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6623))

Abstract

Finding protein complexes and their functions is essential work for understanding biological process. However, one of the difficulties in inferring protein complexes from protein-protein interaction(PPI) network originates from the fact that protein interactions suffer from high false positive rate. We propose a complex finding algorithm which is not strongly dependent on topological traits of the protein interaction network. Our method exploits a new measure, GECSS(Gene Expression Condition Set Similarity) which considers mRNA expression data for a set of PPI. The complexes we found exhibit a higher match with reference complexes than the existing methods. Also we found several novel protein complexes, which are significantly enriched on Gene Ontology database.

This work was supported by National Research Foundation of Korea funded by the Korean Government under Grant (No. 2010-0003965).

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References

  1. Kumar, A., Snyder, M.: Protein complexes take the bait. Nature 415, 123–124 (2002)

    Article  Google Scholar 

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

    Google Scholar 

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

    Article  Google Scholar 

  4. Altaf-Ul-Amin, M., Shinbo, Y., Mihara, K., Kurokawa, K., Kanaya, S.: Development and implementation of an algorithm for detection of protein complexes in large interaction networks. BMC Bioinformatics 7, 207 (2006)

    Article  Google Scholar 

  5. Deane, C.M., Salwinski, L., Xenarios, I., Eisenberg, D.: Protein interactions: Two methods for assessment of the reliability of high throughput observations. Mol. Cell Proteomics 1, 349–356 (2002)

    Article  Google Scholar 

  6. Salwinski, L., Miller, C.S., Smith, A.J., Pettit, F.K., Bowie, J.U., Eisenberg, D.: The database of interacting proteins:2004 update. Nucleic Acids Research 32(Database issue), D449–D451 (2004)

    Article  Google Scholar 

  7. Stark, C., Breitkreutz, B.J., Reguly, T., Boucher, L., Breitkreutz, A., Tyers, M.: BioGRID: a. general repository for interaction datasets. Nucleic Acids Research 34(Database issue), D535–D539 (2006)

    Article  Google Scholar 

  8. Güldener, U., et al.: CYGD: the comprehensive yeast genome database. Nucleic Acids Research 33(Database issue), D364–D368 (2005)

    Article  Google Scholar 

  9. Pu, S., Wong, J., Turner, B., Cho, E., Wodak, S.: Up-to-date catalogues of yeast protein complexes. Nucleic Acids Research 37(3), 825–831 (2009)

    Article  Google Scholar 

  10. Gasch, A.P., et al.: Genomic expression programs in the response of yeast cells to environmental changes. Molecular Biology of the Cell 11(12), 4241–4257 (2000)

    Article  Google Scholar 

  11. Berriz, G.F., King, O.D., Bryant, B., Sander, C., Roth, F.P.: Characterizing gene sets with FuncAssociate. Bioinformatics 19(18), 2502–2504 (2003)

    Article  Google Scholar 

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Yeu, Y., Ahn, J., Yoon, Y., Park, S. (2011). Protein Complex Discovery from Protein Interaction Network with High False-Positive Rate. In: Pizzuti, C., Ritchie, M.D., Giacobini, M. (eds) Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics. EvoBIO 2011. Lecture Notes in Computer Science, vol 6623. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20389-3_19

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  • DOI: https://doi.org/10.1007/978-3-642-20389-3_19

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-20388-6

  • Online ISBN: 978-3-642-20389-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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