ArticleViruses2020
Convolutional Neural Network Based Approach to in Silico Non-Anticipating Prediction of Antigenic Distance for Influenza Virus.
Article in Viruses, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed, 28 citations in OpenAlex.
- FluAttn: Antigenicity prediction of influenza A/H3N2 through attention-based feature mining.Infectious Disease Modelling · 2026Article
- FluNexus: A versatile web platform for antigenic prediction and visualization of influenza A viruses.iMeta · 2026Article
- A CNN-Based Model of Cross-Immunity to Influenza A(H3N2) Virus: Testing Under "Real-World" Conditions.Viruses · 2026Article
- Unsupervised classification of influenza virus surface spikes from cryo-EM reconstructions using head-to-stem width ratio.Virology journal · 2025Article
- Different antigenic distance metrics generate similar predictions of influenza vaccine response breadth despite moderate correlation.PLoS computational biology · 2025Article
- Opportunities for machine learning to predict cross-neutralization in FMDV serotype O.PLoS computational biology · 2025Article
- PREDAC-FluB: predicting antigenic clusters of seasonal influenza B viruses with protein language model embedding based convolutional neural network.Briefings in bioinformatics · 2025Article
- Random forest algorithm reveals novel sites in HA protein that shift receptor binding preference of the H9N2 avian influenza virus.Virologica Sinica · 2025Article
- Sequence-based detection of emerging antigenically novel influenza A viruses.Proceedings. Biological sciences · 2024Article
- MetaFluAD: meta-learning for predicting antigenic distances among influenza viruses.Briefings in bioinformatics · 2024Article
- PREDAC-CNN: predicting antigenic clusters of seasonal influenza A viruses with convolutional neural network.Briefings in bioinformatics · 2024Article
- Article
- Predicting Antigenic Distance from Genetic Data for PRRSV-Type 1: Applications of Machine Learning.Microbiology spectrum · 2023Article
- End-to-end antigenic variant generation for H1N1 influenza HA protein using sequence to sequence models.PloS one · 2022Article
- Application of Big Data and Artificial Intelligence in COVID-19 Prevention, Diagnosis, Treatment and Management Decisions in China.Journal of medical systems · 2021Review
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Evaluation of the antigenic similarity degree between the strains of the influenza virus is highly important for vaccine production. The conventional method used to measure such a degree is related to performing the immunological assays of hemagglutinin inhibition. Namely, the antigenic distance between two strains is calculated on the basis of HI assays. Usually, such distances are visualized by using some kind of antigenic cartography method. The known drawback of the HI assay is that it is rather time-consuming and expensive. In this paper, we propose a novel approach for antigenic distance approximation based on deep learning in the feature spaces induced by hemagglutinin protein sequences and Convolutional Neural Networks (CNNs). To apply a CNN to compare the protein sequences, we utilize the encoding based on the physical and chemical characteristics of amino acids. By varying (hyper)parameters of the CNN architecture design, we find the most robust network. Further, we provide insight into the relationship between approximated antigenic distance and antigenicity by evaluating the network on the HI assay database for the H1N1 subtype. The results indicate that the best-trained network gives a high-precision approximation for the ground-truth antigenic distances, and can be used as a good exploratory tool in practical tasks.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.