The Medical Action Ontology: A tool for annotating and analyzing treatments and clinical management of human disease
Research output: Contribution to journal › Research article › Contributed › peer-review
Contributors
Abstract
BACKGROUND: Navigating the clinical literature to determine the optimal clinical management for rare diseases presents significant challenges. We introduce the Medical Action Ontology (MAxO), an ontology specifically designed to organize medical procedures, therapies, and interventions.
METHODS: MAxO incorporates logical structures that link MAxO terms to numerous other ontologies within the OBO Foundry. Term development involves a blend of manual and semi-automated processes. Additionally, we have generated annotations detailing diagnostic modalities for specific phenotypic abnormalities defined by the Human Phenotype Ontology (HPO). We introduce a web application, POET, that facilitates MAxO annotations for specific medical actions for diseases using the Mondo Disease Ontology.
FINDINGS: MAxO encompasses 1,757 terms spanning a wide range of biomedical domains, from human anatomy and investigations to the chemical and protein entities involved in biological processes. These terms annotate phenotypic features associated with specific disease (using HPO and Mondo). Presently, there are over 16,000 MAxO diagnostic annotations that target HPO terms. Through POET, we have created 413 MAxO annotations specifying treatments for 189 rare diseases.
CONCLUSIONS: MAxO offers a computational representation of treatments and other actions taken for the clinical management of patients. Its development is closely coupled to Mondo and HPO, broadening the scope of our computational modeling of diseases and phenotypic features. We invite the community to contribute disease annotations using POET (https://poet.jax.org/). MAxO is available under the open-source CC-BY 4.0 license (https://github.com/monarch-initiative/MAxO).
FUNDING: NHGRI 1U24HG011449-01A1 and NHGRI 5RM1HG010860-04.
Details
Original language | English |
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Pages (from-to) | 913-927.e3 |
Number of pages | 19 |
Journal | MED |
Volume | 4 |
Issue number | 12 |
Early online date | 9 Nov 2023 |
Publication status | Published - 8 Dec 2023 |
Peer-reviewed | Yes |
External IDs
Scopus | 85176350085 |
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ORCID | /0009-0003-6519-0482/work/147674510 |
Keywords
Sustainable Development Goals
Keywords
- Biological Ontologies, Computer Simulation, Humans, Rare Diseases, Software