Institution identification

PURI

https://redirect.ema.europa.eu/resource/37910

Institution ID

37910

Institution full name and acronym

University of Edinburgh (UofE)

Institution countries

United Kingdom

Type of institution

Educational Institution
Hospital/Clinic/Other health care facility

Institution role

Data holder
Researcher

Institution website

ENCePP partner

Yes
Institution description
The University of Edinburgh is one of the world’s leading research universities, ranked fourth in the UK for research power. Based on our established excellence in discovery science and ambitions for our research, we are aiming to make a significant difference in research areas such as shaping the future of health and care, and harnessing data, digital and artificial intelligence. The Institute of Genetics and Cancer (IGC) and Usher Institute sit within the College of Medicine and Veterinary Medicine (CMVM). The IGC constitutes one of the largest aggregates of human molecular genetics and biology research capacity in the UK and brings together the scientific expertise, technology and support services needed to maximise scientific discovery, enable the rapid translation of basic scientific discoveries into new treatments/clinical guidelines/innovative products  that have a significant impact on society. The Usher Institute brings together colleagues from public health, primary care, biomedical and social sciences backgrounds with expertise in epidemiology, statistics/modelling, social policy, economics and medicine.
Our research uses large scale population-based approaches to further our understanding of the pathogenesis and prevention of common diseases and related complications. We are harnessing the increasing availability of e-health record data (EHR) and new technologies for acquiring high dimensional molecular ‘omics data. We quantify contemporaneous absolute risks of complications, evaluate risk factors for complications, and build prediction models using e-health record data. These data are used to inform current health care policy and clinical practice. We also use large bioresources linked to these data to quantify the marginal improvement gained by genetics and biomarker panels beyond that achieved by EHR data. Our aim is that these prediction algorithms will be incorporated into prediction tools for clinical and self-management, and clinical trial design.
Institution details
Experience with collecting data directly from individual patients or respondents:
Yes
Interest in carrying out research that is funded by pharmaceutical companies:
Yes

Contact