Poster Open Access

Developing Semantic Technology for Toxicology Applications: Interdisciplinary Collaborations and Community Development

Thessen, Anne E; Callahan, Tiffany J; Chan, Lauren E; Willis, Mary; Vasilevsky, Nicole; Boyles, Rebecca; Kulkarni, Resham; Grondin, Cynthia J; Walls, Ramona L; Brander, Susanne; Haendel, Melissa A

Modern toxicology is evolving to leverage data science methodologies to better address complex public health and environmental concerns. Understanding the potential adverse impacts of environmental exposures requires working across a variety of domains and data types that are often siloed and require manual curation and extraction. Ontologies and semantic engineering can facilitate meaningful data integration, but existing semantic standards have not been widely used in toxicology. Development of semantic standards for toxicology requires sustained interdisciplinary collaborations. One place where the community came together to discuss this need is the “Computable Exposures” workshop, held at Oregon State University in September 2019. Ontologists, toxicologists, epidemiologists, exposure scientists, ecotoxicologists, clinicians, computer scientists, computational biologists, and data scientists from academia, government, and industry were in attendance. Here we describe community-building efforts, standards development, and plans for future work. Objectives include building a semantic exposure data model using the Environmental Conditions, Treatments, and Exposures Ontology (ECTO), developing toxicology-driven use cases and competency questions, creating a mailing list, and planning a larger computable exposures conference. Twelve use cases were developed during the workshop, for example, using semantic technology to complete Adverse Outcomes Pathway (AOP) and Aggregate Exposure Pathway (AEP) given the initiating and terminal key events. Broader adoption of ontologies, together with increased data sharing, has the potential to improve a toxicologist’s ability to integrate, navigate, and analyze vast amounts of heterogeneous data—allowing for more rapid safety assessment of chemical and environmental exposures, and increased understanding of underlying biological mechanisms across species.

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