Human Disease Ontology (DOID)

The Disease Ontology (DOID) is a standardized, machine-readable ontology that provides consistent, reusable and sustainable descriptions of human diseases, medical conditions, and disease-related phenotypic characteristics. Developed collaboratively by the biomedical research community, DOID comprehensively covers disease classifications across diverse medical domains including infectious diseases, genetic disorders, cancers, cardiovascular diseases, and mental health conditions. The ontology employs hierarchical relationships to organize diseases from general categories to specific disease subtypes, enabling both broad and fine-grained disease annotation. DOID integrates with other biomedical ontologies (e.g., phenotype ontologies, gene ontologies) to link disease concepts with associated genes, symptoms, and environmental factors. The ontology is widely used in biomedical databases, genomics research, and clinical informatics for disease annotation and knowledge integration.

Example Usage: Annotate a disease research paper or dataset with DOID terms such as “DOID:2841 (lymphoma)” or “DOID:1816 (diabetes mellitus)” to enable automated discovery of disease-related research and clinical data.

Metrics & Statistics

Graph Statistics

Total Nodes

136876

Total Edges

288142

Root Nodes

14035

Leaf Nodes

95185
Knowledge Coverage Statistics

Classes

15343

Individuals

0

Properties

2
Hierarchical Metrics

Maximum Depth

26

Minimum Depth

0

Average Depth

1.59

Depth Variance

1.07
Breadth Metrics

Maximum Breadth

61852

Minimum Breadth

1

Average Breadth

4291.67

Breadth Variance

172233228.89
LLMs4OL Dataset Statistics

Term Types

0

Taxonomic Relations

41569

Non-taxonomic Relations

25

Average Terms per Type

0.00

Usage Example

Use the following code to import this ontology programmatically:

from ontolearner.ontology import DOID

ontology = DOID()
ontology.load("path/to/DOID-ontology.owl")

# Extract datasets
data = ontology.extract()

# Access specific relations
term_types = data.term_typings
taxonomic_relations = data.type_taxonomies
non_taxonomic_relations = data.type_non_taxonomic_relations