UKB-KG: Knowledge Graph for Integrating and Enhancing Biomedical Insights from the UK Biobank

Music City Center 

The UK Biobank (UKB) is a cornerstone of modern biomedical research, providing
unparalleled data to advance the understanding, prediction, and treatment of
diseases. Its contributions span genetics, genomics, disease prediction, and long-term
follow-up studies, driving transformative advancements in public health and precision
medicine. However, the fragmentation of research outcomes across numerous
publications limits analytic efficiency and cross-study integration. To address this,
we developed UKB Knowledge Graph (UKB-KG), a high-quality medical knowledge
graph constructed using large language models (LLMs) with a precision rate of 85.6%.
Integrating data from approximately 7,000 UKB-related publications. UKB-KG comprises
137,328 triples enriched with contextual attributes such as source information
and demographic details. It reveals intricate relationships among genes, diseases,
environment variables, and lifestyle factors, while a dynamic scoring mechanism enhances
triple retrieval accuracy. Evaluations highlight UKB-KG's transformative
potential: (i) Embedding UKB-KG into multi-disease prediction models improves
AUROC, AUPRC, and F1 scores by 8.4%, 6.6%, and 3.2%, respectively, for rare diseases;
(ii) A tailored retrieval-augmented generation (RAG) approach boosted LLM
accuracy by 21% on PubMedQA; and (iii) A user-friendly platform enhances accessibility
for researchers. By unifying fragmented research and enabling robust data
exploration, UKB-KG emerges as a powerful tool for advancing biomedical research
and driving innovative healthcare applications.