primekg

Query PrimeKG for genes, drugs, diseases, and phenotypes via Python scripts.

Updated Mar 10, 2026

Search Tags:#knowledge graph #drug discovery #biology #genetics #precision medicine #primekg

Ready to run

npx skills add https://github.com/felixboehm/biochem-allergy --skill primekg-felixboehm

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: primekg
Source: https://github.com/felixboehm/biochem-allergy/tree/main/.claude/skills/primekg
Command: npx skills add https://github.com/felixboehm/biochem-allergy --skill primekg-felixboehm

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

This Skill provides programmatic access to the Precision Medicine Knowledge Graph (PrimeKG), enabling users to query complex biological data and uncover relationships between genes, drugs, diseases, and phenotypes.

Core Features & Use Cases

Entity Search: Find nodes (genes, drugs, diseases) within the graph.
Relationship Exploration: Retrieve direct associations and neighbors for any given node.
Disease Context Analysis: Summarize key associated genes, drugs, and phenotypes for a specific disease.
Use Case: A researcher can use this Skill to find all known drugs associated with a specific gene implicated in a rare disease, potentially identifying repurposing opportunities.

Quick Start

Use the primekg skill to find all genes associated with Alzheimer's disease.

Frequently Asked Questions about primekg

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I query a biological knowledge graph for drug discovery and disease associations?▼

Query a biological knowledge graph for drug discovery by using Python scripts to search multiscale data nodes like genes, drugs, and diseases. You retrieve direct associations and local disease contexts to uncover relationships.

Can I find drug repurposing opportunities by exploring gene associations in a precision medicine dataset?▼

Find drug repurposing opportunities in a precision medicine dataset by querying specific gene nodes to retrieve their direct drug associations. This identifies known relationships implicated in specific disease contexts.

What is the best way to analyze disease contexts using a precision medicine knowledge graph?▼

Analyze disease contexts using a precision medicine knowledge graph by querying a specific disease node to summarize its associated genes, drugs, and phenotypes. This reveals local biological relationships.

Do I need Python to query the Precision Medicine Knowledge Graph for phenotype analysis?▼

You need Python to query the Precision Medicine Knowledge Graph for phenotype analysis because programmatic access via scripts is required to explore the CSV-based multiscale biological data.

How does exploring node neighbors help with precision medicine research?▼

Exploring node neighbors helps precision medicine research by retrieving direct associations for any given entity, allowing you to map multiscale biological relationships between genes, drugs, diseases, and phenotypes.

Are there limitations when using a CSV-based knowledge graph for complex phenotype analysis?▼

Limitations of a CSV-based knowledge graph for phenotype analysis include the requirement for local Python script execution to query data, which may constrain real-time exploration of highly complex multiscale biological relationships.