HostNet: improved sequence representation in deep neural networks for virus-host prediction.

ABSTRACT:

Background

The escalation of viruses over the past decade has highlighted the need to determine their respective hosts, particularly for emerging ones that pose a potential menace to the welfare of both human and animal life. Yet, the traditional means of ascertaining the host range of viruses, which involves field surveillance and laboratory experiments, is a laborious and demanding undertaking. A computational tool with the capability to reliably predict host ranges for novel viruses can provide timely responses in the prevention and control of emerging infectious diseases. The intricate nature of viral-host prediction involves issues such as data imbalance and deficiency. Therefore, developing highly accurate computational tools capable of predicting virus-host associations is a

SUBMITTER: Ming Z 

PROVIDER: S-EPMC10691023 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature