Identifying and prioritizing potential human-infecting viruses from their genome sequences.

ABSTRACT: Determining which animal viruses may be capable of infecting humans is currently intractable at the time of their discovery, precluding prioritization of high-risk viruses for early investigation and outbreak preparedness. Given the increasing use of genomics in virus discovery and the otherwise sparse knowledge of the biology of newly discovered viruses, we 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, our approach outperformed models based on the phylogenetic relatedness of viruses to known human-infecting viruses (area under the receiver operating characteristic curve [AUC] = 0.773), distinguishing high-risk viruses within families that c

SUBMITTER: Mollentze N 

PROVIDER: S-EPMC8478193 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature