Rebekka Burkholz

The paper identifies sex-biased regulation of cancer genes in normal tissues, linking these differences to sex-specific cancer incidence patterns.

PHOENIX uses biologically informed neural ODEs to infer genome-wide regulatory dynamics, demonstrating scalability and interpretability in simulated and real data.

The paper introduces netZoo, an open-source software suite for analyzing biological networks, demonstrating its use on cancer cell line data to identify drug resistance and cancer development factors.

GRAND is a database of gene regulatory network models across human conditions, enabling comparison between biological states and drug response prediction.

Glycowork is a Python package for glycan data science and machine learning, offering tools for annotation, visualization, and analysis of glycan data.

This paper introduces SweetNet, a graph convolutional neural network for glycan sequence representation, showing it outperforms existing methods and predicts organismal traits and viral glycan-binding.

This paper presents a theoretical framework for PANDA, a gene regulatory network inference method, and shows it outperforms state-of-the-art methods in predicting transcription factor binding.