WaveSeekerNet: accurate prediction of influenza A virus subtypes and host source using attention-based deep learning.

ABSTRACT:

Background

Influenza A virus (IAV) poses a significant threat to animal health globally, with its ability to overcome species barriers and cause pandemics. Rapid and accurate IAV subtypes and host source prediction is crucial for effective surveillance and pandemic preparedness. Deep learning has emerged as a powerful tool for analyzing viral genomic sequences, offering new ways to uncover hidden patterns associated with viral characteristics and host adaptation.

Findings

We introduce WaveSeekerNet, a novel deep learning model for accurate and rapid prediction of IAV subtypes and host source. The model leverages attention-based mechanisms and efficient token mixing schemes, including the Fourier Transform and the Wavelet Transform, to capture intricate patterns within viral

SUBMITTER: Nguyen HH 

PROVIDER: S-EPMC12395966 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature