tooluniverse-disease-research

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ToolUniverse Disease Research

ToolUniverse 疾病研究

Generate a comprehensive, detailed 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, even if the user writes in another language. Only try original-language terms as a fallback if English returns no results. Respond in the user's language.

生成包含完整来源引用的全面、详细的疾病研究报告。报告将以Markdown文件形式创建，并在研究过程中逐步更新。

重要提示：在工具调用中始终使用英文疾病名称和搜索词，即使用户使用其他语言提问。只有当英文搜索无结果时，才尝试使用原语言术语作为备选。回复时使用用户的语言。

When to Use

使用场景

Apply when the user:

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

当用户有以下需求时适用：

询问任何疾病、综合征或医疗状况
需要全面的疾病情报
想要带有引用的详细研究报告
提问“关于[疾病]我们了解哪些信息？”

Core Workflow: Report-First Approach

核心工作流：报告优先方法

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 to file after each dimension
Include citations - Every fact must reference its source tool

User: "Research Parkinson's disease"

Agent Actions (internal, not shown to user):
1. Create "parkinsons_disease_research_report.md" with template
2. Research DIM 1 → Update Identity section
3. Research DIM 2 → Update Clinical section
4. ... continue for all 10 dimensions
5. Present final report to user

请勿向用户展示搜索过程。而是：

先创建报告文件 - 初始化
```
{disease_name}_research_report.md
```
研究每个维度 - 使用所有相关工具
逐步更新报告 - 完成每个维度的研究后，将结果写入文件
包含引用 - 每个事实都必须引用其来源工具

User: "Research Parkinson's disease"

Agent Actions (internal, not shown to user):
1. Create "parkinsons_disease_research_report.md" with template
2. Research DIM 1 → Update Identity section
3. Research DIM 2 → Update Clinical section
4. ... continue for all 10 dimensions
5. Present final report to user

Report Template

报告模板

Create this file structure at the start:

markdown

undefined

开始时创建以下文件结构：

markdown

undefined

Disease Research Report: {Disease Name}

疾病研究报告：{疾病名称}

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

报告生成时间：{日期} 疾病标识：待填充

Executive Summary

执行摘要

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

（3-5句简要概述 - 完成所有研究后填充）

1. Disease Identity & Classification

1. 疾病标识与分类

Ontology Identifiers

本体标识

System	ID	Source
EFO
ICD-10
UMLS CUI
SNOMED CT

系统	ID	来源
EFO
ICD-10
UMLS CUI
SNOMED CT

Synonyms & Alternative Names

同义词与别名

(list with source)

（带来源的列表）

Disease Hierarchy

疾病层级

Parent:
Subtypes:

Sources: (list tools used)

父类：
亚型：

来源：（使用的工具列表）

2. Clinical Presentation

2. 临床表现

Phenotypes (HPO)

表型（HPO）

HPO ID	Phenotype	Description	Source

HPO ID	表型	描述	来源

Symptoms & Signs

症状与体征

(list with source)

（带来源的列表）

Diagnostic Criteria

诊断标准

(from literature/MedlinePlus)

Sources: (list tools used)

（来自文献/MedlinePlus）

来源：（使用的工具列表）

3. Genetic & Molecular Basis

3. 遗传与分子基础

Associated Genes

GWAS Associations

GWAS关联

SNP	P-value	Odds Ratio	Study	Source

SNP	P值	优势比	研究	来源

Pathogenic Variants (ClinVar)

致病性变异（ClinVar）

Variant	Clinical Significance	Condition	Source

Sources: (list tools used)

变异	临床意义	病症	来源

来源：（使用的工具列表）

4. Treatment Landscape

4. 治疗现状

Approved Drugs

获批药物

Drug	ChEMBL ID	Mechanism	Phase	Target	Source

药物	ChEMBL ID	作用机制	阶段	靶点	来源

Clinical Trials

临床试验

NCT ID	Title	Phase	Status	Intervention	Source

NCT ID	标题	阶段	状态	干预措施	来源

Treatment Guidelines

治疗指南

(from literature)

Sources: (list tools used)

（来自文献）

来源：（使用的工具列表）

5. Biological Pathways & Mechanisms

5. 生物通路与机制

Key Pathways

关键通路

Pathway	Reactome ID	Genes Involved	Source

通路	Reactome ID	涉及基因	来源

Protein-Protein Interactions

蛋白质-蛋白质相互作用

(tissue-specific networks)

