Gene Function Prediction and Functional Network: The Role of Gene Ontology

  • Erliang ZengEmail author
  • Chris Ding
  • Kalai Mathee
  • Lisa Schneper
  • Giri Narasimhan
Part of the Intelligent Systems Reference Library book series (ISRL, volume 25)


Almost every cellular process requires the interactions of pairs or larger complexes of proteins. The organization of genes into networks has played an important role in characterizing the functions of individual genes and the interplay between various cellular processes. The Gene Ontology (GO) project has integrated information from multiple data sources to annotate genes to specific biological process. Recently, the semantic similarity (SS) between GO terms has been investigated and used to derive semantic similarity between genes. Such semantic similarity provides us with a new perspective to predict protein functions and to generate functional gene networks. In this chapter, we focus on investigating the semantic similarity between genes and its applications. We have proposed a novel method to evaluate the support for PPI data based on gene ontology information. If the semantic similarity between genes is computed using gene ontology information and using Resniks formula, then our results show that we can model the PPI data as a mixture model predicated on the assumption that true protein-protein interactions will have higher support than the false positives in the data. Thus semantic similarity between genes serves as a metric of support for PPI data. Taking it one step further, new function prediction approaches are also being proposed with the help of the proposed metric of the support for the PPI data. These new function prediction approaches outperform their conventional counterparts. New evaluation methods are also proposed. In another application, we present a novel approach to automatically generate a functional network of yeast genes using Gene Ontology (GO) annotations. An semantic similarity (SS) is calculated between pairs of genes. This SS score is then used to predict linkages between genes, to generate a functional network. Functional networks predicted by SS and other methods are compared. The network predicted by SS scores outperforms those generated by other methods in the following aspects: automatic removal of a functional bias in network training reference sets, improved precision and recall across the network, and higher correlation between a genes lethality and centrality in the network. We illustrate that the resulting network can be applied to generate coherent function modules and their associations. We conclude that determination of semantic similarity between genes based upon GO information can be used to generate a functional network of yeast genes that is comparable or improved with respect to those that are directly based on integrated heterogeneous genomic and proteomic data.


Semantic Similarity Gene Ontology Gene Function Prediction Functional Gene Network 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Ashburner, M., Ball, C., Blake, J., Botstein, D., Butler, H., Cherry, J., Davis, A., Dolinski, K., Dwight, S., Eppig, J., Harris, M., Hill, D., Issel-Tarver, L., Kasarskis, A., Lewis, S., Matese, J., Richardson, J., Ringwald, M., Rubin, G., Sherlock, G.: Gene ontology: tool for the unification of biology. the gene ontology consortium. Nat. Genet. 25(1), 25–29 (2000)CrossRefGoogle Scholar
  2. 2.
    Resnik, P.: Using information content to evaluate semantic similarity. In: Proceedings of the 14th International Joint Conference on Artificial Intelligence, pp. 448–453 (1995)Google Scholar
  3. 3.
    Jiang, J.J., Conrath, D.W.: Semantic similarity based on corpus statistics and lexical taxonomy. In: Proceedings of International Conference on Research in Computational Linguistics (1997)Google Scholar
  4. 4.
    Lin, D.: An information-theoretic definition of similarity. In: Proceedings of the 15th International Conference on Machine Learning (1998)Google Scholar
  5. 5.
    Schlicker, A., Domingues, F.S., Rahnenfuhrer, J., Lengauer, T.: A new measure for functional similarity of gene products based on gene ontology. BMC Bioinformatics 7, 302–317 (2006)CrossRefGoogle Scholar
  6. 6.
    Lord, P.W., Stevens, R.D., Brass, A., Goble, C.A.: Semantic similarity measures as tools for exploring the gene ontology. In: Pac. Symp. Biocomput., pp. 601–612 (2003)Google Scholar
  7. 7.
    Sharan, R., Ulitsky, I., Shamir, R.: Network-based prediction of protein function. Molecular Systems Biology 3(88), 1–13 (2007)Google Scholar
  8. 8.
    Schwikowski, B., Uetz, P., Fields, S.: A network of protein-protein interactions in yeast. Nat. Biotechnol. 18, 1257–1261 (2000)CrossRefGoogle Scholar
  9. 9.
    Hishigaki, H., Nakai, K., Ono, T., Tanigami, A., Takagi, T.: Assessment of prediction accuracy of protein function from protein-protein interaction data. Yeast 18, 523–531 (2001)CrossRefGoogle Scholar
  10. 10.
    Chua, H.N., Sung, W.K., Wong, L.: Exploiting indirect neighbours and topological weight to predict protein function from proteinprotein interactions. Bioinformatics 22, 1623–1630 (2006)CrossRefGoogle Scholar
  11. 11.
    Letovsky, S., Kasif, S.: Predicting protein function from protein/protein interaction data: a probabilistic approach. Bioinformatics 204(suppl. 1), i197–i204 (2003)Google Scholar
  12. 12.
