[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6ECEVBJW98EHPX341SY2GQH":3,"skill-same:01M6ECEVBJW98EHPX341SY2GQH":56},{"skillNo":4,"name":5,"nameZh":6,"description":6,"descriptionZh":6,"repositoryFullName":7,"ownerLogin":8,"ownerAvatarUrl":9,"ownerAccountNo":10,"ownerDisplayName":11,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":15,"categories":16,"keywords":22,"publishedAt":23,"linkOnly":24,"markdownContent":25,"markdownBody":25,"frontMatter":26,"refName":27,"commitSha":28,"skillDirectory":29,"forksCount":30,"repositoryHtmlUrl":31,"repositoryHomepageUrl":32,"ownerHtmlUrl":33,"ownerBlogUrl":6,"ownerDescription":34,"updatedAt":35,"install":36,"resources":49},"01M6ECEVBJW98EHPX341SY2GQH","primekg",null,"ComeOnOliver\u002Fskillshub","ComeOnOliver","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F43215937?v=4","01M6E5JNFECJAAVFE0W7SMWWD2","Haoran Shu","01M6E5JNNDQS7AJZDYFQ8TPZCK",66,"MIT","https:\u002F\u002Fgithub.com\u002FComeOnOliver\u002Fskillshub\u002Fblob\u002Fmain\u002Fskills\u002FK-Dense-AI\u002Fclaude-scientific-skills\u002Fprimekg\u002FSKILL.md",[17],{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[5],"2026-11-10 10:12:30",false,"# PrimeKG Knowledge Graph Skill\n\n## Overview\n\nPrimeKG 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.\n\n**Key capabilities:**\n- Search for nodes (genes, proteins, drugs, diseases, phenotypes)\n- Retrieve direct neighbors (associated entities and clinical evidence)\n- Analyze local disease context (related genes, drugs, phenotypes)\n- Identify drug-disease paths (potential repurposing opportunities)\n\n**Data access:** Programmatic access via `query_primekg.py`. Data is stored at `C:\\Users\\eamon\\Documents\\Data\\PrimeKG\\kg.csv`.\n\n## When to Use This Skill\n\nThis skill should be used when:\n\n- **Knowledge-based drug discovery:** Identifying targets and mechanisms for diseases.\n- **Drug repurposing:** Finding existing drugs that might have evidence for new indications.\n- **Phenotype analysis:** Understanding how symptoms\u002Fphenotypes relate to diseases and genes.\n- **Multiscale biology:** Bridging the gap between molecular targets (genes) and clinical outcomes (diseases).\n- **Network pharmacology:** Investigating the broader network effects of drug-target interactions.\n\n## Core Workflow\n\n### 1. Search for Entities\n\nFind identifiers for genes, drugs, or diseases.\n\n```python\nfrom scripts.query_primekg import search_nodes\n\n# Search for Alzheimer's disease nodes\nresults = search_nodes(\"Alzheimer\", node_type=\"disease\")\n# Returns: [{\"id\": \"EFO_0000249\", \"type\": \"disease\", \"name\": \"Alzheimer's disease\", ...}]\n```\n\n### 2. Get Neighbors (Direct Associations)\n\nRetrieve all connected nodes and relationship types.\n\n```python\nfrom scripts.query_primekg import get_neighbors\n\n# Get all neighbors of a specific disease ID\nneighbors = get_neighbors(\"EFO_0000249\")\n# Returns: List of neighbors like {\"neighbor_name\": \"APOE\", \"relation\": \"disease_gene\", ...}\n```\n\n### 3. Analyze Disease Context\n\nA high-level function to summarize associations for a disease.\n\n```python\nfrom scripts.query_primekg import get_disease_context\n\n# Comprehensive summary for a disease\ncontext = get_disease_context(\"Alzheimer's disease\")\n# Access: context['associated_genes'], context['associated_drugs'], context['phenotypes']\n```\n\n## Relationship Types in PrimeKG\n\nThe graph contains several key relationship types including:\n- `protein_protein`: Physical PPIs\n- `drug_protein`: Drug target\u002Fmechanism associations\n- `disease_gene`: Genetic associations\n- `drug_disease`: Indications and contraindications\n- `disease_phenotype`: Clinical signs and symptoms\n- `gwas`: Genome-wide association studies evidence\n\n## Best Practices\n\n1. **Use specific IDs:** When using `get_neighbors`, ensure you have the correct ID from `search_nodes`.\n2. **Context first:** Use `get_disease_context` for a broad overview before diving into specific genes or drugs.\n3. **Filter relationships:** Use the `relation_type` filter in `get_neighbors` to focus on specific evidence (e.g., only `drug_protein`).\n4. **Multiscale integration:** Combine with `OpenTargets` for deeper genetic evidence or `Semantic Scholar` for the latest literature context.\n\n## Resources\n\n### Scripts\n- `scripts\u002Fquery_primekg.py`: Core functions for searching and querying the knowledge graph.\n\n### Data Path\n- Data: `\u002Fmnt\u002Fc\u002FUsers\u002Feamon\u002FDocuments\u002FData\u002FPrimeKG\u002Fkg.csv`\n- Total nodes: ~129,000\n- Total edges: ~4,000,000\n- Database: CSV-based, optimized for pandas querying.\n",{},"main","def8531e65114c0fca8fb8551c1871ee0eed705c","skills\u002FK-Dense-AI\u002Fclaude-scientific-skills\u002Fprimekg",24,"https:\u002F\u002Fgithub.com\u002FComeOnOliver\u002Fskillshub","https:\u002F\u002Fskillshub.wtf","https:\u002F\u002Fgithub.com\u002FComeOnOliver","AI & Blockchain Engineer\r\n","2026-06-24 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