Data driven technologies

Programme stream(s): Early detection / diagnosis and prognosis

Chair: David Weller, University of Edinburgh, UK
Speaker: Andrew Morris, Health Data Research UK
Speaker: Eva Morris, University of Leeds, UK
Speaker: Richard Martin, University of Bristol, UK

4:35 pm-6:35 pm

Room: Carron

This session will provide an overview of the use of data science in cancer risk assessment, prevention, screening, early diagnosis and survivorship. Over the last ten to fifteen years there have been huge advances in data technology and cancer intelligence systems in the UK and around the world they are rapidly becoming more refined and more useful in clinical medicine and public health. This session will examine how data science is transforming the way we approach cancer control; techniques such as linkage and big data analysis, machine learning and artificial intelligence will be included, and the session will provide some practical examples of how we can use collect and use data more effectively.

Options and Opportunities for Health Data Science
Speaker: Andrew Morris
Affiliation: Health Data Research UK


Healthcare is arguably the last major industry to be transformed by the information age.  Deployments of information technology have only scratched the surface of possibilities for the potential influence of information and computer science on the quality and cost-effectiveness of healthcare. In this talk, the vision, objectives and scientific strategy of HDR UK will be discussed; specifically, the opportunities provided by computer science and “big data” to transform health care delivery models.  Examples will be given from nationwide research and development programmes that integrate electronic patient records with biologic and health system data. Two themes will be explored; specifically:How the size of the UK (65M residents), allied to a relatively stable population and unified health care structures facilitate the application of health informatics to support nationwide quality-assured provision of care.How population-based datasets and disease registries can be integrated with biologic information to facilitate (i) epidemiology; (ii) drug safety studies; (iii) enhanced efficiency of clinical trials through automated follow-up of clinical events and treatment response; and, (iv) the conduct of large-scale genetic, pharmacogenetics, and family-based studies essential for precision medicine

Mining genomic data to inform cancer prevention & treatment strategies
Speaker: Richard Martin
Affiliation: University of Bristol


Genome-wide association studies (GWAS), based on large research consortia and population biobanks, test associations of human traits with genetic variation across millions of loci in tens of thousands of individuals. Since 2005, >3540 GWAS publications have reported nearly 70,000 SNP-phenotype associations. In the basic sciences, high-throughput assays (measuring the genome, methylome, metabolome, transcriptome, and proteome) have enriched understanding of genome function.

These diverse studies provide an un-paralleled resource for understanding the causal underpinnings of human traits. Mendelian randomization (MR) uses genetic instrumental variables to test the causal effects of potentially modifiable exposures or novel drug targets on outcomes. MR has become a major tool in: understanding cancer and CVD aetiology and mechanisms; predicting the benefits of novel therapies and their combinations; and the identification of adverse drug effects and drug repurposing opportunities.  We can readily integrate epigenomics, transcriptomics, metabolomics and proteomics with data on hundreds of diseases in our openly accessible MR-Base database (, mapping the influence of these molecular traits on complex human diseases.

I will discuss the use of MR and MR-Base as an open resource to allow the international scientific community to more effectively and rapidly capitalize on the opportunities for developing new public health and clinical interventions provided by novel high-throughput assays and causal analysis methods to enhance disease prevention and therapeutic research.