In our paper "Emergence of power-law distributions in protein-protein interaction networks through study bias", we show that biased research interest in proteins and aggregation of interactions from multiple studies can explain why node degree distributions in PPI networks follow a power law.
In our paper "Spatial cell graph analysis reveals skin tissue organization characteristic for cutaneous T cell lymphoma", we present the Python tool SHouT to quantify tissue heterogeneity based on spatial omics data and use it to identify skin tissue patterns separating CTCL from benign conditions.
In our paper "DysRegNet: Patient-specific and confounder-aware dysregulated network inference towards precision therapeutics", we present a statistical model and Python tool to mine gene expression data for gene dysregulation events in individual samples in comparison to a control cohort.
In our paper "Network medicine-based epistasis detection in complex diseases: ready for quantum computing", we combine combinatorial optimization on graphs with quantum computing to identify SNPs involved in epistatic interactions.
In our paper "Guiding questions to avoid data leakage in biological machine learning applications", we present 7 questions that should be asked to prevent data leakage when constructing machine learning models in biological domains.
In our paper "ZEB1-mediated fibroblast polarization controls inflammation and sensitivity to immunotherapy in colorectal cancer", we investigate how the transcription factor ZEB1 promotes plasticity of cancer-associated fibroblasts that can shield tumours from the immune system. Great collaboration ...
On July 1, Nicolai Meyerhöfer started as a doctoral researcher in the BIONETS lab. He will work on graph neural network models for prediction of tissue-specific protein-protein interactions.
In our joint retreat with the DaiSyBio group (TUM) in Peschiera del Garda, we had inspiring talks and discussions on hot topics and off topics in computational systems biology, ran two hackathons, and engaged in a series of team building events.
In our paper "Efficiently Labeling and Retrieving Temporal Anomalies in Relational Databases", we show how to flag and retrieve temporal inconsistencies in healthcare information systems.
In our paper "Drugst.One — a plug-and-play solution for online systems medicine and network-based drug repurposing", we present a versatile plugin to turn any tool that outputs a list of genes or proteins into a feature rich, drug repurposing web tool with interactive network visualization.
