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 exis-ting 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.

Key words: Influenza A, H3N2, Antigenic similarity, Graph convolutional network, Neural network