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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.

Scheidwasser, N. et al. (2024) “Deep learning from videography as a tool for measuring<i>E. coli</i>infection in poultry”, bioRxiv.
Mills, C., Kraemer, M. and Donnelly, C. (2024) “Interdisciplinary modelling and forecasting of dengue”, medRxiv.
Dauncey, S. et al. (2024) “Approximations to the Fisher Information Metric of Deep Generative Models for Out-Of-Distribution Detection”, Transactions on Machine Learning Research, 2024.
Stricker, M. et al. (2024) “Genome-wide classification of epigenetic activity reveals regions of enriched heritability in immune-related traits”, Cell Genomics, 4(1), p. 100469.
Clivio, O., Feller, A. and Holmes, C. (2024) “Towards Representation Learning for Weighting Problems in Design-Based Causal Inference”, in Proceedings of Machine Learning Research, pp. 856–880.
Hayes, S. et al. (2024) “Ecology and environment predict spatially stratified risk of highly pathogenic avian influenza in wild birds across Europe”, bioRxiv.
Hayes, S. et al. (2024) “Ecology and environment predict spatially stratified risk of highly pathogenic avian influenza in wild birds across Europe”, bioRxiv.
Ferreira, L., Hu, S. and Myers, S. (2024) “Unravelling genetic interactions affecting quantitative traits in the UK Biobank”, in EUROPEAN JOURNAL OF HUMAN GENETICS, pp. 791–792.
Falck, F., Wang, Z. and Holmes, C. (2024) “Is In-Context Learning in Large Language Models Bayesian? A Martingale Perspective”, in Proceedings of Machine Learning Research, pp. 12784–12805.
McCabe, R. and Donnelly, C. (2023) “Public awareness of and opinions on the use of mathematical transmission modelling to inform public health policy in the United Kingdom”, Journal of the Royal Society Interface, 20.