ArticleNature communications2024
Predictive evolutionary modelling for influenza virus by site-based dynamics of mutations.
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 15 papers.
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Who cites it
15 citing papers in PubMed, 17 citations in OpenAlex.
- Alignment-free prediction of cross-reactivity in influenza A (H3N2) anticipates antigenic drift.PLoS computational biology · 2026Article
- Quantifying the predictability of evolution by analysis of coalescent rate variation.Molecular biology and evolution · 2026Article
- Limited evidence of AI superiority in seasonal influenza vaccine strain selection.Nature medicine · 2026Article
- Reply to: Limited evidence of AI superiority in seasonal influenza vaccine strain selection.Nature medicine · 2026Article
- Mapping hemagglutinin residues driving antigenic diversity in H5Nx avian influenza viruses.Journal of virology · 2026Article
- The Therapeutic Administration ofMicroorganisms · 2026Article
- Next-generation intranasal influenza vaccines: mechanisms, platforms, and translational progress.Frontiers in immunology · 2026Review
- Artificial intelligence for coordinating vaccine design, antiviral discovery, and real-world monitoring in the era of emerging and endemic viral threats.Frontiers in pharmacology · 2026Review
- Stochastic phenotypic switching arises in response to directional selection in experimentally evolved multicellular yeast.Communications biology · 2025Article
- Design and application of expression constructs for FMDV serotype O structural proteins.Biotechnology letters · 2025Article
- Bioengineering hybrid artificial life.Frontiers in bioinformatics · 2025Review
- AI-powered analysis of viral metagenomic sequencing data for rapid outbreak investigation and novel pathogen discovery.Frontiers in microbiology · 2025Review
- A T cell-based ubiquitin-mediated mRNA vaccine provides cross-protection against H1N1 and B influenza viruses in mice.Current research in microbial sciences · 2025Article
- Deep learning architectures for influenza dynamics and treatment optimization: a comprehensive review.Frontiers in artificial intelligence · 2025Review
- Artificial intelligence-based prediction of pathogen emergence and evolution in the world of synthetic biology.Microbial biotechnology · 2024Review
Corrections and comments
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Authors and funding
18 authors at 6 institutions in 4 countries.
Funding
Abstract
Influenza virus continuously evolves to escape human adaptive immunity and generates seasonal epidemics. Therefore, influenza vaccine strains need to be updated annually for the upcoming flu season to ensure vaccine effectiveness. We develop a computational approach, beth-1, to forecast virus evolution and select representative virus for influenza vaccine. The method involves modelling site-wise mutation fitness. Informed by virus genome and population sero-positivity, we calibrate transition time of mutations and project the fitness landscape to future time, based on which beth-1 selects the optimal vaccine strain. In season-to-season prediction in historical data for the influenza A pH1N1 and H3N2 viruses, beth-1 demonstrates superior genetic matching compared to existing approaches. In prospective validations, the model shows superior or non-inferior genetic matching and neutralization against circulating virus in mice immunization experiments compared to the current vaccine. The method offers a promising and ready-to-use tool to facilitate vaccine strain selection for the influenza virus through capturing heterogeneous evolutionary dynamics over genome space-time and linking molecular variants to population immune response.
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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.