Most emerging infectious diseases of humans (like COVID-19) are zoonotic – caused by viruses originating from other animal species. Identifying high-risk viruses earlier can improve research and surveillance priorities. A study published in PLOS Biology on September 28th by Nardus Mollentze, Simon Babayan, and Daniel Streicker at University of Glasgow, United Kingdom suggests that machine learning (a type of artificial intelligence) using viral genomes may predict the likelihood that any animal-infecting virus will infect humans, given biologically relevant exposure. Identifying zoonotic diseases prior to emergence is a major challenge because only a small minority of the estimated 1.67 million animal viruses are able to infect humans. To develop machine learning models using viral genome sequences, the researchers first compiled a dataset of 861 virus species from 36 families.

As artificial intelligence becomes a standard laboratory tool, scientists are quickly discovering both the promise and perils of algorithmically driven research. Artificial intelligence (AI) is cropping up everywhere these days, according to major news sources that are themselves increasingly driven by computer algorithms. Marketers use AI to target advertisements, engineers use it to anticipate device failures, and AI-driven social media platforms wield outsize influence on everything from fashion to politics. While all types of AI--also called machine learning--entail programming a computer to learn from examples and make inferences, practitioners distinguish different forms of it. Within the broader field of AI, a subset of strategies employ artificial neural networks. These mimic biological brains, with elements of a program connecting to each other like neurons.

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.