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BRIEF RESEARCH REPORT article

Front. Syst. Biol.

Sec. Integrative Systems Microbiology

Volume 5 - 2025 | doi: 10.3389/fsysb.2025.1544432

This article is part of the Research TopicInsights in Human and Medical Genomics 2024View all 6 articles

MicrobiomeKG: Bridging Microbiome Research and Host Health Through Knowledge Graphs

Provisionally accepted
Skye  L GoetzSkye L Goetz1Amy  K GlenAmy K Glen2Gwênlyn  GlusmanGwênlyn Glusman1*
  • 1Institute for Systems Biology (ISB), Seattle, United States
  • 2Oregon State University, Corvallis, Oregon, United States

The final, formatted version of the article will be published soon.

The microbiome represents a complex community of trillions of microorganisms residing in various body parts, and plays critical roles in maintaining host health and well-being. Understanding the interactions between microbiota and host offers valuable insights into potential strategies to promote health, including microbiome-targeted interventions. We have created MicrobiomeKG, a Knowledge Graph for microbiome research, bridging various taxa and microbial pathways with host health. This novel knowledge graph derives algorithmically-generated knowledge assertions from the supplementary tables supporting published microbiome papers. By identifying knowledge assertions from supplementary tables, and expressing them as knowledge graphs, we are casting this valuable content into a format that is ideal for hypothesis generation. To address the high heterogeneity of study contexts, methodologies, and reporting standards, we leveraged neural networks to implement a standardized edge scoring system, which we use to perform centrality analyses. We present three example use cases, linking helminth infections with non-alcoholic fatty-liver disease via microbial taxa, exploring connections between the Alistipes genus and inflammation, and identifying the Bifidobacterium genus as the most central connection with Attention Deficit Hyperactivity Disorder. MicrobiomeKG is deployed for integrative analysis and hypothesis generation, both programmatically and via the Biomedical Data Translator ecosystem. By bridging data gaps and facilitating the discovery of new biological relationships, MicrobiomeKG will help advance personalized medicine through a deeper understanding of the microbial contributions to human health and disease mechanisms.

Keywords: Systems Biology, data integration, Supplementary data, Table mining, Knowledge representation, health informatics, hypothesis generation, neural networks

Received: 26 Dec 2024; Accepted: 01 Aug 2025.

Copyright: © 2025 Goetz, Glen and Glusman. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Gwênlyn Glusman, Institute for Systems Biology (ISB), Seattle, United States

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