Human Proteome Variation — Rasmussen Group

The Rasmussen Group focus on computational analysis of large-scale human genome and proteome variation data and couple these with microbiome, health, clinical and registry data. We use these data to generate basic molecular understanding of human health that can guide the development of precision medicine. We therefore work with multi-omics data from the many layers of a cell such as genomics, transcriptomics, proteomics, metabolomics and more to fully characterize the molecular changes in health and disease. Additionally, we have an increasing focus on adding hospital and registry data to define clinically relevant outcomes and their molecular associations.

To investigate such complex biological systems we develop and apply novel bioinformatics and deep learning models for both predictive and investigative analyses. A critical aspect of these approaches is therefore their ability to integrate multiple data types and how they can be used to understand and predict diagnosis or treatment outcomes from all data simultaneously. We try to achieve this by developing state-of-the-art supervised, self-supervised and unsupervised deep learning approaches. Recently, a key focus area of the group has been the development of generative deep learning models for combining molecular and other health measurements and use these to discover new biological hypotheses.