tetano
Editor, Senior Moderator
J R Stat Soc Ser C Appl Stat. 2019 Aug;68(4):859-885. doi: 10.1111/rssc.12338. Epub 2019 Feb 3. [h=1]Improving the identification of antigenic sites in the H1N1 influenza virus through accounting for the experimental structure in a sparse hierarchical Bayesian model.[/h]
Davies V[SUP]1[/SUP], Harvey WT[SUP]1[/SUP], Reeve R[SUP]1[/SUP], Husmeier D[SUP]1[/SUP].
[h=3]Author information[/h] 1 University of Glasgow UK.
[h=3]Abstract[/h] Understanding how genetic changes allow emerging virus strains to escape the protection afforded by vaccination is vital for the maintenance of effective vaccines. We use structural and phylogenetic differences between pairs of virus strains to identify important antigenic sites on the surface of the influenza A(H1N1) virus through the prediction of haemagglutination inhibition (HI) titre: pairwise measures of the antigenic similarity of virus strains. We propose a sparse hierarchical Bayesian model that can deal with the pairwise structure and inherent experimental variability in the H1N1 data through the introduction of latent variables. The latent variables represent the underlying HI titre measurement of any given pair of virus strains and help to account for the fact that, for any HI titre measurement between the same pair of virus strains, the difference in the viral sequence remains the same. Through accurately representing the structure of the H1N1 data, the model can select virus sites which are antigenic, while its latent structure achieves the computational efficiency that is required to deal with large virus sequence data, as typically available for the influenza virus. In addition to the latent variable model, we also propose a new method, the block-integrated widely applicable information criterion biWAIC, for selecting between competing models. We show how this enables us to select the random effects effectively when used with the model proposed and we apply both methods to an A(H1N1) data set.
? 2019 The Authors Journal of the Royal Statistical Society: Series C (Applied Statistics) Published by John Wiley & Sons Ltd on behalf of the Royal Statistical Society.
[h=4]KEYWORDS:[/h] Antigenic variability; Bayesian hierarchical models; Influenza virus; Latent variable models; Markov chain Monte Carlo sampling; Mixed effects models; Spike‐and‐slab prior; Widely applicable information criterion
PMID: 31598013 PMCID: PMC6774336 DOI: 10.1111/rssc.12338
Davies V[SUP]1[/SUP], Harvey WT[SUP]1[/SUP], Reeve R[SUP]1[/SUP], Husmeier D[SUP]1[/SUP].
[h=3]Author information[/h] 1 University of Glasgow UK.
[h=3]Abstract[/h] Understanding how genetic changes allow emerging virus strains to escape the protection afforded by vaccination is vital for the maintenance of effective vaccines. We use structural and phylogenetic differences between pairs of virus strains to identify important antigenic sites on the surface of the influenza A(H1N1) virus through the prediction of haemagglutination inhibition (HI) titre: pairwise measures of the antigenic similarity of virus strains. We propose a sparse hierarchical Bayesian model that can deal with the pairwise structure and inherent experimental variability in the H1N1 data through the introduction of latent variables. The latent variables represent the underlying HI titre measurement of any given pair of virus strains and help to account for the fact that, for any HI titre measurement between the same pair of virus strains, the difference in the viral sequence remains the same. Through accurately representing the structure of the H1N1 data, the model can select virus sites which are antigenic, while its latent structure achieves the computational efficiency that is required to deal with large virus sequence data, as typically available for the influenza virus. In addition to the latent variable model, we also propose a new method, the block-integrated widely applicable information criterion biWAIC, for selecting between competing models. We show how this enables us to select the random effects effectively when used with the model proposed and we apply both methods to an A(H1N1) data set.
? 2019 The Authors Journal of the Royal Statistical Society: Series C (Applied Statistics) Published by John Wiley & Sons Ltd on behalf of the Royal Statistical Society.
[h=4]KEYWORDS:[/h] Antigenic variability; Bayesian hierarchical models; Influenza virus; Latent variable models; Markov chain Monte Carlo sampling; Mixed effects models; Spike‐and‐slab prior; Widely applicable information criterion
PMID: 31598013 PMCID: PMC6774336 DOI: 10.1111/rssc.12338