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?:abstract
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Not everyone gets sick after an exposure to influenza A viruses (IAV). It was unclear whether forecasting symptomatic flu infection based on pre-exposure host gene expression might be possible. We utilized the human gene expression data infected with IAV subtype H1N1 or H3N2 viruses to examine this problem using the state-of-the-art deep learning approach. The results indicate that such forecast is possible and, in other words, gene expression could reflect the strength of host immune systems. Here we have developed DeepFlu—based on deep neural networks—to predict who would catch the flu prior to an exposure to IAV. We validated DeepFlu both on a leave-one-person-out internal cross-validation and an independent external cohort. DeepFlu performed better than the models using convolutional neural network, random forest, or support vector machine, achieving 70.0% accuracy, 0.787 AUROC, and 0.758 AUPR for H1N1 and 73.8% accuracy, 0.847 AUROC, and 0.901 AUPR for H3N2. Besides, DeepFlu which was trained only by pre-exposure data worked the best than by other time spans and mixed training data of H1N1 and H3N2 did not necessarily enhance prediction. These findings suggest that deep learning models could moderately recognize individuals susceptible to flu to prevent the spread of IAV. Author summary Influenza A, one of the leading infectious diseases, causes major health and socioeconomic burdens in the world. Hence, it is important to develop methods to accurately forecast who will succumb to influenza A virus (IAV) in advance. To demonstrate that forecasting influenza susceptibility is possible, we develop DeepFlu, based on the powerful deep learning method, to predict who will catch the flu based on ‘healthy’ pre-viral-exposure host gene expression. DeepFlu, which outperforms other machine learning methods, can be used to forecast susceptibility for two IAV subtypes H1N1 and H3N2. The results suggest that we could identify people at risk for the flu and alarm them in advance to lessen the spread of IAV and reduce the death tolls caused by the flu and its complications.
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