Tempel: time-series mutation prediction of influenza A viruses via attention-based recurrent neural networks
Tempel: time-series mutation prediction of influenza A viruses via attention-based recurrent neural networks
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DOI：
10.1093/bioinformatics/btaa050
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发表时间：
2020-05-01
期刊：
影响因子：
5.8
通讯作者：
Kwoh, Chee Keong
中科院分区：
文献类型：
--
作者：
Yin, Rui;Luusua, Emil;Kwoh, Chee Keong
动机：流感病毒持续威胁公共卫生，造成年度流行和散发性大流行。由于流感病毒的快速突变，其进化仍然是抗病毒治疗有效性的主要障碍。这项工作的目标是利用历史糖蛋白血凝素序列数据预测下一个流感季节是否可能发生突变。其中一个主要的挑战是模拟时序流感病毒株的时间性和维数，并解释预测结果：在这篇文章中，我们提出了一个有效的和强大的时间序列突变预测模型（Tempel）的突变预测甲型流感病毒。我们首先用分裂和嵌入构造序列训练样本。通过使用具有注意机制的递归神经网络，Tempel能够考虑历史残差信息。注意力机制正越来越多地用于通过选择性地关注残基的部分来提高突变预测的性能。基于Tempel建立了一个框架，使我们能够预测任何特定残基位点的突变。在三个流感数据集上的实验结果表明，与广泛使用的方法相比，Tempel可以显着提高预测性能，并为病毒突变和进化的动力学提供新的见解。
Motivation: Influenza viruses are persistently threatening public health, causing annual epidemics and sporadic pandemics. The evolution of influenza viruses remains to be the main obstacle in the effectiveness of antiviral treatments due to rapid mutations. The goal of this work is to predict whether mutations are likely to occur in the next flu season using historical glycoprotein hemagglutinin sequence data. One of the major challenges is to model the temporality and dimensionality of sequential influenza strains and to interpret the prediction results.Results: In this article, we propose an efficient and robust time-series mutation prediction model (Tempel) for the mutation prediction of influenza A viruses. We first construct the sequential training samples with splittings and embeddings. By employing recurrent neural networks with attention mechanisms, Tempel is capable of considering the historical residue information. Attention mechanisms are being increasingly used to improve the performance of mutation prediction by selectively focusing on the parts of the residues. A framework is established based on Tempel that enables us to predict the mutations at any specific residue site. Experimental results on three influenza datasets show that Tempel can significantly enhance the predictive performance compared with widely used approaches and provide novel insights into the dynamics of viral mutation and evolution.