# The Disease Ontology Project: mechanistic profiles of human disease for biomedical and clinical research

> **NIH NIH U41** · UNIVERSITY OF MARYLAND BALTIMORE · 2020 · $52,892

## Abstract

Human disease data is a cornerstone of biomedical research for identifying drug targets,
connecting genetic variations to phenotypes, understanding molecular pathways relevant to
novel treatments and coupling clinical care and biomedical research. Consequently, there is a
significant need for a standardized representation of human disease to connect disease
concepts across resources, to support development of computational tools that will enable
robust data analysis and integration and to continually incorporate new insights regarding our
understanding of disease pathogenesis. For the past 13 years, the Disease Ontology team has
been focusing on developing and applying an etiology based Human Disease Ontology (DO)
and providing the biomedical community with a knowledgebase of integrated rare and common
disease terms to support disease annotations for genomes, genes, genetic variants, associated
biomedical data and literature. Conservatively, based on available resource statistics, terms
from the DO have been annotated to over 150,000 biomedical data elements and citations.
We have developed the DO, representing 6,782 human diseases and the DO web interface
(http://www.disease-ontology.org) and RESTful API to enable semantic exploration of disease
etiology and aligned disease concepts representing 36,711 clinical vocabulary cross-
references. The 10-fold increase of the number of published clinical and experimental studies
per year (PubMed: Clinical Study) in the past four decades, with 43,401 PubMed articles in
2014 compared to 3,269 in 1975, has markedly expanded our understanding of disease
mechanisms. We have identified two main areas of improvement in the DO (1.0) necessary to
represent this growing body of knowledge: (1) representing cellular, molecular and
environmental mechanisms of disease as distinct disease profiles within the DO and (2)
representing alternative classifications of complex disease in order to address clinical use cases
for complex diseases. We thus propose to develop the DO (2.0), an integrative disease
mechanism framework for disease characterization and annotation, with the goal to represent
distinct disease profiles and improve upon the existing single profile (DO 1.0) or mixed profile
classifications (ORDO, NCIthesaurus, MonDO). We believe DO (2.0) will provide both genomic
and clinical research communities with a versatile system that will enable researchers to
perform more accurate and comprehensive analysis of common cellular, molecular or
environmental disease mechanisms. Utilization of the DO will be promoted in the clinical and
biomedical communities through high profile publications, conferences and workshops.

## Key facts

- **NIH application ID:** 9977237
- **Project number:** 5U41HG008735-04
- **Recipient organization:** UNIVERSITY OF MARYLAND BALTIMORE
- **Principal Investigator:** Lynn Marie Schriml
- **Activity code:** U41 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $52,892
- **Award type:** 5
- **Project period:** — → —

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9977237

## Citation

> US National Institutes of Health, RePORTER application 9977237, The Disease Ontology Project: mechanistic profiles of human disease for biomedical and clinical research (5U41HG008735-04). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9977237. Licensed CC0.

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