Sparse Bayesian variable selection for the identification of antigenic variability in the Foot-and-Mouth disease virus

Davies, V. , Reeve, R. , Harvey, W., Maree, F. and Husmeier, D. (2014) Sparse Bayesian variable selection for the identification of antigenic variability in the Foot-and-Mouth disease virus. Proceedings of Machine Learning Research, 33, pp. 149-158.

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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 iden- tify 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.

Item Type:Articles
Glasgow Author(s) Enlighten ID:Reeve, Professor Richard and Husmeier, Professor Dirk and Harvey, Dr William and Davies, Dr Vinny
Authors: Davies, V., Reeve, R., Harvey, W., Maree, F., and Husmeier, D.
College/School:College of Science and Engineering > School of Mathematics and Statistics > Statistics
College of Medical Veterinary and Life Sciences > Institute of Biodiversity Animal Health and Comparative Medicine
College of Science and Engineering > School of Computing Science
Journal Name:Proceedings of Machine Learning Research
Copyright Holders:Copyright © 2014 The Authors
First Published:First published in Proceedings of Machine Learning Research 33: 149-158
Publisher Policy:Reproduced with the permission of the authors

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