ArticleFrontiers in microbiology2024
A sequence-based machine learning model for predicting antigenic distance for H3N2 influenza virus.
Article in Frontiers in microbiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed, 17 citations in OpenAlex.
- 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
- 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
- Forecasting Influenza Epidemics and Pandemics in the Age of AI and Machine Learning.Reviews in medical virology · 2026Review
- Unsupervised classification of influenza virus surface spikes from cryo-EM reconstructions using head-to-stem width ratio.Virology journal · 2025Article
- Language models learn to represent antigenic properties of human influenza A(H3) virus.Scientific reports · 2025Article
- Deep recurrent neural network with fractional addax optimization algorithm for influenza virus host prediction.MethodsX · 2025Article
- Role of artificial intelligence in advancing immunology.Immunologic research · 2025Review
- Review
- Article
- Pathway Activation Analysis for Pan-Cancer Personalized Characterization Based on Riemannian Manifold.International journal of molecular sciences · 2024Article
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: Seasonal influenza A H3N2 viruses are constantly changing, reducing the effectiveness of existing vaccines. As a result, the World Health Organization (WHO) needs to frequently update the vaccine strains to match the antigenicity of emerged H3N2 variants. Traditional assessments of antigenicity rely on serological methods, which are both labor-intensive and time-consuming. Although numerous computational models aim to simplify antigenicity determination, they either lack a robust quantitative linkage between antigenicity and viral sequences or focus restrictively on selected features. Methods: Here, we propose a novel computational method to predict antigenic distances using multiple features, including not only viral sequence attributes but also integrating four distinct categories of features that significantly affect viral antigenicity in sequences. Results: This method exhibits low error in virus antigenicity prediction and achieves superior accuracy in discerning antigenic drift. Utilizing this method, we investigated the evolution process of the H3N2 influenza viruses and identified a total of 21 major antigenic clusters from 1968 to 2022. Discussion: Interestingly, our predicted antigenic map aligns closely with the antigenic map generated with serological data. Thus, our method is a promising tool for detecting antigenic variants and guiding the selection of vaccine candidates.
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