（组织特异性网络）

Expression Patterns

表达模式

Tissue	Expression Level	Source

Sources: (list tools used)

组织	表达水平	来源

来源：（使用的工具列表）

6. Epidemiology & Risk Factors

6. 流行病学与风险因素

Prevalence & Incidence

患病率与发病率

(from literature)

（来自文献）

Risk Factors

风险因素

Factor	Evidence	Source

因素	证据	来源

GWAS Studies

GWAS研究

Study	Sample Size	Findings	Source

Sources: (list tools used)

研究	样本量	发现	来源

来源：（使用的工具列表）

7. Literature & Research Activity

7. 文献与研究动态

Publication Trends

发表趋势

Total publications (5 years):
Current year:
Trend:

总发表量（近5年）：
本年度：
趋势：

Key Publications

关键文献

PMID	Title	Year	Citations	Source

PMID	标题	年份	引用量	来源

Research Institutions

研究机构

(from OpenAlex)

Sources: (list tools used)

（来自OpenAlex）

来源：（使用的工具列表）

8. Similar Diseases & Comorbidities

8. 相似疾病与并发症

Similar Diseases

相似疾病

Disease	Similarity Score	Shared Genes	Source

疾病	相似度评分	共享基因	来源

Comorbidities

并发症

(from literature/clinical data)

Sources: (list tools used)

（来自文献/临床数据）

来源：（使用的工具列表）

9. Cancer-Specific Information (if applicable)

9. 癌症专属信息（如适用）

CIViC Variants

CIViC变异

Gene	Variant	Evidence Level	Clinical Significance	Source

基因	变异	证据等级	临床意义	来源

Molecular Profiles

分子谱

(biomarkers)

（生物标志物）

Targeted Therapies

靶向治疗

Therapy	Target	Evidence	Source

Sources: (list tools used)

疗法	靶点	证据	来源

来源：（使用的工具列表）

10. Drug Safety & Adverse Events

10. 药物安全性与不良事件

Drug Warnings

药物警告

Drug	Warning Type	Description	Source

药物	警告类型	描述	来源

Clinical Trial Adverse Events

临床试验不良事件

Trial	Drug	Adverse Event	Frequency	Source

试验	药物	不良事件	发生率	来源

FAERS Reports

FAERS报告

(FDA adverse event data)

Sources: (list tools used)

（FDA不良事件数据）

来源：（使用的工具列表）

References

参考文献

Data Sources Used

使用的数据源

Tool	Query	Section

工具	查询参数	章节

Database Versions

数据库版本

OpenTargets: (version/date)
ClinVar: (version/date)
GWAS Catalog: (version/date)

---

OpenTargets：（版本/日期）
ClinVar：（版本/日期）
GWAS Catalog：（版本/日期）

---

Research Protocol

研究流程

Step 1: Initialize Report

步骤1：初始化报告

python

from datetime import datetime

def create_report_file(disease_name):
    """Create initial report file with template"""
    filename = f"{disease_name.lower().replace(' ', '_')}_research_report.md"
    
    template = f"""# Disease Research Report: {disease_name}

**Report Generated**: {datetime.now().strftime('%Y-%m-%d %H:%M')}
**Disease Identifiers**: Pending research...

---

python

from datetime import datetime

def create_report_file(disease_name):
    """Create initial report file with template"""
    filename = f"{disease_name.lower().replace(' ', '_')}_research_report.md"
    
    template = f"""# Disease Research Report: {disease_name}

**Report Generated**: {datetime.now().strftime('%Y-%m-%d %H:%M')}
**Disease Identifiers**: Pending research...

---

Executive Summary

Research in progress...

1. Disease Identity & Classification

Researching...

2. Clinical Presentation

Pending...

[... rest of template ...] """

with open(filename, 'w') as f:
    f.write(template)

return filename

undefined

Pending...

