A Semantic Model Leveraging Pattern-based Ontology Terms to Bridge Environmental Exposures and Health Outcomes

Lauren E. Chan, Nicole A. Vasilevsky, Anne Thessen, Nicolas Matentzoglu, William D. Duncan, Christopher J. Mungall, Melissa A. Haendel

Research output: Contribution to journalConference articlepeer-review

Abstract

Chemicals are a critical aspect of modern agriculture and residues of these chemicals are commonly consumed by humans. Consumption, inhalation, or topical exposure to agricultural chemicals can pose a risk for human health through a variety of mechanisms. Similarly, exposures to radiation, nutrient consumption, and many other environmental entities can impact health and thus a wide array of research has been pursued to better understand the mechanisms and impacts of environmental exposures. While extensive exposure research has been conducted and the data stored in environmental health databases, the ability to computationally assess these findings in the larger context of biomedical research to inform our knowledge for improved human health is still challenging. We developed an integrative exposure-disease model based on the Exposure Ontology (ExO) upper level ontology and established four Dead Simple OWL Design Patterns (DOSDP) for Mondo Disease Ontology. These patterns offer coordination of exposure event and exposure stimulus terms with disease terms, utilizing content from Open Biological Ontologies. Our model and pattern set can leverage logical axioms from integrated ontologies including the Food Ontology and the Environmental Conditions, Treatments, and Exposures Ontology (ECTO) for greater data and knowledge enrichment. Development of exposure event component terms and related logical axioms can facilitate the standardization needed for exposure modeling. Exposure content and our model can be utilized for the development of integrative knowledge graphs of exposure health data. Additionally, this model serves as a resource to aid the integration of common exposure data sources such as self-reported survey tools. Future work is needed to incorporate essential exposure data components into a comprehensive model, such as estimated or known exposure values, temporality of exposures, and biologically active exposure dosages that incur toxic effects.

Original languageEnglish (US)
Pages (from-to)48-55
Number of pages8
JournalCEUR Workshop Proceedings
Volume3073
StatePublished - 2021
Externally publishedYes
Event2021 International Conference on Biomedical Ontologies, ICBO 2021 - Bozen-Bolzano, Italy
Duration: Sep 16 2021Sep 18 2021

Keywords

  • Disease
  • Environmental exposure
  • Knowledge graph
  • Ontology
  • Semantic model

ASJC Scopus subject areas

  • Computer Science(all)

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