In our paper "SwitchTFI: identifying transcription factors driving cell differentiation", we present an algorithm to infer regulatory mechanisms that drive the transition from progenitor to offspring cells from scRNA-seq data.
We are very excited to announce that the DFG funds the new CRC "Cellular and Systems Control of Autoimmune Disease" (CASCAID). Together with the lab of Stefan Uderhardt, we will develop graph-based models of histological healing that quantify the target of successful treatment of autoimmune diseases...
In our paper "NApy: Efficient Statistics in Python for Large-Scale Heterogeneous Data with Enhanced Support for Missing Data", we present a Python package with Numba and OpenMP-powered C++ backends for fast and memory-efficient statistical testing in the presence of missing data.
In our paper "Deep learning models for unbiased sequence-based PPI prediction plateau at an accuracy of 0.65" we show that usage of ESM-2 embeddings boosts performance in out-of-distribution PPI prediction to around 0.65 independently of model architecture.
In our paper "Inference of differential kinase interaction networks with KINference", we present the R tool KINference, which can be used to identify kinase-substrate links that are differentially active between two conditions.
In our paper "Data splitting to avoid information leakage with DataSAIL", we present an algorithm and Python package that facilitates leakage-reduced data splitting to enable realistic evaluation of ML models that are intended to be used in out-of-distribution scenarios.
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.