[... rest of template ...] """

with open(filename, 'w') as f:
    f.write(template)

return filename

undefined

Step 2: Research Each Dimension with Citations

步骤2：带引用的维度研究

For EACH piece of information, track:

Tool name that provided the data
Parameters used in the query
Timestamp of the query

python

def research_with_citations(tu, disease_name, report_file):
    """Research and update report with full citations"""
    
    references = []  # Track all sources
    
    # === DIMENSION 1: Identity ===
    
    # Get EFO ID
    efo_result = tu.tools.OSL_get_efo_id_by_disease_name(disease=disease_name)
    efo_id = efo_result.get('efo_id')
    references.append({
        'tool': 'OSL_get_efo_id_by_disease_name',
        'params': {'disease': disease_name},
        'section': 'Identity'
    })
    
    # Get ICD codes
    icd_result = tu.tools.icd_search_codes(query=disease_name, version="ICD10CM")
    references.append({
        'tool': 'icd_search_codes',
        'params': {'query': disease_name, 'version': 'ICD10CM'},
        'section': 'Identity'
    })
    
    # Get UMLS
    umls_result = tu.tools.umls_search_concepts(query=disease_name)
    references.append({
        'tool': 'umls_search_concepts',
        'params': {'query': disease_name},
        'section': 'Identity'
    })
    
    # Get synonyms from EFO
    if efo_id:
        efo_term = tu.tools.ols_get_efo_term(obo_id=efo_id.replace('_', ':'))
        references.append({
            'tool': 'ols_get_efo_term',
            'params': {'obo_id': efo_id},
            'section': 'Identity'
        })
        
        # Get subtypes
        children = tu.tools.ols_get_efo_term_children(obo_id=efo_id.replace('_', ':'), size=20)
        references.append({
            'tool': 'ols_get_efo_term_children',
            'params': {'obo_id': efo_id, 'size': 20},
            'section': 'Identity'
        })
    
    # UPDATE REPORT FILE with Identity section
    update_report_section(report_file, 'Identity', {
        'efo_id': efo_id,
        'icd_codes': icd_result,
        'umls': umls_result,
        'synonyms': efo_term.get('synonyms', []) if efo_term else [],
        'subtypes': children
    }, references[-5:])  # Last 5 references for this section
    
    # === DIMENSION 2: Clinical ===
    # ... continue for all dimensions

对于每一条信息，需记录：

提供数据的工具名称
查询使用的参数
查询的时间戳

python

def research_with_citations(tu, disease_name, report_file):
    """Research and update report with full citations"""
    
    references = []  # Track all sources
    
    # === DIMENSION 1: Identity ===
    
    # Get EFO ID
    efo_result = tu.tools.OSL_get_efo_id_by_disease_name(disease=disease_name)
    efo_id = efo_result.get('efo_id')
    references.append({
        'tool': 'OSL_get_efo_id_by_disease_name',
        'params': {'disease': disease_name},
        'section': 'Identity'
    })
    
    # Get ICD codes
    icd_result = tu.tools.icd_search_codes(query=disease_name, version="ICD10CM")
    references.append({
        'tool': 'icd_search_codes',
        'params': {'query': disease_name, 'version': 'ICD10CM'},
        'section': 'Identity'
    })
    
    # Get UMLS
    umls_result = tu.tools.umls_search_concepts(query=disease_name)
    references.append({
        'tool': 'umls_search_concepts',
        'params': {'query': disease_name},
        'section': 'Identity'
    })
    
    # Get synonyms from EFO
    if efo_id:
        efo_term = tu.tools.ols_get_efo_term(obo_id=efo_id.replace('_', ':'))
        references.append({
            'tool': 'ols_get_efo_term',
            'params': {'obo_id': efo_id},
            'section': 'Identity'
        })
        
        # Get subtypes
        children = tu.tools.ols_get_efo_term_children(obo_id=efo_id.replace('_', ':'), size=20)
        references.append({
            'tool': 'ols_get_efo_term_children',
            'params': {'obo_id': efo_id, 'size': 20},
            'section': 'Identity'
        })
    
    # UPDATE REPORT FILE with Identity section
    update_report_section(report_file, 'Identity', {
        'efo_id': efo_id,
        'icd_codes': icd_result,
        'umls': umls_result,
        'synonyms': efo_term.get('synonyms', []) if efo_term else [],
        'subtypes': children
    }, references[-5:])  # Last 5 references for this section
    
    # === DIMENSION 2: Clinical ===
    # ... continue for all dimensions

Step 3: Update Report File After Each Dimension

步骤3：完成每个维度后更新报告

python

def update_report_section(filename, section_name, data, sources):
    """Update a specific section in the report file"""
    
    # Read current file
    with open(filename, 'r') as f:
        content = f.read()
    
    # Format section content with citations
    if section_name == 'Identity':
        section_content = format_identity_section(data, sources)
    elif section_name == 'Clinical':
        section_content = format_clinical_section(data, sources)
    # ... etc
    
