AIMC Topic: Influenza A Virus, H3N2 Subtype
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Language models learn to represent antigenic properties of human influenza A(H3) virus.
Scientific reports
Jul 1, 2025
Given that influenza vaccine effectiveness depends on a good antigenic match between the vaccine and circulating viruses, it is important to assess the antigenic properties of newly emerging variants continuously. With the increasing application of r...
Leveraging pre-vaccination antibody titres across multiple influenza H3N2 variants to forecast the post-vaccination response.
EBioMedicine
May 26, 2025
BACKGROUND: Despite decades of research on the influenza virus, we still lack a predictive understanding of how vaccination reshapes each person's antibody response, which impedes efforts to design better vaccines. Models using pre-vaccination antibo...
Utilizing machine learning and hemagglutinin sequences to identify likely hosts of influenza H3Nx viruses.
Preventive veterinary medicine
Sep 26, 2024
Influenza is a disease that represents both a public health and agricultural risk with pandemic potential. Among the subtypes of influenza A virus, H3 influenza virus can infect many avian and mammalian species and is therefore a virus of interest to...
Integrated epigenomic exposure signature discovery.
Epigenomics
Sep 3, 2024
The epigenome influences gene regulation and phenotypes in response to exposures. Epigenome assessment can determine exposure history aiding in diagnosis. Here we developed and implemented a machine learning algorithm, the exposure signature discove...
Seasonal antigenic prediction of influenza A H3N2 using machine learning.
Nature communications
May 7, 2024
Antigenic characterization of circulating influenza A virus (IAV) isolates is routinely assessed by using the hemagglutination inhibition (HI) assays for surveillance purposes. It is also used to determine the need for annual influenza vaccine update...
DeepFlu: a deep learning approach for forecasting symptomatic influenza A infection based on pre-exposure gene expression.
Computer methods and programs in biomedicine
Oct 25, 2021
BACKGROUND AND OBJECTIVE: Not everyone gets sick after an exposure to influenza A viruses (IAV). Although KLRD1 has been identified as a potential biomarker for influenza susceptibility, it remains unclear whether forecasting symptomatic flu infectio...
A Deep Learning Approach for Predicting Antigenic Variation of Influenza A H3N2.
Computational and mathematical methods in medicine
Oct 16, 2021
Modeling antigenic variation in influenza (flu) virus A H3N2 using amino acid sequences is a promising approach for improving the prediction accuracy of immune efficacy of vaccines and increasing the efficiency of vaccine screening. Antigenic drift a...
Machine Learning Prediction and Experimental Validation of Antigenic Drift in H3 Influenza A Viruses in Swine.
mSphere
Mar 17, 2021
The antigenic diversity of influenza A viruses (IAV) circulating in swine challenges the development of effective vaccines, increasing zoonotic threat and pandemic potential. High-throughput sequencing technologies can quantify IAV genetic diversity,...
Discovery of Influenza A virus neuraminidase inhibitors using support vector machine and Naïve Bayesian models.
Molecular diversity
Dec 21, 2015
Neuraminidase (NA) is a critical enzyme in the life cycle of influenza virus, which is known as a successful paradigm in the design of anti-influenza agents. However, to date there are no classification models for the virtual screening of NA inhibito...
Time series computational prediction of vaccines for influenza A H3N2 with recurrent neural networks.
Journal of bioinformatics and computational biology
Feb 1, 2020
Influenza viruses are persistently threatening public health, causing annual epidemics and sporadic pandemics due to rapid viral evolution. Vaccines are used to prevent influenza infections but the composition of the influenza vaccines have to be upd...
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