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BioC-compatible full-text passage detection for protein-protein interactions using extended dependency graph.

Yifan Peng1, Cecilia Arighi2, Cathy H Wu2

  • 1Computer & Information Sciences, University of Delaware and yfpeng@udel.edu.

Database : the Journal of Biological Databases and Curation
|May 13, 2016
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Summary

This study introduces a novel protein-protein interaction (PPI) detection system that leverages extended dependency graphs for improved accuracy in identifying PPIs within biomedical literature. The system achieves high recall and F-value, streamlining biocuration workflows.

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Area of Science:

  • Biomedical Informatics
  • Computational Biology
  • Text Mining

Background:

  • Increasing volume of biomedical publications reporting experimental results, particularly protein-protein interactions (PPIs).
  • Need for efficient systems to automatically detect and extract PPI information from full-text articles to aid biocuration.

Purpose of the Study:

  • To develop and evaluate a system for detecting text passages containing protein-protein interactions (PPIs) in full-text biomedical articles.
  • To integrate the system output seamlessly into the biocuration pipeline using the BioC format.
  • To assess the system's generalizability across different corpora.

Main Methods:

  • Development of a PPI detection system utilizing extended dependency graphs to abstract syntactic variations.
  • Application of a limited set of rules for extracting PPI pairs and identifying relevant passages.
  • Evaluation using BioC task articles, BioGRID database, and the AIMed corpus.

Main Results:

  • Achieved 83.5% recall for unique PPIs against the BioGRID database.
  • Obtained an F-value of 80.5% for detecting passages with PPIs.
  • Demonstrated generalizability with an F-value of 76.1% for sentence detection and 64.7% for unique PPI detection on the AIMed corpus.

Conclusions:

  • The developed PPI system effectively detects protein-protein interactions and relevant text passages in biomedical literature.
  • The use of extended dependency graphs enhances extraction accuracy and reduces the need for extensive rule sets.
  • The system's compatibility with the BioC format facilitates integration into existing biocuration pipelines, improving efficiency.