A predictive language model for SARS-CoV-2 evolution.

ABSTRACT: Modeling and predicting mutations are critical for COVID-19 and similar pandemic preparedness. However, existing predictive models have yet to integrate the regularity and randomness of viral mutations with minimal data requirements. Here, we develop a non-demanding language model utilizing both regularity and randomness to predict candidate SARS-CoV-2 variants and mutations that might prevail. We constructed the "grammatical frameworks" of the available S1 sequences for dimension reduction and semantic representation to grasp the model's latent regularity. The mutational profile, defined as the frequency of mutations, was introduced into the model to incorporate randomness. With this model, we successfully identified and validated several variants with significantly enhanced viral infecti

SUBMITTER: Ma E 

PROVIDER: S-EPMC11663983 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature