SynthesisEmerging microbes & infections2021
Influenza virus genotype to phenotype predictions through machine learning: a systematic review.
Synthesis in Emerging microbes & infections, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 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.
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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
25 citing papers in PubMed, 42 citations in OpenAlex.
- Using AI to Assess Potential Zoonotic Threats.Journal of medical Internet research · 2026Article
- Influ-BERT: a domain-adaptive genomic language model for advancing influenza A virus research.Briefings in bioinformatics · 2026Article
- Risk perception and influenza vaccine hesitancy among university students in Saudi Arabia.Frontiers in pharmacology · 2026Article
- Viral emergence and pandemic preparedness in a One Health framework.Nature reviews. Microbiology · 2026Review
- Different antigenic distance metrics generate similar predictions of influenza vaccine response breadth despite moderate correlation.PLoS computational biology · 2025Article
- Development of an early prediction model for risk of influenza A and influenza B based on complete blood count examination.BMC infectious diseases · 2025Article
- AI Methods Tailored to Influenza, RSV, HIV, and SARS-CoV-2: A Focused Review.Pathogens (Basel, Switzerland) · 2025Review
- Predictive models of influenza A virus lethal disease yield insights from ferret respiratory tract and brain tissues.Scientific reports · 2025Article
- Deep learning architectures for influenza dynamics and treatment optimization: a comprehensive review.Frontiers in artificial intelligence · 2025Review
- Exploring genetic signatures of zoonotic influenza A virus at the swine-human interface with phylogenetic and ancestral sequence reconstruction.Virus evolution · 2025Article
- Integrating multi-dimensional data to reveal the mechanisms and molecular targets of baikening granules for treatment of pediatric influenza.Frontiers in molecular biosciences · 2025Article
- Machine learning in infectious diseases: potential applications and limitations.Annals of medicine · 2024Review
- Review
- Data alchemy, from lab to insight: Transforming in vivo experiments into data science gold.PLoS pathogens · 2024Article
- Machine learning approaches for influenza A virus risk assessment identifies predictive correlates using ferret model in vivo data.Communications biology · 2024Article
- Transmission restriction and genomic evolution co-shape the genetic diversity patterns of influenza A virus.Virologica Sinica · 2024Article
- Sequence-based detection of emerging antigenically novel influenza A viruses.Proceedings. Biological sciences · 2024Article
- Tackling the Antimicrobial Resistance "Pandemic" with Machine Learning Tools: A Summary of Available Evidence.Microorganisms · 2024Review
- Predicting host species susceptibility to influenza viruses and coronaviruses using genome data and machine learning: a scoping review.Frontiers in veterinary science · 2024Article
- Supervised Machine Learning Methods for Seasonal Influenza Diagnosis.Diagnostics (Basel, Switzerland) · 2023Article
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 1 country.
Funding
Abstract
backgroundThere is great interest in understanding the viral genomic predictors of phenotypic traits that allow influenza A viruses to adapt to or become more virulent in different hosts. Machine learning techniques have demonstrated promise in addressing this critical need for other pathogens because the underlying algorithms are especially well equipped to uncover complex patterns in large datasets and produce generalizable predictions for new data. As the body of research where these techniques are applied for influenza A virus phenotype prediction continues to grow, it is useful to consider the strengths and weaknesses of these approaches to understand what has prevented these models from seeing widespread use by surveillance laboratories and to identify gaps that are underexplored with this technology. METHODS AND
resultsWe present a systematic review of English literature published through 15 April 2021 of studies employing machine learning methods to generate predictions of influenza A virus phenotypes from genomic or proteomic input. Forty-nine studies were included in this review, spanning the topics of host discrimination, human adaptability, subtype and clade assignment, pandemic lineage assignment, characteristics of infection, and antiviral drug resistance.
conclusionsOur findings suggest that biases in model design and a dearth of wet laboratory follow-up may explain why these models often go underused. We, therefore, offer guidance to overcome these limitations, aid in improving predictive models of previously studied influenza A virus phenotypes, and extend those models to unexplored phenotypes in the ultimate pursuit of tools to enable the characterization of virus isolates across surveillance laboratories.
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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.