ArticlemSphere2021
Machine Learning Prediction and Experimental Validation of Antigenic Drift in H3 Influenza A Viruses in Swine.
Article in mSphere, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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.
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Who cites it
17 citing papers in PubMed, 27 citations in OpenAlex.
- Intrinsic dataset features drive mutational effect prediction by protein language models.bioRxiv : the preprint server for biology · 2026Article
- The application of CRISPR gene-editing technology in influenza prevention and control.Frontiers in genome editing · 2026Review
- Translating viral genetic data to PRRSV-2 cross-neutralization using machine learning.Frontiers in immunology · 2026Article
- Opportunities for machine learning to predict cross-neutralization in FMDV serotype O.PLoS computational biology · 2025Article
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- Unveiling the ghost: machine learning's impact on the landscape of virology.The Journal of general virology · 2025Review
- Data alchemy, from lab to insight: Transforming in vivo experiments into data science gold.PLoS pathogens · 2024Article
- Machine learning approaches for influenza A virus risk assessment identifies predictive correlates using ferret model in vivo data.Communications biology · 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
- PARNAS: Objectively Selecting the Most Representative Taxa on a Phylogeny.Systematic biology · 2023Article
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- Predicting Antigenic Distance from Genetic Data for PRRSV-Type 1: Applications of Machine Learning.Microbiology spectrum · 2023Article
- Ecological and evolutionary dynamics of multi-strain RNA viruses.Nature ecology & evolution · 2022Review
- Evidence of influenza A infection and risk of transmission between pigs and farmworkers.Zoonoses and public health · 2022Article
- Genetic and Antigenic Characterization of an Expanding H3 Influenza A Virus Clade in U.S. Swine Visualized by Nextstrain.mSphere · 2022Article
- Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
The antigenic diversity of influenza A viruses (IAV) circulating in swine challenges the development of effective vaccines, increasing zoonotic threat and pandemic potential. High-throughput sequencing technologies can quantify IAV genetic diversity, but there are no accurate approaches to adequately describe antigenic phenotypes. This study evaluated an ensemble of nonlinear regression models to estimate virus phenotype from genotype. Regression models were trained with a phenotypic data set of pairwise hemagglutination inhibition (HI) assays, using genetic sequence identity and pairwise amino acid mutations as predictor features. The model identified amino acid identity, ranked the relative importance of mutations in the hemagglutinin (HA) protein, and demonstrated good prediction accuracy. Four previously untested IAV strains were selected to experimentally validate model predictions by HI assays. Errors between predicted and measured distances of uncharacterized strains were 0.35, 0.61, 1.69, and 0.13 antigenic units. These empirically trained regression models can be used to estimate antigenic distances between different strains of IAV in swine by using sequence data. By ranking the importance of mutations in the HA, we provide criteria for identifying antigenically advanced IAV strains that may not be controlled by existing vaccines and can inform strain updates to vaccines to better control this pathogen.
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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.