Tom Gaunt is Professor of Health and Biomedical Informatics and MRC Investigator in the MRC Integrative Epidemiology Unit at the University of Bristol. He leads a multidisciplinary team of health data scientists developing novel approaches to investigating disease mechanisms and identifying intervention targets. He co-leads the BRC’s translational data science theme.

Professor Gaunt co-leads the development of key open research data resources and software platforms, including OpenGWAS and EpiGraphDB, used by thousands of researchers worldwide. His team collaborate with pharmaceutical companies to use these tools and resources to identify novel intervention targets and repurposing opportunities. They also develop innovative approaches to data integration and mining using AI, supporting the systematic identification of risk factors and intervention targets for multiple diseases.

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Biomarkers for screening and diagnosing lung cancer

In the UK, only 15 per cent of people diagnosed with lung cancer will still…

Theme Translational data science

Workstream Omics for prediction and prognosis

Data driven approaches to drug target prioritisation

Despite more money going towards developing drugs, the success rate of getting new drugs to…

Theme Translational data science

Workstream Genetic evidence to prioritise intervention

Using Mendelian randomization to improve how drugs are tested for different populations

Randomised controlled trials (RCTs) are the gold standard for testing health interventions. However, they…

Theme Translational data science

Workstream Genetic evidence to prioritise intervention

Developing a genetic data platform for use in pharmaceutical testing

OpenGWAS is one of the largest and most used genetic research databases in the…

Theme Translational data science

Workstream Genetic evidence to prioritise intervention

Using genetics to improve how drug targets are identified

Many modern drugs target very specific biological processes. When a drug is being developed,…

Theme Translational data science

Workstream Genetic evidence to prioritise intervention

Exploring inflammation as a driver for post-operative complications

One in seven patients develop a serious medical problem after surgery. These types of complications…

Theme Translational data science

Workstream Omics for prediction and prognosis

Investigating new approaches to drug development using human genetics

Developing new drugs is an important part of improving our ability to treat disease. To…

Theme Translational data science

Workstream Genetic evidence to prioritise intervention

Exploring the link between obesity and heart failure using genetics

Heart failure is a condition that develops when the heart unable to pump blood around…

Theme Translational data science

Workstream Genetic evidence to prioritise intervention