Mining Protein Interaction from Biomedical Literature with Relation Kernel Method

  • Jae-Hong Eom
  • Byoung Tak Zhang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3973)

Abstract

Many interaction data still exist only in the biomedical literature and they require much effort to construct well-structured data. Discovering useful knowledge from large collections of papers is becoming more important for efficient biological and biomedical researches as genomic research advances. In this paper, we present a relation kernel-based interaction extraction method to extract knowledge efficiently. We extract protein interactions of from text documents with relation kernel and Yeast was used as an example target organism. Kernel for relation extraction is constructed with predefined interaction corpus and set of interaction patterns. The proposed method only exploits shallow parsed documents. Experimental results show that the proposed kernel method achieves a recall rate of 79.0% and precision rate of 80.8% for protein interaction extraction from biomedical document without full parsing efforts.

Keywords

Kernel Method Recall Rate Parse Tree Biomedical Literature Relation Extraction 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Deng, M., et al.: Inferring Domain–Domain Interactions from Protein–Protein Interactions. Genome Res. 12, 1540–1548 (2002)CrossRefGoogle Scholar
  2. 2.
    Goffeau, A., et al.: Life with 6000 Genes. Science 274, 546–567 (1996)CrossRefGoogle Scholar
  3. 3.
    Yakushiji, A., et al.: Event Extraction from Biomedical Parsers Using a Full Parser. In: Proc. of the 6th Pacific Symposium on Biocomputing, pp. 408–419 (2001)Google Scholar
  4. 4.
    Park, J.C., et al.: Bidirectional Incremental Parsing for Automatic Pathway Identification with Combinatory Categorical Grammar. In: Proc. of the 6th Pacific Symposium on Biocomputing, pp. 396–407 (2001)Google Scholar
  5. 5.
    Temkin, J.M., et al.: Extraction of Protein Interaction Information from Unstructured Text Using a Content–free Grammar. Bioinformatics 19(16), 2046–2053 (2003)CrossRefGoogle Scholar
  6. 6.
    Leroy, G., et al.: Filling Preposition–based Templates to Capture Information from Medical Abstracts. In: Proc. of the 7th Pacific Symposium on Biocomputing, pp. 350–361 (2002)Google Scholar
  7. 7.
    Pustejovsky, J., et al.: Robust Relational Parsing Over Biomedical Literature: Extracting Inhibit Relations. In: Proc. of the 7th Pacific Symposium on Biocomputing, pp. 362–373 (2002)Google Scholar
  8. 8.
    Zelenko, D., et al.: Kernel Methods for Relation Extraction. J. Machine Learning Res. 3, 1083–1106 (2003)MATHCrossRefMathSciNetGoogle Scholar
  9. 9.
    Lodi, H., et al.: Text Classification Using String Kernels. J. Machine Learning Res. 2, 419–444 (2002)CrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Jae-Hong Eom
    • 1
  • Byoung Tak Zhang
    • 1
  1. 1.Biointelligence Laboratory, School of Computer Science and EngineeringSeoul National UniversitySeoulSouth Korea

Personalised recommendations