Precision Medicine Oriented Knowledge Graph

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.

We introduce 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 scale, and the entire range of approved and experimental drugs with their therapeutic action, considerably expanding previous efforts in disease-rooted knowledge graphs.

PrimeKG supports drug-disease prediction by including an abundance of ’indications’, ’contradictions’ and ’off-label use’ edges, which are usually missing in other knowledge graphs. We accompany PrimeKG's graph structure with text descriptions of clinical guidelines for drugs and diseases to enable multi-modal analyses.

The figure below provides an overview of PrimeKG. Panel a shows a schematic overview of the various types of nodes in PrimeKG and the relationships they have with other nodes in the graph.

Panel b shows all disease nodes in PrimeKG visualized in a circular layout together with disease-associated information. Shown are relationships between disease nodes and any other node type. Disease nodes are densely connected to four other node types in PrimeKG through seven types of relations.

Panel c shows an example of paths in PrimeKG between the disease node ‘Autism’ and the drug node ‘Risperidone’. Intermediate nodes are colored by their node type from panel a. We also display snippets of text features for both nodes to demonstrate the multimodal nature of PrimeKG.

Abbreviations - MF: molecular function, BP: biological process, CC: cellular component, APZ: Apiprazole, EPI: epilepsy, ABP: abdominal pain, + / - associations: positive and negative associations.

Publication

Building a knowledge graph to enable precision medicine
Payal Chandak*, Kexin Huang*, and Marinka Zitnik
In Review 2022 [bioRxiv]

@article{chandak2022building,
  title={Building a knowledge graph to enable precision medicine},
  author={Chandak, Payal and Huang, Kexin and Zitnik, Marinka},
  journal={bioRxiv},
  doi={10.1101/2022.05.01.489928},
  url={https://www.biorxiv.org/content/early/2022/05/01/2022.05.01.489928},
  year={2022},
  publisher={Cold Spring Harbor Laboratory}
}

Code

The code to reproduce results, together with documentation and tutorials, is available in PrimeKG’s Github repository.

Data availability

PrimeKG is hosted on Harvard Dataverse. We deposited the knowledge graph along with all relevant intermediate files at this repository.

Authors

Latest News

May 2022:   George Named the 2022 Wojcicki Troper Fellow

May 2022:   New preprint on PrimeKG

New preprint on building knowledge graphs to enable precision medicine applications.

Apr 2022:   Webster on the Cover of Cell Systems

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Apr 2022:   NASA Space Biology

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Mar 2022:   Yasha's Graduate Research Fellowship

Yasha won the National Defense Science and Engineering Graduate (NDSEG) Fellowship. Congratulations!

Mar 2022:   AI4Science at ICML 2022

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Mar 2022:   Graph Algorithms in Biomedicine at PSB 2023

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Mar 2022:   Multimodal Learning on Graphs

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Owen has been selected to present our research on explainable biomedical AI to members of the US Congress at the “Posters on the Hill” symposium. Congrats Owen!

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Feb 2022:   Biomedical Graph ML Tutorial Accepted to ISMB

Excited to present a tutorial at ISMB 2022 on graph representation learning for precision medicine. Congratulations, Michelle!

Feb 2022:   Marissa Won the Gates Cambridge Scholarship

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Jan 2022:   Inferring Gene Multifunctionality

Jan 2022:   Deep Graph AI for Time Series Accepted to ICLR

Paper on graph representation learning for time series accepted to ICLR. Congratulations, Xiang!

Jan 2022:   Probing GNN Explainers Accepted to AISTATS

Jan 2022:   Marissa Sumathipala selected as Churchill Scholar

Marissa Sumathipala is selected for the prestigious Churchill Scholarship. Congratulations, Marissa!

Jan 2022:   Therapeutics Data Commons User Meetup

We invite you to join the growing open-science community at the User Group Meetup of Therapeutics Data Commons! Register for the first live user group meeting on Tuesday, January 25 at 11:00 AM EST.

Jan 2022:   Workshop on Graph Learning Benchmarks

Dec 2021:   NASA: Precision Space Health System

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