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@saezlab

Saez Lab

Institute for Computational Biomedicine - Julio Saez-Rodriguez's group

Saez Lab

Welcome to Saez Lab!

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.

Resources

Legend:     Home page     R code     Python code     Package     Article       Docs


BioCypher CARNIVAL CellNOpt CollecTRI
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 COSMOS Decoupler DoRothEA
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 LIANA+ MetalinksDB MISTy
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 OmniPath PHONEMeS PROGENy
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.

Popular repositories

  1. decoupleR decoupleR Public

    R package to infer biological activities from omics data using a collection of methods.

    R 166 21

  2. liana liana Public

    LIANA: a LIgand-receptor ANalysis frAmework

    R 157 24

  3. decoupler-py decoupler-py Public

    Python package to perform enrichment analysis from omics data.

    Python 143 20

  4. pypath pypath Public

    Python module for prior knowledge integration. Builds databases of signaling pathways, enzyme-substrate interactions, complexes, annotations and intercellular communication roles.

    Python 128 43

  5. liana-py liana-py Public

    LIANA+: an all-in-one framework for cell-cell communication

    Python 126 15

  6. dorothea dorothea Public

    R package to access DoRothEA's regulons

    R 123 25

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