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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.

Okell, L. et al. (2020) “Host or pathogen-related factors in COVID-19 severity? – Authors’ reply”, The Lancet, 396(10260), p. 1397.
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.
Radhakrishnan, S. et al. (2020) “Rabies as a Public Health Concern in India-A Historical Perspective.”, Tropical medicine and infectious disease, 5(4), p. E162.
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.
Charniga, K. et al. (2020) “Spatial and temporal invasion dynamics of the 2014-2017 Zika and chikungunya epidemics in Colombia”, p. 2020.09.11.20189811.
Investigators:, R. et al. (2020) “Resurgence of SARS-CoV-2 in England: detection by community antigen surveillance”, p. 2020.09.11.20192492.
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.