MicrobiomeKG: Bridging Microbiome Research and Host Health Through Knowledge Graphs

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.