tooluniverse-disease-research

name	tooluniverse-disease-research
description	Generate comprehensive disease research reports using 100+ ToolUniverse tools. Creates a detailed markdown report file and progressively updates it with findings from 10 research dimensions. All information includes source references. Use when users ask about diseases, syndromes, or need systematic disease analysis.

ToolUniverse Disease Research

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.

When to Use

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]?"

Core Workflow: Report-First Approach

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

1. Create report file first - Initialize {disease_name}_research_report.md
2. Research each dimension - Use all relevant tools
3. Update report progressively - Write findings to file after each dimension
4. 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

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 |
|--------|-----|--------|
| EFO | | |
| ICD-10 | | |
| UMLS CUI | | |
| SNOMED CT | | |

### Synonyms & Alternative Names
- (list with source)

### Disease Hierarchy
- Parent: 
- Subtypes:

**Sources**: (list tools used)

---

## 2. Clinical Presentation

### Phenotypes (HPO)
| HPO ID | Phenotype | Description | Source |
|--------|-----------|-------------|--------|

### Symptoms & Signs
- (list with source)

### Diagnostic Criteria
- (from literature/MedlinePlus)

**Sources**: (list tools used)

---

## 3. Genetic & Molecular Basis

### Associated Genes
| Gene | Score | Ensembl ID | Evidence | Source |
|------|-------|------------|----------|--------|

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

### Pathogenic Variants (ClinVar)
| Variant | Clinical Significance | Condition | Source |
|---------|----------------------|-----------|--------|

**Sources**: (list tools used)

---

## 4. Treatment Landscape

### Approved Drugs
| Drug | ChEMBL ID | Mechanism | Phase | Target | Source |
|------|-----------|-----------|-------|--------|--------|

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

### Treatment Guidelines
- (from literature)

**Sources**: (list tools used)

---

## 5. Biological Pathways & Mechanisms


| Pathway | Reactome ID | Genes Involved | Source |
|---------|-------------|----------------|--------|


 (tissue-specific networks)


| Tissue | Expression Level | Source |
|--------|------------------|--------|

: (list tools used)

---




 (from literature)


| Factor | Evidence | Source |
|--------|----------|--------|


| Study | Sample Size | Findings | Source |
|-------|-------------|----------|--------|

: (list tools used)

---




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


| PMID | Title | Year | Citations | Source |
|------|-------|------|-----------|--------|


 (from OpenAlex)

: (list tools used)

---




| Disease | Similarity Score | Shared Genes | Source |
|---------|-----------------|--------------|--------|


 (from literature/clinical data)

: (list tools used)

---




| Gene | Variant | Evidence Level | Clinical Significance | Source |
|------|---------|----------------|----------------------|--------|


 (biomarkers)


| Therapy | Target | Evidence | Source |
|---------|--------|----------|--------|

: (list tools used)

---




| Drug | Warning Type | Description | Source |
|------|--------------|-------------|--------|


| Trial | Drug | Adverse Event | Frequency | Source |
|-------|------|---------------|-----------|--------|


 (FDA adverse event data)

: (list tools used)

---




| Tool | Query | Section |
|------|-------|---------|


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

---

Research Protocol

Step 1: Initialize Report

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

Step 2: Research Each Dimension with Citations

For EACH piece of information, track:

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

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': 
        })
        
        
        children = tu.tools.ols_get_efo_term_children(obo_id=efo_id.replace(, ), size=)
        references.append({
            : ,
            : {: efo_id, : },
            : 
        })
    
    
    update_report_section(report_file, , {
        : efo_id,
        : icd_result,
        : umls_result,
        : efo_term.get(, [])  efo_term  [],
        : children
    }, references[-:])  
    
    
    

Step 3: Update Report File After Each Dimension

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 |

### Synonyms & Alternative Names
{format_list_with_source(data[], )}

### Disease Subtypes


**Sources**: 
"""

---

Complete Tool Usage by Section

Section 1: Identity (use ALL of these)

# 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)

Section 2: Clinical Presentation (use ALL of these)

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)

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)

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)

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)

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

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

Section 8: Cancer-Specific (if cancer)

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

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)

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

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

In Tables

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

In Lists

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

In Prose

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

References Section

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

## References

### Tools Used
| # | 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 |
| ... | ... | ... | ... | ... |

### Data Retrieved Summary
- Total tools used: 45
- Total API calls: 78
- Sections completed: 10/10

---

Progressive Update Pattern

After researching EACH dimension, immediately update the report file:

# 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\n*Pending...*",
    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

Before presenting to user, verify:

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

---

Example Output Structure

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.

---

Tool Reference

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