Identification of Pandemic Risk for Avian Influenza Virus With Graph Cross Attention Networks.

Abstract

Influenza A virus poses a significant threat to global health due to its high mutability and potential for pandemics. Accurate prediction of influenza virus pandemic risk can mitigate their threat to public health systems and societal stability. Current sequence-based deep learning methods predict the risk of viral pandemics by extracting features from the viral genome sequence. However, these methods ignore the gene flow between different virus strains through genetic reassortment, which is essential for understanding the dynamics of viral evolution. To leverage the gene flow of virus strains to predict the pandemic, we propose Graph Cross Attention Networks for influenza pandemic identification (GCAN-Flu). GCAN-Flu begins with constructing an influenza virus gene flow network based on the genetic reassortment relationship between viruses, and then encodes the gene flow information between strains as a latent modal representation. We further use Graph Cross Attention (GCA) layers to integrate the viral genome sequence with the gene flow information, and predict pandemic risks based on the multimodal representations. Experimental results demonstrate that GCAN-Flu outperforms current methods, providing a powerful tool for pandemic forecasting.