Sparse Bayesian Variable Selection for the Identification of Antigenic Variability in the Foot-and-Mouth Disease Virus

Vinny Davies, Richard Reeve, William Harvey, Francois Maree, Dirk Husmeier
Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:149-158, 2014.

Abstract

Vaccines created from closely related viruses are vital for offering protection against newly emerging strains. For Foot-and-Mouth disease virus (FMDV), where multiple serotypes co-circulate, testing large numbers of vaccines can be infeasible. Therefore the development of an in silico predictor of cross-protection between strains is important to help optimise vaccine choice. Here we describe a novel sparse Bayesian variable selection model using spike and slab priors which is able to predict antigenic variability and identify sites which are important for the neutralisation of the virus. We are able to identify multiple residues which are known to be key indicators of antigenic variability. Many of these were not identified previously using Frequentist mixed-effects models and still cannot be found when an L1 penalty is used. We further explore how the Markov chain Monte Carlo (MCMC) proposal method for the inclusion of variables can offer significant reductions in computational requirements, both for spike and slab priors in general, and our hierarchical Bayesian model in particular.

Cite this Paper

BibTeX
@InProceedings{pmlr-v33-davies14, title = {{Sparse Bayesian Variable Selection for the Identification of Antigenic Variability in the Foot-and-Mouth Disease Virus}}, author = {Davies, Vinny and Reeve, Richard and Harvey, William and Maree, Francois and Husmeier, Dirk}, booktitle = {Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics}, pages = {149--158}, year = {2014}, editor = {Kaski, Samuel and Corander, Jukka}, volume = {33}, series = {Proceedings of Machine Learning Research}, address = {Reykjavik, Iceland}, month = {22--25 Apr}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v33/davies14.pdf}, url = {https://proceedings.mlr.press/v33/davies14.html}, abstract = {Vaccines created from closely related viruses are vital for offering protection against newly emerging strains. For Foot-and-Mouth disease virus (FMDV), where multiple serotypes co-circulate, testing large numbers of vaccines can be infeasible. Therefore the development of an in silico predictor of cross-protection between strains is important to help optimise vaccine choice. Here we describe a novel sparse Bayesian variable selection model using spike and slab priors which is able to predict antigenic variability and identify sites which are important for the neutralisation of the virus. We are able to identify multiple residues which are known to be key indicators of antigenic variability. Many of these were not identified previously using Frequentist mixed-effects models and still cannot be found when an L1 penalty is used. We further explore how the Markov chain Monte Carlo (MCMC) proposal method for the inclusion of variables can offer significant reductions in computational requirements, both for spike and slab priors in general, and our hierarchical Bayesian model in particular.} }
Endnote
%0 Conference Paper %T Sparse Bayesian Variable Selection for the Identification of Antigenic Variability in the Foot-and-Mouth Disease Virus %A Vinny Davies %A Richard Reeve %A William Harvey %A Francois Maree %A Dirk Husmeier %B Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2014 %E Samuel Kaski %E Jukka Corander %F pmlr-v33-davies14 %I PMLR %P 149--158 %U https://proceedings.mlr.press/v33/davies14.html %V 33 %X Vaccines created from closely related viruses are vital for offering protection against newly emerging strains. For Foot-and-Mouth disease virus (FMDV), where multiple serotypes co-circulate, testing large numbers of vaccines can be infeasible. Therefore the development of an in silico predictor of cross-protection between strains is important to help optimise vaccine choice. Here we describe a novel sparse Bayesian variable selection model using spike and slab priors which is able to predict antigenic variability and identify sites which are important for the neutralisation of the virus. We are able to identify multiple residues which are known to be key indicators of antigenic variability. Many of these were not identified previously using Frequentist mixed-effects models and still cannot be found when an L1 penalty is used. We further explore how the Markov chain Monte Carlo (MCMC) proposal method for the inclusion of variables can offer significant reductions in computational requirements, both for spike and slab priors in general, and our hierarchical Bayesian model in particular.
RIS
TY - CPAPER TI - Sparse Bayesian Variable Selection for the Identification of Antigenic Variability in the Foot-and-Mouth Disease Virus AU - Vinny Davies AU - Richard Reeve AU - William Harvey AU - Francois Maree AU - Dirk Husmeier BT - Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics DA - 2014/04/02 ED - Samuel Kaski ED - Jukka Corander ID - pmlr-v33-davies14 PB - PMLR DP - Proceedings of Machine Learning Research VL - 33 SP - 149 EP - 158 L1 - http://proceedings.mlr.press/v33/davies14.pdf UR - https://proceedings.mlr.press/v33/davies14.html AB - Vaccines created from closely related viruses are vital for offering protection against newly emerging strains. For Foot-and-Mouth disease virus (FMDV), where multiple serotypes co-circulate, testing large numbers of vaccines can be infeasible. Therefore the development of an in silico predictor of cross-protection between strains is important to help optimise vaccine choice. Here we describe a novel sparse Bayesian variable selection model using spike and slab priors which is able to predict antigenic variability and identify sites which are important for the neutralisation of the virus. We are able to identify multiple residues which are known to be key indicators of antigenic variability. Many of these were not identified previously using Frequentist mixed-effects models and still cannot be found when an L1 penalty is used. We further explore how the Markov chain Monte Carlo (MCMC) proposal method for the inclusion of variables can offer significant reductions in computational requirements, both for spike and slab priors in general, and our hierarchical Bayesian model in particular. ER -
APA
Davies, V., Reeve, R., Harvey, W., Maree, F. & Husmeier, D.. (2014). Sparse Bayesian Variable Selection for the Identification of Antigenic Variability in the Foot-and-Mouth Disease Virus. Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 33:149-158 Available from https://proceedings.mlr.press/v33/davies14.html.

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