We are a research group at Heidelberg University and the European Bioinformatics Institute, part of the European Molecular Biology Laboratory (EMBL-EBI).
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.
Legend:
Home page
R code
Python code
Package
Article
Docs
 |
 |
 |
 |
|---|---|---|---|
| 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 |
| PYPI |
BIOC |
BIOC |
|
 |
 |
 |
 |
| 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 |
| PYPI |
BIOC |
PYPI BIOC |
|
 |
 |
 |
 |
| 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 | MetaProViz Metabolomics functional analysis and visualization |
| |
|
|
|
 |
 |
 |
 |
| MISTy Explainable machine learning models for single-cell, highly multiplexed, spatially resolved data | NetworkCommons Context specific networks from omics data and prior-knowledge | ocEAn Metabolic enzyme enrichment analysis | OmniPath Networks, pathways, gene annotations from 180+ databases |
| BIOC |
PYPI |
|
BIOC PYPI PYPI  CYTO |
 |
 |
||
| PHONEMeS Logic modeling of phosphoproteomics | PROGENy Activities of canonical pathways from transcriptomics data | ||
| |
BIOC |
More resources: See them in the Resources section of our webpage. Docker: A container with all our tools is available.