论文标题
用于预测流感A宿主和抗原类型的多通道神经网络
Multi-channel neural networks for predicting influenza A virus hosts and antigenic types
论文作者
论文摘要
流感每个季节都会发生，偶尔会引起大流行。尽管死亡率较低，但流感是一个主要的公共卫生问题，因为肺炎等严重疾病可能会使它复杂化。一种快速，准确和低成本的方法来预测流感病毒的原始宿主和亚型，可以帮助减少病毒的传播并使资源贫乏的地区受益。在这项工作中，我们提出多通道神经网络，以预测具有黑凝集素和神经氨酸酶蛋白序列的流感类型和宿主的抗原类型和宿主。包含完整蛋白质序列的集成数据集用于生成预训练的模型，并使用了其他两个数据集来测试模型的性能。一个测试组包含完整的蛋白质序列，另一个测试组包含不完整的蛋白质序列。结果表明，多通道神经网络适用，有望可以预测具有完整和部分蛋白质序列的病毒宿主和抗原亚型。
Influenza occurs every season and occasionally causes pandemics. Despite its low mortality rate, influenza is a major public health concern, as it can be complicated by severe diseases like pneumonia. A fast, accurate and low-cost method to predict the origin host and subtype of influenza viruses could help reduce virus transmission and benefit resource-poor areas. In this work, we propose multi-channel neural networks to predict antigenic types and hosts of influenza A viruses with hemagglutinin and neuraminidase protein sequences. An integrated data set containing complete protein sequences were used to produce a pre-trained model, and two other data sets were used for testing the model's performance. One test set contained complete protein sequences, and another test set contained incomplete protein sequences. The results suggest that multi-channel neural networks are applicable and promising for predicting influenza A virus hosts and antigenic subtypes with complete and partial protein sequences.
