Frontiers in Toxicology | |
The AOP-DB RDF: Applying FAIR Principles to the Semantic Integration of AOP Data Using the Research Description Framework | |
Marvin Martens1  Egon L. Willighagen1  Chris T. Evelo2  Jonathan Senn3  Trevor Levey4  Thomas Exner5  Holly M. Mortensen6  | |
[1] Department of Bioinformatics (BiGCaT), Maastricht University, Maastricht, Netherlands;Maastricht Centre for Systems Biology, Maastricht University, Maastricht, Netherlands;Oak Ridge Associated Universities, Oak Ridge, TN, United States;SAS Institute, Cary, NC, United States;Seven Past Nine, Cerknica, Slovenia;United States Environmental Protection Agency, Office of Research and Development, Center for Public Health and Environmental Assessment, Research Triangle Park, Durham, NC, United States; | |
关键词: semantic web; adverse outcome pathway; toxcast assays; disease; pathway; ontological mapping; | |
DOI : 10.3389/ftox.2022.803983 | |
来源: DOAJ |
【 摘 要 】
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.
【 授权许可】
Unknown