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    This study predicts Influenza A virus host tropism using Hemagglutinin (HA) protein sequences. The novel method achieves higher accuracy in identifying avian, human, and swine infections.

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    Area of Science:

    • Virology
    • Bioinformatics
    • Machine Learning

    Background:

    • Influenza virus poses a significant threat across multiple species.
    • Hemagglutinin (HA) protein is crucial for viral entry and a target for antiviral development.
    • Accurate host tropism prediction is vital for managing influenza outbreaks.

    Purpose of the Study:

    • To develop a predictive model for Influenza A virus host tropism (Human, Avian, Swine).
    • To utilize only the Hemagglutinin (HA) protein sequence for host prediction.
    • To improve upon existing methods for influenza host prediction accuracy and balance.

    Main Methods:

    • Encoding HA protein sequences into numerical signals using the Hydrophobicity Index.
    • Utilizing a Convolutional Neural Network (CNN)-based predictive model.
    • Training and validating the model on a large dataset of HA protein sequences from the Influenza Research Database (IRD).

    Main Results:

    • The proposed model achieved higher accuracy for individual classes (up to 10% for Avian) and overall (5%) compared to a previous study.
    • The model demonstrated more balanced accuracy across all host classes (Human, Avian, Swine).
    • High accuracy was achieved in distinguishing HA protein sequences for different host types.

    Conclusions:

    • The developed CNN model effectively predicts Influenza A virus host tropism using HA protein sequences.
    • This approach offers a more accurate and balanced method for identifying potential hosts of influenza viruses.
    • The findings contribute to better understanding and control of influenza transmission.