Flu-CNN: identifying host specificity of Influenza A virus using convolutional networks

Area of Science:

• Virology
• Genomics
• Computational Biology

Background:

• Influenza A viruses (IAVs) pose significant pandemic risks, with host specificity limiting but not preventing cross-species transmission.
• Early detection of shifts in IAV host specificity is critical for public health but remains challenging using genomic data.
• Identifying potential zoonotic threats from avian influenza viruses is essential for timely outbreak management.

Purpose of the Study:

• To develop and validate a computational model for predicting Influenza A virus host specificity from genomic sequences.
• To focus on avian influenza viruses with zoonotic potential, such as H5N1, H7N9, and H9N2.
• To provide a tool for real-time risk assessment and surveillance during influenza outbreaks.

Main Methods:

• Development of a Convolutional Neural Network (CNN) model named Flu-CNN.
• Training and evaluation of Flu-CNN on a large dataset of 911,098 viral genomic sequences.
• Analysis of individual genomic segments to determine host specificity and identify key mutations.

Main Results:

• Flu-CNN achieved 99% accuracy in predicting host specificity from a single genomic segment.
• The model demonstrated high accuracy even for high-risk avian influenza subtypes with limited strains.
• Key mutations associated with zoonotic potential were identified, and the model assessed the zoonotic risk of specific strains.

Conclusions:

• Flu-CNN offers a pioneering and highly accurate approach for predicting IAV host specificity using genomic data.
• The model significantly enhances early outbreak surveillance and risk mitigation strategies for potential human-infecting influenza viruses.
• This computational tool provides novel insights into viral evolution and aids global efforts to prevent widespread influenza transmission.