Oct 11, 2012

Ranking relations between diseases, drugs and genes for a curation task

Journal of Biomedical Semantics
Simon Clematide, Fabio Rinaldi

Abstract

One of the key pieces of information which biomedical text mining systems are expected to extract from the literature are interactions among different types of biomedical entities (proteins, genes, diseases, drugs, etc.). Several large resources of curated relations between biomedical entities are currently available, such as the Pharmacogenomics Knowledge Base (PharmGKB) or the Comparative Toxicogenomics Database (CTD).Biomedical text mining systems, and in particular those which deal with the extraction of relationships among entities, could make better use of the wealth of already curated material. We propose a simple and effective method based on logistic regression (also known as maximum entropy modeling) for an optimized ranking of relation candidates utilizing curated abstracts. Furthermore, we examine the effects and difficulties of using widely available metadata (i.e. MeSH terms and chemical substance index terms) for relation extraction. Cross-validation experiments result in an improvement of the ranking quality in terms of AUCiP/R by 39% (PharmGKB) and 116% (CTD) against a frequency-based baseline of 0.39 (PharmGKB) and 0.21 (CTD). For the TAP-10 metrics, we achieve an improvement of 53% (PharmGKB) and 134% (CTD) a...Continue Reading

  • References20
  • Citations10

References

  • References20
  • Citations10

Citations

Mentioned in this Paper

Pharmacogenomics
Chemicals
Biomedicine
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Toxicogenomics

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