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

Journal article

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.Comment: Accepted version submitted to the SN Computer Science; Published in the SN Computer Science 202

Authors: Xu, Y; Wojtczak, D

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Springer
• Journal: SN Computer Science
• Volume: 4
• Issue: 5
• Article number: 435
• Publication date: 2023-06-08
• DOI: 10.1007/s42979-023-01839-5
• EISSN: 2661-8907
• ISSN: 2661-8907
• Language: English
• Keywords: Antigenic drift; Hemagglutinin (influenza); Antigenic shift; Genetics; Artificial neural network; Host (biology); Artificial intelligence; Immunology; Virology; Computer science; Virus; Neuraminidase; Computational biology; Antigen; Biology