基于注意力机制与集成学习的甲型H5N1流感病毒抗原相似性预测

doi:10.11896/jsjkx.210900032

Abstract

Abstract: Influenza A virus can lead to seasonal influenza virus outbreaks or even global outbreaks.Continued and cumulative changes in the hemagglutinin protein of influenza viruses can lead to the antigenic variants that reduce vaccine effectiveness or even cause vaccine failure.Therefore,antigenic similarity prediction is critical for influenza outbreak surveillance and vaccine selection.Although A/H5N1 virus originates in poultry,they can cause pneumonia and multiple organ failure in humans.In view of influenza virus and the antigenic characteristics,this paper designs a neural network model to predict the antigenic similarity between viruses.Specifically,the model represents amino acid sequences based on the K-mer embedding and position specific scoring matrices(PSSM),then integrates the features.Furthermore,integrated deep learning model fused with attention mechanism for antigen similarity prediction.Experimental results show that the model significantly improves the accuracy,precision,F1 and MCC compares with the baseline models.Experimental results show that the model has good robustness and extensibility,and has good application potential in the field of antigenic similarity prediction.

Key words: Antigenic similarity, Influenza A, H5N1, Ensemble learning, Attention mechanism

CLC Number:

TP391

WANG Ying-hui, LI Wei-hua, LI Chuan, CHEN Wei, WEN Jun-ying. Prediction of Antigenic Similarity of Influenza A/H5N1 Virus Based on Attention Mechanism and Ensemble Learning[J].Computer Science, 2022, 49(11A): 210900032-6.

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Prediction of Antigenic Similarity of Influenza A/H5N1 Virus Based on Attention Mechanism and Ensemble Learning

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