A curated knowledge base of disease pathophysiology, with structured evidence from the literature.
View all disorders online | QC Dashboard | Priority Dashboard
Each disorder page includes:
- Disease mechanisms and pathophysiology
- Clinical phenotypes with HPO term links
- Genetic factors and variants
- Treatment options with MAXO term links
- All claims backed by PubMed evidence
The knowledge base is stored as structured YAML files in kb/disorders/. Each disorder has its own file (e.g., Asthma.yaml) containing:
name: Asthma
pathophysiology:
- name: Airway Inflammation
description: Chronic inflammation of the airways
biological_processes:
- preferred_term: inflammatory response
term:
id: GO:0006954
label: inflammatory response
evidence:
- reference: PMID:12345678
supports: SUPPORT
snippet: "Exact quote from the paper"
explanation: "Why this supports the claim"
phenotypes:
- name: Wheezing
frequency: FREQUENT
phenotype_term:
preferred_term: Wheezing
term:
id: HP:0030828
label: Wheezing
treatments:
- name: Bronchodilators
description: Relax airway smooth muscle
treatment_term:
preferred_term: respiratory tract agent therapy
term:
id: MAXO:0000312
label: respiratory tract agent therapyEntities are linked to authoritative ontologies:
- Phenotypes: Human Phenotype Ontology (HP)
- Cell types: Cell Ontology (CL)
- Biological processes: Gene Ontology (GO)
- Diseases: Mondo Disease Ontology (MONDO)
- Treatments: Medical Action Ontology (MAXO)
- Anatomy: Uberon (UBERON)
All claims must cite PubMed references with exact quotes. This prevents misinformation and enables verification.
Multiple layers of automated validation ensure data quality and prevent AI hallucinations:
- Schema validation: Ensures correct YAML structure against the LinkML schema
- Ontology term validation: Verifies term IDs exist and labels match authoritative sources (HPO, MONDO, GO, etc.)
- Reference validation: Confirms that quoted snippets actually appear in cited PubMed abstracts
- Compliance analysis: Measures coverage of recommended fields (descriptions, evidence, ontology terms)
# Run schema + term validation
just qc
# Validate a single file
just validate kb/disorders/Asthma.yaml
# Validate references against PubMed abstracts
just validate-references kb/disorders/Asthma.yaml
# Analyze compliance with recommended field coverage
just compliance-all
# Compliance with weighted scoring and threshold checks
just compliance-weightedAll evidence snippets must be exact quotes from paper abstracts, not paraphrases. The reference validator fetches abstracts from PubMed and checks that the quoted text appears verbatim. This catches:
- AI-generated paraphrases that don't match the actual paper
- Wrong PMIDs (e.g., a PMID that points to an unrelated paper)
- Fabricated citations
When validation fails, either fix the snippet to match the actual abstract or remove the evidence item.
Generate a visual dashboard showing compliance metrics across all disorder files:
just gen-dashboardThis creates dashboard/index.html with:
- Summary metrics (files analyzed, average compliance, violations)
- A dedicated
capability_metrics.htmlreport with aggregate coverage, evidence, ontology, mechanism, treatment, and compliance metrics - Slot compliance comparison chart
- Detailed views of the 10 lowest-compliance files (priority curation targets)
- Full table of all files sorted by compliance
- A dedicated
not_yet_curated.htmlreport listing referenced MONDO diseases that do not yet have local DisMech pages - A dedicated
priority.htmlreport ranking MONDO curation candidates with action recommendations and coverage summaries
View online: QC Dashboard or locally: open dashboard/index.html
Generate only the MONDO curation priority view:
just gen-priority-dashboardGenerate a local-only dashboard across all uncurated MONDO disease descendants:
just gen-priority-dashboard-all-mondoThis writes large artifacts under tmp/priority-dashboard-all-mondo/ so they remain
gitignored and do not get committed to GitHub by accident.
YAML files are rendered to browsable HTML pages with clickable ontology term links.
uv run python -m dismech.render --allDisorder pathographs can also be exported as CX2 networks for publication to NDEx.
# Write CX2 JSON to a file
uv run dismech-cx2 kb/disorders/Stargardt_Disease.yaml -o Stargardt_Disease.cx2.json
# Same flow via justfile target
just export-cx2 kb/disorders/Stargardt_Disease.yaml -o Stargardt_Disease.cx2.json
# Export all disorders to a directory, skipping those with no pathograph edges
just export-cx2-all -o /tmp/cx2
# Upload directly to NDEx using env vars
export NDEX_USERNAME=...
export NDEX_PASSWORD=...
uv run dismech-cx2 kb/disorders/Stargardt_Disease.yaml --ndex-upload
# Bulk upload all disorders to the NDEx test server
export NDEX_USERNAME=...
export NDEX_PASSWORD=...
just upload-cx2-test-alldismech-cx2 uses the existing pathograph graph model, preserves node and edge metadata,
and applies a deterministic layout so the uploaded network is immediately viewable in
NDEx. Add --dot-layout if Graphviz and pydot are available and you want a Graphviz
layout instead of the built-in layered layout. The default NDEx upload visibility is
PUBLIC, and the just upload targets default the host to https://test.ndexbio.org.
This knowledge base is curated with Claude Code — an AI agent that knows the schema, validates ontology terms, and checks evidence against PubMed abstracts. There are two ways to start curating:
Best for power users who want full control, deep research providers, and iterative curation sessions.
Prerequisites: A Claude Pro/Max subscription and just installed (installation).
# 1. Clone and enter the repo
git clone https://github.com/monarch-initiative/dismech.git
cd dismech
# 2. Launch Claude Code
claude
# 3. Ask it anything — or run a curation skill directly
> Give me a tour of the dismech project
> What disorders are missing ontology terms?
> /curate Parkinson DiseaseThe /curate command triggers a full curation workflow: deep literature research, YAML generation, ontology term binding, evidence validation, and a ready-to-review PR.
You can also ask the agent open-ended questions, request targeted edits, or run QC:
> Add MAXO treatment terms to the Asthma entry
> Validate references in kb/disorders/Lupus.yaml
> Which files have the lowest compliance scores?
Optional — deep research providers:
For narrative-style deep research, set up an Edison Scientific API key:
export EDISON_API_KEY=your_key_hereFor literature-search-style retrieval via Asta, request an API key from Allen AI Asta and export:
export ASTA_API_KEY=your_key_hereThen run:
just research-disorder asta Liver_CirrhosisAsta outputs are typically literature packets rather than polished narrative reports: they prioritize relevant papers, summaries, snippets, and metadata such as PMID/DOI.
Best for quick contributions — no local setup required.
- Go to claude.ai/code
- Open the
monarch-initiative/dismechrepository - Set up an environment with the following settings:
- Allow access to external websites — needed for fetching PubMed abstracts, ClinicalTrials.gov data, and ontology lookups
- Set
EDISON_API_KEY— for deep literature research via Edison Scientific (sign up → Account → Profile → Create new token)
- Interact with the agent in the web UI — ask questions, request edits, or run
/curate - When you're done, click the Create pull request button to submit your changes for review
That's it. The web UI handles git branching, commits, and PR creation automatically.
Regardless of which route you use, the agent follows the same workflow:
- Research — gathers pathophysiology, phenotypes, treatments, and genetic factors from the literature
- Draft — generates a structured YAML file with ontology term bindings and PubMed-backed evidence
- Validate — runs schema validation, ontology term checks, and reference verification (
just qc) - Submit — commits changes and opens a pull request for human review
For detailed contribution guidelines, see CONTRIBUTING.md.
- LinkML Schema Docs
- Schema source:
src/dismech/schema/dismech.yaml