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 "Inference of differential gene regulatory networks using boosted differential trees", we present BoostDiff, a tree-based method for identifying differences in transcriptional regulation between two conditions.

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In our paper "Cracking the black box of deep sequence-based protein-protein interaction prediction", we show that the near-perfect performances reported for deep learning methods for PPI prediction can be entirely attributed to data leakage.

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The kick-off meeting for the DyHealthNet project, held in Feldthurns (Italy) from February 1-3, 2024, was a collaborative effort involving the project partners from the University of Bozen-Bolzano, the Eurac Research Institute, the FAU Erlangen, and the TU Munich. The gathering facilitated a compreh...

Category: News