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Automated risk factor discovery for large-scale biobank data

OPH/23/3

BACKGROUND

Large-scale biobanks have captured lifestyle, medical and epidemiological records on millions of people around the world, unlocking the potential to discover diverse risk factors for common diseases, and improve global health. To date, the investigation of risk factors typically proceeds one disease at a time, with painstaking selection of potential risk factors from among 1,000s of candidates. We aim to automate the process of risk factor discovery using computationally sophisticated Bayesian approaches, and – in consultation with experts in the field – apply them to the analysis of many diseases. This studentship offers the opportunity to identify new risk factors that may point to new interventions for preventing or treating common diseases.

RESEARCH EXPERIENCE, RESEARCH METHODS AND TRAINING

This project offers the student the opportunity to

  1. develop and apply big data approaches to large-scale biobank data, specifically to analyse half a million individuals in the UK Biobank,
  2. develop expertise in computationally sophisticated Bayesian Monte Carlo methods run in a modern high performance computing environment,
  3. develop knowledge and understanding of the lifestyle, medical and epidemiological factors influencing the risk of important common diseases.

FIELD WORK, SECONDMENTS, INDUSTRY PLACEMENTS AND TRAINING

Group members will join weekly lab meetings and attend conferences. Training support is available through a range of courses including in R, python and scientific computing, many through the Medical Sciences Division and the Department for Continuing Education.

PROSPECTIVE STUDENT

We accept academically excellent students with a variety of backgrounds including scientists and clinicians who have graduated with Bachelors or Masters degrees in biology, medicine, statistics, computing and mathematics. The best performing students have an overriding interest and understanding in the biological problems at hand, and they come with, or develop, numerical competence and a natural affinity for computer-based work.

Supervisors

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