ArticleViruses2023
Prediction of Antigenic Distance in Influenza A Using Attribute Network Embedding.
Article in Viruses, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Multi-View Transformers for Structure-Aware HA-NA Drift Risk Scoring and Mutation Hotspot Mapping.Viruses · 2026Article
- Review
- Deep recurrent neural network with fractional addax optimization algorithm for influenza virus host prediction.MethodsX · 2025Article
- MetaFluAD: meta-learning for predicting antigenic distances among influenza viruses.Briefings in bioinformatics · 2024Article
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Authors and funding
3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Owing to the rapid changes in the antigenicity of influenza viruses, it is difficult for humans to obtain lasting immunity through antiviral therapy. Hence, tracking the dynamic changes in the antigenicity of influenza viruses can provide a basis for vaccines and drug treatments to cope with the spread of influenza viruses. In this paper, we developed a novel quantitative prediction method to predict the antigenic distance between virus strains using attribute network embedding techniques. An antigenic network is built to model and combine the genetic and antigenic characteristics of the influenza A virus H3N2, using the continuous distributed representation of the virus strain protein sequence (ProtVec) as a node attribute and the antigenic distance between virus strains as an edge weight. The results show a strong positive correlation between supplementing genetic features and antigenic distance prediction accuracy. Further analysis indicates that our prediction model can comprehensively and accurately track the differences in antigenic distances between vaccines and influenza virus strains, and it outperforms existing methods in predicting antigenic distances between strains.
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