Disease Ontology Terms Scraper

Disease Ontology Terms Scraper

Scrape disease terms, synonyms, and relationships from the Disease Ontology by keyword or DOID, up to a million per run. Every term returns its definition, cross-references, parent and child IDs, and computed metadata. No API key required. Export to CSV, JSON, Excel, or XML.

The Disease Ontology is the standard vocabulary for human disease, but its OBO files and SPARQL endpoint are hard to query without bioinformatics tooling. This Actor reads the public REST API directly, so you can pull structured disease terms by keyword or DOID and get a flat, analysis-ready row for each match. It handles pagination, deprecation filtering, and synonym expansion automatically.

Who uses it	What they scrape Disease Ontology for
Bioinformaticians	Building a local disease vocabulary for NLP pipelines or annotation tools.
Clinical researchers	Mapping free-text diagnoses to standard DOIDs for cohort analysis.
Health data engineers	Populating a knowledge graph with disease nodes and their parent-child links.
Pharma analysts	Identifying all subtypes of a disease for competitive landscape reports.

What it does

This Actor collects disease terms from the Disease Ontology by keyword search or DOID and returns each term as a flat row with its metadata, synonyms, and ontological relationships.

• 🔍 Keyword search: type a disease name like 'leukemia' and get every matching term.
• 🆔 DOID lookup: paste a DOID like DOID:1240 to fetch that exact term and its tree.
• 🗑️ Deprecation filter: skip obsolete terms so your dataset stays clean.
• 📊 Flat row output: each term is one row with synonyms, parents, children, and xrefs in consistent columns.

Results export to CSV, JSON, Excel, or XML, or straight from the API.

What you can do with Disease Ontology data

🧬 Build a disease NLP dictionary.

A bioinformatician scrapes all leukemia subtypes with their synonyms to train a named-entity recognition model on clinical notes.

📋 Normalize clinical data.

A data engineer maps thousands of free-text diagnoses from EHR records to standard DOIDs for a multi-site research study.

🔗 Populate a knowledge graph.

A knowledge graph developer pulls parent and child DOIDs for 'autoimmune disease' to build a hierarchical disease network.

📈 Map a disease landscape.

A pharma analyst fetches all subtypes of 'breast cancer' with their definitions to understand the competitive trial landscape.

Why choose this scraper

	What you get
Structured disease data	Synonyms, definitions, cross-references, and parent-child relationships in one row per term
No bioinformatics setup	No OBO parser, no SPARQL queries, no ontology tooling required
Flexible input	Search by common disease name or fetch by exact DOID identifier
Scalable	From a single term to a million, with automatic pagination

How it compares

No other Store actor targets Disease Ontology the same way, so the honest comparison is with the alternatives teams actually weigh.

	Disease Ontology Terms Scraper	Build it in-house	By hand
Setup	Run it now, zero config	Days of engineering	None, but hours per pull
When Disease Ontology changes	Maintained for you	You fix it	You re-learn the page
Proxies, retries, anti-bot	Built in	Your problem	Browser only
Output	Fixed JSON schema, CSV/Excel export	Whatever you build	Copy-paste
Cost	Pay per result	Engineering time	Analyst hours

Configure the run

Drive the Actor with a disease keyword or a specific DOID, and set a maximum number of terms. The deprecation filter runs as each term is read so only current terms reach your dataset. The Input tab lists every parameter.

A first run with the defaults:

{
 "searchTerm": "leukemia",
 "maxItems": 10
}

A larger pull:

{
 "searchTerm": "leukemia",
 "maxItems": 200
}

Pricing

Pay-per-result: $0.0085 per result collected. You pay only for the results written to your dataset.

Results collected	Approximate cost
100 results	$0.85
1,000 results	$8.50
10,000 results	$85.00

New Apify accounts start with $5 in free credit.

Free users

Free-plan runs return up to 10 results as a preview. Upgrade your Apify plan to collect up to 1,000,000 results per run.

Run it

1. Create a free Apify account with $5 in credit.
2. Open the Disease Ontology Terms Scraper.
3. Set your inputs and any filters, then click Start.
4. Export the results as CSV, Excel, JSON, or XML from the Dataset tab.

Run it programmatically through the Apify API (run-sync-get-dataset-items) or the ApifyClient for JavaScript and Python.

Use with AI agents (MCP)

Give an AI agent live access to Disease Ontology through the Model Context Protocol. Add the Actor to Claude, Cursor, or any MCP client:

claude mcp add --transport http apify "https://mcp.apify.com?tools=parseforge/disease-ontology-terms-scraper"

Then prompt it in plain language to run the scraper and read back the results.

Troubleshooting

Why am I getting no results for my search term?

Check the spelling of your keyword. The Disease Ontology uses standard medical terminology. Try a broader term or look up the exact disease name on the Disease Ontology website first.

Why does my run stop at 10 items?

Free Apify users are limited to 10 disease terms per run. Upgrade to a paid plan to increase the maxItems limit up to 1,000,000.

Why are some disease terms missing from my results?

Deprecated terms are skipped by default. Set includeDeprecated to true in the input if you need obsolete terms. Also check that your maxItems value is high enough to capture all matches.

Why does my DOID lookup return an error?

Make sure the DOID is formatted correctly, for example DOID:1240. The prefix must be uppercase 'DOID' followed by a colon and the numeric ID.

Why are parent and child DOID fields empty for some terms?

Some disease terms in the ontology are leaf nodes with no children, or root-level terms with no parents. Empty fields are expected for those cases.

FAQ

Question	Answer
What is the Disease Ontology?	The Disease Ontology is a community-driven, open-source vocabulary that provides standard identifiers (DOIDs) for human diseases, linking them to synonyms, phenotypes, and other biomedical vocabularies.
Do I need an API key to scrape the Disease Ontology?	No. This Actor uses the public Disease Ontology REST API, which requires no authentication or API key.
What data does each disease term row include?	Each row includes the DOID, disease name, definition, synonyms, parent DOIDs, child DOIDs, cross-references to other vocabularies, and whether the term is deprecated.
Can I search by a disease name instead of a DOID?	Yes. Enter a keyword like 'leukemia' in the searchTerm field and the Actor will return all matching disease terms.
How do I fetch a specific disease term by its DOID?	Enter the DOID directly in the searchTerm field, for example DOID:1240 for leukemia. The Actor will fetch that exact term.
What happens to deprecated disease terms?	By default, deprecated terms are skipped. Set includeDeprecated to true if you need historical or obsolete terms in your dataset.
How many disease terms can I scrape in one run?	Free Apify users can scrape up to 10 terms. Paid users can scrape up to 1,000,000 terms per run.
What output formats are supported?	You can export your disease terms dataset to CSV, JSON, Excel, or XML from the Apify platform.
Does this Actor handle pagination automatically?	Yes. The Actor follows pagination links from the Disease Ontology API until it reaches your maxItems limit or exhausts the results.
Can I get the full parent-child hierarchy for a disease?	Each row includes direct parent and child DOIDs. You can reconstruct the full hierarchy by chaining lookups or scraping recursively.

Related actors

Browse the full ParseForge collection for more scrapers.

🆘 Need help? Email parseforge@protonmail.com with your run ID, your input, and what you expected.

⚠️ Disclaimer. This Actor is unofficial and is not affiliated with, endorsed by, or sponsored by the Disease Ontology Consortium. It collects only publicly available data. You are responsible for using the collected data in compliance with the source's terms of service and applicable data-protection laws, including GDPR, CCPA, and PIPL. Do not use it to collect personal data unlawfully.