    # Replace placeholder with actual content
    placeholder = f"## {section_number}. {section_name}\n*Researching...*"
    content = content.replace(placeholder, section_content)
    
    # Write back
    with open(filename, 'w') as f:
        f.write(content)


def format_identity_section(data, sources):
    """Format Identity section with proper citations"""
    
    source_list = ', '.join([s['tool'] for s in sources])
    
    return f"""## 1. Disease Identity & Classification

python

def update_report_section(filename, section_name, data, sources):
    """Update a specific section in the report file"""
    
    # Read current file
    with open(filename, 'r') as f:
        content = f.read()
    
    # Format section content with citations
    if section_name == 'Identity':
        section_content = format_identity_section(data, sources)
    elif section_name == 'Clinical':
        section_content = format_clinical_section(data, sources)
    # ... etc
    
    # Replace placeholder with actual content
    placeholder = f"## {section_number}. {section_name}\n*Researching...*"
    content = content.replace(placeholder, section_content)
    
    # Write back
    with open(filename, 'w') as f:
        f.write(content)


def format_identity_section(data, sources):
    """Format Identity section with proper citations"""
    
    source_list = ', '.join([s['tool'] for s in sources])
    
    return f"""## 1. Disease Identity & Classification

Ontology Identifiers

System	ID	Source
EFO	{data['efo_id']}	OSL_get_efo_id_by_disease_name
ICD-10	{data['icd_codes']}	icd_search_codes
UMLS CUI	{data['umls']}	umls_search_concepts

System	ID	Source
EFO	{data['efo_id']}	OSL_get_efo_id_by_disease_name
ICD-10	{data['icd_codes']}	icd_search_codes
UMLS CUI	{data['umls']}	umls_search_concepts

Synonyms & Alternative Names

{format_list_with_source(data['synonyms'], 'ols_get_efo_term')}

Disease Subtypes

{format_list_with_source(data['subtypes'], 'ols_get_efo_term_children')}

Sources: {source_list} """

---

{format_list_with_source(data['subtypes'], 'ols_get_efo_term_children')}

Sources: {source_list} """

---

Complete Tool Usage by Section

各章节工具使用清单

Section 1: Identity (use ALL of these)

章节1：疾病标识（全部使用）

python

undefined

python

undefined

Required tools - use all

tu.tools.OSL_get_efo_id_by_disease_name(disease=disease_name) tu.tools.OpenTargets_get_disease_id_description_by_name(diseaseName=disease_name) tu.tools.ols_search_efo_terms(query=disease_name) tu.tools.ols_get_efo_term(obo_id=efo_id) tu.tools.ols_get_efo_term_children(obo_id=efo_id, size=30) tu.tools.umls_search_concepts(query=disease_name) tu.tools.umls_get_concept_details(cui=cui) tu.tools.icd_search_codes(query=disease_name, version="ICD10CM") tu.tools.snomed_search_concepts(query=disease_name)

undefined

undefined

Section 2: Clinical Presentation (use ALL of these)

章节2：临床表现（全部使用）

python

tu.tools.OpenTargets_get_associated_phenotypes_by_disease_efoId(efoId=efo_id)
tu.tools.get_HPO_ID_by_phenotype(query=symptom)  # for each key symptom
tu.tools.get_phenotype_by_HPO_ID(id=hpo_id)  # for top phenotypes
tu.tools.MedlinePlus_search_topics_by_keyword(term=disease_name, db="healthTopics")
tu.tools.MedlinePlus_get_genetics_condition_by_name(condition=disease_slug)
tu.tools.MedlinePlus_connect_lookup_by_code(cs=icd_oid, c=icd_code)

python

tu.tools.OpenTargets_get_associated_phenotypes_by_disease_efoId(efoId=efo_id)
tu.tools.get_HPO_ID_by_phenotype(query=symptom)  # for each key symptom
tu.tools.get_phenotype_by_HPO_ID(id=hpo_id)  # for top phenotypes
tu.tools.MedlinePlus_search_topics_by_keyword(term=disease_name, db="healthTopics")
tu.tools.MedlinePlus_get_genetics_condition_by_name(condition=disease_slug)
tu.tools.MedlinePlus_connect_lookup_by_code(cs=icd_oid, c=icd_code)

