Machine learning model helps scientists identify source of deadly viruses

Researchers created a machine learning software that analyzes virus' genetic information to predict which groups of animals the virus will likely spread to, according to a paper published Nov. 1 in Science.

For the study, researchers at the University of Glasgow in the U.K. collected epidemiological and genetic data on several hundred viruses with known animal hosts that can spread to humans. Using machine learning, researchers created a computer model to predict which animal groups would most likely host a virus, based on its RNA genome. The model can predict one of 11 likely animal groups, such as rodents or primates, but cannot identify a specific species as the likely virus carrier.

"We would love to know the species," said Daniel Streicker, PhD, a disease ecologist at the University of Glasgow and lead study author. "But this is a way to hopefully get to the species faster."

When used on known viruses, the system identified the general vector type 90.8 percent of the time and the host reservoir type 71.9 percent of the time, according to STAT.

At present, scientists mostly rely on circumstantial evidence to link an emerging human virus to its animal reservoir. The machine learning model could help scientists prevent future outbreaks from occurring in humans, according to Dr. Streicker.

"Until you know what the reservoir is, it's difficult to gauge risk, and it's difficult to do anything to stop a disease from emerging," he said.

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