ArticlePloS one2018
Predicting antigenic variants of H1N1 influenza virus based on epidemics and pandemics using a stacking model.
Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed, 71 citations in OpenAlex.
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- Highly pathogenic avian influenza: pandemic preparedness for a scenario of high lethality with no vaccines.Frontiers in public health · 2025Article
- VarPPUD: Variant post prioritization developed for undiagnosed genetic disorders.medRxiv : the preprint server for health sciences · 2024Article
- Development of PREDAC-H1pdm to model the antigenic evolution of influenza A/(H1N1) pdm09 viruses.Virologica Sinica · 2023Article
- Early warning of emerging infectious diseases based on multimodal data.Biosafety and health · 2023Review
- Editorial: Computational methods to analyze RNA data for human diseases.Frontiers in genetics · 2023Article
- Identification and analysis of B cell epitopes of hemagglutinin of H1N1 influenza virus.Archives of microbiology · 2022Article
- Development of the H3N2 influenza microneedle vaccine for cross-protection against antigenic variants.Scientific reports · 2022Article
- Antigenic characterization of influenza and SARS-CoV-2 viruses.Analytical and bioanalytical chemistry · 2022Review
- End-to-end antigenic variant generation for H1N1 influenza HA protein using sequence to sequence models.PloS one · 2022Article
- Advances in the Rapid Diagnostic of Viral Respiratory Tract Infections.Frontiers in cellular and infection microbiology · 2022Review
- Sagacious epitope selection for vaccines, and both antibody-based therapeutics and diagnostics: tips from virology and oncology.Antibody therapeutics · 2022Review
- COVID-19 forecasting and intervention planning using gated recurrent unit and evolutionary algorithm.Neural computing & applications · 2022Article
- Exploring the Lethality of Human-Adapted Coronavirus Through Alignment-Free Machine Learning Approaches Using Genomic Sequences.Current genomics · 2021Article
- Review
- Disaster and Pandemic Management Using Machine Learning: A Survey.IEEE internet of things journal · 2021Article
- A Herbal Mixture Formula of OCD20015-V009 Prophylactic Administration to Enhance Interferon-Mediated Antiviral Activity Against Influenza A Virus.Frontiers in pharmacology · 2021Article
- Enhanced Gaussian process regression-based forecasting model for COVID-19 outbreak and significance of IoT for its detection.Applied intelligence (Dordrecht, Netherlands) · 2021Article
- Determinants of the infection rate of the COVID-19 in the U.S. using ANFIS and virus optimization algorithm (VOA).Chaos, solitons, and fractals · 2020Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
H1N1 is the earliest emerging subtype of influenza A viruses with available genomic sequences, has caused several pandemics and seasonal epidemics, resulting in millions of deaths and enormous economic losses. Timely determination of new antigenic variants is crucial for the vaccine selection and flu prevention. In this study, we chronologically divided the H1N1 strains into several periods in terms of the epidemics and pandemics. Computational models have been constructed to predict antigenic variants based on epidemic and pandemic periods. By sequence analysis, we demonstrated the diverse mutation patterns of HA1 protein on different periods and that an individual model built upon each period can not represent the variations of H1N1 virus. A stacking model was established for the prediction of antigenic variants, combining all the variation patterns across periods, which would help assess a new influenza strain's antigenicity. Three different feature extraction methods, i.e. residue-based, regional band-based and epitope region-based, were applied on the stacking model to verify its feasibility and robustness. The results showed the capability of determining antigenic variants prediction with accuracy as high as 0.908 which performed better than any of the single models. The prediction performance using the stacking model indicates clear distinctions of mutation patterns and antigenicity between epidemic and pandemic strains. It would also facilitate rapid determination of antigenic variants and influenza surveillance.
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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.