PrimeKG Knowledge Graph Skill
Overview
PrimeKG is a precision medicine knowledge graph that integrates 20 high-quality primary resources
into a single edge list. It describes 17,080 diseases with 4,050,249 relationships across ten
major biological scales — drug-target, disease-gene, phenotype-disease, pathway and anatomical
associations among them — over roughly 129,000 nodes.
Its distinguishing feature is drug-disease coverage: PrimeKG carries indication,
contraindication, and off-label use edges that most disease knowledge graphs lack, which is
what makes repurposing questions answerable here rather than merely askable.
Cite: Chandak P, Huang K, Zitnik M. Building a knowledge graph to enable precision medicine.
Sci Data 10, 67 (2023). PMID 36732524.
Key capabilities:
- Search for nodes (genes, proteins, drugs, diseases, phenotypes)
- Retrieve direct neighbors (associated entities and clinical evidence)
- Analyze local disease context (related genes, drugs, phenotypes)
- Identify drug-disease paths (potential repurposing opportunities)
Data access: scripts/query_primekg.py reads kg.csv from the path in the PRIMEKG_DATA
environment variable (default data/PrimeKG/kg.csv). Download the CSV first — see
Data Path. The script works as a CLI or as an importable module:
python skills/primekg/scripts/query_primekg.py search Alzheimer --node-type disease
python skills/primekg/scripts/query_primekg.py neighbors EFO_0000249 --relation disease_protein
python skills/primekg/scripts/query_primekg.py context "Alzheimer's disease"
python skills/primekg/scripts/query_primekg.py paths CHEMBL1 D001 --max-depth 2
Add --format json for machine-readable output, or --data /path/to/kg.csv to override
PRIMEKG_DATA for one run. Every subcommand exits non-zero when the data file is missing.
When to Use This Skill
This skill should be used when:
- Knowledge-based drug discovery: Identifying targets and mechanisms for diseases.
- Drug repurposing: Finding existing drugs that might have evidence for new indications.
- Phenotype analysis: Understanding how symptoms/phenotypes relate to diseases and genes.
- Multiscale biology: Bridging the gap between molecular targets (genes) and clinical outcomes (diseases).
- Network pharmacology: Investigating the broader network effects of drug-target interactions.
Core Workflow
1. Search for Entities
Find identifiers for genes, drugs, or diseases.
import sys
sys.path.insert(0, "skills/primekg/scripts")
from query_primekg import search_nodes
results = search_nodes("Alzheimer", node_type="disease")
2. Get Neighbors (Direct Associations)
Retrieve all connected nodes and relationship types.
from query_primekg import get_neighbors
neighbors = get_neighbors("EFO_0000249")
3. Analyze Disease Context
A high-level function to summarize associations for a disease.
from query_primekg import get_disease_context
context = get_disease_context("Alzheimer's disease")
4. Connect Two Entities (Repurposing Hypotheses)
Find how a drug and a disease are linked, either directly or through one shared
intermediate node. Edges are traversed as undirected.
from query_primekg import find_paths
paths = find_paths("CHEMBL1", "D001")
paths = find_paths("CHEMBL1", "D001", max_depth=1)
for hops in paths:
print(" -> ".join(hop["display_relation"] for hop in hops))
Only depths 1 and 2 are supported; any other max_depth raises ValueError.
Three or more hops through a 4-million-edge graph run through hub nodes and are
rarely interpretable.
Relationship Types in PrimeKG
The graph contains several key relationship types including:
protein_protein: Physical PPIsdrug_protein: Drug target/mechanism associationsdisease_gene: Genetic associationsdrug_disease: Indications and contraindicationsdisease_phenotype: Clinical signs and symptomsgwas: Genome-wide association studies evidence
Best Practices
- Use specific IDs: When using
get_neighbors, ensure you have the correct ID from search_nodes.
- Context first: Use
get_disease_context for a broad overview before diving into specific genes or drugs.
- Filter relationships: Use the
relation_type filter in get_neighbors to focus on specific evidence (e.g., only drug_protein).
- Multiscale integration: see Composing below — PrimeKG asserts that a relationship exists,
not how strong the evidence is. Pair it with a scored source before acting.
Composing with the rest of the bundle
open-targets → alongside: PrimeKG tells you an edge exists; Open Targets scores how strong
the evidence is and names the datatype behind it. A PrimeKG disease_protein edge and an Open
Targets association driven only by literature are the same claim at different resolutions.ncats-arax → instead, when provenance matters: ARAX returns Biolink-typed relationships with
source attribution per edge. PrimeKG gives you the graph but not the citation for each edge.target-safety → after: a disease_protein edge says nothing about whether inhibiting the
protein is tolerated. gnomAD constraint does.depmap → after: whether the gene is actually required in cells, not merely associated.chembl → after: what has been made against a protein this graph implicates.clinicaltrials → after: PrimeKG's indication and off-label edges are a hypothesis generator;
the registry says whether anyone has tested it.
Two-hop paths are hypotheses, not evidence. Traversal through a hub node connects almost
anything to almost anything — read the intermediate node before believing the path.
Resources
Scripts
scripts/query_primekg.py: search, neighbours, disease context and path finding, usable as a
CLI or as an importable module.
Data Path
- Data:
kg.csv, downloaded from the PrimeKG Harvard Dataverse.
- Point the scripts at it with
export PRIMEKG_DATA=/path/to/kg.csv (default: data/PrimeKG/kg.csv).
- Total nodes: ~129,000
- Total edges: ~4,000,000
- Database: CSV-based, optimized for pandas querying.
