Human health and disease emerge from complex interactions across molecular, cellular, and population scales.
We use AI to integrate and analyse heterogeneous data across all layers of health - from genomics and medical imaging to clinical records and population studies - to develop interpretable and robust models that support medical decision-making. Building on expertise in machine learning and biomedicine, we strive to improve health outcomes through earlier diagnosis, better risk assessment, and more precise prevention and treatment.
Life is complex, and complicated: living systems are composed of millions of different molecular entities, interacting in space and time to create emergent properties.
We use AI to design and execute sophisticated and large-scale experiments and studies using modern biotechnologies and instruments; organise, navigate and reason with massive amounts of facts and data; and create interpretable, predictive, scientifically founded models of biological systems at scales from nano- to Megameters.
Going to smaller and smaller distances, the physical laws behind Life turn into the fundamental laws behind chemistry and physics.
From a scientific AI perspective, the collisions of elementary particles, the astrophysical evolution of our Universe, the dynamics of molecules and cells, and the collective behavior of tissues, organisms, and even ecosystems can be understood with similar numerical methods. Our unifying goal is to understand fundamental structures and principles across scales from complex, high-dimensional data. We develop AI-methods to uncover the underlying fundamental mechanisms by combining theory, simulation, and optimal analysis of large-scale experimental and observational data. The requirements of the different research fields in terms of accuracy, uncertainty quantification, robustness, and interpretability challenge our methods and tools and drive our interdisciplinary program on AI in the sciences.
Rare genetic variants can have large effects on human health, but their impacts are difficult to detect with standard methods. We developed DeepRVAT, a rare variant analysis framework for modeling disease risk and biological traits through the integration of rare variant annotations.
Using deep set networks, DeepRVAT learns gene-level impairment scores from variant annotations (e.g. conservation and splicing-related scores), boosting power for association testing in large biobank datasets. Compared with existing approaches, it increases gene discovery and improves identification of individuals with high genetic risk in population-scale studies - supporting deeper insights into disease biology and more informative genetic risk stratification.
