Building a knowledge graph to enable precision medicine
Harvard–MIT Division of Health Sciences and Technology · Stanford University · +3 more institutions
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 repositories, and evolving ontologies describing various scales of biological organization between genotypes and clinical phenotypes. Here, we present PrimeKG, a multimodal knowledge graph for precision medicine analyses. 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…
Citation impact
- FWCI
- 60.84
- Percentile
- 100%
- References
- 120
Authors
3Topics & keywords
- Disease
- Precision medicine
- Computer science
- Personalized medicine
- Clinical phenotype
- Data science
- Computational biology
- Knowledge graph