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

Knowledge graphs (KGs) have emerged as a powerful tool for knowledge discovery. In this perspective paper, we present a framework for KG construction, graph representation learning, and predictive modeling towards AI-driven discovery in biohealth. We illustrate this through two case studies: (1) Protein Knowledge Network (ProKN) and KSMoFinder, a KG embedding-based model that predicts protein kinase and phosphorylation site associations with state-of-the-art accuracy by learning from biological context in a biomedical knowledge network for drug discovery; (2) Social Determinants of Health (SDoH) KG, built from synthetic data of Veteran Health Administration with a veteran suicide-risk prediction model that uncovers latent, multifactorial risk patterns. These use cases spanning biomedical and population health research, demonstrate how KG-driven AI can bridge the gap between molecular and population level studies. We highlight how such open, interoperable knowledge networks offer a reusable framework for accelerating discovery and addressing complex health challenges. Finally, we provide targeted recommendations for Delaware's health innovation ecosystem to leverage this paradigm for public health strategy, clinical decision-making, and translational research.