Section 3: Genetics (use ALL of these)

章节3：遗传学（全部使用）

python

tu.tools.OpenTargets_get_associated_targets_by_disease_efoId(efoId=efo_id)
tu.tools.OpenTargets_target_disease_evidence(efoId=efo_id, ensemblId=gene_id)  # for top genes
tu.tools.clinvar_search_variants(condition=disease_name, max_results=50)
tu.tools.clinvar_get_variant_details(variant_id=vid)  # for top variants
tu.tools.clinvar_get_clinical_significance(variant_id=vid)
tu.tools.gwas_search_associations(disease_trait=disease_name, size=50)
tu.tools.gwas_get_variants_for_trait(disease_trait=disease_name, size=50)
tu.tools.gwas_get_associations_for_trait(disease_trait=disease_name, size=50)
tu.tools.gwas_get_studies_for_trait(disease_trait=disease_name, size=30)
tu.tools.GWAS_search_associations_by_gene(gene_name=gene)  # for top genes
tu.tools.gnomad_get_variant_frequency(variant=variant)  # for key variants

python

tu.tools.OpenTargets_get_associated_targets_by_disease_efoId(efoId=efo_id)
tu.tools.OpenTargets_target_disease_evidence(efoId=efo_id, ensemblId=gene_id)  # for top genes
tu.tools.clinvar_search_variants(condition=disease_name, max_results=50)
tu.tools.clinvar_get_variant_details(variant_id=vid)  # for top variants
tu.tools.clinvar_get_clinical_significance(variant_id=vid)
tu.tools.gwas_search_associations(disease_trait=disease_name, size=50)
tu.tools.gwas_get_variants_for_trait(disease_trait=disease_name, size=50)
tu.tools.gwas_get_associations_for_trait(disease_trait=disease_name, size=50)
tu.tools.gwas_get_studies_for_trait(disease_trait=disease_name, size=30)
tu.tools.GWAS_search_associations_by_gene(gene_name=gene)  # for top genes
tu.tools.gnomad_get_variant_frequency(variant=variant)  # for key variants

Section 4: Treatment (use ALL of these)

章节4：治疗（全部使用）

python

tu.tools.OpenTargets_get_associated_drugs_by_disease_efoId(efoId=efo_id, size=100)
tu.tools.OpenTargets_get_drug_chembId_by_generic_name(drugName=drug)  # for each drug
tu.tools.OpenTargets_get_drug_mechanisms_of_action_by_chemblId(chemblId=chembl_id)
tu.tools.search_clinical_trials(condition=disease_name, pageSize=50)
tu.tools.get_clinical_trial_descriptions(nct_ids=nct_list)
tu.tools.get_clinical_trial_conditions_and_interventions(nct_ids=nct_list)
tu.tools.get_clinical_trial_eligibility_criteria(nct_ids=nct_list)
tu.tools.get_clinical_trial_outcome_measures(nct_ids=nct_list)
tu.tools.extract_clinical_trial_outcomes(nct_ids=nct_list)
tu.tools.GtoPdb_list_diseases(name=disease_name)
tu.tools.GtoPdb_get_disease(disease_id=gtopdb_id)

python

tu.tools.OpenTargets_get_associated_drugs_by_disease_efoId(efoId=efo_id, size=100)
tu.tools.OpenTargets_get_drug_chembId_by_generic_name(drugName=drug)  # for each drug
tu.tools.OpenTargets_get_drug_mechanisms_of_action_by_chemblId(chemblId=chembl_id)
tu.tools.search_clinical_trials(condition=disease_name, pageSize=50)
tu.tools.get_clinical_trial_descriptions(nct_ids=nct_list)
tu.tools.get_clinical_trial_conditions_and_interventions(nct_ids=nct_list)
tu.tools.get_clinical_trial_eligibility_criteria(nct_ids=nct_list)
tu.tools.get_clinical_trial_outcome_measures(nct_ids=nct_list)
tu.tools.extract_clinical_trial_outcomes(nct_ids=nct_list)
tu.tools.GtoPdb_list_diseases(name=disease_name)
tu.tools.GtoPdb_get_disease(disease_id=gtopdb_id)

Section 5: Pathways (use ALL of these)

