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Mining Overlapping Protein Complexes in PPI Network Based on Granular Computation in Quotient Space

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Intelligent Computing Theories and Application (ICIC 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10954))

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Abstract

Proteins complexes play a critical role in many biological processes. The existing protein complex detection algorithms are mostly cannot reflect the overlapping protein complexes. In this paper, a novel algorithm is proposed to detect overlapping protein complexes based on granular computation in quotient space. Firstly, problems are expressed by quotient space and different quotient space embodies the quotient set of different granular. Then the method estimates the relationship between particles to make up for the inadequacy of data in combination with the PPI data and Gene Ontology data, deals with the network based on quotient space theory. Graining the network to construct the quotient space and merging the particles layer by layer. The final protein complexes is obtained after purification. The experimental results on Saccharomyces cerevisiae and Homo sapiens turned out that the proposed method could exploit protein complexes more accurately and efficiently.

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References

  1. Zhao, J., Lei, X., Wu, F.X.: Predicting protein complexes in weighted dynamic PPI networks based on ICSC. Complexity 2017, 1–11 (2017)

    MathSciNet  MATH  Google Scholar 

  2. Lei, X., Ding, Y., Fujita, H., Zhang, A.: Identification of dynamic protein complexes based on fruit fly optimization algorithm. Knowl. Based Syst. 105, 270–277 (2016)

    Article  Google Scholar 

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

    Article  Google Scholar 

  4. Nepusz, T., Yu, H., Paccanaro, A.: Detecting overlapping protein complexes in protein-protein interaction networks. Nat. Meth. 9, 471 (2012)

    Article  Google Scholar 

  5. Gavin, A.C., Aloy, P., Grandi, P., Krause, R., Boesche, M., Marzioch, M., Rau, C., Jensen, L.J., Bastuck, S., Dümpelfeld, B.: Proteome survey reveals modularity of the yeast cell machinery. Nature 440, 631–636 (2006)

    Article  Google Scholar 

  6. Leung, H.C., Xiang, Q., Yiu, S.M., Chin, F.Y.: Predicting protein complexes from PPI data: a core-attachment approach. J. Comput. Biol. J. Comput. Mol. Cell Biol. 16, 133 (2009)

    Article  MathSciNet  Google Scholar 

  7. Min, W., Li, X., Kwoh, C.K., Ng, S.K.: A core-attachment based method to detect protein complexes in PPI networks. BMC Bioinf. 10, 1–16 (2009)

    Google Scholar 

  8. Van Dongen, S.: Graph clustering by flow simulation. Ph.D. thesis University of Utrecht (2000)

    Google Scholar 

  9. Wu, H., Gao, L., Dong, J., Yang, X.: Detecting overlapping protein complexes by rough-fuzzy clustering in protein-protein interaction networks. PLoS ONE 9, e91856 (2014)

    Article  Google Scholar 

  10. Lei, X., Wang, F., Wu, F.X., Zhang, A., Pedrycz, W.: Protein complex identification through Markov clustering with firefly algorithm on dynamic protein–protein interaction networks. Inf. Sci. 329, 303–316 (2016)

    Article  Google Scholar 

  11. Ling, Z., Bo, Z.: Theory of fuzzy quotient space (methods of fuzzy granular computing). J. Softw. 14, 770–776 (2003)

    MATH  Google Scholar 

  12. Xu, F., Zhang, L., Wang, L.: Approach of the fuzzy granular computing based on the theory of quotient space. Pattern Recognit. Artif. Intell. 17, 424–429 (2004)

    Google Scholar 

  13. Xenarios, I., Salwínski, L., Duan, X.J., Higney, P., Kim, S.M., Eisenberg, D.: DIP, the Database of interacting Proteins: a research tool for studying cellular networks of protein interactions. Nucleic Acids Res. 30, 303 (2002)

    Article  Google Scholar 

  14. Mathivanan, S., Periaswamy, B., et al.: An evaluation of human protein-protein interaction data in the public domain. BMC Bioinf. 7, S19 (2006)

    Article  Google Scholar 

  15. Zhao, S., Wang, K.E., Chen, J., et al.: Community detection algorithm based on clustering granulation. J. Comput. Appl. 34, 2812–2815 (2014)

    Google Scholar 

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

    Article  Google Scholar 

  17. Baldi, P., Brunak, S., Chauvin, Y., Nielsen, H.: Assessing the accuracy of prediction algorithms for classification: an overview. Bioinformatics 16, 412 (2000)

    Article  Google Scholar 

  18. Lei, X., Ding, Y., Wu, F.X.: Detecting protein complexes from DPINs by density based clustering with Pigeon-Inspired Optimization Algorithm. Sci. China Inf. Sci. 59, 070103 (2016)

    Article  Google Scholar 

  19. Tang, Y., Min, L.I.: A cytoscape plugin for visualization and clustering analysis of protein interaction networks. Chin. J. Bioinf. (2014)

    Google Scholar 

Download references

Acknowledgement

This paper is supported by the National Natural Science Foundation of China (61672334, 61502290, 61401263) and the Fundamental Research Funds for the Central Universities, Shaanxi Normal University (GK201804006).

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Authors and Affiliations

  1. School of Computer Science, Shaanxi Normal University, Xi’an, 710062, China

    Jie Zhao & Xiujuan Lei

Authors
  1. Jie Zhao
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  2. Xiujuan Lei
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Corresponding author

Correspondence to Xiujuan Lei .

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Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. Polytechnic of Bari, Bari, Italy

    Vitoantonio Bevilacqua

  3. University of Wollongong, North Wollongong, New South Wales, Australia

    Prashan Premaratne

  4. Indian Institute of Technology Kanpur, Kanpur, India

    Phalguni Gupta

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Zhao, J., Lei, X. (2018). Mining Overlapping Protein Complexes in PPI Network Based on Granular Computation in Quotient Space. In: Huang, DS., Bevilacqua, V., Premaratne, P., Gupta, P. (eds) Intelligent Computing Theories and Application. ICIC 2018. Lecture Notes in Computer Science(), vol 10954. Springer, Cham. https://doi.org/10.1007/978-3-319-95930-6_69

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  • DOI: https://doi.org/10.1007/978-3-319-95930-6_69

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-95929-0

  • Online ISBN: 978-3-319-95930-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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