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A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

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Published on: October 13, 2023

Improving Biomedical Knowledge Graph Quality: A Community Approach.

Katherina G Cortes¹, Shilpa Sundar², Sarah Gehrke³

¹Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus.

|September 5, 2025

Summary

This summary is machine-generated.

Biomedical knowledge graphs (KGs) need better standards for creation and sharing. Current KGs lack transparency, hindering reuse and comparability in biomedical research.

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Area of Science:

Biomedical Informatics
Data Science
Knowledge Representation

Background:

Biomedical knowledge graphs (KGs) are crucial tools in research and translation.
Existing KGs often lack consistent design, documentation, and dissemination practices, unlike ontologies.
This opacity limits their external reuse and comparability.

Purpose of the Study:

To establish and apply a set of evaluation criteria for biomedical KGs.
To assess the transparency and reusability of existing biomedical KGs.
To identify areas for standardization and improvement in KG development.

Main Methods:

Developed evaluation criteria based on data standards and principles from related fields.
Applied these criteria to a diverse set of 16 biomedical KGs.
Analyzed variations in KG models, source integration, and terminology.

Main Results:

Many biomedical KGs, even those appearing to follow best practices, obscure essential information for reuse.
Significant variations exist in KG models, data integration, and node type terminology across different KGs.
Lack of transparency is a common issue, impeding external use.

Conclusions:

Community-wide adoption of shared criteria and standards (e.g., BioLink, KGX) is essential.
Improved transparency and standardization will enhance KG reusability and comparability.
Maturing KG standards is critical for maximizing their utility in biomedical research.