Isabella Friis Jørgensen
Novo Nordisk Foundation Center for Protein Research, University of Copenhagen
The Brunak Group aims for understanding multi-morbidity disease progression patterns and their relation to treatment events. The group integrates heterogeneous life science data from the molecular and clinical domains and is also engaged in methodology of translational utility, such as techniques of relevance within precision medicine.
The Brunak Group has specific interest in genes and proteins, which play a role in several diseases, genes that may rationalize clinically observed patterns of multi-morbidity, or be of interest in relation to treatment strategies in the domain of chronic pathology.
The group aims for discriminating between treatment-related disease correlations and other comorbidities, stratifying patients not only from their genotype but also on phenotypic data from resources such as clinical descriptions in electronic medical records.
“Together with our secure supercomputing infrastructure, that is designed to handle population-wide data from Denmark and other countries, our goal is to complement classical epidemiology towards disease-spectrum wide analyses in a lifelong perspective, that can take events separated by long time periods into account,” says Professor and Group Leader Søren Brunak.
The human genome, proteome variation and personalized medicine are themes with a strong focus in the group. In particular the ranking of treatment options and the reduction of patient-specific adverse drug reactions. Data integration and machine learning methods development in the big biomedical data domain is a major theme, as is the design of supercomputing infrastructure and private cloud solutions needed for person-sensitive data integrity.
From Danish population-wide health data, we have developed a disease trajectory concept that can stratify patients according to longitudinal patterns rather than conventional subgroups of a single disease. The concept can, for example, estimate the mortality risk from a long-term prehistory of a single diagnosis. The approach can aggregate long and short timescales, for example combining 20-40 years of disease history with high-frequency data from short timescales from a single admission.