A sequence-based machine learning model for predicting antigenic distance for H3N2 influenza virus.

ABSTRACT:

Introduction

Seasonal influenza A H3N2 viruses are constantly changing, reducing the effectiveness of existing vaccines. As a result, the World Health Organization (WHO) needs to frequently update the vaccine strains to match the antigenicity of emerged H3N2 variants. Traditional assessments of antigenicity rely on serological methods, which are both labor-intensive and time-consuming. Although numerous computational models aim to simplify antigenicity determination, they either lack a robust quantitative linkage between antigenicity and viral sequences or focus restrictively on selected features.

Methods

Here, we propose a novel computational method to predict antigenic distances using multiple features, including not only viral sequence attributes but also integrating four

SUBMITTER: Li X 

PROVIDER: S-EPMC10834737 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature