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Quantitative evaluation of ontology design patterns for combining pathology and anatomy ontologies

Data are increasingly annotated with multiple ontologies to capture rich information about the features of the subject under investigation. Analysis may be performed over each ontology separately, but recently there has been a move to combine multiple ontologies to provide more powerful analytical p...

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Autores principales: Alghamdi, Sarah M., Sundberg, Beth A., Sundberg, John P., Schofield, Paul N., Hoehndorf, Robert
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6411989/
https://www.ncbi.nlm.nih.gov/pubmed/30858527
http://dx.doi.org/10.1038/s41598-019-40368-1
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author Alghamdi, Sarah M.
Sundberg, Beth A.
Sundberg, John P.
Schofield, Paul N.
Hoehndorf, Robert
author_facet Alghamdi, Sarah M.
Sundberg, Beth A.
Sundberg, John P.
Schofield, Paul N.
Hoehndorf, Robert
author_sort Alghamdi, Sarah M.
collection PubMed
description Data are increasingly annotated with multiple ontologies to capture rich information about the features of the subject under investigation. Analysis may be performed over each ontology separately, but recently there has been a move to combine multiple ontologies to provide more powerful analytical possibilities. However, it is often not clear how to combine ontologies or how to assess or evaluate the potential design patterns available. Here we use a large and well-characterized dataset of anatomic pathology descriptions from a major study of aging mice. We show how different design patterns based on the MPATH and MA ontologies provide orthogonal axes of analysis, and perform differently in over-representation and semantic similarity applications. We discuss how such a data-driven approach might be used generally to generate and evaluate ontology design patterns.
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spelling pubmed-64119892019-03-13 Quantitative evaluation of ontology design patterns for combining pathology and anatomy ontologies Alghamdi, Sarah M. Sundberg, Beth A. Sundberg, John P. Schofield, Paul N. Hoehndorf, Robert Sci Rep Article Data are increasingly annotated with multiple ontologies to capture rich information about the features of the subject under investigation. Analysis may be performed over each ontology separately, but recently there has been a move to combine multiple ontologies to provide more powerful analytical possibilities. However, it is often not clear how to combine ontologies or how to assess or evaluate the potential design patterns available. Here we use a large and well-characterized dataset of anatomic pathology descriptions from a major study of aging mice. We show how different design patterns based on the MPATH and MA ontologies provide orthogonal axes of analysis, and perform differently in over-representation and semantic similarity applications. We discuss how such a data-driven approach might be used generally to generate and evaluate ontology design patterns. Nature Publishing Group UK 2019-03-11 /pmc/articles/PMC6411989/ /pubmed/30858527 http://dx.doi.org/10.1038/s41598-019-40368-1 Text en © The Author(s) 2019 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Alghamdi, Sarah M.
Sundberg, Beth A.
Sundberg, John P.
Schofield, Paul N.
Hoehndorf, Robert
Quantitative evaluation of ontology design patterns for combining pathology and anatomy ontologies
title Quantitative evaluation of ontology design patterns for combining pathology and anatomy ontologies
title_full Quantitative evaluation of ontology design patterns for combining pathology and anatomy ontologies
title_fullStr Quantitative evaluation of ontology design patterns for combining pathology and anatomy ontologies
title_full_unstemmed Quantitative evaluation of ontology design patterns for combining pathology and anatomy ontologies
title_short Quantitative evaluation of ontology design patterns for combining pathology and anatomy ontologies
title_sort quantitative evaluation of ontology design patterns for combining pathology and anatomy ontologies
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6411989/
https://www.ncbi.nlm.nih.gov/pubmed/30858527
http://dx.doi.org/10.1038/s41598-019-40368-1
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