A team of scientists led by researchers at the University of Georgia Center for Food Safety in Griffin has developed a machine-learning approach that could lead to quicker identification of the animal source of certain Salmonella outbreaks.
In the research, published in the January 2019 issue of Emerging Infectious Diseases, Xiangyu Deng and his colleagues used more than 1,000 genomes to predict the animal sources, especially livestock, of Salmonella Typhimurium.
Deng, an assistant professor of food microbiology at the center, and Shaokang Zhang, a postdoctoral associate with the center, led the project, which also included experts from the Centers for Disease Control and Prevention, the U.S. Food and Drug Administration, the Minnesota Department of Health and the Translational Genomics Research Institute. Read more