Mapping the Global Antigenic Evolution of Human Influenza A/H3N2 Neuraminidase Based on a Machine Learning Model - 1968-2024.

ABSTRACT:

Introduction

Human influenza A/H3N2 imposes a substantial global disease burden. Beyond hemagglutinin (HA), neuraminidase (NA) also plays a critical role in the antigenic evolution of influenza viruses. However, a comprehensive understanding of NA antigenic evolution remains lacking.

Methods

NA inhibition (NAI) data were collected and structural epitopes for A/H3N2 NA were identified. A machine learning model was developed to accurately predict antigenic relationships by integrating four feature groups: epitopes, physicochemical properties, N-glycosylation, and catalytic sites. An antigenic correlation network (ACNet) was constructed and antigenic clusters were identified using the Markov clustering algorithm.

Results

The best random forest model (PREDEC-N2) achieved

SUBMITTER: Feng J 

PROVIDER: S-EPMC12360319 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature