Finding Bicliques in Digraphs: Application into Viral-Host Protein Interactome

  • Malay Bhattacharyya
  • Sanghamitra Bandyopadhyay
  • Ujjwal Maulik
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6744)

Abstract

We provide the first formalization true to the best of our knowledge to the problem of finding bicliques in a directed graph. The problem is addressed employing a two-stage approach based on an existing biclustering algorithm. This novel problem is useful in several biological applications of which we focus only on analyzing the viral-host protein interaction graphs. Strong and significant bicliques of HIV-1 and human proteins are derived using the proposed methodology, which provides insights into some novel regulatory functionalities in case of the acute immunodeficiency syndrome in human.

Keywords

Gene Ontology Bipartite Graph Human Protein Interaction Matrix Biclustering Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Barkow, S., Bleuler, S., Prelić, A., Zimmermann, P., Zitzler, E.: BicAT: a Biclustering Analysis Toolbox. Bioinformatics 22(10), 1282–1283 (2006)CrossRefGoogle Scholar
  2. 2.
    Brass, A.L., Dykxhoorn, D.M., Benita, Y., Yan, N., Engelman, A., Xavier, R.J., Lieberman, J., Elledge, S.J.: Identification of Host Proteins Required for HIV Infection Through a Functional Genomic Screen. Science 319(5865), 921–926 (2008)CrossRefGoogle Scholar
  3. 3.
    Cheng, Y., Church, G.: Biclustering of Expression Data. In: Proceedings of the 8th ISMB Conference, AAAI Press, pp. 93–103. AAAI Press, Menlo Park (2000)Google Scholar
  4. 4.
    Ding, C., Zhang, Y., Li, T.: Biclustering Protein Complex Interactions with a Biclique Finding Algorithm. In: Proceedings of the Sixth International Conference on Data Mining, Hong Kong, pp. 178–187 (2006)Google Scholar
  5. 5.
    Fu, W., Sanders-Beer, B.E., Katz, K.S., Maglott, D.R., Pruitt, K.D., Ptak, R.G.: Human immunodeficiency virus type 1, human protein interaction database at NCBI. Nucleic Acids Research 37(Database Issue), D417–D422 (2009)CrossRefGoogle Scholar
  6. 6.
    Madeira, S.C., Oliveira, A.L.: Biclustering algorithms for biological data analysis: a survey. IEEE/ACM Transactions on Computational Biology and Bioinformatics 1, 24–45 (2004)CrossRefGoogle Scholar
  7. 7.
    Pandey, G., Atluri, G., Steinbach, M., Myers, C.L., Kumar, V.: An Association Analysis Approach to Biclustering. In: Proceedings of the 15th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Paris, France (2009)Google Scholar
  8. 8.
    Prelić, A., Bleuler, S., Zimmermann, P., Wille, A., Bühlmann, P., Gruissem, P., Hennig, L., Thiele, L., Zitzler, E.: A systematic comparison and evaluation of biclustering methods for gene expression data. Bioinformatics 22(9), 1122–1129 (2006)CrossRefGoogle Scholar
  9. 9.
    Tanay, A., Sharan, R., Shamir, R.: Discovering statistically significant biclusters in gene expression data. Bioinformatics 18, S136–S144 (2002)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Malay Bhattacharyya
    • 1
  • Sanghamitra Bandyopadhyay
    • 1
  • Ujjwal Maulik
    • 2
  1. 1.Machine Intelligence UnitIndian Statistical InstituteKolkataIndia
  2. 2.Department of Computer Science and EngineeringJadavpur UniversityKolkataIndia

Personalised recommendations