章节5：通路（全部使用）

python

tu.tools.Reactome_get_diseases()
tu.tools.Reactome_map_uniprot_to_pathways(id=uniprot_id)  # for top genes
tu.tools.Reactome_get_pathway(stId=pathway_id)  # for key pathways
tu.tools.Reactome_get_pathway_reactions(stId=pathway_id)
tu.tools.humanbase_ppi_analysis(gene_list=top_genes, tissue=relevant_tissue)
tu.tools.gtex_get_expression_by_gene(gene=gene)  # for top genes
tu.tools.HPA_get_protein_expression(gene=gene)
tu.tools.geo_search_datasets(query=disease_name)

python

tu.tools.Reactome_get_diseases()
tu.tools.Reactome_map_uniprot_to_pathways(id=uniprot_id)  # for top genes
tu.tools.Reactome_get_pathway(stId=pathway_id)  # for key pathways
tu.tools.Reactome_get_pathway_reactions(stId=pathway_id)
tu.tools.humanbase_ppi_analysis(gene_list=top_genes, tissue=relevant_tissue)
tu.tools.gtex_get_expression_by_gene(gene=gene)  # for top genes
tu.tools.HPA_get_protein_expression(gene=gene)
tu.tools.geo_search_datasets(query=disease_name)

Section 6: Literature (use ALL of these)

章节6：文献（全部使用）

python

tu.tools.PubMed_search_articles(query=f'"{disease_name}"', limit=100)
tu.tools.PubMed_search_articles(query=f'"{disease_name}" AND epidemiology', limit=50)
tu.tools.PubMed_search_articles(query=f'"{disease_name}" AND mechanism', limit=50)
tu.tools.PubMed_search_articles(query=f'"{disease_name}" AND treatment', limit=50)
tu.tools.PubMed_get_article(pmid=pmid)  # for top 10 articles
tu.tools.PubMed_get_related(pmid=key_pmid)
tu.tools.PubMed_get_cited_by(pmid=key_pmid)
tu.tools.OpenTargets_get_publications_by_disease_efoId(efoId=efo_id)
tu.tools.openalex_search_works(query=disease_name, limit=50)
tu.tools.europe_pmc_search_abstracts(query=disease_name, limit=50)
tu.tools.semantic_scholar_search_papers(query=disease_name, limit=50)

python

tu.tools.PubMed_search_articles(query=f'"{disease_name}"', limit=100)
tu.tools.PubMed_search_articles(query=f'"{disease_name}" AND epidemiology', limit=50)
tu.tools.PubMed_search_articles(query=f'"{disease_name}" AND mechanism', limit=50)
tu.tools.PubMed_search_articles(query=f'"{disease_name}" AND treatment', limit=50)
tu.tools.PubMed_get_article(pmid=pmid)  # for top 10 articles
tu.tools.PubMed_get_related(pmid=key_pmid)
tu.tools.PubMed_get_cited_by(pmid=key_pmid)
tu.tools.OpenTargets_get_publications_by_disease_efoId(efoId=efo_id)
tu.tools.openalex_search_works(query=disease_name, limit=50)
tu.tools.europe_pmc_search_abstracts(query=disease_name, limit=50)
tu.tools.semantic_scholar_search_papers(query=disease_name, limit=50)

Section 7: Similar Diseases

章节7：相似疾病

python

tu.tools.OpenTargets_get_similar_entities_by_disease_efoId(efoId=efo_id, threshold=0.3, size=30)

python

tu.tools.OpenTargets_get_similar_entities_by_disease_efoId(efoId=efo_id, threshold=0.3, size=30)

Section 8: Cancer-Specific (if cancer)

章节8：癌症专属（如为癌症）

python

tu.tools.civic_search_diseases(limit=100)
tu.tools.civic_search_genes(query=gene, limit=20)  # for cancer genes
tu.tools.civic_get_variants_by_gene(gene_id=civic_gene_id, limit=50)
tu.tools.civic_get_variant(variant_id=vid)
tu.tools.civic_get_evidence_item(evidence_id=eid)
tu.tools.civic_search_therapies(limit=100)
tu.tools.civic_search_molecular_profiles(limit=50)

python

tu.tools.civic_search_diseases(limit=100)
tu.tools.civic_search_genes(query=gene, limit=20)  # for cancer genes
tu.tools.civic_get_variants_by_gene(gene_id=civic_gene_id, limit=50)
tu.tools.civic_get_variant(variant_id=vid)
tu.tools.civic_get_evidence_item(evidence_id=eid)
tu.tools.civic_search_therapies(limit=100)
tu.tools.civic_search_molecular_profiles(limit=50)

