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

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Desiderata for a biomedical knowledge network: opportunities, challenges and future Directions.

Chunlei Wu¹, Hongfang Liu², Jason Flannick³

¹Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037, United States.

|October 3, 2025

Summary

Biomedical knowledge graphs require domain-specific reasoning, standardized data, and robust validation for effective AI-driven discovery. Addressing these challenges is key to advancing biomedicine through a connected knowledge network.

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Area of Science:

Biomedical Informatics
Knowledge Representation
Artificial Intelligence

Background:

Knowledge graphs are vital for knowledge discovery in complex biomedical systems.
Biomedical data complexity necessitates dynamic reasoning, abstraction, and standardization in knowledge graphs.

Purpose of the Study:

To outline key desiderata for developing and utilizing biomedical knowledge graphs.
To address challenges and define future directions for a biomedical knowledge network.

Main Methods:

Synthesized insights from a meeting of leading scientists.
Identified six critical areas for advancing biomedical knowledge graphs.

Main Results:

Domain-centric reasoning is needed for biomedical knowledge graphs.
Harmonized standards, robust validation, and integration with large language models are essential.
Secure sharing frameworks and ethical governance are critical for trustworthy knowledge graphs.

Conclusions:

Addressing these six desiderata will enable trustworthy and actionable biomedical knowledge discovery.
Realizing the potential of a biomedical knowledge network is crucial for advancing the field of biomedicine.