DSFAS: MASH - Machine learning and advanced omics data Analysis for improved food Safety and public Health

The overall goal of this project is to develop novel methods, pipelines, and tools to predict genomic risk factors for food safety-related outcomes, such as microbial presence/persistence and interplay with other microorganisms in the agricultural and food processing ecosystems. This project aims to improve the safety of our food supply by developing a reproducible, easy-to-use analytical pipeline to detect and predict the presence and behavior of recurring, emerging, and persistent (REP) pathogens using novel computational methods. Specifically, we will use machine learning (ML), computational methods, and the latest experimental techniques to achieve the following objectives. Objective 1: Develop bioinformatics pipeline to identify genetic patterns in foodborne pathogens associated with changes in environmental conditions. Objective 2: Develop ML-based predictive model to detect pathogenic microorganisms in microbiomes (metagenomes) from food production environments. Objective 3: Demonstrate real-world applications of the computational and modeling pipeline by predicting microbial persistence in sustainable-leaning farm and processing environments. Objective 4: Launch artificial intelligence (AI)-based dashboard to detect the genetic patterns of foodborne pathogens present in microbial communities within the food production ecosystem.