MC-NN: An End-to-End Multi-Channel Neural Network Approach for Predicting Influenza A Virus Hosts and Antigenic Types

• URL: http://arxiv.org/abs/2306.05587v4
• Date: Wed, 21 Feb 2024 22:52:08 GMT
• Title: MC-NN: An End-to-End Multi-Channel Neural Network Approach for Predicting Influenza A Virus Hosts and Antigenic Types
• Authors: Yanhua Xu and Dominik Wojtczak
• Abstract summary: Influenza poses a significant threat to public health, particularly among the elderly, young children, and people with underlying dis-eases. We propose a multi-channel neural network model to predict the host and antigenic sub-types of influenza A viruses.
• Score: 5.067354030054702
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Influenza poses a significant threat to public health, particularly among the elderly, young children, and people with underlying dis-eases. The manifestation of severe conditions, such as pneumonia, highlights the importance of preventing the spread of influenza. An accurate and cost-effective prediction of the host and antigenic sub-types of influenza A viruses is essential to addressing this issue, particularly in resource-constrained regions. In this study, we propose a multi-channel neural network model to predict the host and antigenic subtypes of influenza A viruses from hemagglutinin and neuraminidase protein sequences. Our model was trained on a comprehensive data set of complete protein sequences and evaluated on various test data sets of complete and incomplete sequences. The results demonstrate the potential and practicality of using multi-channel neural networks in predicting the host and antigenic subtypes of influenza A viruses from both full and partial protein sequences.

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