Integrative Neural Network Approach for Protein Interaction Prediction from Heterogeneous Data

  • Xue-wen Chen
  • Mei Liu
  • Yong Hu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5139)

Abstract

Protein interactions are essential in discovery of protein functions and fundamental biological processes. In this paper, we aim to create a reliable computational model for protein interaction prediction by integrating information from complementary data sources. An integrative Artificial Neural Network (ANN) framework is developed to predict protein-protein interactions (PPIs) from heterogeneous data in Human. Performance of our proposed framework is empirically investigated by combining protein domain data, molecular function and biological process annotations in Gene Ontology. Experimental results demonstrate that our approach can predict PPIs with high sensitivity of 82.43% and specificity of 78.67%. The results suggest that combining multiple data sources can result in a 7% increase in sensitivity compared to using only domain information. We are able to construct a protein interaction network with proteins around mitotic spindle checkpoint of the human interactome map. Novel predictions are made and some are supported by evidences in literature.

Keywords

Protein-Protein Interaction Interaction Network Prediction Heterogeneous Data Integration Integrative Neural Network 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Uetz, P., Giot, L., Cagney, G., Mansfield, T.A., Judson, R.S., et al.: A comprehensive analysis of protein-protein interactions in Saccharomyces cerevisiae. Nature 403, 623–627 (2000)CrossRefGoogle Scholar
  2. 2.
    Ito, T., Tashiro, K., Muta, S., Ozawa, R., Chiba, T., et al.: Toward a protein-protein interaction map of the budding yeast: A comprehensive system to examine two-hybrid interactions in all possible combinations between the yeast proteins. Proc. Natl. Acad. Sci. USA 97, 1143–1147 (2000)CrossRefGoogle Scholar
  3. 3.
    Bader, G.D., Hogue, C.W.: Analyzing yeast protein-protein interaction data obtained from different sources. Nat. Biotechnol. 20, 991–997 (2000)CrossRefGoogle Scholar
  4. 4.
    Ho, Y., Gruhler, A., Heilbut, A., Bader, G.D., Moore, L., et al.: Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spectrometry. Nature 415, 180–183 (2002)CrossRefGoogle Scholar
  5. 5.
    Gavin, A.C., Bosche, M., Krause, R., Grandi, P., Marzioch, M., et al.: Functional organization of the yeast proteome by systematic analysis of protein complexes. Nature 415, 141–147 (2002)CrossRefGoogle Scholar
  6. 6.
    Sprinzak, E., Margalit, H.: Correlated sequence-signatures as markers of protein-protein interaction. J. Mol. Biol. 311, 681–692 (2001)CrossRefGoogle Scholar
  7. 7.
    Kim, W.K., Park, J., Suh, J.K.: Large scale statistical prediction of protein-protein interaction by potentially interacting domain (PID) pair. Genome Inform. 13, 42–50 (2002)Google Scholar
  8. 8.
    Han, D., Kim, H.S., Seo, J., Jang, W.: A domain combination based probabilistic framework for protein-protein interaction prediction. Genome Inform. 14, 250–259 (2003)Google Scholar
  9. 9.
    Deng, M., Mehta, S., Sun, F., Chen, T.: Inferring domain-domain interactions from protein-protein interactions. Genome Res. 12, 1540–1548 (2002)CrossRefGoogle Scholar
  10. 10.
    Chen, X.W., Liu, M.: Prediction of protein-protein interactions using random decision forest framework. Bioinformatics 21, 4394–4400 (2005)CrossRefGoogle Scholar
  11. 11.
    Chen, X.W., Liu, M.: Domain Based Predictive Models for Protein-Protein Interaction Prediction. In: EURASIP (2006)Google Scholar
  12. 12.
    Ashburner, M., Ball, C.A., Blake, J.A., Botstein, D., Butler, H., et al.: Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat. Genet. 25, 25–29 (2000)Google Scholar
  13. 13.
    Peri, S., Navarro, J.D., Amanchy, R., Kristiansen, T.Z., Jonnalagadda, C.K., et al.: Development of human protein reference database as an initial platform for approaching systems biology in humans. Genome Res. 13, 2363–2371 (2003)CrossRefGoogle Scholar
  14. 14.
    Bateman, A., Coin, L., Durbin, R., Finn, R.D., Hollich, V., et al.: The Pfam protein families database. Nucleic Acids Res. 32, D138-141 (2004)CrossRefGoogle Scholar
  15. 15.
    Cahill, D.P., Lengauer, C., Yu, J., Riggins, G.J., Willson, J.K., et al.: Mutations of mitotic checkpoint genes in human cancers. Nature 392, 300–303 (1998)CrossRefGoogle Scholar
  16. 16.
    Bharadwaj, R., Yu, H.: The spindle checkpoint, aneuploidy, and cancer. Oncogene 23, 2016–2027 (2004)CrossRefGoogle Scholar
  17. 17.
    Rhodes, D.R., Tomlins, S.A., Varambally, S., Mahavisno, V., Barrette, T., et al.: Probabilistic model of the human protein-protein interaction network. Nat. Biotechnol. 23, 951–959 (2005)CrossRefGoogle Scholar
  18. 18.
    Chen, J., Fang, G.: MAD2B is an inhibitor of the anaphase-promoting complex. Genes Dev. 15, 1765–1770 (2001)CrossRefGoogle Scholar
  19. 19.
    Ghiselli, G., Coffee, N., Munnery, C.E., Koratkar, R., Siracusa, L.D.: The cohesin SMC3 is a target the for beta-catenin/TCF4 transactivation pathway. J. Biol. Chem. 278, 20259–20267 (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Xue-wen Chen
    • 1
  • Mei Liu
    • 1
  • Yong Hu
    • 2
  1. 1.Bioinformatics and Computational Life-Sciences Laboratory, ITTC, Department of Electrical Engineering and Computer ScienceThe University of KansasLawrenceUSA
  2. 2.Department of Management Science, School of Business ManagementSun Yat-sen University, Guangdong University of Foreign Studies, GuangzhouGuangdongChina

Personalised recommendations