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LitPathExplorer: a confidence-based visual text analytics tool for exploring literature-enriched pathway models.

Axel J Soto1, Chrysoula Zerva1, Riza Batista-Navarro1

  • 1National Centre for Text Mining, School of Computer Science, University of Manchester, Manchester M1 7DN, UK.

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Summary

LitPathExplorer is a new tool that uses text mining and machine learning to automatically extract biological pathway events from scientific literature, improving model curation and maintenance.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Text Mining

Background:

  • Pathway models are crucial for understanding biological processes.
  • Manual curation of pathway models from literature is time-consuming and difficult to maintain.
  • Automated support is needed for efficient pathway model updating.

Purpose of the Study:

  • To develop LitPathExplorer, a visual text analytics tool for exploring and analyzing biological pathway models.
  • To facilitate the extraction and organization of pathway events from scientific literature.
  • To provide a confidence measure for automatically extracted events.

Main Methods:

  • Integration of text mining, semi-supervised learning, and interactive visualization.
  • Automatic extraction of biological events (statements) from literature.
  • Computation of confidence values for extracted events based on linguistic features and metadata.

Main Results:

  • LitPathExplorer achieved 89% precision and 71% recall in event extraction.
  • Confidence measure showed 61-73% average precision in ranking events, improving to 95% with user involvement.
  • Qualitative evaluation confirmed the tool's utility for pathway model exploration.

Conclusions:

  • LitPathExplorer automates the extraction of evidence for pathway models.
  • The tool aids in discovering new events for model updates.
  • Confidence scores enhance the reliability of automatically extracted information.