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.

本研究提出了一种基于多通道神经网络模型的方法，从血凝素和神经氨酸酰化酶蛋白序列来预测甲型流感病毒的宿主和抗原亚型，结果表明多通道神经网络在预测从全蛋白序列到部分蛋白序列的甲型流感病毒宿主和抗原亚型方面具有潜力和实用性。

MC-NN: 一种端到端的多通道神经网络方法，用于预测甲型流感病毒宿主和抗原类型