drug-discovery-prompts/upstream/mims-harvard-ToolUniverse/skills/tooluniverse-disease-research/RESEARCH_PROTOCOL.md

title, task, lineage_type, upstream_source, upstream_sha, imported_at, prompt_class, upstream_changes, author, validated

title	task	lineage_type	upstream_source	upstream_sha	imported_at	prompt_class	upstream_changes	author	validated
Research Protocol: Step-by-Step Procedures		import	https://github.com/mims-harvard/ToolUniverse/blob/e2520a96/skills/tooluniverse-disease-research/RESEARCH_PROTOCOL.md	e2520a96	2026-06-26	prompt	accepted	upstream	false

Research Protocol: Step-by-Step Procedures

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': '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

# 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

Step 4: Format Section 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['synonyms'], 'ols_get_efo_term')}

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

**Sources**: {source_list}
"""

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

Expected Output Scale

For a disease like "Alzheimer's Disease", 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.