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

摘要/Abstract

摘要： 甲型流感病毒可能导致季节性流感病毒疫情甚至全球大爆发。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,致使疫苗效力降低甚至失效。抗原相似性预测对流感疫情监测和疫苗选择是至关重要的。甲型H5N1病毒源于禽类,可引起人类肺炎和多器官衰竭。针对流感病毒及其抗原特点,设计一个预测病毒抗原相似性的神经网络模型,该模型分别基于K-mer嵌入与位置特异性矩阵表示序列信息并进行融合;在此基础上,设计融合注意力机制的集成深度学习模型用于抗原相似性预测。实验结果表明,相比基准模型,该模型显著提高了模型预测的准确率、精确率、F1值和MCC值。从实验中可以看出该模型具有良好的鲁棒性和扩展性,在抗原相似性预测领域有很好的应用潜力。

关键词: 抗原性相似性, 甲型流感, H5N1, 集成学习, 注意力机制

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

中图分类号:

TP391

王迎晖, 李维华, 李川, 陈伟, 文俊颖. 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[J]. 计算机科学, 2022, 49(11A): 210900032-6. https://doi.org/10.11896/jsjkx.210900032

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. https://doi.org/10.11896/jsjkx.210900032

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