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Related Experiment Video

Updated: Jul 30, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

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From language models to large-scale food and biomedical knowledge graphs.

Gjorgjina Cenikj1,2, Lidija Strojnik3, Risto Angelski4

  • 1Jožef Stefan Institute, Ljubljana, 1000, Slovenia. gjorgjina.cenikj@ijs.si.

Scientific Reports
|May 15, 2023
PubMed
Summary

Automated relation mining pipelines extract dietary and biomedical interactions from research, achieving 70% precision. This aids medical professionals by structuring scattered knowledge, reducing manual research efforts.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Medical Informatics

Background:

  • Dietary and biomedical factor interactions are dispersed across numerous unstructured research articles.
  • Existing biomedical knowledge graphs lack comprehensive relations between food and biomedical entities.
  • Automatic structuring of this knowledge is crucial for medical professionals.

Purpose of the Study:

  • To evaluate the performance of three relation-mining pipelines (FooDis, FoodChem, ChemDis) for extracting food-chemical-disease relations.
  • To assess the feasibility of automatically structuring scattered knowledge on dietary and biomedical interactions.
  • To reduce the human effort required for medical professionals to access novel scientific discoveries.

Main Methods:

  • Utilized three state-of-the-art relation-mining pipelines: FooDis, FoodChem, and ChemDis.
  • Extracted relations between food, chemical, and disease entities from textual data.
  • Conducted two case studies involving automatic relation extraction and validation by domain experts.

Main Results:

  • The relation-mining pipelines achieved an average precision of approximately 70%.
  • Automated extraction successfully identified interactions between dietary and biomedical factors.
  • Domain experts validated the extracted relations, confirming the pipelines' utility.

Conclusions:

  • Relation-mining pipelines offer a viable solution for structuring complex dietary and biomedical knowledge.
  • These tools can significantly reduce the time and effort medical professionals spend on literature review.
  • The study demonstrates the potential for automated knowledge discovery in bridging the gap between food science and medicine.