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Related Experiment Video

Updated: Jun 11, 2026

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

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

Published on: October 13, 2023

Knowledge Graph-Driven AI in Biohealth: From Biomedical Discovery to Health Risk Prediction.

Chuming Chen^1,2, Manju Anandakrishnan², Cathy H Wu^1,2

¹Department of Computer and Information Sciences, University of Delaware.

Delaware Journal of Public Health

|June 10, 2026

Summary

This summary is machine-generated.

Knowledge graphs (KGs) accelerate biohealth discovery by integrating molecular and population data. This AI-driven framework enhances drug discovery and predicts health risks, offering reusable solutions for complex challenges.

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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

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Published on: June 13, 2025

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Last Updated: Jun 11, 2026

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

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

Area of Science:

Biohealth Informatics
Artificial Intelligence
Knowledge Representation

Background:

Knowledge graphs (KGs) are pivotal for knowledge discovery in biohealth.
Existing approaches often lack integration across molecular and population health levels.
AI-driven methods are needed to unlock complex health insights.

Purpose of the Study:

To present a framework for AI-driven discovery in biohealth using KGs.
To demonstrate the framework's application in drug discovery and population health.
To highlight the potential of open, interoperable knowledge networks.

Main Methods:

KG construction, graph representation learning, and predictive modeling.
Developed Protein Knowledge Network (ProKN) and KSMoFinder for kinase prediction.
Built a Social Determinants of Health (SDoH) KG for suicide-risk prediction.

Main Results:

KSMoFinder achieved state-of-the-art accuracy in predicting protein kinase associations.
The SDoH KG model uncovered latent, multifactorial risk patterns for veteran suicide.
Demonstrated KG-driven AI's ability to bridge molecular and population health studies.

Conclusions:

KG-driven AI provides a reusable framework for accelerating biohealth discovery.
This approach can address complex health challenges from molecular to population levels.
Recommendations are provided for leveraging KGs in public health and translational research.