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Assessing the Quality of Emergent Adverse Outcome Pathways Using Semantic Analysis
Citation:
Pollesch, N., J. Olker, AND R. Wang. Assessing the Quality of Emergent Adverse Outcome Pathways Using Semantic Analysis. SETAC, Pittsburgh, PA, November 13 - 17, 2022. https://doi.org/10.23645/epacomptox.21466638
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Description:
Contribution, collaboration, and curation of the Adverse Outcome Pathway knowledgebase has enabled data-mining andcomputational knowledge discovery research that utilizes the information within the AOP knowledgebase. One active area of research is the discovery and identification of Emergent Adverse Outcome Pathways (emAOPs). The creation of emAOPs is a result of key event sharing by AOP authors and network analytic techniques provide methods by which emAOPs can be identified. Previous research has shown that thousands of emAOPs exist in the AOP-Wiki. Given the large number of emAOPs, computational methods need to bemust be developed to assess their biological integrity and validity. Semantic coherence analysis has been shown recently to be a useful technique to compare and assess the quality of AOPs. In this presentation, results from a semantic coherence analysis analyses of emAOPs are shared that indicates many emAOPs are of high semantic quality. The combination of emergent AOP knowledge discovery methods using network analysis and quality assessment using semantic analysis is demonstrated as a novel workflow to take advantage of, and contribute to, the AOP knowledgebase.
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DOI: Assessing the Quality of Emergent Adverse Outcome Pathways Using Semantic Analysis
ASSESSING THE QUALITY OF EMERGENT AOPS USING SEMANTIC ANALYSIS - POLLESCH ET AL., 2022.PDF (PDF, NA pp, 1247.559 KB, about PDF)