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primekg

查询精准医学知识图谱（PrimeKG），以获取包含基因、药物、疾病、表型等多尺度生物数据。

作者

@K-Dense-AI

分类

开发工具

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name:primekgdescription:Query the Precision Medicine Knowledge Graph (PrimeKG) for multiscale biological data including genes, drugs, diseases, phenotypes, and more.license:Unknownmetadata:[object Object]

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.