Search the Precision Medicine Knowledge Graph for genes, drugs, diseases, and phenotypes.
npx skills add https://github.com/jasrajtulsi/GRAD-SCOPE --skill primekg-jasrajtulsi
Please help me install this Agent Skill. Skill: primekg Source: https://github.com/jasrajtulsi/GRAD-SCOPE/tree/main/.claude/skills/primekg Command: npx skills add https://github.com/jasrajtulsi/GRAD-SCOPE --skill primekg-jasrajtulsi
Finds precision-medicine relationships in PrimeKG without manually exploring a large biomedical graph, helping users move from a disease, gene, or drug name to actionable evidence quickly.
Ask the skill to search PrimeKG for a disease or drug name and return its direct neighbors and disease context.
High-intent search queries and answers about installing and using this skill.
You can search a biomedical knowledge graph by querying the Precision Medicine Knowledge Graph (PrimeKG) to retrieve genes, drugs, diseases, and phenotypes for a given query. It uses pandas-backed loading to search CSV-based graph data and return direct neighbors.
Disease context exploration in precision medicine research summarizes a specific disease's associated genes, drugs, phenotypes, and related diseases. It retrieves direct associations from the knowledge graph to support multiscale biology tasks and hypothesis generation.
Yes, you can find drug repurposing paths using a pandas knowledge graph workflow by discovering drug-disease paths within PrimeKG. The process performs direct neighbor retrieval and optional relation filtering on CSV-based graph data for deterministic analysis.
The best way to retrieve direct neighbors of a gene in a biomedical graph is querying PrimeKG by entity name. It loads the graph data with pandas, performs entity searches, and returns direct associations with their specific relationship types.
Yes, you need pandas to query PrimeKG for biomedical entities. The skill requires pandas as a dependency for CSV-based graph querying, loading the data to perform entity searches, neighbor lookups, and optional relation filtering.
You use relation filtering when analyzing a disease's biological network to ensure deterministic analysis of specific associations. It restricts the direct neighbors retrieved from the knowledge graph, narrowing the precision medicine relationships to relevant biological and clinical evidence.
dralkhQuery PrimeKG CSV data to retrieve biomedical entities and relationships.
shushuznSearch biomedical entities and extract neighbor associations from the PrimeKG knowledge graph.
yf8578Query PrimeKG to retrieve genes, drugs, diseases, and phenotypes.
OwnLabAIQuery PrimeKG knowledge graph nodes and neighbors using Python and pandas.
