ArticleScientific reports2017
Predicting influenza antigenicity from Hemagglutintin sequence data based on a joint random forest method.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers.
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42 citing papers in PubMed, 71 citations in OpenAlex.
- Alignment-free prediction of cross-reactivity in influenza A (H3N2) anticipates antigenic drift.PLoS computational biology · 2026Article
- FluAttn: Antigenicity prediction of influenza A/H3N2 through attention-based feature mining.Infectious Disease Modelling · 2026Article
- A CNN-Based Model of Cross-Immunity to Influenza A(H3N2) Virus: Testing Under "Real-World" Conditions.Viruses · 2026Article
- High-throughput pseudovirus neutralisation maps the antigenic landscape of influenza A/H1N1 viruses.EBioMedicine · 2025Article
- Opportunities for machine learning to predict cross-neutralization in FMDV serotype O.PLoS computational biology · 2025Article
- PREDAC-FluB: predicting antigenic clusters of seasonal influenza B viruses with protein language model embedding based convolutional neural network.Briefings in bioinformatics · 2025Article
- Language models learn to represent antigenic properties of human influenza A(H3) virus.Scientific reports · 2025Article
- Unveiling the ghost: machine learning's impact on the landscape of virology.The Journal of general virology · 2025Review
- Leveraging large language models to predict antibody biological activity against influenza A hemagglutinin.Computational and structural biotechnology journal · 2025Article
- Deep learning-guided structural analysis of a novel bacteriophage KPP105 against multidrug-resistantComputational and structural biotechnology journal · 2025Article
- A deep learning approach predicting the activity of COVID-19 therapeutics and vaccines against emerging variants.NPJ systems biology and applications · 2024Article
- Sequence-based detection of emerging antigenically novel influenza A viruses.Proceedings. Biological sciences · 2024Article
- MetaFluAD: meta-learning for predicting antigenic distances among influenza viruses.Briefings in bioinformatics · 2024Article
- Article
- 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
- Early warning of emerging infectious diseases based on multimodal data.Biosafety and health · 2023Review
- 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
Corrections and comments
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Authors and funding
10 authors at 7 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Timely identification of emerging antigenic variants is critical to influenza vaccine design. The accuracy of a sequence-based antigenic prediction method relies on the choice of amino acids substitution matrices. In this study, we first compared a comprehensive 95 substitution matrices reflecting various amino acids properties in predicting the antigenicity of influenza viruses by a random forest model. We then proposed a novel algorithm called joint random forest regression (JRFR) to jointly consider top substitution matrices. We applied JRFR to human H3N2 seasonal influenza data from 1968 to 2003. A 10-fold cross-validation shows that JRFR outperforms other popular methods in predicting antigenic variants. In addition, our results suggest that structure features are most relevant to influenza antigenicity. By restricting the analysis to data involving two adjacent antigenic clusters, we inferred a few key amino acids mutation driving the 11 historical antigenic drift events, pointing to experimentally validated mutations. Finally, we constructed an antigenic cartography of all H3N2 viruses with hemagglutinin (the glycoprotein on the surface of the influenza virus responsible for its binding to host cells) sequence available from NCBI flu database, and showed an overall correspondence and local inconsistency between genetic and antigenic evolution of H3N2 influenza viruses.
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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.