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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.

Watson, J. and Holmes, C. (2020) “Correction to: Graphing and reporting heterogeneous treatment effects through reference classes”, Trials, 21(1), p. 865.
Watson, J. and Holmes, C. (2020) “Graphing and reporting heterogeneous treatment effects through reference classes”, Trials, 21(1), p. 386.
Kendall, M. et al. (2020) “Epidemiological changes on the Isle of Wight after the launch of the NHS Test and Trace programme: a preliminary analysis”, The Lancet Digital Health, 2(12), pp. e658 - e666.
Watson, J. et al. (2020) “A cautionary note on the use of unsupervised machine learning algorithms to characterise malaria parasite population structure from genetic distance matrices”, PLoS Genetics, 16(10).
Pompe, E., Holmes, C. and Łatuszyński, K. (2020) “A framework for adaptive MCMC targeting multimodal distributions”, The Annals of Statistics, 48(5), pp. 2930–2952.
O’Cathail, S. et al. (2020) “NRF2 metagene signature is a novel prognostic biomarker in colorectal cancer”, Cancer Genetics, 248, pp. 1–10.
Mateen, B. et al. (2020) “Improving the quality of machine learning in health applications and clinical research”, Nature Machine Intelligence, 2(10), pp. 554–556.
Bitoun, E. et al. (2020) “ZCWPW1 is recruited to recombination hotspots by PRDM9, and is essential for meiotic double strand break repair”, eLife, 9.
Liu, X. et al. (2020) “Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension”, Lancet Digital Health, 2(10), pp. e537 - e548.
Cruz Rivera, S. et al. (2020) “Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension”, Lancet Digital Health, 2(10), pp. e549 - e560.