MC-NN: An End-to-End Multi-Channel Neural Network Approach for Predicting Influenza A Virus Hosts and Antigenic Types
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.
Keywords
Cite
@article{arxiv.2306.05587,
title = {MC-NN: An End-to-End Multi-Channel Neural Network Approach for Predicting Influenza A Virus Hosts and Antigenic Types},
author = {Yanhua Xu and Dominik Wojtczak},
journal= {arXiv preprint arXiv:2306.05587},
year = {2024}
}
Comments
Accepted version submitted to the SN Computer Science; Published in the SN Computer Science 2023; V2: minor updates were made to the Results section; V3: minor updates regarding data description; V4: correct the time stamps mentioned in the legends of Figures 1 and 2