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.
In our paper "Network medicine-based epistasis detection in complex diseases: ready for quantum computing", we combine combinatorial optimization on graphs with quantum computing to identify SNPs involved in epistatic interactions.
In our paper "Guiding questions to avoid data leakage in biological machine learning applications", we present 7 questions that should be asked to prevent data leakage when constructing machine learning models in biological domains.
In our paper "ZEB1-mediated fibroblast polarization controls inflammation and sensitivity to immunotherapy in colorectal cancer", we investigate how the transcription factor ZEB1 promotes plasticity of cancer-associated fibroblasts that can shield tumours from the immune system. Great collaboration ...
On July 1, Nicolai Meyerhöfer started as a doctoral researcher in the BIONETS lab. He will work on graph neural network models for prediction of tissue-specific protein-protein interactions.
In our joint retreat with the DaiSyBio group (TUM) in Peschiera del Garda, we had inspiring talks and discussions on hot topics and off topics in computational systems biology, ran two hackathons, and engaged in a series of team building events.
