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TransMiner: mining transitive associations among biological objects from text.

Vijay Narayanasamy1, Snehasis Mukhopadhyay, Mathew Palakal

  • 1School of Informatics, Indiana University School of Medicine, Indiana University Purdue University Indianapolis, Ind., USA.

Journal of Biomedical Science
|December 14, 2004
PubMed
Summary

TransMiner automatically discovers direct and transitive associations between biological objects like genes and proteins from scientific literature. This system aids in generating novel hypotheses by identifying previously unstated relationships in biomedical research.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Biomedical Informatics

Background:

  • Scientific literature contains vast amounts of information on associations between biological objects (genes, proteins, drugs).
  • Automated methods are needed to efficiently extract these complex relationships from large databases like Medline.
  • Discovering both direct and indirect (transitive) associations can reveal novel biological insights and generate hypotheses.

Purpose of the Study:

  • To introduce TransMiner, a novel system for discovering direct and transitive associations among biological objects from the Medline database.
  • To develop and validate a method for identifying potential transitive associations and distinguishing them from direct ones.
  • To enable hypothesis generation by uncovering associations not explicitly stated in the literature.

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Main Methods:

  • Utilized the Medline database for mining scientific literature.
  • Employed the principle of co-occurrence to build an association graph of direct relationships.
  • Applied the principle of transitive closure to identify potential indirect associations.
  • Developed a ranking system for transitive associations based on co-occurring terms.
  • Integrated a graph visualization applet for displaying discovered associations.

Main Results:

  • Successfully identified direct and transitive associations among biological objects.
  • Demonstrated the system's capability by analyzing breast cancer genes, the calpain signal transduction pathway, and magnesium-migraine associations.
  • Validated the rediscovery of known associations, confirming the system's efficacy.

Conclusions:

  • TransMiner effectively mines the scientific literature to discover direct and transitive associations among biological entities.
  • The system provides a valuable tool for hypothesis generation in biomedical research by uncovering implicit relationships.
  • Visualization of association graphs aids in understanding complex biological networks.