ArticleNature communications2024
Seasonal antigenic prediction of influenza A H3N2 using machine learning.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
27 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Literature review: Current trends and future prospects of digital vaccine supply chain support technology.Human vaccines & immunotherapeutics · 2025Pooled it
- Emergence of the novel PA-D27G mutation conferring reduced baloxavir susceptibility in influenza A viruses circulating in China, 2018-2025.Emerging microbes & infections · 2026Article
- VirPLM: antigenic prediction of influenza A/H3N2 viruses with a fine-tuned protein language model.Bioinformatics (Oxford, England) · 2026Article
- From sites to structure to serology: a roadmap for structure-aware molecular evolution of antigenically evolving viruses.Journal of virology · 2026Review
- Advances in the Development of a Universal Influenza Vaccine.Antibodies (Basel, Switzerland) · 2026Review
- Alignment-free prediction of cross-reactivity in influenza A (H3N2) anticipates antigenic drift.PLoS computational biology · 2026Article
- Epistasis facilitates the long-term antigenic evolution of the influenza B virus hemagglutinin.bioRxiv : the preprint server for biology · 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
- Large language models for bioinformatics.Quantitative biology (Beijing, China) · 2026Review
- Article
- Forecasting Influenza Epidemics and Pandemics in the Age of AI and Machine Learning.Reviews in medical virology · 2026Review
- A taxonomically harmonized global dataset of wild bird hosts for avian influenza virus surveillance.Scientific data · 2025Article
- Recent advances in avian influenza virus: Molecular pathogenesis, emerging strains, and next-generation therapeutics.World journal of virology · 2025Review
- Development of an early prediction model for risk of influenza A and influenza B based on complete blood count examination.BMC infectious diseases · 2025Article
- Research Progress of Universal Influenza Vaccine.Vaccines · 2025Review
- Microbiome-Immune Interaction and Harnessing for Next-Generation Vaccines Against Highly Pathogenic Avian Influenza in Poultry.Vaccines · 2025Review
- PREDAC-FluB: predicting antigenic clusters of seasonal influenza B viruses with protein language model embedding based convolutional neural network.Briefings in bioinformatics · 2025Article
- Evaluating the performance of the PREDAC method in flu vaccine recommendations over the past decade (2013-2023).Virologica Sinica · 2025Article
- Challenges in Humoral Immune Response to Adeno-Associated Viruses Determination.International journal of molecular sciences · 2025Review
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Antigenic characterization of circulating influenza A virus (IAV) isolates is routinely assessed by using the hemagglutination inhibition (HI) assays for surveillance purposes. It is also used to determine the need for annual influenza vaccine updates as well as for pandemic preparedness. Performing antigenic characterization of IAV on a global scale is confronted with high costs, animal availability, and other practical challenges. Here we present a machine learning model that accurately predicts (normalized) outputs of HI assays involving circulating human IAV H3N2 viruses, using their hemagglutinin subunit 1 (HA1) sequences and associated metadata. Each season, the model learns an updated nonlinear mapping of genetic to antigenic changes using data from past seasons only. The model accurately distinguishes antigenic variants from non-variants and adaptively characterizes seasonal dynamics of HA1 sites having the strongest influence on antigenic change. Antigenic predictions produced by the model can aid influenza surveillance, public health management, and vaccine strain selection activities.
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Registered trials
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.