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Creating a Structured AOP Knowledgebase via Ontology-Based Annotations (OpenTox USA 2018)
Citation:
Ives, C., I. Campia, R. Wang, C. Wittwehr, AND S. Edwards. Creating a Structured AOP Knowledgebase via Ontology-Based Annotations (OpenTox USA 2018). OpenTox USA 2018, RTP, NC, July 11 - 12, 2018.
Impact/Purpose:
This poster describes ontological annotations of AOPwiki to enable advanced query and aid AOP development.
Description:
The Adverse Outcome Pathway (AOP) framework is increasingly adopted to integrate data from traditional and emerging toxicity screenings, as well as those from disease research. As the number of AOPs has increased, so has the need to define an AOP in terms that can be interpreted computationally. We will present a comprehensive collection of 172 AOPs annotated by terms from existing biological ontologies and housed in the AOP-Wiki as of December 4, 2016. Following review by the original AOP authors, these terms are being included in the AOP-Wiki, and the selected ontologies will be used to drive author-selected annotation of new AOPs as they are entered. This expanded annotation of AOPs allows computational reasoners to aid in both AOP development and use, and to enable advanced queries of individual Key Events (KEs) based on semantically equivalent entities. In addition, the incorporation of explicit biological objects will reduce the lead time to convert a qualitative AOP description into a conceptual model that can support computational modeling. As genomics becomes a more important part of the high throughput toxicity testing pipeline, the new methods described for annotating KEs will also promote the visualization and analysis of genomics data in an AOP context. The study, “Linking Complex Disease and Exposure Data to Established Data Standards”, incorporates community-based data standard consensus measures from Phenotypes and Exposures (PhenX) (www.phenxtoolkit.org) to link existing studies, and make them available for the assessment of environmental contributors to complex diseases. To identify variable linkages and promote data harmonization, existing formal ontologies in exposures and complex diseases will be extended to include additional concepts.
