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Hypothesizing mechanistic links between microbes and disease using knowledge graphs.

Brook E Santangelo1, Michael Bada2, Lawrence E Hunter2

  • 1Department of Biomedical Informatics, University of Colorado Denver Anschutz Medical Campus, Aurora, CO, USA. brook.santangelo@cuanschutz.edu.

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|February 26, 2025
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Summary
This summary is machine-generated.

Knowledge graphs can now generate mechanistic hypotheses for gut microbiome and disease links. This approach, using MGMLink, aids in understanding host-microbe interactions for conditions like inflammatory bowel disease and Parkinson's disease.

Keywords:
Computational biologyEmbedding methodsKnowledge graphsMicrobiome

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Area of Science:

  • Biomedical informatics
  • Microbiome research
  • Systems biology

Background:

  • Gut microbiome alterations correlate with various diseases.
  • Mechanistic understanding of host-microbe interactions in disease is challenging.
  • Knowledge graphs effectively represent complex biological concepts.

Purpose of the Study:

  • To demonstrate the potential of knowledge graphs for hypothesizing mechanistic explanations of host-microbe interactions in disease.
  • To develop a scalable and comprehensive approach for generating these hypotheses.

Main Methods:

  • Construction of a knowledge graph (MGMLink) linking microbes, genes, and metabolites.
  • Utilizing shortest path or template-based searches within the knowledge graph.
  • Application of a novel path-prioritization methodology for hypothesis inference.

Main Results:

  • The developed methodology supports the inference of mechanistic hypotheses for observed microbe-disease phenotype relationships.
  • Demonstrated applications in inflammatory bowel disease and Parkinson's disease.
  • The approach enables scalable and comprehensive generation of mechanistic hypotheses.

Conclusions:

  • Knowledge graphs, specifically MGMLink, offer a powerful tool for elucidating mechanistic links between the gut microbiome and disease.
  • This methodology facilitates a deeper understanding of complex host-microbe interactions.
  • The approach has significant implications for biomedical research and disease mechanism discovery.