biomarker_discovery: Скилл для поиска биомаркеров

--- name: biomarker_discovery description: "Biomarker Discovery Pipeline - Discover biomarkers: TCGA differential expression, NCBI gene data, OpenTargets associations, and clinical relevance. Use this skill for precision medicine tasks involving tcga differential expression analysis get gene metadata by gene name get associated targets by disease efoId clinvar search. Combines 4 tools from 4 SCP server(s)." --- # Biomarker Discovery Pipeline **Discipline**: Precision Medicine | **Tools Used**: 4 | **Servers**: 4 ## Description Discover biomarkers: TCGA differential expression, NCBI gene data, OpenTargets associations, and clinical relevance. ## Tools Used - **`tcga_differential_expression_analysis`** from `tcga-server` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/11/Origene-TCGA` - **`get_gene_metadata_by_gene_name`** from `ncbi-server` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/9/Origene-NCBI` - **`get_associated_targets_by_disease_efoId`** from `opentargets-server` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/15/Origene-OpenTargets` - **`clinvar_search`** from `search-server` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/7/Origene-Search` ## Workflow 1. Run TCGA differential expression 2. Get gene metadata 3. Get OpenTargets associations 4. Search ClinVar variants ## Test Case ### Input ```json { "query": "biomarkers for breast cancer", "gene": "BRCA1", "disease_efo": "EFO_0000305" } ``` ### Expected Steps 1. Run TCGA differential expression 2. Get gene metadata 3. Get OpenTargets associations 4. Search ClinVar variants ## Usage Example > **Note:** Replace `sk-b04409a1-b32b-4511-9aeb-22980abdc05c` with your own SCP Hub API Key. You can obtain one from the [SCP Platform](https://scphub.intern-ai.org.cn). ```python import asyncio import json from contextlib import AsyncExitStack from mcp import ClientSession from mcp.client.streamable_http import streamablehttp_client from mcp.client.sse import sse_client SERVERS = { "tcga-server": "https://scp.intern-ai.org.cn/api/v1/mcp/11/Origene-TCGA", "ncbi-server": "https://scp.intern-ai.org.cn/api/v1/mcp/9/Origene-NCBI", "opentargets-server": "https://scp.intern-ai.org.cn/api/v1/mcp/15/Origene-OpenTargets", "search-server": "https://scp.intern-ai.org.cn/api/v1/mcp/7/Origene-Search" } async def connect(url, stack): transport = streamablehttp_client(url=url, headers={"SCP-HUB-API-KEY": "sk-b04409a1-b32b-4511-9aeb-22980abdc05c"}) read, write, _ = await stack.enter_async_context(transport) ctx = ClientSession(read, write) session = await stack.enter_async_context(ctx) await session.initialize() return session def parse(result): try: if hasattr(result, 'content') and result.content: c = result.content[0] if hasattr(c, 'text'): try: return json.loads(c.text) except: return c.text return str(result) except: return str(result) async def main(): async with AsyncExitStack() as stack: # Connect to required servers sessions = {} sessions["tcga-server"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/11/Origene-TCGA", stack) sessions["ncbi-server"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/9/Origene-NCBI", stack) sessions["opentargets-server"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/15/Origene-OpenTargets", stack) sessions["search-server"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/7/Origene-Search", stack) # Execute workflow steps # Step 1: Run TCGA differential expression result_1 = await sessions["tcga-server"].call_tool("tcga_differential_expression_analysis", arguments={}) data_1 = parse(result_1) print(f"Step 1 result: {json.dumps(data_1, indent=2, ensure_ascii=False)[:500]}") # Step 2: Get gene metadata result_2 = await sessions["ncbi-server"].call_tool("get_gene_metadata_by_gene_name", arguments={}) data_2 = parse(result_2) print(f"Step 2 result: {json.dumps(data_2, indent=2, ensure_ascii=False)[:500]}") # Step 3: Get OpenTargets associations result_3 = await sessions["opentargets-server"].call_tool("get_associated_targets_by_disease_efoId", arguments={}) data_3 = parse(result_3) print(f"Step 3 result: {json.dumps(data_3, indent=2, ensure_ascii=False)[:500]}") # Step 4: Search ClinVar variants result_4 = await sessions["search-server"].call_tool("clinvar_search", arguments={}) data_4 = parse(result_4) print(f"Step 4 result: {json.dumps(data_4, indent=2, ensure_ascii=False)[:500]}") # Cleanup print("Workflow complete!") if __name__ == "__main__": asyncio.run(main()) ```