    Deng, M., Tu, Z., Sun, F., Chen, T.: Mapping gene ontology to proteins based on protein–protein interaction data. Bioinformatics 20(6), 895–902 (2004)CrossRefGoogle Scholar
  13. 13.
    Vazquez, A., Flammini, A., Maritan, A., Vespignani, A.: Global protein function prediction from protein-protein interaction networks. Nat. Biotechnol. 21, 697–700 (2003)CrossRefGoogle Scholar
  14. 14.
    Karaoz, U., Murali, T.M., Letovsky, S., Zheng, Y., Ding, C., Cantor, C.R., Kasif, S.: Whole-genome annotation by using evidence integration in functional-linkage networks. Proc. Natl. Acad. Sci. USA 101, 2888–2893 (2004)CrossRefGoogle Scholar
  15. 15.
    Nabieva, E., Jim, K., Agarwal, A., Chazelle, B., Singh, M.: Whole-proteome prediction of protein function via graph-theoretic analysis of interaction maps. Bioinformatics 21(suppl. 1), i302–i310 (2005)Google Scholar
  16. 16.
    Joshi, T., Chen, Y., Becker, J.M., Alexandrov, N., Xu, D.: Genome-scale gene function prediction using multiple sources of high-throughput data in yeast saccharomyces cerevisiae. OMICS 8(4), 322–333 (2004)CrossRefGoogle Scholar
  17. 17.
    Lee, H., Tu, Z., Deng, M., Sun, F., Chen, T.: Diffusion kernel-based logistic regression models for protein function prediction. OMICS 10(1), 40–55 (2006)CrossRefGoogle Scholar
  18. 18.
    Lanckriet, G.R., De Bie, T., Cristianini, N., Jordan, M.I., Noble, W.S.: A statistical framework for genomic data fusion. Bioinformatics 20(16), 2626–2635 (2004)CrossRefGoogle Scholar
  19. 19.
    Tsuda, K., Shin, H., Schölkopf, B.: Fast protein classification with multiple networks. Bioinformatics 21(suppl. 2) (2005)Google Scholar
  20. 20.
    Bader, G.D., Hogue, C.W.: An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinformatics 4(1) (2003)Google Scholar
  21. 21.
    Sharan, R., Ideker, T., Kelley, B., Shamir, R., Karp, R.M.: Identification of protein complexes by comparative analysis of yeast and bacterial protein interaction data. J. Comput. Biol. 12(6), 835–846 (2005)CrossRefGoogle Scholar
  22. 22.
    Arnau, V., Mars, S., Marin, I.: Iterative cluster analysis of protein interaction data. Bioinformatics 21(3), 364–378 (2005)CrossRefGoogle Scholar
  23. 23.
    Segal, E., Wang, H., Koller, D.: Discovering molecular pathways from protein interaction and gene expression data. Bioinformatics 19(suppl. 1), i264–i271 (2003)Google Scholar
  24. 24.
    Kelley, R., Ideker, T.: Systematic interpretation of genetic interactions using protein networks. Nature Biotechnology 23(5), 561–566 (2005)CrossRefGoogle Scholar
  25. 25.
    Wu, Y., Lonardi, S.: A linear-time algorithm for predicting functional annotations from proteinprotein interaction networks. In: Proceedings of the Workshop on Data Mining in Bioinformatics (BIOKDD 2007), pp. 35–41 (2007)Google Scholar
  26. 26.
    Jansen, R., Yu, H., Greenbaum, D., Kluger, Y., Krogan, N.J., Chung, S., Emili, A., Snyder, M., Greenblatt, J.F., Gerstein, M.: A Bayesian networks approach for predicting protein-protein interactions from genomic data. Science 302, 449–453 (2003)CrossRefGoogle Scholar
  27. 27.
    Zhang, L.V., Wong, S.L., King, O.D., Roth, F.P.: Predicting co-complexed protein pairs using genomic and proteomic data integration. BMC Bioinformatics 5 (April 2004)Google Scholar
  28. 28.
    Ben-Hur, A., Noble, W.S.: Kernel methods for predicting protein-protein interactions. Bioinformatics 21(suppl. 1) (June 2005)Google Scholar
  29. 29.
    Qi, Y., Bar-Joseph, Z., Klein-Seetharaman, J.: Evaluation of different biological data and computational classification methods for use in protein interaction prediction. PROTEINS: Structure, Function, and Bioinformatics 3, 490–500 (2006)CrossRefGoogle Scholar
  30. 30.
    Lee, I., Date, S.V., Adai, A.T., Marcotte, E.M.: A probabilistic functional network of yeast genes. Science 306, 1555–1558 (2004)CrossRefGoogle Scholar
  31. 31.
