primekg

PrimeKG Knowledge Graph Skill

Overview

PrimeKG is a precision medicine knowledge graph that integrates over 20 primary databases and high-quality scientific literature into a single resource. It contains over 100,000 nodes and 4 million edges across 29 relationship types, including drug-target, disease-gene, and phenotype-disease associations.

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: Programmatic access via query_primekg.py. Data is stored at C:\Users\eamon\Documents\Data\PrimeKG\kg.csv.

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.

from scripts.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 scripts.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 scripts.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']

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

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: Combine with OpenTargets for deeper genetic evidence or Semantic Scholar for the latest literature context.

Resources

Scripts

scripts/query_primekg.py: Core functions for searching and querying the knowledge graph.

Data Path

Data: /mnt/c/Users/eamon/Documents/Data/PrimeKG/kg.csv

Total nodes: ~129,000

Total edges: ~4,000,000

Database: CSV-based, optimized for pandas querying.

Author

Category

Install