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. The published Dataverse files are a fixed 2022 snapshot. > **Superseded upstream.** The PrimeKG maintainers now state that PrimeKG has been superseded by **OptimusKG** (same lab; a superset with more current data — 190,531 nodes, 21.8M edges, 26 relation types) and recommend it for almost all new work. Use OptimusKG for new analyses (`uv pip install optimuskg`, Python ≥ 3.12; `import optimuskg; nodes, edges = optimuskg.load_graph(lcc=True)` or `G = optimuskg.load_networkx(lcc=True)`; data: Harvard Dataverse doi:10.7910/DVN/IYNGEV, docs: https://optimuskg.ai). Keep this PrimeKG workflow for reproducing or comparing against published PrimeKG results and benchmarks. **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...