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Artificial Intelligence, Data Science and Biostatistics in Diabetes

The group's mission is to advance the understanding of diabetes, its causes and its effective treatment, through the use of innovative Statistics, Artificial Intelligence and Data Science methods. It is an interdisciplinary team, with a shared use of computational based analysis. Its expertise centres around statistical genetics, Mendelian randomization, Bayesian statistics, machine learning, risk prediction, stratified medicine, and infectious disease modelling.

Key areas of research are focused on:

• The development of Causal Inference methods by combining genetics and observational study data using the technique of Mendelian randomization, and through the design and analysis of clinical trials.
• Large-scale genetic analyses of diabetes and related traits. Expertise in genome-wide association studies and genome sequencing analyses. More recently activity monitor data has been used to derive estimates of sleep measures to perform epidemiological and genetic analyses.
• Developing patented point of care testing (POCT) devices for diabetes, smartphone neuropathy diagnosis and medical virtual reality (VR) training simulators in collaborations between HEI, NHS and medical device companies.
• Bayesian statistical methodology used for inference and prediction in complex, partially observed systems, most notably in infectious disease modelling.

Professor Jack Bowden Professor of Biomedical Data Science
Dr Ninon Mounier Postdoctoral Research Fellow
Pedro Lopes Cardoso PhD Student
Dr John Dennis Independent Research Fellow in Medical Statistics
Laura Guedemann PhD Student
Vasileios Karageorgiou PhD Student
Dr TJ McKinley Senior Lecturer in Bayesian Statistics
Dr Nick Owens Lecturer in Data Science and Artificial Intelligence
Cat Russon PhD Student
Dr Beverley Shields Senior Lecturer in Medical Statistics
Professor Neil Vaughan Associate Professor of Data Science and Artificial Intelligence
Professor Mike Weedon Associate Professor of Bioinformatics
Timothy Hall

Postdoctoral Research Associate

  • MRC Integrative Unit at the University of Bristol
  • Professor Bowden previously held a Mendelian randomization MRC Programme grant at the IEU. He maintains an honorary position within the IEU, continuing to support PhD students and research staff going forward.
  • Since 2010, Professor Neil Vaughan has been actively involved in the UK Government’s All Party Parliamentary Engineering Group (APPEG) and APPG-AI (Stephen Metcalfe MP) at the House of Lords, and the Science Engineering and Technology for Britain (SETforBritain), (Andrew Miller MP) in House of Commons.

Please find a selection of our key and landmark papers below.  Full publications lists are available on individual biographies:

Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. J. Bowden, Davey Smith G and Burgess S (2015).  International Journal of Epidemiology 44: 512-525

Statistical inference in two-sample summary-data Mendelian randomization using robust profile adjusted score. Zhao Q, Wang J, Hemani G, Bowden J, and Small D. Annals of Statistics (2020) 48: 1742-1769 

Approximate Bayesian Computation and simulation based inference for complex stochastic epidemic models McKinley, T. et al., Statistical Science, 33 (1), 4–18, 2018.

Vaughan N., Gabrys B., (2020) Scoring and assessment in medical VR training simulators with dynamic time series classification. Elsevier Journal, Engineering Applications of Artificial Intelligence, 103760, (H-Index 86, Quartile 1 for AI, Impact 3.5),

Biological and clinical insights from genetics of insomnia symptoms, Lane JM, Jones SE, Dashti HS, Wood AR, Aragam KG, van Hees VT, Strand LB, Winsvold BS, Wang H, Bowden J, Song Y, Patel K, Anderson SG, Beaumont RN, Bechtold DA, Cade BE, Haas M, Kathiresan S, Little MA, Luik AI, Loudon AS, Purcell S, Richmond RC, Scheer FAJL, Schormair B, Tyrrell J, Winkelman JW, Winkelmann J; HUNT All In Sleep, Hveem K, Zhao C, Nielsen JB, Willer CJ, Redline  S, Spiegelhalder K, Kyle SD, Ray DW, Zwart JA, Brumpton B, Frayling TM, Lawlor DA, Rutter MK*, Weedon MN*, Saxena R*.. Nat Genet. 2019 Mar *

Heterozygous RFX6 protein truncating variants cause Maturity-Onset Diabetes of the Young (MODY) with reduced penetrance. Kashyap A Patel, Markku Laakso, Alena Stancakova, Thomas W Laver, Kevin Colclough, Matthew B Johnson, Jarno Kettunen, Tiinamaija Tuomi, Miriam Cnop, Maggie H Shepherd, Sarah E Flanagan, Sian Ellard, Andrew T Hattersley, Michael N Weedon. Nature Communications. 2017

J Dennis (PI) Understanding the association between diabetes and severity of COVID-19 infection. Diabetes UK rapid response grant: £40,000

J Bowden (Co-PI) Sleep disturbance: defining the clinical impact of Diabetes. Diabetes UK. £247,607 (2019-2020)

J Bowden (Co-PI) and others. Genetic Evaluation of Multi-morbidity towards INdividualisation of Interventions – GEMINI. MRC consolidator grant: £110,000

T McKinley (PI), R McDonald, D Hodgson and R Delahay, Efficient Bayesian modelling of infectious diseases in wildlife, NERC Standard Grant: £396,000 (2020-2023)

T McKinley (PI), S Das and T Tregenza, Innovate UK Knowledge Transfer Partnership with Chelonia Ltd. £167,000 (2019-2021)

T McKinley (Co-I), R White and others, Improving scientific and public health decision making by developing technologies to increase use of robust methods to calibrate and analyse complex mathematical models, Wellcome Trust Technology Transfer Grant £494,000 (2019-2021)

N Vaughan (PI) Royal Academy of Engineering, Frameworks for Virtual Reality Medical Training Simulators, £971,502, 2018-2023. 

N Vaughan, (PI) Wessex Academic Health Science Network WAHSN, Development of orthopaedic virtual reality NHS training, 2015, £136,000.

N Vaughan (Co-I) Health and Care Research Wales: Pathway to Portfolio Funding. ParaVR CPR Training Simulator for Schools, 2019, £18,423.

N Vaughan (Co-I) Association of Anaesthetists in Great Britain and Ireland (AAGBI) Large Research Grant, Development of an Epidural Training Simulator, 2016, £15,000.

N Vaughan, (Co-I) NHS Acorn Innovation Prize, Neuropathy Smartphone Health Device for Diabetes (Patented), 2015, £10,000.

N Vaughan, (Co-I) Obstetric Anaesthetists Association (OAA), Large Project Grant, 2012, NHS Clinical Trial: Quantification of pressures in labouring women of varying BMIs, £32,354.

N Vaughan, (PI) NHS Health Education and Training Award, Digital and TEL Innovator of the Year, 2019 

Medical Research Council Grants

2020-2023: PI on Diabetes UK grant: “Using UK Biobank to assess the clinical presentation of MODY in an unselected population”. £165,000

2020-2023: Co-PI on MRC grant: “Assessing the penetrance, pathogenicity and clinical presentation of monogenic disease genes”. PI (Caroline Wright). £634,000

2017-2019: Co-I on Diabetes UK grant: “Sleep disturbance: new insights into the clinical impact in diabetes”. With Martin Rutter. £257,928

2017-2019: PI on MRC grant: “The genetics of sleep patterns and their relationship to obesity and Type 2 diabetes”. £401,206

2015-2018: PI on Diabetes UK grant: “Developing a Type 1 diabetes genetic risk score to get the right diagnosis and the right treatment for patients with diabetes”. £233,000

The AI, Data Science and Biostatistics research group uses the following research facility:

  • High Performance Computing

More details of all of this can be found here.