Human Disease Ontology (DOID)¶
The Disease Ontology (DOID) is a standardized, machine-readable ontology for describing and classifying human diseases [1] [2]. It provides stable disease identifiers and a structured classification that supports the integration of disease-related knowledge across biomedical resources [1]. The ontology was developed to provide a common semantic framework for human disease concepts and to improve interoperability between biological and clinical data sources [1] [2].
DOID supports disease annotation, data integration, search, and reuse across biomedical databases, genomics resources, and related research applications [1] [2]. Its continued development has expanded disease classification, cross-references, definitions, and interoperability with other biomedical resources, supporting consistent disease representation and cross-resource comparison [2].
Example Usage:
Annotate a disease-related dataset or research resource with DOID identifiers, such as DOID:2841 for lymphoma or DOID:9352 for diabetes mellitus, to provide standardized disease annotations that can be linked and compared across biomedical databases and knowledge resources [1] [2].
Metrics & Statistics¶
Total Nodes |
136876 |
Total Edges |
288142 |
Root Nodes |
14035 |
Leaf Nodes |
95185 |
Classes |
15343 |
Individuals |
0 |
Properties |
2 |
Maximum Depth |
26 |
Minimum Depth |
0 |
Average Depth |
1.59 |
Depth Variance |
1.07 |
Maximum Breadth |
61852 |
Minimum Breadth |
1 |
Average Breadth |
4291.67 |
Breadth Variance |
172233228.89 |
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