News

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.

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.

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...

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.

BIONETS lab goes digital history: In our paper "On the role of network topology in German-Jewish recommendation letter networks", we analyze the role of professional networks for emigration of German-Jewish academics from Nazi Germany.

Our paper "TF-Prioritizer: a Java pipeline to prioritize condition-specific transcription factors" has just appeared in GigaScience. TF-Prioritizer accepts ATAC, DNase, or ChIP sequencing and RNA sequencing data as input and identifies transcription factors with differential activity.