--- name: biomarker-discovery-30079a86f6f5 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 `` 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 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, transport_type): transport = streamablehttp_client(url=url, headers={"SCP-HUB-API-KEY": ""}) read, write, _ = await transport.__aenter__() ctx = ClientSession(read, write) session = await ctx.__aenter__() await session.initialize() return session, ctx, transport 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(): # Connect to required servers sessions = {} sessions["tcga-server"], _, _ = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/11/Origene-TCGA", "streamable-http") sessions["ncbi-server"], _, _ = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/9/Origene-NCBI", "streamable-http") sessions["opentargets-server"], _, _ = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/15/Origene-OpenTargets", "streamable-http") sessions["search-server"], _, _ = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/7/Origene-Search", "streamable-http") # 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()) ``` --- Source attribution: InternScience Original project: https://github.com/InternScience/scp/blob/cea5398564032aea65a78e246d06c30ae945e03f/skills/biomarker_discovery/SKILL.md License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice MIT License Copyright (c) 2024 Anthropic, PBC Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # Mega AI Hub resource metadata Creator / publisher: InternScience Original project: https://github.com/InternScience/scp/blob/cea5398564032aea65a78e246d06c30ae945e03f/skills/biomarker_discovery/SKILL.md Attribution basis: Source repository record Fields of work: Data & Analytics, Education & Research, Healthcare & Life Sciences Roles: Clinical Research, Data Scientist, Researcher Specialties: API Tools: Ncbi Server, Opentargets Server, Search Server, Tcga Server Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: 8fa467d7c8e71392b71986580df7a143d371345481fa486fe9249547f4fb8509