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bioRxiv · 10.1101/2022.05.01.489928

Building a knowledge graph to enable precision medicine

Chandak, P.·Huang, K.·Zitnik, M.
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Abstract

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.

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Chandak, P., Huang, K., Zitnik, M.. 2022-05-01. Building a knowledge graph to enable precision medicine. https://doi.org/10.1101/2022.05.01.489928

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