Overview
Currently studying for a PhD in translational immunology and machine learning, I would describe myself as a passionate data scientist with an interest in the application of machine learning to diagnosis and management of disease. In my research I am using existing technologies, as well as generating new methodologies, to mine immunological data sets with the objective to uncover patterns that are predictive of cause or outcome in infectious disease (with a particular focus on acute severe sepsis).
I have a rich background with over 5 years experience in routine diagnostic microbiology, working for organisations such as the John Radcliffe Hospital in Oxford, and Public Health England in Bristol. Whilst working for the NHS, I always addressed my work with a multi-disciplinary mindset, eager to apply my programming and technical skills to problems faced day-to-day. This ambition for problem solving pathed the way for my PhD studies and current activities, where I combine my biomedical background with data science expertise. During the COVID-19 pandemic I worked as a data scientist for the Department of Health and Social Care, where I was responsible for the deployment of epidemiological models that generate the R number.
I am active in the open-source programming community and many of my projects can be found on GitHub. Notable contributions are: CytoPy, an open-source Python framework for the analysis of large and complex Cytometry data, and CHoRD, a research database for COVID-19 biomarker discovery.
Publication
2023
- Burton, R. J. et al. 2023. Conventional and unconventional T cell responses contribute to the prediction of clinical outcome and causative bacterial pathogen in sepsis patients. [Online]. medRxiv: Cold Spring Harbor Laboratory. (10.1101/2023.09.13.23295490v1) Available at: https://www.medrxiv.org/content/10.1101/2023.09.13.23295490v1
- Burton, R. J., Cuff, S. M., Morgan, M. P., Artemiou, A. and Eberl, M. 2023. GeoWaVe: Geometric median clustering with weighted voting for ensemble clustering of cytometry data. Bioinformatics 39(1), article number: btac751. (10.1093/bioinformatics/btac751)
2022
- Burton, R. J. 2022. Identifying immunological biomarkers of sepsis using cytometry bioinformatics and machine learning. PhD Thesis, Cardiff University.
- Ponsford, M. J. et al. 2022. Examining the utility of extended laboratory panel testing in the emergency department for risk stratification of patients with COVID-19: a single-centre retrospective service evaluation. Journal of Clinical Pathology 75(4), pp. 255-262. (10.1136/jclinpath-2020-207157)
2021
- Burton, R. J., Ahmed, R., Cuff, S. M., Baker, S., Artemiou, A. and Eberl, M. 2021. CytoPy: An autonomous cytometry analysis framework. PLoS Computational Biology 17(6), article number: e1009071. (10.1371/journal.pcbi.1009071)
2019
- Burton, R. J., Albur, M., Eberl, M. and Cuff, S. M. 2019. Using artificial intelligence to reduce diagnostic workload without compromising detection of urinary tract infections. BMC Medical Informatics and Decision Making 19(1), article number: 171. (10.1186/s12911-019-0878-9)
Articles
- Burton, R. J., Cuff, S. M., Morgan, M. P., Artemiou, A. and Eberl, M. 2023. GeoWaVe: Geometric median clustering with weighted voting for ensemble clustering of cytometry data. Bioinformatics 39(1), article number: btac751. (10.1093/bioinformatics/btac751)
- Ponsford, M. J. et al. 2022. Examining the utility of extended laboratory panel testing in the emergency department for risk stratification of patients with COVID-19: a single-centre retrospective service evaluation. Journal of Clinical Pathology 75(4), pp. 255-262. (10.1136/jclinpath-2020-207157)
- Burton, R. J., Ahmed, R., Cuff, S. M., Baker, S., Artemiou, A. and Eberl, M. 2021. CytoPy: An autonomous cytometry analysis framework. PLoS Computational Biology 17(6), article number: e1009071. (10.1371/journal.pcbi.1009071)
- Burton, R. J., Albur, M., Eberl, M. and Cuff, S. M. 2019. Using artificial intelligence to reduce diagnostic workload without compromising detection of urinary tract infections. BMC Medical Informatics and Decision Making 19(1), article number: 171. (10.1186/s12911-019-0878-9)
Thesis
- Burton, R. J. 2022. Identifying immunological biomarkers of sepsis using cytometry bioinformatics and machine learning. PhD Thesis, Cardiff University.
Websites
- Burton, R. J. et al. 2023. Conventional and unconventional T cell responses contribute to the prediction of clinical outcome and causative bacterial pathogen in sepsis patients. [Online]. medRxiv: Cold Spring Harbor Laboratory. (10.1101/2023.09.13.23295490v1) Available at: https://www.medrxiv.org/content/10.1101/2023.09.13.23295490v1
Research
Burton RJ, Ahmed R, Cuff SM, Baker S, Artemiou A, et al. (2021) CytoPy: An autonomous cytometry analysis framework. PLOS Computational Biology 17(6): e1009071. https://doi.org/10.1371/journal.pcbi.1009071
Raffray L, Burton RJ, Baker SE, Morgan MP, Eberl M., 2020. Zoledronate rescues immunosuppressed monocytes in sepsis patients. Immunology 159:88-95.
Burton RJ, Albur M, Eberl M, Cuff SM. 2019. Using artificial intelligence to reduce diagnostic workload without compromising detection of urinary tract infections. BMC Medical Informatics and Decision Making 19:171.
Thesis
Systemic immunophenotyping in acute severe sepsis
Supervisors
Matthias Eberl
Professor of Translational Immunology, Division of Infection and Immunity. Joint Academic Lead for Public Involvement and Engagement, School of Medicine.