基于深度学习的禽流感病毒溢出风险预测研究

Prediction of spillover risk for avian influenza virus based on deep learning

禽流感病毒基因组由8个长短不一的基因节段组成,全长约为14~16 kb。由于病毒本身特殊的分子遗传机制,病毒通过基因点突变和基因组重排快速变异,引发病毒感染宿主范围的改变,持续威胁人类健康,因此,自然界禽流感病毒溢出风险预测具有重要公共卫生意义。文章联合使用卷积神经网络和循环神经网络表征病毒基因组序列,分别在特定类群数据集和全类群数据集上进行训练和测试,并对模型的迁移预测能力进行评估。实验结果显示:①特定类群模型对各自数据集的预测性能良好,AUROC值和AUPR值分别达到0.966和0.876以上,但泛化能力较差;②除H9N2类群外,全局模型性能良好,AUROC值和AUPR值均达到1.000;③基于消融实验,发现注意力机制和Embedding层对模型性能影响较大;④进一步测试模型的泛化能力,迁移预测的AUROC值和AUPR值分别可达0.984和0.941以上;⑤可视化注意力权重系数矩阵,为模型提供生物学可解释性。性能良好的深度学习预测模型可用于禽流感病毒跨种感染的早期预警。

The influenza virus genome consists of eight genetic segments of varying lengths,with a total length of approximately 14~16 kb.Due to the special molecular genetic mechanism of the virus,it undergoes rapid mutations through gene point mutation and genome rearrangement,which leads to changes in its biological infection characteristics and poses a continuous threat to health.Therefore,accurate prediction of natural avian influenza virus spillovers is crucial for public health.This paper,employs a combination of convolutional neural network(CNN)and recurrent neural network(RNN)to represent viral genome sequences.The model's transferability on both specific group datasets and entire datasets was evaluated.The experimental results demonstrate excellent prediction performance of the specific group model on the respective datasets,with AUROC exceeding O.966 and AUPR values surpassing 0.876.However,its generalization ability is limited.Conversely,except for the H9N2 group,the global model performs well with AUROC and AUPR values reaching 1.000 across all groups.Based on ablation experiments,it was found that attention mechanism and sequence embedding method significantly impact model performance while further testing its generalization ability reveals AUROC values above 0.984 and AUPR values over 0.941 for transfer predictions respectively.Visualizing the attention weight matrix provides biological interpretability for the model.The high-performing deep learning prediction model can be effectively utilized for early warning systems against cross-species infections caused by avian influenza viruses.