Science Daily  September 28, 2021 Researchers in the UK developed machine learning models that identify candidate zoonoses solely using signatures of host range encoded in viral genomes. Within a dataset of 861 viral species with known zoonotic status, their approach outperformed models based on the phylogenetic relatedness of viruses to known human-infecting viruses distinguishing high-risk viruses within families that contain a minority of human-infecting species. The model predictions suggested the existence of generalizable features of viral genomes that are independent of virus taxonomic relationships and that may preadapt viruses to infect humans. Their model reduced a second set of 645 […]