We've updated our Privacy Policy to make it clearer how we use your personal data. We use cookies to provide you with a better experience. You can read our Cookie Policy here.


Machine Learning Identifies the Next Deadly Virus

Virus particles coming out of a nostril.
Credit: iStock.
Listen with
Register for free to listen to this article
Thank you. Listen to this article using the player above.

Want to listen to this article for FREE?

Complete the form below to unlock access to ALL audio articles.

Read time: 1 minute

Researchers from the University of Waterloo have successfully classified 191 previously unidentified astroviruses using a new machine learning-enabled classification process.

Astroviruses are some of the most damaging and widespread viruses in the world. These viruses cause severe diarrhea, which kills more than 440,000 children under the age of five annually. In the poultry industry, astroviruses like avian flu have an 80 per cent infection rate and a 50 per cent mortality rate among livestock, leading to economic devastation, supply chain disruption, and food shortages.

Astroviruses mutate quickly and can spread easily across their more than 160 host species, putting researchers and public health officials in a constant race to classify and understand new astroviruses as they emerge. In 2023, there were 322 unidentified astroviruses with distinct genomes. This year, that number has risen to 479.

Want more breaking news?

Subscribe to Technology Networks’ daily newsletter, delivering breaking science news straight to your inbox every day.

Subscribe for FREE

“At any given point, between two and nine per cent of humans carry one of these viruses. That number can be as high as 30 per cent in some countries,” said Fatemeh Alipour, PhD candidate in computer science at Waterloo and the lead computer science author of the research study. “Understanding and classifying these viruses effectively is essential for developing vaccines.”

The astrovirus research team included computer science researchers at Waterloo and biology researchers at the University of Western Ontario.

The new three-part classification method includes supervised machine learning, unsupervised machine learning, and manual labelling of each astrovirus’s host.

“The main idea behind the classification method is to leverage machine learning to classify species by learning from their ‘genomic signatures’,” said Lila Kari, professor in the David R. Cheriton School of Computer Science. “The classification method is exciting both in its speed and general applicability.”

“This method can help us understand how viruses are transmitted between different animals. It can also be used to classify viruses in other virus families like HIV and Dengue.”

Reference: Alipour F, Holmes C, Lu YY, Hill KA, Kari L. Leveraging machine learning for taxonomic classification of emerging astroviruses. Front Mol Biosci. 2024;10:1305506. doi: 10.3389/fmolb.2023.1305506

This article has been republished from the following materials. Note: material may have been edited for length and content. For further information, please contact the cited source. Our press release publishing policy can be accessed here.