Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Analysis of Causal Relationships in Integrated Ontologies of Diseases, Phenotypes, and Radiological Diagnosis.

Studies in health technology and informatics - 6 Jun 2022

Kahn Charles E

Abstract excerpt

Biomedical ontologies encode knowledge in a form that makes it computable. The current study used the integration of three large biomedical ontologies-the Disease Ontology (DO), Human Phenotype Ontology (HPO), and Radiology Gamuts Ontology (RGO)-to explore inferred causal relationships between high-level DO and HPO concepts. The principal DO categories were defined as the 7 direct subclasses of the top-level...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Biological Ontologies
Share to Causality
Share to Phenotype

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Radiography

Source

Identifiers

PMID: 35673013
DOI: 10.3233/SHTI220074

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 6 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Analysis of Causal Relationships in Integrated Ontologies of Diseases, Phenotypes, and Radiological Diagnosis.PMID 35673013Read ↗

The Medical Action Ontology: A Tool for Annotating and Analyzing Treatments and Clinical Management of Human DiseaseSemantic proximity Read ↗

Interoperability between phenotypes in research and healthcare terminologies--Investigating partial mappings between HPO and SNOMED CT.Semantic proximity Read ↗

The Human Phenotype Ontology: Semantic Unification of Common and Rare Disease.Semantic proximity Read ↗

Integrating Bio-ontologies and Controlled Clinical Terminologies: From Base Pairs to Bedside Phenotypes.Semantic proximity Read ↗

[Rare-disease data standards].Semantic proximity Read ↗

Modeling quantitative traits for COVID-19 case reportsSemantic proximity Read ↗

The Human Phenotype Ontology in 2021.Semantic proximity Read ↗

Formal axioms in biomedical ontologies improve analysis and interpretation of associated dataSemantic proximity Read ↗

Klarigi: Characteristic explanations for semantic biomedical data.Semantic proximity Read ↗

Phenotypic Analysis of Clinical Narratives Using Human Phenotype Ontology.Semantic proximity Read ↗

Dictionary of disease ontologies (DODO): a graph database to facilitate access and interaction with disease and phenotype ontologiesSemantic proximity Read ↗

The Disease Ontology: fostering interoperability between biological and clinical human disease-related data.Semantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.