ArticleBioinformatics (Oxford, England)2019
Graph-guided multi-task sparse learning model: a method for identifying antigenic variants of influenza A(H3N2) virus.
Article in Bioinformatics (Oxford, England), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Alignment-free prediction of cross-reactivity in influenza A (H3N2) anticipates antigenic drift.PLoS computational biology · 2026Article
- Epitope-spanning antigenic variation reprograms immunodominance and broadens immunity in sequential influenza vaccination.Nature communications · 2026Article
- Intrahost HA polymorphisms and culture adaptation shape antigenic profiles of H3N2 influenza viruses.Journal of virology · 2026Article
- Different antigenic distance metrics generate similar predictions of influenza vaccine response breadth despite moderate correlation.PLoS computational biology · 2025Article
- Topolow: a mapping algorithm for antigenic cross-reactivity and binding affinity assays.Bioinformatics (Oxford, England) · 2025Article
- Neutralization potency of the 2023-24 seasonal influenza vaccine against circulating influenza H3N2 strains.Human vaccines & immunotherapeutics · 2024Article
- Sequence-based detection of emerging antigenically novel influenza A viruses.Proceedings. Biological sciences · 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
- A sequence-based machine learning model for predicting antigenic distance for H3N2 influenza virus.Frontiers in microbiology · 2024Article
- Novel graph-based machine-learning technique for viral infectious diseases: application to influenza and hepatitis diseases.Annals of medicine · 2023Article
- Antigenic characterization of influenza and SARS-CoV-2 viruses.Analytical and bioanalytical chemistry · 2022Review
- Detection and Prevention of Virus Infection.Advances in experimental medicine and biology · 2022Article
- Triple reassortment increases compatibility among viral ribonucleoprotein genes of contemporary avian and human influenza A viruses.PLoS pathogens · 2021Article
- Measuring Site-specific Glycosylation Similarity between Influenza a Virus Variants with Statistical Certainty.Molecular & cellular proteomics : MCP · 2020Article
- Multi-task learning sparse group lasso: a method for quantifying antigenicity of influenza A(H1N1) virus using mutations and variations in glycosylation of Hemagglutinin.BMC bioinformatics · 2020Article
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Authors and funding
6 authors.
Funding
Abstract
Motivation: Influenza virus antigenic variants continue to emerge and cause disease outbreaks. Time-consuming, costly and middle-throughput serologic methods using virus isolates are routinely used to identify influenza antigenic variants for vaccine strain selection. However, the resulting data are notoriously noisy and difficult to interpret and integrate because of variations in reagents, supplies and protocol implementation. A novel method without such limitations is needed for antigenic variant identification. Results: We developed a Graph-Guided Multi-Task Sparse Learning (GG-MTSL) model that uses multi-sourced serologic data to learn antigenicity-associated mutations and infer antigenic variants. By applying GG-MTSL to influenza H3N2 hemagglutinin sequences, we showed the method enables rapid characterization of antigenic profiles and identification of antigenic variants in real time and on a large scale. Furthermore, sequences can be generated directly by using clinical samples, thus minimizing biases due to culture-adapted mutation during virus isolation. Availability and implementation: MATLAB source codes developed for GG-MTSL are available through http://sysbio.cvm.msstate.edu/files/GG-MTSL/. Supplementary information: Supplementary data are available at Bioinformatics online.
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