    Lee, I., Li, Z., Marcotte, E.M.: An improved, bias-reduced probabilistic functional gene network of baker’s yeast, saccharomyces cerevisiae. PLoS ONE, e988 (2007)Google Scholar
  32. 32.
    von Mering, C., Krause, R., Snel, B., Cornell, M., Oliver, S.G., Fields, S., Bork, P.: Comparative assessment of large-scale data sets of protein-protein interactions. Nature 417, 399–403 (2002)CrossRefGoogle Scholar
  33. 33.
    Yu, J., Fotouhi, F.: Computational approaches for predicting protein-protein interactions: A survey. J. Med. Syst. 30(1), 39–44 (2006)CrossRefGoogle Scholar
  34. 34.
    Bader, J.S.: Greedily building protein networks with confidence. Bioinformatics 19(15), 1869–1874 (2003)CrossRefGoogle Scholar
  35. 35.
    Ashburner, M., Ball, C.A., Blake, J.A., Botstein, D., Butler, H., Cherry, J.M., Davis, A.P., Dolinski, K., Dwight, S.S., Eppig, J.T., Harris, M.A., Hill, D.P., Issel-Tarver, L., Kasarskis, A., Lewis, S., Matese, J.C., Richardson, J.E., Ringwald, M., Rubin, G.M., Sherlock, G.: Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat. Genet. 25, 25–29 (2000)CrossRefGoogle Scholar
  36. 36.
    Stark, C., Breitkreutz, B.J., Reguly, T., Boucher, L., Breitkreutz, A., Tyers, M.: BioGRID: a general repository for interaction datasets. Nucleic Acids Res. 34 (January 2006)Google Scholar
  37. 37.
    Mewes, H., Gruber, F., Geier, C., Haase, B., Kaps, D., Lemcke, A., Mannhaupt, K., Pfeiffer, G., Schuller, F.: MIPS: a database for genomes and protein sequences. Nucleic Acids Res. 30(1), 31–34 (2002)CrossRefGoogle Scholar
  38. 38.
    Murali, T., Wu, C., Kasif, S.: The art of gene function prediction. Nat. Biotechnol. 24(12), 1474–1475 (2006)CrossRefGoogle Scholar
  39. 39.
    Giaever, G., Chu, A., Ni, L., Connelly, C., Riles, L., et al.: Functional profiling of the saccharomyces cerevisiae genome. Nature 418(6896), 387–391 (2002)CrossRefGoogle Scholar
  40. 40.
    Myers, C.L., Robson, D., Wible, A., Hibbs, M.A., Chiriac, C., Theesfeld, C.L., Dolinski, K., Troyanskaya, O.G.: Discovery of biological networks from diverse functional genomic data. Genome Biology 6, R114 (2005)Google Scholar
  41. 41.
    Rhodes, D.R., Tomlins, S.A., Varambally, S., Mahavisno, V., Barrette, T., Kalyana-Sundaram, S., Ghosh, D., Pandey, A., Chinnaiyan, A.M.: Probabilistic model of the human protein-protein interaction network. Nature Biotechnology 23(8), 951–959 (2005)CrossRefGoogle Scholar
  42. 42.
    Pan, X., Ye, P., Yuan, D.S., Wang, X., Bader, J.S., Boeke, J.D.: A DNA integrity network in the yeast Saccharomyces cerevisiae. Cell 124, 1069–1081 (2006)CrossRefGoogle Scholar
  43. 43.
    Zhong, W., Sternberg, P.W.: Genome-wide prediction of c. elegans genetic interactions. Science 311, 1481–1484 (2006)CrossRefGoogle Scholar
  44. 44.
    Huang, H., Zhang, L.V., Roth, F.P., Bader, J.S.: Probabilistic paths for protein complex inference, pp. 14–28 (2006)Google Scholar
  45. 45.
    Jeong, H., Mason, S.P., Barabasi, A.L., Oltvai, Z.N.: Lethality and centrality in protein networks. Nature 411, 41–42 (2001)CrossRefGoogle Scholar
  46. 46.
    Sevilla, J.L., Segura, V., Podhorski, A., Guruceaga, E., Mato, J.M., Martinez-Cruz, L.A., Corrales, F.J., Rubio, A.: Correlation between gene expression and go semantic similarity. IEEE/ACM Trans. Comput. Biol. Bioinformatics 2(4), 330–338 (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Erliang Zeng
    • 1
    Email author
  • Chris Ding
    • 2
  • Kalai Mathee
    • 3
  • Lisa Schneper
    • 3
  • Giri Narasimhan
    • 4
  1. 1.Department of Computer Science and EngineeringUniversity of Notre DameNotre DameUSA
  2. 2.Department of Computer Science and EngineeringUniversity of Texas at ArlingtonTexasUSA
  3. 3.Department of Molecular Microbiology, College of MedicineFlorida International UniversityMiamiUSA
  4. 4.Bioinformatics Research Group (BioRG), School of Computing and Information SciencesFlorida International UniversityMiamiUSA

Personalised recommendations