ArticleScience advances2023
Phylogenetic identification of influenza virus candidates for seasonal vaccines.
Article in Science advances, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
10 citing papers in PubMed.
- Large language models for bioinformatics.Quantitative biology (Beijing, China) · 2026Review
- Hydroxypropyl Cellulose Enhances Immune Responses to the Current Seasonal Influenza Vaccine in Mice.Microbiology and immunology · 2026Article
- A taxonomically harmonized global dataset of wild bird hosts for avian influenza virus surveillance.Scientific data · 2025Article
- Review
- Species-specific RNA barcoding technology for rapid and accurate identification of four types of influenza virus.BMC genomics · 2025Article
- Evaluating the performance of the PREDAC method in flu vaccine recommendations over the past decade (2013-2023).Virologica Sinica · 2025Article
- Neutralization potency of the 2023-24 seasonal influenza vaccine against circulating influenza H3N2 strains.Human vaccines & immunotherapeutics · 2024Article
- Article
- Review
- PREDAC-CNN: predicting antigenic clusters of seasonal influenza A viruses with convolutional neural network.Briefings in bioinformatics · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The seasonal influenza (flu) vaccine is designed to protect against those influenza viruses predicted to circulate during the upcoming flu season, but identifying which viruses are likely to circulate is challenging. We use features from phylogenetic trees reconstructed from hemagglutinin (HA) and neuraminidase (NA) sequences, together with a support vector machine, to predict future circulation. We obtain accuracies of 0.75 to 0.89 (AUC 0.83 to 0.91) over 2016-2020. We explore ways to select potential candidates for a seasonal vaccine and find that the machine learning model has a moderate ability to select strains that are close to future populations. However, consensus sequences among the most recent 3 years also do well at this task. We identify similar candidate strains to those proposed by the World Health Organization, suggesting that this approach can help inform vaccine strain selection.
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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.