Forecasting SARS-CoV-2 spike protein evolution from small data by deep learning and regression.

ABSTRACT: The emergence of SARS-CoV-2 variants during the COVID-19 pandemic caused frequent global outbreaks that confounded public health efforts across many jurisdictions, highlighting the need for better understanding and prediction of viral evolution. Predictive models have been shown to support disease prevention efforts, such as with the seasonal influenza vaccine, but they require abundant data. For emerging viruses of concern, such models should ideally function with relatively sparse data typically encountered at the early stages of a viral outbreak. Conventional discrete approaches have proven difficult to develop due to the spurious and reversible nature of amino acid mutations and the overwhelming number of possible protein sequences adding computational complexity. We hypothesized that

SUBMITTER: King S 

PROVIDER: S-EPMC12341966 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature