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.

Behr, M., Holmes, C. and Munk, A. (2018) “Multiscale blind source separation”, The Annals of Statistics, 46(2), pp. 711–744.

Bycroft, C. et al. (2018) “Patterns of genetic differentiation and the footprints of historical migrations in the Iberian Peninsula”, Nature Communications [Preprint].

Harrar, H., Myers, S. and Ghanem, A. (2018) “Art or Science? An Evidence-Based Approach to Human Facial Beauty a Quantitative Analysis Towards an Informed Clinical Aesthetic Practice”, Aesthetic Plastic Surgery, 42(1), pp. 137–146.

Kafkafi, N. et al. (2018) “Reproducibility and replicability of rodent phenotyping in preclinical studies”, Neuroscience and biobehavioral reviews, 87, pp. 218–232.

Rukat, T., Holmes, C. and Yau, C. (2018) “Probabilistic Boolean Tensor Decomposition”, in Proceedings of Machine Learning Research, pp. 4413–4422.

Abdalla, M. et al. (2018) “A general framework for predicting the transcriptomic consequences of non-coding variation.”

Rukat, T., Holmes, C. and Yau, C. (2018) “Probabilistic Boolean tensor decomposition”, in 35th International Conference on Machine Learning, ICML 2018, pp. 7007–7020.

Holmes, C. and Watson, J. (2018) “Machine learning for randomised controlled trials: identifying treatment effectheterogeneity with strict control of type I error.”

Lyddon, S., Walker, S. and Holmes, C. (2018) “Nonparametric learning from Bayesian models with randomized objective functions”, in Advances in Neural Information Processing Systems, pp. 2071–2081.

Rahmioglu, N. et al. (2017) “Variability of genome-wide DNA methylation and mRNA expression profiles in reproductive and endocrine disease related tissues”, Epigenetics, 12(10), pp. 897–908.