The Regents of the University of Minnesota received a $799,993 Project Grant award from the United States Department of Agriculture's National Institute of Food and Agriculture under the Agriculture and Food Research Initiative (AFRI) federal grant program (CFDA #10.310).
The grant will fund the creation of an integrative data science platform to predict the immunogenic and epidemiologic phenotype of Porcine Reproductive and Respiratory Syndrome Virus-Type 2 (PRRSV-2) variants through applying machine learning and artificial intelligence across structural biology, computational immunology, and genomic epidemiology. Specifically, the University will use unsupervised learning to cluster closely related genetic sequences, measure structural divergence between variants using artificial intelligence protein structure prediction, build a model to predict antigenic distance based on genetic and structural metrics, and develop a machine learning algorithm to predict a variant's epidemiologic fitness. The University will also create a visualization platform allowing stakeholders to contextualize their own sequences within the broader phenotypic diversity of the virus. This will improve human-data interactions and support decision-making in the swine industry regarding disease management and immunization practices.
Generated 3/5/24, 3:25 PM