Publications by Computational Biology and Bioinformatics

Clarkson, J. et al. (2022) “DAMNETS: a deep autoregressive model for generating Markovian network time series”, in Proceedings of the First Learning on Graphs Conference. Journal of Machine Learning Research, pp. 23:1 – 23:19.

Hayes, S. et al. (2022) “Understanding the incidence and timing of rabies cases in domestic animals and wildlife in south-east Tanzania in the presence of widespread domestic dog vaccination campaigns”, Veterinary Research, 53(1).

Eales, O. et al. (2022) “SARS-CoV-2 lineage dynamics in England from September to November 2021: high diversity of Delta sub-lineages and increased transmissibility of AY.4.2”, BMC Infectious Diseases, 22(1), p. 647.

Crook, O. et al. (2022) “Semi-Supervised Non-Parametric Bayesian Modelling of Spatial Proteomics.”, The annals of applied statistics, 16(4), pp. 22 - aoas1603.

Crook, O. et al. (2022) “Analysis of the first genetic engineering attribution challenge”, Nature Communications, 13(1), p. 7374.

Charniga, K. et al. (2022) “Estimating Zika virus attack rates and risk of Zika virus-associated neurological complications in Colombian capital cities with a Bayesian model”, Royal Society Open Science, 9(11).

Eales, O. et al. (2022) “Trends in SARS-CoV-2 infection prevalence during England’s roadmap out of lockdown, January to July 2021”, PLoS Computational Biology, 18(11).

Cooper, J. et al. (2022) “Topological analysis of credit data: preliminary findings”, in Proceedings of the 23rd International Conference on Intelligent Data Engineering and Automated Learning (IDEAL). Springer, pp. 432–442.

Parag, K., Thompson, R. and Donnelly, C. (2022) “Authors’ reply to the discussion of ‘Are epidemic growth rates more informative than reproduction numbers?’ by Parag et al. in Session 1 of the Royal Statistical Society’s Special Topic Meeting on COVID-19 Transmission: 9 June 2021”, Journal of the Royal Statistical Society: Statistics in Society Series A, 185(S1), pp. S55 - S60.

Whitaker, M. et al. (2022) “Variant-specific symptoms of COVID-19 in a study of 1,542,510 adults in England”, Nature Communications, 13.