Section 9: Pharmacology

章节9：药理学

python

tu.tools.GtoPdb_get_targets(target_type=type, limit=50)  # GPCR, ion channel, etc
tu.tools.GtoPdb_get_target(target_id=tid)  # for disease-relevant targets
tu.tools.GtoPdb_get_target_interactions(target_id=tid)
tu.tools.GtoPdb_search_interactions(approved_only=True)
tu.tools.GtoPdb_list_ligands(ligand_type="Approved")

python

tu.tools.GtoPdb_get_targets(target_type=type, limit=50)  # GPCR, ion channel, etc
tu.tools.GtoPdb_get_target(target_id=tid)  # for disease-relevant targets
tu.tools.GtoPdb_get_target_interactions(target_id=tid)
tu.tools.GtoPdb_search_interactions(approved_only=True)
tu.tools.GtoPdb_list_ligands(ligand_type="Approved")

Section 10: Safety (use ALL of these)

章节10：安全性（全部使用）

python

tu.tools.OpenTargets_get_drug_warnings_by_chemblId(chemblId=cid)  # for each drug
tu.tools.OpenTargets_get_drug_blackbox_status_by_chembl_ID(chemblId=cid)
tu.tools.extract_clinical_trial_adverse_events(nct_ids=nct_list)
tu.tools.FAERS_count_reactions_by_drug_event(drug=drug_name, event=event)
tu.tools.AdverseEventPredictionQuestionGenerator(disease_name=disease, drug_name=drug)

python

tu.tools.OpenTargets_get_drug_warnings_by_chemblId(chemblId=cid)  # for each drug
tu.tools.OpenTargets_get_drug_blackbox_status_by_chembl_ID(chemblId=cid)
tu.tools.extract_clinical_trial_adverse_events(nct_ids=nct_list)
tu.tools.FAERS_count_reactions_by_drug_event(drug=drug_name, event=event)
tu.tools.AdverseEventPredictionQuestionGenerator(disease_name=disease, drug_name=drug)

Citation Format

引用格式

Every piece of data MUST include its source. Use this format:

每一条数据都必须包含来源。使用以下格式：

In Tables

表格中

markdown

| Gene | Score | Source |
|------|-------|--------|
| APOE | 0.92 | OpenTargets_get_associated_targets_by_disease_efoId |
| APP | 0.88 | OpenTargets_get_associated_targets_by_disease_efoId |

markdown

| 基因 | 评分 | 来源 |
|------|-------|--------|
| APOE | 0.92 | OpenTargets_get_associated_targets_by_disease_efoId |
| APP | 0.88 | OpenTargets_get_associated_targets_by_disease_efoId |

In Lists

列表中

markdown

- Memory loss [Source: OpenTargets_get_associated_phenotypes_by_disease_efoId]
- Cognitive decline [Source: MedlinePlus_get_genetics_condition_by_name]

markdown

- 记忆力减退 [来源：OpenTargets_get_associated_phenotypes_by_disease_efoId]
- 认知能力下降 [来源：MedlinePlus_get_genetics_condition_by_name]

In Prose

正文中

markdown

The disease affects approximately 6.5 million Americans (Source: PubMed_search_articles, 
query: "Alzheimer disease epidemiology").

markdown

该疾病影响约650万美国人（来源：PubMed_search_articles，查询词："Alzheimer disease epidemiology"）。

References Section

参考文献章节

At the end of the report, include complete tool usage log:

markdown

undefined

在报告末尾，需包含完整的工具使用日志：

markdown

undefined

References

参考文献

Tools Used

使用的工具

#	Tool	Parameters	Section	Items Retrieved
1	OSL_get_efo_id_by_disease_name	disease="Alzheimer disease"	Identity	1
2	ols_get_efo_term	obo_id="EFO:0000249"	Identity	1
3	OpenTargets_get_associated_targets_by_disease_efoId	efoId="EFO_0000249"	Genetics	245
...	...	...	...	...

序号	工具	参数	章节
1	OSL_get_efo_id_by_disease_name	disease="Alzheimer disease"	疾病标识
2	ols_get_efo_term	obo_id="EFO:0000249"	疾病标识
3	OpenTargets_get_associated_targets_by_disease_efoId	efoId="EFO_0000249"	遗传学
...	...	...	...

