Publications by Statistical Genetics and Epidemiology

Our research spans areas of statistical genetics, in particular the development of powerful statistical approaches to analyse genetic data, as well as studying infectious diseases.

Nicholson, G. et al. (2021) “Local prevalence of transmissible SARS-CoV-2 infection: an integrative causal model for debiasing fine-scale targeted testing data”, p. 2021.05.17.21256818.

Jiang, X. et al. (2021) “Modelling the impact of rapid tests, tracing and distancing in lower-income countries suggest that optimal policies vary with rural-urban settings”, p. 2021.03.17.21253853.

Camuto, A. et al. (2021) “Towards a theoretical understanding of the robustness of variational autoencoders”, in. Journal of Machine Learning Research, pp. 3565–3573.

Lagou, V. et al. (2021) “Publisher Correction: Sex-dimorphic genetic effects and novel loci for fasting glucose and insulin variability”, Nature Communications, 12(1), p. 995.

Lyons, J. et al. (2021) “Protocol for the development of the Wales Multimorbidity e-Cohort (WMC): data sources and methods to construct a population-based research platform to investigate multimorbidity”, BMJ Open, 11(1).

Lehmann, B. et al. (2021) “Optimal strategies for learning multi-ancestry polygenic scores vary across traits”, p. 2021.01.15.426781.

Fong, E. and Holmes, C. (2021) “Conformal Bayesian Computation”, in Advances in Neural Information Processing Systems, pp. 18268–18279.

Zaidi, S. et al. (2021) “Neural Ensemble Search for Uncertainty Estimation and Dataset Shift”, in Advances in Neural Information Processing Systems, pp. 7898–7911.

Camuto, A. et al. (2021) “Learning Bijective Feature Maps for Linear ICA”, in Proceedings of Machine Learning Research, pp. 3655–3663.

Camuto, A. et al. (2021) “Asymmetric Heavy Tails and Implicit Bias in Gaussian Noise Injections”, in Proceedings of Machine Learning Research, pp. 1249–1260.