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.

Padellini, T. et al. (2022) “Time varying association between deprivation, ethnicity and SARS-CoV-2 infections in England: A population-based ecological study”, The Lancet Regional Health - Europe, 15, p. 100322.

Unsworth, H. et al. (2022) “Building an evidence standards framework for artificial intelligence-enabled digital health technologies”, The Lancet Digital Health, 4(4), pp. e216 - e217.

Ramisch, A. et al. (2022) “Dynamics of gene regulatory organization in longitudinal twin RNA-seq data”, in EUROPEAN JOURNAL OF HUMAN GENETICS, pp. 67–67.

Ghalebikesabi, S. et al. (2022) “Mitigating Statistical Bias within Differentially Private Synthetic Data”, in Proceedings of Machine Learning Research, pp. 685–695.

Ghalebikesabi, S. et al. (2022) “Mitigating Statistical Bias within Differentially Private Synthetic Data”, in Proceedings of the 38th Conference on Uncertainty in Artificial Intelligence, UAI 2022, pp. 696–705.

Venkatesh, S. et al. (2022) “Genetic architecture of longitudinal obesity trajectories in primary care electronic health records”, in HUMAN HEREDITY, pp. 21–22.

Clivio, O. et al. (2022) “Neural Score Matching for High-Dimensional Causal Inference”, in Proceedings of Machine Learning Research, pp. 7076–7110.

Nicholson, G. et al. (2022) “Improving local prevalence estimates of SARS-CoV-2 infections using a causal debiasing framework”, Nature Microbiology, 7(1), pp. 97–107.

Mallon, A.-M. et al. (2021) “Advancing data science in drug development through an innovative computational framework for data sharing and statistical analysis”, BMC Medical Research Methodology, 21(1).