We employ this model to predict the prevalent sequence of the H3N2 viruses sampled from 2006 to 2017. The identity between our predicted sequence and ...
By constructing time-series training samples with splittings and embeddings, we develop a computational method for predicting suitable strains as the ...
Feb 19, 2020 · Schematic overview of the computational model to predict vaccine strain selection. a) The general diagram of the prediction model with recurrent ...
Apr 9, 2020 · By constructing time-series training samples with splittings and embeddings, we develop a computational method for predicting suitable strains ...
We employ this model to predict the prevalent sequence of the H3N2 viruses sampled from 2006 to 2017. The identity between our predicted sequence and ...
May 7, 2024 · Here we present a machine learning model that accurately predicts (normalized) outputs of HI assays involving circulating human IAV H3N2 viruses.
Missing: recurrent | Show results with:recurrent
In this article, we propose an efficient and robust time-series mutation prediction model (Tempel) for the mutation prediction of influenza A viruses.
For viral isolates sampled between 2002 and 2007, we used this method to predict the antigenic evolution of the H3N2 viruses in retrospective testing scenarios.
In this study, we designed deep convolutional neural networks (CNNs) to predict Influenza antigenicity. Our model is the first that systematically analyzed 566 ...
Missing: series | Show results with:series
By employing recurrent neural networks with attention mechanisms, Tempel is capable of considering the historical residue information. Attention mechanisms are ...