ArticleBMC bioinformatics2020
Multi-task learning sparse group lasso: a method for quantifying antigenicity of influenza A(H1N1) virus using mutations and variations in glycosylation of Hemagglutinin.
Article in BMC bioinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Post-translational modification networks as master regulators of influenza virus replication, host adaptation, and immune evasion.Frontiers in immunology · 2026Review
- Different antigenic distance metrics generate similar predictions of influenza vaccine response breadth despite moderate correlation.PLoS computational biology · 2025Article
- Sparse multitask group Lasso for genome-wide association studies.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
- Characterization of Conserved Evolution in H7N9 Avian Influenza Virus Prior Mass Vaccination.Virulence · 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
- Development of PREDAC-H1pdm to model the antigenic evolution of influenza A/(H1N1) pdm09 viruses.Virologica Sinica · 2023Article
- 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
- Selecting Representative Samples From Complex Biological Datasets Using K-Medoids Clustering.Frontiers in genetics · 2022Article
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6 authors.
Funding
Abstract
backgroundIn addition to causing the pandemic influenza outbreaks of 1918 and 2009, subtype H1N1 influenza A viruses (IAVs) have caused seasonal epidemics since 1977. Antigenic property of influenza viruses are determined by both protein sequence and N-linked glycosylation of influenza glycoproteins, especially hemagglutinin (HA). The currently available computational methods are only considered features in protein sequence but not N-linked glycosylation.
resultsA multi-task learning sparse group least absolute shrinkage and selection operator (LASSO) (MTL-SGL) regression method was developed and applied to derive two types of predominant features including protein sequence and N-linked glycosylation in hemagglutinin (HA) affecting variations in serologic data for human and swine H1N1 IAVs. Results suggested that mutations and changes in N-linked glycosylation sites are associated with the rise of antigenic variants of H1N1 IAVs. Furthermore, the implicated mutations are predominantly located at five reported antibody-binding sites, and within or close to the HA receptor binding site. All of the three N-linked glycosylation sites (i.e. sequons NCSV at HA 54, NHTV at HA 125, and NLSK at HA 160) identified by MTL-SGL to determine antigenic changes were experimentally validated in the H1N1 antigenic variants using mass spectrometry analyses. Compared with conventional sparse learning methods, MTL-SGL achieved a lower prediction error and higher accuracy, indicating that grouped features and MTL in the MTL-SGL method are not only able to handle serologic data generated from multiple reagents, supplies, and protocols, but also perform better in genetic sequence-based antigenic quantification.
conclusionsIn summary, the results of this study suggest that mutations and variations in N-glycosylation in HA caused antigenic variations in H1N1 IAVs and that the sequence-based antigenicity predictive model will be useful in understanding antigenic evolution of IAVs.
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