Using Machine Learning to Detect Coronavirus Threats

An artificial intelligence model has successfully identified coronaviruses capable of infecting humans, out of the thousands of viruses that circulate in wild animals. The model, developed by a team of biologists, mathematicians and physicists at the University of California, Davis, could be used in surveillance for new pandemic threats. The work was published June 8 in Scientific Reports. 

Professor Javier Arsuaga, departments of molecular and cellular biology and of mathematics, Professor Mariel Vazquez, departments of microbiology and molecular genetics and of mathematics, and research specialist Georgina Gonzalez-Isunza, developed a neural network model that produces a human binding potential (h-BiP) score for coronaviruses based on the ability of the virus spike protein to bind to human cells. The model was trained on data from known coronaviruses.

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