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Exploiting semantic patterns over biomedical knowledge graphs for predicting treatment and causative relations.

Gokhan Bakal1, Preetham Talari2, Elijah V Kakani2

  • 1Department of Computer Science, University of Kentucky, United States.

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Summary
This summary is machine-generated.

This study introduces a novel method using semantic graph patterns to predict biomedical treatment and causative relations. The approach achieves high accuracy, aiding in the discovery of new drug treatments and understanding disease mechanisms.

Keywords:
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Area of Science:

  • Biomedical Informatics
  • Computational Biology
  • Drug Discovery

Background:

  • Biomedical research aims to find new treatments and understand disease causes.
  • In vitro methods are crucial for initial drug candidate screening.
  • Natural Language Processing (NLP) and machine learning predict relations between biomedical entities.

Purpose of the Study:

  • Develop accurate predictive models for unknown treatment and causative relations.
  • Utilize semantic graph pattern features from biomedical knowledge graphs.

Main Methods:

  • Trained models on curated relations from UMLS Metathesaurus (7000 treats, 2918 causes).
  • Extracted graph pattern features from SemMedDB knowledge graph paths.
  • Employed logistic regression and decision tree models with 1:10 class imbalance.

Main Results:

  • Achieved high F-scores: 99% for treatment relations, 90% for causative relations.
  • Identified discriminative graph patterns with intuitive interpretations.
  • Predicted plausible new relations and retrieved over 50% of treatment relations from an external dataset.

Conclusions:

  • Semantic graph patterns effectively predict biomedical treatment/causative relations.
  • Presents novel evidence for direct prediction of biomedical relations using graph features.
  • Complements existing lexical approaches by providing additional features for relation prediction.