Gene Functional Annotation with Dynamic Hierarchical Classification Guided by Orthologs

  • Kazuhiro Seki
  • Yoshihiro Kino
  • Kuniaki Uehara
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5808)

Abstract

This paper proposes an approach to automating Gene Ontology (GO) annotation in the framework of hierarchical classification that uses known, already annotated functions of the orthologs of a given gene. The proposed approach exploits such known functions as constraints and dynamically builds classifiers based on the training data available under the constraints. In addition, two unsupervised approaches are applied to complement the classification framework. The validity and effectiveness of the proposed approach are empirically demonstrated.

Keywords

Gene ontology String matching Information retrieval 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Baumgartner, Jr.,W.A., Cohen, K.B., Fox, L.M., Acquaah-Mensah, G., Hunter, L.: Manual curation is not sufficient for annotation of genomic databases. Bioinformatics 23(13), 41–48 (2007)CrossRefGoogle Scholar
  2. 2.
    Hersh, W., Bhuptiraju, R.T., Ross, L., Cohen, A.M., Kraemer, D.F.: TREC 2004 genomics track overview. In: Proc. of the 13th Text REtrieval Conference (2004)Google Scholar
  3. 3.
    Blaschke, C., Leon, E., Krallinger, M., Valencia, A.: Evaluation of BioCreAtIvE assessment of task 2. BMC Bioinformatics 16(1), S16 (2005)CrossRefGoogle Scholar
  4. 4.
    Seki, K., Mostafa, J.: Gene ontology annotation as text categorization: An empirical study. Information Processing & Management 44(5), 1754–1770 (2008)CrossRefGoogle Scholar
  5. 5.
    Ray, S., Craven, M.: Learning statistical models for annotating proteins with function information using biomedical text. BMC Bioinformatics 6(1), S18 (2005)CrossRefGoogle Scholar
  6. 6.
    Stoica, E., Hearst, M.: newblock Predicting gene functions from text using a cross-species approach. In: Proc. of the Pacific Symposium on Biocomputing, pp. 88–99 (2006)Google Scholar
  7. 7.
    McCallum, A., Rosenfeld, R., Mitchell, T.M., Ng, A.Y.: Improving text classification by shrinkage in a hierarchy of classes. In: Proc. of the 15th International Conference on Machine Learning, pp. 359–367 (1998)Google Scholar
  8. 8.
    Ruch, P.: Automatic assignment of biomedical categories: toward a generic approach. Bioinformatics 22(6), 658–664 (2006)CrossRefGoogle Scholar
  9. 9.
    Chiang, J., Yu, H.: Extracting functional annotations of proteins based on hybrid text mining approaches. In: Proc. of the BioCreAtIvE (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Kazuhiro Seki
    • 1
  • Yoshihiro Kino
    • 1
  • Kuniaki Uehara
    • 1
  1. 1.Kobe UniversityKobeJapan

Personalised recommendations