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 "Demographic confounders distort inference of gene regulatory and gene co-expression networks in cancer", we show that age and sex are important confounders for GRN and GCN inference.
In our paper "Federated singular value decomposition for high-dimensional data", we present federated privacy-aware SVD algorithms for both horizontal and vertical cross-silo data distribution scenarios.
In our paper "The specific DNA methylation landscape in focal cortical dysplasia ILAE type 3D", we show that histopathological epilepsy subtypes have distinct DNA methylation profiles.
We're happy to announce that Anne has received funding from FAU's Emerging Talents Initiative for her project "CAB: Carbon-aware bioinformatics". She'll develop tools to automatically shift CO2-intensive computations to time slots when green energy is available.
Our new web tool ROBUST-Web (https://robust-web.net/) for study-bias-aware disease module mining in PPI networks has been published in Bioinformatics.