Antigenicity prediction and vaccine recommendation of human influenza virus A (H3N2) using convolutional neural networks.

ABSTRACT: The rapid evolution of influenza A viruses poses a great challenge to vaccine development. Analytical and machine learning models have been applied to facilitate the process of antigenicity determination. In this study, we designed deep convolutional neural networks (CNNs) to predict Influenza antigenicity. Our model is the first that systematically analyzed 566 amino acid properties and 141 amino acid substitution matrices for their predictability. We then optimized the structure of the CNNs using particle swarm optimization. The optimal neural networks outperform other predictive models with a blind validation accuracy of 95.8%. Further, we applied our model to vaccine recommendations in the period 1997 to 2011 and contrasted the performance of previous vaccine recommendations using trad

SUBMITTER: Lee EK 

PROVIDER: S-EPMC7734114 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature