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MicrobiomeKG: Bridging Microbiome Research and Host Health Through Knowledge Graphs.

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MicrobiomeKG is a novel knowledge graph connecting microbes and their functions to host health. It aids in understanding the microbiome for health interventions and generating research hypotheses.

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Area of Science:

  • Microbiome research
  • Bioinformatics
  • Computational biology

Background:

  • The human microbiome, a vast community of microorganisms, significantly impacts host health.
  • Understanding host-microbe interactions is key for developing health-promoting strategies.
  • Microbiome-targeted interventions show promise for maintaining well-being.

Purpose of the Study:

  • To develop MicrobiomeKG, a Knowledge Graph for microbiome research.
  • To bridge the gap between microbial taxa, pathways, and host health.
  • To facilitate integrative analysis and hypothesis generation in microbiome studies.

Main Methods:

  • Created MicrobiomeKG, a novel knowledge graph.
  • Derived knowledge assertions algorithmically from supplemental tables of published microbiome papers.
  • Deployed the knowledge graph within the Biomedical Data Translator ecosystem.

Main Results:

  • Established a comprehensive Knowledge Graph (MicrobiomeKG) for microbiome research.
  • Enabled algorithmic extraction of knowledge from scientific literature.
  • Provided a platform for integrative analysis and hypothesis generation.

Conclusions:

  • MicrobiomeKG offers a valuable resource for exploring microbiome-host interactions.
  • The knowledge graph facilitates novel insights and accelerates research in the field.
  • This approach supports the development of microbiome-targeted health interventions.