ArticlemBio2013
Using sequence data to infer the antigenicity of influenza virus.
Article in mBio, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 68 papers.
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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.
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Who cites it
68 citing papers in PubMed, 102 citations in OpenAlex.
- Alignment-free prediction of cross-reactivity in influenza A (H3N2) anticipates antigenic drift.PLoS computational biology · 2026Article
- Different antigenic distance metrics generate similar predictions of influenza vaccine response breadth despite moderate correlation.PLoS computational biology · 2025Article
- A Novel Matching Pursuit Modeling Strategy Based on Adaptive Fourier Decomposition Theory for Predicting Antigenic Variation of Influenza A (H1N1).China CDC weekly · 2025Article
- Protein structure prediction via deep learning: an in-depth review.Frontiers in pharmacology · 2025Review
- Neu5Gc binding loss of subtype H7 influenza A virus facilitates adaptation to gallinaceous poultry following transmission from waterbirds.Journal of virology · 2024Article
- MetaFluAD: meta-learning for predicting antigenic distances among influenza viruses.Briefings in bioinformatics · 2024Article
- Seasonal antigenic prediction of influenza A H3N2 using machine learning.Nature communications · 2024Article
- MAIVeSS: streamlined selection of antigenically matched, high-yield viruses for seasonal influenza vaccine production.Nature communications · 2024Article
- Neu5Gc binding loss of subtype H7 influenza A virus facilitates adaptation to gallinaceous poultry following transmission from waterbirds but restricts spillback.bioRxiv : the preprint server for biology · 2024Article
- A sequence-based machine learning model for predicting antigenic distance for H3N2 influenza virus.Frontiers in microbiology · 2024Article
- Article
- A Bayesian approach to incorporate structural data into the mapping of genotype to antigenic phenotype of influenza A(H3N2) viruses.PLoS computational biology · 2023Article
- Genetic and Antigenic Characterization of an Expanding H3 Influenza A Virus Clade in U.S. Swine Visualized by Nextstrain.mSphere · 2022Article
- Antigenic characterization of influenza and SARS-CoV-2 viruses.Analytical and bioanalytical chemistry · 2022Review
- Librator: a platform for the optimized analysis, design, and expression of mutable influenza viral antigens.Briefings in bioinformatics · 2022Article
- Informative SNP Selection Based on a Fuzzy Clustering and Improved Binary Particle Swarm Optimization Algorithm.Computational and mathematical methods in medicine · 2022Article
- A biophysical model of viral escape from polyclonal antibodies.Virus evolution · 2022Article
- End-to-end antigenic variant generation for H1N1 influenza HA protein using sequence to sequence models.PloS one · 2022Article
- B.1.1.7 (Alpha) variant is the most antigenic compared to Wuhan strain, B.1.351, B.1.1.28/triple mutant and B.1.429 variants.Frontiers in microbiology · 2022Article
- Selecting Representative Samples From Complex Biological Datasets Using K-Medoids Clustering.Frontiers in genetics · 2022Article
8 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 3 institutions in 1 country.
Funding
Abstract
unlabelledThe efficacy of current influenza vaccines requires a close antigenic match between circulating and vaccine strains. As such, timely identification of emerging influenza virus antigenic variants is central to the success of influenza vaccination programs. Empirical methods to determine influenza virus antigenic properties are time-consuming and mid-throughput and require live viruses. Here, we present a novel, experimentally validated, computational method for determining influenza virus antigenicity on the basis of hemagglutinin (HA) sequence. This method integrates a bootstrapped ridge regression with antigenic mapping to quantify antigenic distances by using influenza HA1 sequences. Our method was applied to H3N2 seasonal influenza viruses and identified the 13 previously recognized H3N2 antigenic clusters and the antigenic drift event of 2009 that led to a change of the H3N2 vaccine strain. IMPORTANCE: This report supplies a novel method for quantifying antigenic distance and identifying antigenic variants using sequences alone. This method will be useful in influenza vaccine strain selection by significantly reducing the human labor efforts for serological characterization and will increase the likelihood of correct influenza vaccine candidate selection.
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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.