# The Human Disease Ontology: An integrated, mechanistic knowledge resource for biomedical research.

> **NIH NIH U24** · UNIVERSITY OF MARYLAND BALTIMORE · 2022 · $839,112

## Abstract

The 2020 NHGRI Strategic plan highlights the need for facilitating data and resource interoperability for
advancing genomic research and promoting data reuse. The Human Disease Ontology (DO) Knowledgebase
will provide a sustainable approach for linking the growing bodies of information related to core datasets across
genomic and proteomic resources, as interoperable genomic resources enable precision medicine and
knowledge dissemination. The DO plays a key role in disease knowledge organization, representation, and
standardization, serving as a reference framework for multiscale biomedical data integration and analysis across
thousands of clinical, biomedical and computational researchers and genomic resources around the world.
Expanding the DO’s disease data and models for complex diseases will provide a comprehensive network of
disease to disease relationships (DO’s diseasome) that represents a disease feature similarity network for clinical
differential diagnosis exploration. We will deepen our knowledge and understanding of the interrelationships
between genomics and the social and environmental factors that influence human health. We will deliver an
increasingly comprehensive view of the roles and relationships of genomic variation, biomolecules,
environmental drivers and regulatory elements on biological processes, and address the need for genomics
training in the clinical workforce. We will build beyond the current set of coordinating genomic resources, offering
increasingly automated approaches for aggregating and linking disease metadata in a scalable and cost-effective
manner. The DO Knowledgebase will expand content, capacity to support the development of genomic data
science and machine learning/artificial intelligence (ML/AI) methods. The overall goal of this proposal is to
facilitate the linking of disease data via the DO’s diseaseome across broadly useful biomedical, clinical genomic,
proteomic and epigenomic resources, to drive innovative machine learning research and to provide a resource
for optimizing clinical care. The DO serves as the de facto standard for disease etiology across biomedical data
repositories. Conservatively, based on available resource statistics, terms from the DO have been annotated to
over 1.5 million biomedical data elements and citations, a 10x increase in the past 5 years. Our proposed
aims position us well for providing a comprehensive disease resource for the genomic community. We have
identified three main areas of improvement in the DO Knowledgebase to achieve our goals: (1) aggregating
disease information across genomic resources, modeling complex disease and defining the disease
environmental exposome; (2) automating the DO’s production workflow, enabling federated resource querying,
producing a multi-lingual DO and dissemination of ML/AI ready datasets; (3) maintaining and expanding the
DO’s collaborations, establishing a clinical training nosology program and convening topical focus groups.

## Key facts

- **NIH application ID:** 10497271
- **Project number:** 1U24HG012557-01
- **Recipient organization:** UNIVERSITY OF MARYLAND BALTIMORE
- **Principal Investigator:** Lynn Marie Schriml
- **Activity code:** U24 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $839,112
- **Award type:** 1
- **Project period:** 2022-09-06 → 2027-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10497271

## Citation

> US National Institutes of Health, RePORTER application 10497271, The Human Disease Ontology: An integrated, mechanistic knowledge resource for biomedical research. (1U24HG012557-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10497271. Licensed CC0.

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