ArticleProceedings. Biological sciences2020
Predicting the short-term success of human influenza virus variants with machine learning.
Article in Proceedings. Biological sciences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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
17 citing papers in PubMed, 24 citations in OpenAlex.
- Alignment-free prediction of cross-reactivity in influenza A (H3N2) anticipates antigenic drift.PLoS computational biology · 2026Article
- Implications From the Analogous Relationship Between Evolutionary and Learning Processes.BioEssays : news and reviews in molecular, cellular and developmental biology · 2025Review
- Measures of Population Immunity Can Predict the Dominant Clade of Influenza A (H3N2) in the 2017-2018 Season and Reveal Age-Associated Differences in Susceptibility and Antibody-Binding Specificity.Influenza and other respiratory viruses · 2024Article
- Measures of population immunity can predict the dominant clade of influenza A (H3N2) in the 2017-2018 season and reveal age-associated differences in susceptibility and antibody-binding specificity.medRxiv : the preprint server for health sciences · 2024Article
- Review
- Sequence-based detection of emerging antigenically novel influenza A viruses.Proceedings. Biological sciences · 2024Article
- Predicting Potential PRRSV-2 Variant Emergence through Phylogenetic Inference.Transboundary and emerging diseases · 2024Article
- Optimal thresholds and key parameters for predicting influenza A virus transmission events in ferrets.Npj viruses · 2024Article
- Phylogenetic identification of influenza virus candidates for seasonal vaccines.Science advances · 2023Article
- Supervised Machine Learning Methods for Seasonal Influenza Diagnosis.Diagnostics (Basel, Switzerland) · 2023Article
- Identification and In Silico Characterization of a Conserved Peptide on Influenza Hemagglutinin Protein: A New Potential Antigen for Universal Influenza Vaccine Development.Nanomaterials (Basel, Switzerland) · 2023Article
- Quantitative systems-based prediction of antimicrobial resistance evolution.NPJ systems biology and applications · 2023Review
- Towards evolutionary predictions: Current promises and challenges.Evolutionary applications · 2023Review
- Deep clustering of bacterial tree images.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2022Article
- 12 Plagues of AI in Healthcare: A Practical Guide to Current Issues With Using Machine Learning in a Medical Context.Frontiers in digital health · 2022Review
- Utility of Artificial Intelligence Amidst the COVID 19 Pandemic: A Review.Journal of medical systems · 2020Review
- A new resolution function to evaluate tree shape statistics.PloS one · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Seasonal influenza viruses are constantly changing and produce a different set of circulating strains each season. Small genetic changes can accumulate over time and result in antigenically different viruses; this may prevent the body's immune system from recognizing those viruses. Due to rapid mutations, in particular, in the haemagglutinin (HA) gene, seasonal influenza vaccines must be updated frequently. This requires choosing strains to include in the updates to maximize the vaccines' benefits, according to estimates of which strains will be circulating in upcoming seasons. This is a challenging prediction task. In this paper, we use longitudinally sampled phylogenetic trees based on HA sequences from human influenza viruses, together with counts of epitope site polymorphisms in HA, to predict which influenza virus strains are likely to be successful. We extract small groups of taxa (subtrees) and use a suite of features of these subtrees as key inputs to the machine learning tools. Using a range of training and testing strategies, including training on H3N2 and testing on H1N1, we find that successful prediction of future expansion of small subtrees is possible from these data, with accuracies of 0.71-0.85 and a classifier 'area under the curve' 0.75-0.9.
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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.