Data Science for Population-scale Registry and Biobank Data
Our group is interested in finding new ways to early identify common preventable diseases. To do that we develop novel statistical and deep learning approaches and apply them to millions of health information from electronic health record/national health registries. We then integrate registry-based information with genetic information from large biobank-based studies (e.g. FinnGen) to help identify groups of individuals that can most benefit from existing pharmacological interventions. Finally, we aim to implement these approaches in the clinic and evaluate their cost-effectiveness.
We are also interested in using trans-national Scandinavian registries to ask basic question about human nature/nurture and evolution. For example, we are interested in understanding which diseases are currently under strongest selection and if we can see the impact of selection within large-scale genetic data.