CovTransformer: A transformer model for SARS-CoV-2 lineage frequency forecasting.

ABSTRACT: With hundreds of SARS-CoV-2 lineages circulating in the global population, there is an ongoing need for predicting and forecasting lineage frequencies and thus identifying rapidly expanding lineages. Accurate prediction would allow for more focused experimental efforts to understand pathogenicity of future dominating lineages and characterize the extent of their immune escape. Here, we first show that the inherent noise and biases in lineage frequency data make a commonly-used regression-based approach unreliable. To address this weakness, we constructed a machine learning model for SARS-CoV-2 lineage frequency forecasting, called CovTransformer, based on the transformer architecture. We designed our model to navigate challenges such as a limited amount of data with high levels of noise an

SUBMITTER: Feng Y 

PROVIDER: S-EPMC11631054 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature