疾病研究与证据全景整理｜个人思维图谱小站

正文

获取原文件

SKILL.md

中文导读

查询疾病定义、表型、遗传、通路、治疗研究和流行病学资料，组织具有来源引用的研究概览。

输入材料

疾病名称与研究范围
可核查的数据库访问条件

交付结果

疾病研究资料
引用、检索与限制记录

阅读与使用

本页保存指定版本的原始 Skill 和配套文件，可查看中文导读、原文与参考资料。获取完整包后，按原文准备所需工具与材料。执行需要 ToolUniverse SDK 或 MCP 服务，部分数据库需要独立访问权限。医疗资料供研究、资料整理和专业人员复核，试验资格、用药与诊疗结论需要核实当前来源。

来源仓库为 mims-harvard/ToolUniverse，采用 Apache-2.0 许可。Stars 表示仓库关注度，核验日期为 2026-10-15。分类表示适用的工作环节，实际执行条件以原文为准。

原始 Skill

Generate a comprehensive disease research report with full source citations. The report is created as a markdown file and progressively updated during research.

IMPORTANT: Always use English disease names and search terms in tool calls. Respond in the user's language.

LOOK UP, DON'T GUESS

When asked about a disease, query Orphanet/OMIM/DisGeNET FIRST. Don't rely on memory for prevalence, genetics, or treatment — these change over time. When you're not sure about a fact, your first instinct should be to SEARCH for it using tools, not to reason harder from memory.

When to Use

User asks about any disease, syndrome, or medical condition
Needs comprehensive disease intelligence or a detailed research report
Asks "what do we know about [disease]?"

Core Workflow: Report-First Approach

DO NOT show the search process to the user. Instead:

Create report file first - Initialize {disease_name}_research_report.md
Research each dimension - Use all relevant tools
Update report progressively - Write findings after each dimension
Include citations - Every fact must reference its source tool

Disease Mechanism Reasoning

When synthesizing disease etiology, trace the full pathogenic cascade:

Genetic basis - Which variants (rare or common) confer risk, and in which genes?
Molecular mechanism - How do those variants alter protein function, expression, or regulation?
Cellular effect - What downstream cellular processes are disrupted (signaling, metabolism, stress response)?
Tissue/organ manifestation - How does cellular dysfunction present as organ-level pathology?

This chain structures the Genetic & Molecular Basis (Section 3) and Biological Pathways (Section 5) sections.

10 Research Dimensions

Dim	Section	Key Tools
1	Identity & Classification	OSL_get_efo_id_by_disease_name, ols_search_efo_terms, ols_get_efo_term, umls_search_concepts, icd_search_codes, snomed_search_concepts
2	Clinical Presentation	OpenTargets phenotypes, HPO lookup, MedlinePlus
3	Genetic & Molecular Basis	OpenTargets targets, ClinVar variants, GWAS associations, gnomAD
4	Treatment Landscape	OpenTargets drugs, clinical trials, GtoPdb
5	Biological Pathways	Reactome pathways, humanbase_ppi_analysis, GTEx expression, HPA
6	Epidemiology & Literature	PubMed, OpenAlex, Europe PMC, Semantic Scholar
7	Similar Diseases	OpenTargets similar entities
8	Cancer-Specific (if applicable)	CIViC genes/variants/therapies
9	Pharmacology	GtoPdb targets/interactions/ligands
10	Drug Safety	OpenTargets warnings, clinical trial AEs, FAERS

See: tool_usage_details.md for complete tool calls per section.

Normalizing free text to ontology IDs (Dimension 1)

When the input is messy free text (a sample attribute, a synonym, a tissue/organism label) rather than a clean disease name, use ZOOMA_annotate_text to map it to standardized ontology terms (EFO/MONDO/UBERON/etc.) before lookup. It returns each match as an ontology IRI with a confidence rating (HIGH/GOOD/MEDIUM/LOW), so you can keep only high-confidence hits and feed the resolved ID into OLS / OpenTargets.

tu.run_tool("ZOOMA_annotate_text", {
    "property_value": "asthma",        # free text to resolve
    "property_type": "disease",         # optional context hint
    "min_confidence": "HIGH",           # drop fuzzy matches
    "max_results": 3,
})
# -> [{"semantic_tags": ["http://purl.obolibrary.org/obo/MONDO_0004979"],
#      "curies": ["MONDO:0004979"], "confidence": "HIGH", "source": "zooma", ...}]

# Restrict to one ontology source (e.g. EFO) when you need a specific namespace:
tu.run_tool("ZOOMA_annotate_text", {"property_value": "diabetes", "ontologies": "efo"})

# Inspect which curated datasources back ZOOMA annotations (for provenance):
tu.run_tool("ZOOMA_list_datasources", {})
# -> [{"name": "eva-clinvar", "type": "DATABASE", "uri": "https://www.ebi.ac.uk/eva"}, ...]

Each match also carries a ready-to-use curies field (e.g. MONDO:0004979) so you can feed the resolved ID straight into OLS / OpenTargets without parsing the IRI. ZOOMA is the live replacement for the retired OxO cross-reference service; pair it with ols_get_efo_term to expand the resolved IRI into labels, synonyms, and hierarchy.

