(P26-027-23) Multi-Ontology Nutrition Informatics Infrastructure (MONII)

Objectives: MONII brings ontology and systems integration approaches to nutrition accelerating the speed of nutrition discoveries, enable convergence of nutrition research across domains, and build a knowledge base for eventual use in translational research. With a convergence approach, MONII enables the wheels of nutrition science to move faster and be more rapidly translatable across use cases. MONII coheres heterogeneous food and nutrition science data with foundational reusable cyberinfrastructure which, when instantiated with data, enables modular, extensible data integration, machine and deep learning models, and other forms of artificial intelligence.

Methods: We conducted a review of all Open Biological and Biomedical Ontology (OBO) ontologies related to nutrition as well as the food science and nutrition literature to build a conceptual model for the foundation of MONII. To create the framework, we identified common concepts for across ontologies, essential for integrating food and health data and then analyzed relationships and interactions across these terms.

Results: The conceptual model has 18 ontologies related to nutrition groups into 4 categories—“Clinical and Public Health Nutrition Science,” “Agricultural and Food Science and Technology,” “Health and Medical Science,” and “Basic Life Sciences.” Of the 18 existing nutrition-related ontologies, 17 of 18 came from the OBO Foundry. The exception was the Processing and Observation Ontology. The model shows 8 existing ontologies connected to “Agricultural and Food Science and Technology” and 2 more under development, 4 existing ontologies connected to “Health and Medical Science” and 3 more under development, 5 connected to “Clinical and Public Health Nutrition Science,” and 4 to “Basic Life Sciences.”

Conclusions: The current work provides a conceptual framework for MONII. Further development of MONII will expand and enable integration of food systems level data about organizations and actors together with nutrition to expand the granularity of food system and food flow information so for instance, communities might better understand their net inflow and outflow of Vitamin C—or any other nutrient, and it’s relative availability in the food supply to a given community.

Funding Sources: NSF AI Institute Award #2112606; NSF SCC-RCN Award #1737573; USDA AMS Coop Agreement #22-TMMSD-ME-002