Predicting host tropism in influenza a viruses: insights from multi-segment nucleotide signatures.

ABSTRACT:

Background

Influenza A virus (IAV) poses a significant public health threat due to its cross-species transmission and complex host adaptation mechanisms. This study integrated whole-genome data from avian, human, swine, and bovine IAV strains, using machine learning to predict viral host tropism based on nucleotide site features and to identify key sites driving host adaptation along with their synergistic effects.

Methods

A total of 64,000 IAV sequences from avian, human, swine, and bovine hosts were analyzed to build host-prediction models. A four-class classification framework (avian, human, swine, bovine) was constructed using nucleotide site features from all eight genomic segments (PB2, PB1, PA, HA, NP, NA, MP, NS). Eight machine learning algorithms (logistic regressio

SUBMITTER: Chen W 

PROVIDER: S-EPMC12903447 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature