Building a Knowledge Graph to Enable Precision Medicine

Developing personalized diagnostic strategies and targeted treatments requires a deep understanding of disease biology and the ability to dissect the relationship between molecular and genetic factors and their phenotypic consequences. However, such knowledge is fragmented across publications, non-standardized research repositories, and evolving ontologies describing various scales of biological organization between genotypes and clinical phenotypes. Here, we present PrimeKG, a precision medicine-oriented knowledge graph that provides a holistic view of diseases. PrimeKG integrates 20 high-quality resources to describe 17,080 diseases with 4,050,249 relationships representing ten major biological scales, including disease-associated protein perturbations, biological processes and pathways, anatomical and phenotypic scales, and the entire range of approved and experimental drugs with their therapeutic action, considerably expanding previous efforts in disease-rooted knowledge graphs. In addition, PrimeKG supports artificial intelligence analyses of how drugs might target disease-associated molecular perturbations by containing an abundance of indications, contradictions, and off-label use drug-disease edges lacking in other knowledge graphs. We accompany PrimeKGs graph structure with text descriptions of clinical guide-lines to enable multimodal analyses.