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Creating a Structured AOP Knowledgebase via Ontology-Based Annotations
Citation:
Ives, C., I. Campia, R. Wang, C. Wittwehr, AND S. Edwards. Creating a Structured AOP Knowledgebase via Ontology-Based Annotations. OpenTox USA 2017, Durham, NC, July 12 - 13, 2017.
Impact/Purpose:
This work describes efforts to expand the AOP ontology developed as part of the international AOP-KB effort via the incorporation of existing biological ontologies. This work is essential for the long-term sustainability of AOP development and use. First, the expert-derived AOPs housed in the AOP-KB must be identifiable by controlled terminology to promote interoperability among the different modules of the knowledgebase and to facilitate the emergence of AOP networks. Second, having key events tied to biological ontologies is necessary for the incorporation of computationally predicted AOPs derived from data mining of toxicological databases. Finally, having the key events within an AOP tied to formal biological ontologies will promote direct use of the AOP information by computational methods and should facilitate the development of computational models describing the AOPs and AOP networks.
Description:
The Adverse Outcome Pathway (AOP) framework is increasingly used to integrate data from traditional and emerging toxicity testing paradigms. As the number of AOP descriptions has increased, so has the need to define the AOP in terms that can be interpreted computationally. We will present a comprehensive annotation of AOPs housed in the AOP-Wiki as of December 4, 2017 using terms from existing biological ontologies. 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 be developed to aid in both AOP development and use, including advanced queries of individual Key Events (KEs) based on semantically equivalent entities. In addition, the incorporation of explicit biological objects will reduce the time required for converting a qualitative AOP description into a conceptual model that can support computational modeling. As high throughput genomics becomes a more important part of the high throughput toxicity testing landscape, the new methods described for annotating KEs will also promote the visualization and analysis of genomics data in an AOP context. [This is an abstract or a proposed presentation and does not necessarily reflect EPA policy. Mention of trade names or commercial products does not constitute endorsement or recommendation for use.]