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A Tree Kernel-Based Method for Protein-Protein Interaction Mining from Biomedical Literature

  • Jae-Hong Eom
  • Sun Kim
  • Seong-Hwan Kim
  • Byoung-Tak Zhang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3886)

Abstract

As genomic research advances, the knowledge discovery from a large collection of scientific papers becomes more important for efficient biological and biomedical research. Even though current databases continue to update new protein-protein interactions, valuable information still remains in biomedical literature. Thus data mining techniques are required to extract the information. In this paper, we present a tree kernel-based method to mine protein-protein interactions from biomedical literature. The tree kernel is designed to consider grammatical structures for given sentences. A support vector machine classifier is combined with the tree kernel and trained on predefined interaction corpus and set of interaction patterns. Experimental results show that the proposed method gives promising results by utilizing the structure patterns.

Keywords

Support Vector Machine Kernel Method Biomedical Literature Sentence Length Extraction Performance 
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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

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

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