Biomedical Network Science Lab
Welcome to the Biomedical Network Science Lab!
The Biomedical Network Science (BIONETS) lab investigates molecular disease mechanisms using techniques from network science, combinatorial optimization, and artificial intelligence. We develop algorithms and tools to mine multi-omics data for such mechanisms and to individuate novel strategies for mechanistically grounded drug repurposing and causally effective treatments of complex diseases. We also develop privacy-preserving decentralized biomedical AI solutions, which enable cross-institutional studies on sensitive data. Finally, we are interested in meta-scientific questions such as reproducibility and the impact of data bias on biomedical AI systems.
We are happy to announce that our paper "The edge-preservation similarity for comparing rooted, unordered, node-labeled trees" has just appeared in Pattern Recognition Letters.
In this three-year project, we will develop a network-based software platform for dynamic and explorative analysis of of the CHRIS cohort data. The project will be carried out in collaboration with TUM, Eurac Research, and UNIBZ.
Together with partners from TUM and University of Hamburg, we will develop dimensionality reduction techniques for scRNA-seq data based on inference of differential gene regulatory network. The new methods will be used to investigate CD4 helper T cell exhaustion, a limiting factor in immunotherapy.
Klaudia's poster presentation of our recently published systems medicine validation tool DIGEST won the best poster award at GCB 2022.
Our paper "Querying Temporal Anomalies in Healthcare Information Systems and Beyond" won the best paper award at ADBIS 2022!
