An artificial intelligence algorithm has been used to analyze patient data from past viral pandemics including SARS, MERS and swine flu to find patterns in which genes turned on or off during infection, and identified two sets of genes that can be used to predict patient outcomes in future pandemics.
The multidisciplinary study reveals how gene expressions in patients with viral infections — including the novel coronavirus — can predict severity of illness and immune response, and how the model can be used to predict the outcomes of test therapies.
The research, which bridges a gap between medicine and computer science, was led by Pradipta Ghosh, Debashis Sahoo and Soumita Das at the University of California-San Diego.
Researchers identified two sets of genes involved in the process. A set of 166 genes reveals how a person’s immune system responds to infection, while a set of 20 signature genes can predict disease severity, including whether the patient will need to be hospitalized or put on a ventilator.
