计算机科学
卷积神经网络
特征(语言学)
甲型流感病毒
人工智能
相似性(几何)
代表(政治)
病毒
病毒学
模式识别(心理学)
生物
政治
图像(数学)
哲学
法学
语言学
政治学
作者
Rui Yin,Nyi Nyi Thwin,Pei Zhuang,Zhuoyi Lin,Chee Keong Kwoh
标识
DOI:10.1109/tcbb.2021.3108971
摘要
The rapid evolution of influenza viruses constantly leads to the emergence of novel influenza strains. Many computational models have been developed to predict the antigenic variants without considerations of explicitly modeling the interdependencies between channels of feature maps. Moreover, the influenza sequences consisting of similar distribution of residues will have high degrees of similarity and will affect the prediction outcome. We have proposed a 2D convolutional neural network model to infer influenza antigenic variants. Specifically, we apply a new distributed representation of amino acids, named ProtVec that can be applied to a variety of downstream proteomic machine learning tasks. After splittings and embeddings of influenza strains, a 2D squeeze-and-excitation CNN architecture is constructed that enables networks to focus on informative residue features by fusing both spatial and channel-wise information with local receptive fields at each layer. Experimental results on three influenza datasets show IAV-CNN achieves state-of-the-art performance combining the new distributed representation with our proposed architecture. It outperforms both traditional machine algorithms with the same feature representations and the majority of existing models in the independent test data. Therefore we believe that our model can be served as a reliable and robust tool for the prediction of antigenic variants.
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