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The AOP-DB RDF: Applying FAIR Principles to the Semantic Integration of AOP Data Using the Research Description Framework

Computational toxicology is central to the current transformation occurring in toxicology and chemical risk assessment. There is a need for more efficient use of existing data to characterize human toxicological response data for environmental chemicals in the US and Europe. The Adverse Outcome Path...

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Autores principales: Mortensen, Holly M., Martens, Marvin, Senn, Jonathan, Levey, Trevor, Evelo, Chris T., Willighagen, Egon L., Exner, Thomas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8915825/
https://www.ncbi.nlm.nih.gov/pubmed/35295213
http://dx.doi.org/10.3389/ftox.2022.803983
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author Mortensen, Holly M.
Martens, Marvin
Senn, Jonathan
Levey, Trevor
Evelo, Chris T.
Willighagen, Egon L.
Exner, Thomas
author_facet Mortensen, Holly M.
Martens, Marvin
Senn, Jonathan
Levey, Trevor
Evelo, Chris T.
Willighagen, Egon L.
Exner, Thomas
author_sort Mortensen, Holly M.
collection PubMed
description Computational toxicology is central to the current transformation occurring in toxicology and chemical risk assessment. There is a need for more efficient use of existing data to characterize human toxicological response data for environmental chemicals in the US and Europe. The Adverse Outcome Pathway (AOP) framework helps to organize existing mechanistic information and contributes to what is currently being described as New Approach Methodologies (NAMs). AOP knowledge and data are currently submitted directly by users and stored in the AOP-Wiki (https://aopwiki.org/). Automatic and systematic parsing of AOP-Wiki data is challenging, so we have created the EPA Adverse Outcome Pathway Database. The AOP-DB, developed by the US EPA to assist in the biological and mechanistic characterization of AOP data, provides a broad, systems-level overview of the biological context of AOPs. Here we describe the recent semantic mapping efforts for the AOP-DB, and how this process facilitates the integration of AOP-DB data with other toxicologically relevant datasets through a use case example.
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spelling pubmed-89158252022-03-15 The AOP-DB RDF: Applying FAIR Principles to the Semantic Integration of AOP Data Using the Research Description Framework Mortensen, Holly M. Martens, Marvin Senn, Jonathan Levey, Trevor Evelo, Chris T. Willighagen, Egon L. Exner, Thomas Front Toxicol Toxicology Computational toxicology is central to the current transformation occurring in toxicology and chemical risk assessment. There is a need for more efficient use of existing data to characterize human toxicological response data for environmental chemicals in the US and Europe. The Adverse Outcome Pathway (AOP) framework helps to organize existing mechanistic information and contributes to what is currently being described as New Approach Methodologies (NAMs). AOP knowledge and data are currently submitted directly by users and stored in the AOP-Wiki (https://aopwiki.org/). Automatic and systematic parsing of AOP-Wiki data is challenging, so we have created the EPA Adverse Outcome Pathway Database. The AOP-DB, developed by the US EPA to assist in the biological and mechanistic characterization of AOP data, provides a broad, systems-level overview of the biological context of AOPs. Here we describe the recent semantic mapping efforts for the AOP-DB, and how this process facilitates the integration of AOP-DB data with other toxicologically relevant datasets through a use case example. Frontiers Media S.A. 2022-02-14 /pmc/articles/PMC8915825/ /pubmed/35295213 http://dx.doi.org/10.3389/ftox.2022.803983 Text en Copyright © 2022 Mortensen, Martens, Senn, Levey, Evelo, Willighagen and Exner. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Toxicology
Mortensen, Holly M.
Martens, Marvin
Senn, Jonathan
Levey, Trevor
Evelo, Chris T.
Willighagen, Egon L.
Exner, Thomas
The AOP-DB RDF: Applying FAIR Principles to the Semantic Integration of AOP Data Using the Research Description Framework
title The AOP-DB RDF: Applying FAIR Principles to the Semantic Integration of AOP Data Using the Research Description Framework
title_full The AOP-DB RDF: Applying FAIR Principles to the Semantic Integration of AOP Data Using the Research Description Framework
title_fullStr The AOP-DB RDF: Applying FAIR Principles to the Semantic Integration of AOP Data Using the Research Description Framework
title_full_unstemmed The AOP-DB RDF: Applying FAIR Principles to the Semantic Integration of AOP Data Using the Research Description Framework
title_short The AOP-DB RDF: Applying FAIR Principles to the Semantic Integration of AOP Data Using the Research Description Framework
title_sort aop-db rdf: applying fair principles to the semantic integration of aop data using the research description framework
topic Toxicology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8915825/
https://www.ncbi.nlm.nih.gov/pubmed/35295213
http://dx.doi.org/10.3389/ftox.2022.803983
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