We are a research group at Heidelberg University. We develop software tools for systems level analysis and mechanistic modeling of molecular and biomedical data.
Our goal is to acquire a functional understanding of the deregulation of signalling networks in disease and to apply this knowledge to develop novel therapeutics. We focus on cancer, heart failure, auto-immune and fibrotic disease. Towards this goal, we integrate big (‘omics’) data with mechanistic molecular knowledge into statistical and machine learning methods. To this end, we have developed a range of tools in different areas of biomedical research, mainly using the programming languages R and Python.
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Home page
R code
Python code
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| BioCypher A unifying framework for biomedical research knowledge graphs | CARNIVAL Causal reasoning to explore mechanisms in molecular networks | CellNOpt Train logic models of signaling against omics data | CollecTRI Collection of Transcriptional Regulatory Interactions |
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| CORNETO Unified framework for network inference problems | COSMOS Mechanistic insights across multiple omics | Decoupler Infer biological activities from omics data using a collection of methods | DoRothEA Transcription factor activity inference |
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| DOT Optimization framework for transferring cell features from a reference data to spatial omics | LIANA Framework to infer inter- and intra-cellular signalling from single-cell and spatial omics | MetalinksDB Database of protein-metabolite and small molecule ligand-receptor interactions | MISTy Explainable machine learning models for single-cell, highly multiplexed, spatially resolved data |
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| ocEAn Metabolic enzyme enrichment analysis | OmniPath Networks, pathways, gene annotations from 180 databases | PHONEMeS Logic modeling of phosphoproteomics | PROGENy Activities of canonical pathways from transcriptomics data |
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More resources: See them in the Resources section of our webpage. Docker: A container with all our tools is available.
