About the Project
Students can select from any of the three advertised projects. These projects have been carefully selected to provide students with an excellent scientific training in an important area of diabetes research, the latest laboratory and computing skills, outstanding resources, and with world leading scientists as supervisors. They cover various aspects of diabetes research, including autoimmunity in the pancreas; neuro-endocrinology to understand the relationship between the brain, mental health and the endocrine system; gene regulation in the placenta and fetal development of the pancreas; rare genetic forms of diabetes; muscle physiology; and the use of electronic medical records to understand disease causes, treatments and progression. Students will learn a wide range of state-of-the-art techniques, which could include CRISPR-Cas9 gene editing, DNA methylation, DNA sequence analysis, muscle insulin sensitivity physiology, brain electrophysiology, medical statistics, R for statistics and data visualisation and programming in python, data science including machine learning, in vivo metabolic phenotype skills and cell biology including 3D stem cell culture. Students will have access to outstanding resources, including cohorts of >5000 patients with rare defects in insulin secretion, a world leading collection of samples for study of pancreas pathology, resources of electronic medical records and biobanks from millions of people and unique resources for studying human development of the pancreas and brain.
Type 2 diabetes is a major public health problem that is responsible for around 90% of all diabetes cases. There are many factors that underlie not only an individual’s risk of developing diabetes, but also their subsequent progression and the efficacy of different treatment options. Precision medicine approaches aim to improve patient care by allowing doctors to use personal information to better understand disease risk in individual patients, and also to inform optimal treatment choices.
This PhD will develop novel Bayesian statistical approaches for tackling various key aspects of effective precision medicine, particularly focused on prediction and treatment selection models (e.g. Jones et al. 2016, Dennis et al. 2018, Dennis et al. 2018). The student will develop new methods to overcome the challenges of modelling complex human data, including key statistical issues such as overfitting. The student will also explore Bayesian adaptive learning techniques to improve predictions for assessing the efficacy of treatments and progression of individual patients that can be updated over time as new data becomes available.
The student will have the opportunity to learn and develop cutting-edge statistical and machine learning methodologies, and work with a world-renowned diabetes research team. They will use existing clinical research data on over 400,000 real world UK patients with Type 2 diabetes as well as data from 1000s of individuals who have participated in randomised drug trials.
The project would suit someone with a background in statistics, machine learning or mathematics. Experience in handling complex real-world data sets is desirable. The student will join a vibrant, diverse and world class interdisciplinary research team, including geneticists, biological scientists, mathematicians, computer scientists and clinicians, all studying aspects of diabetes and related conditions.
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