AAV vector design: capsid selection, promoter optimization, payload capacity.
tooluniverse-precision-medicine-stratification
维护者 FreedomIntelligence · 最近更新 2026年4月1日
Comprehensive patient stratification for precision medicine by integrating genomic, clinical, and therapeutic data. Given a disease/condition, genomic data (germline variants, somatic mutations, expression), and optional clinical parameters, performs multi-phase analysis across 9 phases covering disease disambiguation, genetic risk assessment, disease-specific molecular stratification, pharmacogenomic profiling, com….
原始来源
FreedomIntelligence/OpenClaw-Medical-Skills
https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/tooluniverse-precision-medicine-stratification
- 维护者
- FreedomIntelligence
- 许可
- MIT
- 最近更新
- 2026年4月1日
技能摘要
来自 SKILL.md 的关键信息
核心说明
- 转换 patient genomic 、 clinical profiles into actionable risk stratification,treatment recommendations,、 personalized therapeutic strategies. Integrates germline genetics,somatic alterations,pharmacogenomics,pathway biology,、 clinical evidence to produce quantitative risk score ,支持 tiered management recommendations。
- KEY PRINCIPLES:1. Report-first approach - Create report file FIRST,then populate progressively 2. Disease-specific logic - Cancer vs metabolic vs rare disease pipelines diverge at Phase 2 3. Multi-level integration - Germline + somatic + expression + clinical data layers 4. Evidence-graded - Every finding has evidence tier (T1-T4) 5. Quantitative output - Precision Medicine Risk Score (0-100) ,支持 transparent components 6. Pharmacogenomic guidance - Drug selection 、 dosing recommendations 7. Guideline-concordant - Reference NCCN,ACC/AHA,ADA,、 other guidelines 8. Source-referenced - Every statement cites tool/database source 9. Completeness checklist - Mandatory section showing data availability 、 analysis coverage 10. English-first queries - Always use English terms in tool calls. Respond in user's language。
- result = tu.tools.OpenTargets_get_disease_id_description_by_name(diseaseName='breast cancer')。
原始文档
SKILL.md 摘录
When to Use
Apply when user asks:
- "Stratify this breast cancer patient: ER+/HER2-, BRCA1 mutation, stage II"
- "What is the risk profile for this diabetes patient with HbA1c 8.5 and CYP2C19 poor metabolizer?"
- "NSCLC patient with EGFR L858R, stage IV, TMB 25 - treatment strategy?"
- "Predict prognosis and recommend treatment for this cardiovascular patient"
- "Patient has Marfan syndrome with FBN1 mutation - risk stratification"
- "Alzheimer's risk assessment: APOE e4/e4, family history positive"
- "Personalized treatment plan for type 2 diabetes with genetic risk factors"
- "Which therapy is best for this patient's molecular profile?"
NOT for (use other skills instead):
- Single variant interpretation -> Use
tooluniverse-variant-interpretationortooluniverse-cancer-variant-interpretation - Immunotherapy-specific prediction -> Use
tooluniverse-immunotherapy-response-prediction - Drug safety profiling only -> Use
tooluniverse-adverse-event-detection - Target validation -> Use
tooluniverse-drug-target-validation - Clinical trial search only -> Use
tooluniverse-clinical-trial-matching - Drug-drug interaction analysis only -> Use
tooluniverse-drug-drug-interaction - PRS calculation only -> Use
tooluniverse-polygenic-risk-score
Required Input
- Disease/condition: Free-text disease name (e.g., "breast cancer", "type 2 diabetes", "Marfan syndrome")
- At least one of: Germline variants, somatic mutations, gene list, or clinical biomarkers
Strongly Recommended
- Genomic data: Specific variants (e.g., "BRCA1 c.68_69delAG", "EGFR L858R"), gene names, or expression changes
- Clinical parameters: Age, sex, disease stage, biomarkers (HbA1c, PSA, LDL-C)
适用场景
- 适合在clinicians ask about patient risk stratification,treatment selection,prognosis prediction,或 personalized therapeutic strategy across cancer,metabolic,cardiovascular,neurological,或 rare diseases时使用。
不适用场景
- Do not rely on this catalog entry alone ,用于 installation 或 maintenance details。
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