【摘要】 流感病毒血凝素蛋白的持续和累积变化会产生新的抗原株，能够逃避人类免疫并引起季节性流感或流感大爆发。及时识别新的抗原变异体，对疫苗筛选和流感预防是至关重要的。图嵌入模型在部分数据缺失的情况下仍然可以实现有效的相互关系建模。针对甲型流感病毒H3N2，提出一种基于图卷积神经网络的抗原性预测方法，获取流感毒株低维稠密嵌入向量，同时对序列信息进行编码并作为补充特征，利用深度神经网络模型对特征进行融合并学习关键的抗原特征，完成抗原性预测。在两个数据集上的实验结果表明，该方法相比其他同类方法，显著提升了抗原相似性预测性能，具有良好的鲁棒性和可扩展性。此外，从实验中还可以看出，图卷积神经网络可以有效地获取抗原相似关系的抗原特征。更多还原

【Abstract】 Continual and accumulated mutations in the hemagglutinin(HA)protein of influenza A virus generates novel antigenic strains that can evade human immunity and cause seasonal influenza or influenza pandemics.Timely identification of new antigenic variants is crucial for the selection of vaccines and influenza prevention.Graph embedding models can effectively model interactions even when some data is missing.For influenza A virus H3N2,this paper proposes an antigenicity prediction method based on graph convolutional networks to obtain the low-dimensional dense embedding vector of influenza strain.Then,it encodes the sequence information as supplementary features.Furthermore,deep neural networks is adopted to fuse these features and learn the dominative features for antigenicity prediction.Experimental results on two datasets show that,compared with those of existing methods,the proposed method significantly improves the performance of antigenic similarity prediction,and has good robustness and scalability.In addition,it can be seen from experiments that graph convolutional networks can effectively obtain the antigenic features of the antigenic similarity relationship.更多还原