Report Template

Create this file structure at the start:

# Disease Research Report: {Disease Name}

**Report Generated**: {date}
**Disease Identifiers**: (to be filled)

---

## Executive Summary
(Brief 3-5 sentence overview - fill after all research complete)

---

## 1. Disease Identity & Classification
### Ontology Identifiers
| System | ID | Source |

### Synonyms & Alternative Names
### Disease Hierarchy

---

## 2. Clinical Presentation
### Phenotypes (HPO)
| HPO ID | Phenotype | Description | Source |

### Symptoms & Signs
### Diagnostic Criteria

---

## 3. Genetic & Molecular Basis
### Associated Genes
| Gene | Score | Ensembl ID | Evidence | Source |

### GWAS Associations
| SNP | P-value | Odds Ratio | Study | Source |

### Pathogenic Variants (ClinVar)

---

## 4. Treatment Landscape
### Approved Drugs
| Drug | ChEMBL ID | Mechanism | Phase | Target | Source |

### Clinical Trials
| NCT ID | Title | Phase | Status | Source |

---

## 5. Biological Pathways & Mechanisms

## 6. Epidemiology & Risk Factors

## 7. Literature & Research Activity

## 8. Similar Diseases & Comorbidities

## 9. Cancer-Specific Information (if applicable)

## 10. Drug Safety & Adverse Events

---

## References
### Tools Used
| # | Tool | Parameters | Section | Items Retrieved |

Citation Format

Every piece of data MUST include its source:

In tables: Add a Source column with tool name In lists: - Finding [Source: tool_name] In prose: (Source: tool_name, query: "...") References section: Complete tool usage log with parameters

Progressive Update Pattern

# After each dimension's research:
# 1. Read current report
# 2. Replace placeholder with formatted content
# 3. Write back immediately
# 4. Continue to next dimension

Evidence Grading & Interpretation

Every finding in the report should be graded:

Grade	Criteria	Example
T1 (Strong)	Replicated genetic evidence (GWAS, rare variants), FDA-approved therapy	BRCA1 → breast cancer; trastuzumab for HER2+
T2 (Moderate)	Single genetic study, phase II+ trial data, strong biological evidence	FOXO3 → longevity (centenarian studies)
T3 (Association)	Observational data, gene expression changes, pathway membership	IL-6 elevated in Alzheimer's CSF
T4 (Computational)	Network proximity, text mining, predicted associations	DisGeNET text-mined gene-disease link

Synthesis Questions (answer in Executive Summary)

After collecting data from all 10 dimensions, the report MUST answer:

What causes this disease? Summarize the genetic architecture (monogenic vs polygenic, key loci, penetrance)
What are the therapeutic options? Ranked by evidence level and approval status
What biomarkers exist? For diagnosis, prognosis, and treatment selection
What's the unmet need? What aspects lack effective treatment or understanding?
What are the active research frontiers? Based on clinical trials and recent publications

Interpreting Cross-Database Concordance

When multiple databases provide different data for the same disease:

OpenTargets + DisGeNET + OMIM agree on a gene: T1 evidence — high confidence
Only OpenTargets reports an association: Check the datasource scores — genetic_association > literature > animal_model
DisGeNET score > 0.5 but not in OpenTargets: May be text-mined; verify with PubMed
Gene in GWAS but not OMIM: Likely a complex disease susceptibility locus, not Mendelian

Handling Conflicting Data

Conflict	Resolution
Different prevalence estimates across sources	Report range; note the most recent/largest study
Drug approved in one country but not another	Note regulatory status per region
Gene-disease association in one DB but absent in another	Grade by evidence type; text-mining alone is T4
Clinical trial results contradict label indications	The trial result is newer evidence; note both

Final Report Quality Checklist

All 10 sections have content (or marked "No data available")
Every data point has a source citation
Executive summary reflects key findings
References section lists all tools used
Tables properly formatted
No placeholder text remains

Expected Output Scale

For a well-studied disease (e.g., Alzheimer's), the final report should include:

5+ ontology IDs, 10+ synonyms, disease hierarchy
20+ phenotypes with HPO IDs
50+ genes, 30+ GWAS associations, 100+ ClinVar variants
20+ drugs, 50+ clinical trials
10+ pathways, PPI network, expression data
100+ publications
15+ similar diseases
Drug warnings and adverse events

Total: 500+ individual data points, each with source citation.

Cross-Skill References

For rare disease differential diagnosis, run: python3 skills/tooluniverse-rare-disease-diagnosis/scripts/clinical_patterns.py --type differential --symptoms 'symptom1,symptom2'

Reference Files

REPORT_TEMPLATE.md - Full report markdown template and citation format guide
RESEARCH_PROTOCOL.md - Step-by-step code procedures, progressive update pattern, quality checklist
tool_usage_details.md - Complete tool calls for each research dimension
TOOLS_REFERENCE.md - Complete tool documentation
EXAMPLES.md - Sample disease research reports

疾病研究与证据全景整理

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