Machine learning tool can predict viral reservoirs
Research, led by University of Glasgow, suggests that a new machine learning algorithm, designed to use vital genome sequences to predict the likely natural hosts for a broad spectrum of ribonucleic acid viruses, could help inform preventive measures against deadly diseases.
Scientists hope this new machine learning tool will accelerate research, surveillance and disease control activities to target the right species in the wild, with the ultimate aim of preventing deadly and dangerous viruses reaching humans.
Dr Daniel Streicker, Senior Research Fellow within the Institute of Biodiversity, Animal Health and Comparative Medicine, and the senior author of the study recently published in Science, commented: “Genome sequences are just about the first piece of information available when viruses emerge, but until now they have mostly been used to identify viruses and study their spread. Being able to use those genomes to predict the natural ecology of viruses means we can rapidly narrow the search for their animal reservoirs and vectors, which ultimately means earlier interventions.”
Researchers are now developing a web application that will allow scientists from anywhere in the world to submit their virus sequences and get rapid predictions for reservoir hosts, vectors and transmission routes.
First published: 10 December 2018