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")   # scripts/ is not a package
from query_primekg import search_nodes

# Search for Alzheimer's disease nodes
results = search_nodes("Alzheimer", node_type="disease")
# Returns: [{"id": "EFO_0000249", "type": "disease", "name": "Alzheimer's disease", ...}]

2. Get Neighbors (Direct Associations)

Retrieve all connected nodes and relationship types.

from query_primekg import get_neighbors

# Get all neighbors of a specific disease ID
neighbors = get_neighbors("EFO_0000249")
# Returns: List of neighbors like {"neighbor_name": "APOE", "relation": "disease_gene", ...}

3. Analyze Disease Context

A high-level function to summarize associations for a disease.

from query_primekg import get_disease_context

# Comprehensive summary for a disease
context = get_disease_context("Alzheimer's disease")
# Access: context['associated_genes'], context['associated_drugs'], context['phenotypes']

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

# Direct edges first, then two-hop paths through a shared neighbour
paths = find_paths("CHEMBL1", "D001")            # max_depth=2 by default
paths = find_paths("CHEMBL1", "D001", max_depth=1)  # direct edges only

# Each path is a list of edge dicts, ordered start -> end:
# [{'relation': 'drug_protein', ...}, {'relation': 'disease_protein', ...}]
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 PPIs
• drug_protein: Drug target/mechanism associations
• disease_gene: Genetic associations
• drug_disease: Indications and contraindications
• disease_phenotype: Clinical signs and symptoms
• gwas: Genome-wide association studies evidence

Best Practices

1. Use specific IDs: When using get_neighbors, ensure you have the correct ID from search_nodes.
2. Context first: Use get_disease_context for a broad overview before diving into specific genes or drugs.
3. Filter relationships: Use the relation_type filter in get_neighbors to focus on specific evidence (e.g., only drug_protein).
4. 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.