Technology Spotlight
The diversity of backgrounds and research interests in our group is also reflected in the wide range of technologies that we use and develop. The vignettes below provide an overview of tools that were developed by us. Particular areas of expertise are network-based methods, Virtual Reality technology, multi-omics data integration and image analysis. More information on specific projects, links to code and webapps can be found under resources.
Exploring data in Virtual Reality
Our team is pioneering the application of Virtual Reality (VR) technology for exploring large and diverse biological data. On the surface, VR simply drastically increases the amount of information that can be displayed. On a deeper level, and perhaps more importantly, the immersive 3D space also represents the natural environment in which evolution has shaped human cognition. We perceive and interact with the world in 3D, and basic neurological processes of pattern recognition, learning, even social behavior are intimately linked to this fundamental experience. Exploring data in VR thus offers unique opportunities for integrating powerful machine learning with innately human capabilities, e.g. intuition and generalization via experience, incomplete or noisy information.
Multi-media kitchen
We built a multi-media workshop as a central infrastructure for developing and experimenting with new technologies for exploring data. The room is equipped with a range of state-of-the-art 3D technologies. Several Virtual Reality stations enable several users to simultaneously and collaboratively explore a complex dataset in a shared virtual environment. The green screen and custom-built video and audio recording equipment allow us to create mixed reality videos and explore new formats for science communication and remote teaching.
Network cartographs for interpretable visualizations
Networks offer an intuitive visual representation of complex systems. Important network characteristics can often be recognized by eye and, in turn, patterns that stand out visually often have a meaningful interpretation. In conventional network layout algorithms, however, the precise determinants of a node’s position within a layout are difficult to decipher and to control. Here we propose an approach for directly encoding arbitrary structural or functional network characteristics into node positions. We introduce a series of two- and three-dimensional layouts, benchmark their efficiency for model networks, and demonstrate their power for elucidating structure-to-function relationships in large-scale biological networks.
