alterlab-primekg

# PrimeKG Knowledge Graph Skill ## Overview PrimeKG (Chandak, Huang & Zitnik, *Scientific Data* 2023; mims-harvard/PrimeKG) is a precision medicine knowledge graph integrating 20 primary resources. It contains 129,375 nodes and 4,050,249 edges across 30 edge types and 10 node types, including drug-target, disease-gene, and disease-phenotype 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 `scripts/query_primekg.py`. Point the loader at the `kg.csv` released on Harvard Dataverse via the `PRIMEKG_DATA_PATH` environment variable (it defaults to `../data/kg.csv` relative to the script). All functions operate on the `x_*`/`y_*`/`relation`/`display_relation` columns of `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 effec...