logo Netzschleuder network catalog, repository, and centrifuge

primekg — PrimeKG precision medicine knowledge graph (2022)

Description

The Precision Medicine Knowledge Graph (PrimeKG) of Chandak, Huang and Zitnik, which integrates 20 biomedical resources (e.g. DrugBank, DisGeNET, the Human Phenotype Ontology, Reactome, Gene Ontology, UBERON, SIDER) into a single graph centered on diseases. Nodes are biological and medical entities of ten types (genes/proteins, drugs, diseases, phenotypes, exposures, anatomical regions, biological processes, molecular functions, cellular components and pathways), and an undirected edge is a relationship between two of them, of one of 30 types (e.g. protein-protein interaction, drug indication, disease-phenotype association, drug side effect).

There are four networks, the final graph and the intermediate stages of its construction, as released by the authors (version 2.1 of the data set):

  • kg: the final knowledge graph (from nodes.csv and edges.csv).
  • raw: all the relationships of the integrated resources (kg_raw.csv).
  • giant: the largest connected component of raw (kg_giant.csv).
  • grouped: giant after grouping the diseases whose names correspond to subtypes of the same disease into single nodes (kg_grouped.csv); this has the same relationships as the final graph.

The upstream files list each relationship in both directions; each is included here once. In the intermediate networks, nodes are identified by their identifier, type, name and source. As in the authors' final graph, 63 genes appear as two nodes, under two names (e.g. an old and a current gene symbol) with the same NCBI identifier.

The networks have the following properties:

  • node_id: the identifier of the entity in its source (e.g. an NCBI gene ID, a DrugBank ID, a MONDO ID).
  • node_type: the type of the entity (gene/protein, drug, disease, effect/phenotype, exposure, anatomy, biological_process, molecular_function, cellular_component, pathway).
  • name: the name of the entity.
  • source: the source of the identifier (e.g. NCBI, DrugBank, MONDO, HPO, REACTOME).
  • relation (edges): the type of the relationship (e.g. protein_protein, drug_protein, indication, disease_phenotype_positive).
  • display_relation (edges): a shorter name of the type of relationship (e.g. ppi, target, indication).
Tags
Biological Informational Unweighted Multigraph Metadata
Citation
Upstream URL OK
https://doi.org/10.7910/DVN/IXA7BM
https://github.com/mims-harvard/PrimeKG
Upstream license
CC0 1.0, https://creativecommons.org/publicdomain/zero/1.0/
Networks
Tip: click on a column header to sort the list, or hover over it for a description.
Column legend
$\left<k\right>$
Mean degree
$\sigma_k$
Standard deviation of the degrees
$\lambda_h$
Largest eigenvalue of the Hashimoto (non-backtracking) matrix
$\tau$
Random walk mixing time
$r$
Degree assortativity
$c$
Global clustering coefficient
$\oslash$
(Pseudo-) diameter
$S$
Relative size of the largest component
NPs
Node properties
EPs
Edge properties
Name Nodes Edges $\left<k\right>$ $\sigma_k$ $\lambda_h$ $\tau$ $r$ $c$ $\oslash$ $S$ Kind Mode NPs EPs gt GraphML GML csv
kg 129,375 4,050,249 62.61 420.36 1125.67 3508.75 -0.29 0.08 23 1.00 Undirected Unipartite node_id node_type name source relation display_relation 5.6 MiB 18.0 MiB 17.8 MiB 17.1 MiB
raw 134,583 4,098,513 60.91 412.61 1125.68 3508.62 -0.29 0.08 23 1.00 Undirected Unipartite node_id node_type name source relation display_relation 5.7 MiB 18.3 MiB 18.2 MiB 17.5 MiB
giant 134,500 4,098,431 60.94 412.74 1125.68 3508.41 -0.29 0.08 23 1.00 Undirected Unipartite node_id node_type name source relation display_relation 5.8 MiB 18.3 MiB 18.2 MiB 17.4 MiB
grouped 129,375 4,050,249 62.61 420.36 1125.67 3508.14 -0.29 0.08 23 1.00 Undirected Unipartite node_id node_type name source relation display_relation 5.6 MiB 18.0 MiB 17.8 MiB 17.1 MiB