Biomedical Network Science Lab

The Biomedical Network Science (BIONETS) Lab investigates molecular mechanisms, using techniques from network science, (graph-based) AI and combinatorial optimization. We develop algorithms, AI models, and software tools to mine omics data for such mechanisms, with the aim to better understand cellular pathways and, ultimately, pave the way for targeted 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.

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The Bionets group is now on LinkedIn! Feel free to follow us for news and scientific updates.

We went on a joint scientific retreat with the DaiSyBio group at TUM’s Science & Study Center in Raitenhaslach, Burghausen. The two days were packed with great scientific exchange that will keep us busy with new ideas for months to come! We kicked things off with a joint poster session, held focused discussions in a…

Judith Bernett has been awarded the Best Scientific Contribution Award by the German Association for AI in Medicine (KIMED) at the BAIOSPHERE Medical 2026 conference in Erlangen for her publication Critical evaluation of drug response prediction models with DrEval. We had a strong presence at this year’s BAIOSPHERE Medical conference, where around 200 researchers and…

Bionets has attended the RECOMB 2026 conference in Thessaloniki https://recomb.org/recomb2026/.

“Critical evaluation of drug response prediction models with DrEval” has been published in Nature Communications. ML-based cancer cell line drug response models are well-motivated, and significant research effort has gone into developing complex modeling approaches (over 100 papers in 2025). The problem: under rigorous evaluation, we found none that actually works: Most are published based…

On April 1st, Dominik Pysch joined the BIONETS Lab as a new PhD candidate. He will work on graph-based modelling of tissue inflammation in the context of FAU’s and UKER’s new CRC CASCAID.

We are very happy that, on March 1st, Judith Bernett joined the BIONETS Lab as a new research associate. Judith will work on ML models for context-specific filtering of protein-protein interaction networks in the context of the CoBiNet project (https://cobinet.ai).

More than 30 researchers and project partners gathered in Erlangen for the DyHealthNet workshop on March 19-20. Within these two days, several interesting presentations and discussions from professionals in the field of population cohorts took place, including researchers from the SHIP and FinnGen study, and the Pan-Canadian Genome Library. We also presented our experiences from…

We are very happy to announce the birth of AIBeez – “Association for the Promotion of Artificial Intelligence in Health and Engineering Sciences in Erlangen e.V.” New members who want to support AI research for biomedicine in Erlangen are very welcome!