Data Retrieved Summary

数据检索摘要

Total tools used: 45
Total API calls: 78
Sections completed: 10/10

---

使用工具总数：45
API调用总数：78
完成章节数：10/10

---

Progressive Update Pattern

渐进式更新模式

After researching EACH dimension, immediately update the report file:

python

undefined

完成每个维度的研究后，立即更新报告文件：

python

undefined

After each dimension's research completes:

1. Read current report

with open(report_file, 'r') as f: report = f.read()

2. Replace placeholder with formatted content

report = report.replace( "## 3. Genetic & Molecular Basis\nPending...", formatted_genetics_section )

3. Write back immediately

with open(report_file, 'w') as f: f.write(report)

4. Continue to next dimension

---

---

Final Report Quality Checklist

最终报告质量检查清单

Example Output Structure

示例输出结构

For "Alzheimer's Disease" research, the final report should be 2000+ lines with:

Section 1: 5+ ontology IDs, 10+ synonyms, disease hierarchy
Section 2: 20+ phenotypes with HPO IDs, symptoms list
Section 3: 50+ genes with scores, 30+ GWAS associations, 100+ ClinVar variants
Section 4: 20+ drugs, 50+ clinical trials with details
Section 5: 10+ pathways, PPI network, expression data
Section 6: 100+ publications, citation analysis, institution list
Section 7: 15+ similar diseases with similarity scores
Section 8: (if cancer) variants, evidence items
Section 9: Pharmacological targets and interactions
Section 10: Drug warnings, adverse events

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

针对“阿尔茨海默病”的研究，最终报告应不少于2000行，包含：

章节1：5个以上本体ID、10个以上同义词、疾病层级
章节2：20个以上带HPO ID的表型、症状列表
章节3：50个以上带评分的基因、30个以上GWAS关联、100个以上ClinVar变异
章节4：20个以上药物、50个以上带详细信息的临床试验
章节5：10个以上通路、PPI网络、表达数据
章节6：100篇以上文献、引用分析、机构列表
章节7：15个以上带相似度评分的相似疾病
章节8：（如为癌症）变异、证据条目
章节9：药理学靶点与相互作用
章节10：药物警告、不良事件

总计：包含500个以上独立数据点的详细报告，每个数据点均带来源引用。

Tool Reference

工具参考

See TOOLS_REFERENCE.md for complete tool documentation. See EXAMPLES.md for sample reports.

完整工具文档请参阅TOOLS_REFERENCE.md。示例报告请参阅EXAMPLES.md。

tooluniverse-disease-research

Original

Translation

ToolUniverse Disease Research

ToolUniverse 疾病研究

When to Use

使用场景

Core Workflow: Report-First Approach

核心工作流：报告优先方法

Report Template

报告模板

Disease Research Report: {Disease Name}

疾病研究报告：{疾病名称}

Executive Summary

执行摘要

1. Disease Identity & Classification

1. 疾病标识与分类

Ontology Identifiers

本体标识

Synonyms & Alternative Names

同义词与别名

Disease Hierarchy

疾病层级

2. Clinical Presentation

2. 临床表现

Phenotypes (HPO)

表型（HPO）

Symptoms & Signs

症状与体征

Diagnostic Criteria

诊断标准

3. Genetic & Molecular Basis

3. 遗传与分子基础

Associated Genes

相关基因

GWAS Associations

GWAS关联

Pathogenic Variants (ClinVar)

致病性变异（ClinVar）

4. Treatment Landscape

4. 治疗现状

Approved Drugs

获批药物

Clinical Trials

临床试验

Treatment Guidelines

治疗指南

5. Biological Pathways & Mechanisms

5. 生物通路与机制

Key Pathways

关键通路

Protein-Protein Interactions

蛋白质-蛋白质相互作用

Expression Patterns

表达模式

6. Epidemiology & Risk Factors

6. 流行病学与风险因素

Prevalence & Incidence

患病率与发病率

Risk Factors

风险因素

GWAS Studies

GWAS研究

7. Literature & Research Activity

7. 文献与研究动态

Publication Trends

发表趋势

Key Publications

关键文献

Research Institutions

研究机构

8. Similar Diseases & Comorbidities

8. 相似疾病与并发症

Similar Diseases

相似疾病

Comorbidities

并发症

9. Cancer-Specific Information (if applicable)

9. 癌症专属信息（如适用）

CIViC Variants