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Inferring Drug-Protein⁻Side Effect Relationships from Biomedical Text.

Min Song1, Seung Han Baek2, Go Eun Heo3

  • 1Department of Library and Information Science, Yonsei University, Seoul 03722, Korea. min.song@yonsei.ac.kr.

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|February 23, 2019
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Summary

This study uses text mining to identify drug-side effect pathways, uncovering novel hypotheses for understanding drug mechanisms and improving safety. The approach generates predictable paths to aid researchers in elucidating complex biomedical relationships.

Keywords:
Biomedical Text MiningInference of Drug-Protein-Side Effect RelationSemantic Relatedness

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Area of Science:

  • Biomedical Informatics
  • Pharmacology
  • Computational Biology

Background:

  • Mechanisms underlying drug side effects are poorly understood.
  • Identifying specific pathways between drugs and their adverse effects is challenging.

Purpose of the Study:

  • To construct potential drug-side effect pathways using text-mining techniques.
  • To develop ranking metrics for identifying the most probable pathways.

Main Methods:

  • Extracted drug-protein, protein-protein, and protein-side effect relationships from biomedical texts using text mining and relation-extraction rules.
  • Constructed drug-protein-side effect paths and ranked them using a novel function combining semantic similarity and co-occurrence frequency.

Main Results:

  • Identified 13 plausible biomedical paths linking drugs to side effects from PubMed cancer abstracts.
  • The proposed ranking function demonstrated superior performance compared to existing methods.
  • Confirmed the generated paths as novel hypotheses for further investigation.

Conclusions:

  • Automatically generated predictable drug-side effect paths offer valuable insights into underlying biomedical mechanisms.
  • This approach aids researchers in generating plausible hypotheses for understanding complex drug-induced side effects.
  • Facilitates a deeper understanding of drug safety and risk assessment.