We develop integrative and interpretable machine learning algorithms that can help clinicians with predictive models and decision support to tailor patients’ care to their unique clinical and genomic traits.🚀
We are a tech research group. 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 using and also have developed a range of tools （Pipelines）in different areas of bio research, mainly using the programming languages R,Python, and Matlab(Web Design).
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 | MISTy Explainable machine learning models for single-cell, highly multiplexed, spatially resolved data |
| |
|
|
BIOC |
 |
 |
 |
 |
| 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 |
| |
 BIOC  PYPI PYPI  CYTO |
|
BIOC |
More resources: See them in the Resources section of our webpage. Docker: A container with all our tools is available.
| Backend: | |
| Frontend: | |
| Database: | |
| DevOps: | |
| Automated test: | |
| Version Control: | |
| Ides: | |
| Other Tools: | |
| Operating Systems: |