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@saezlab

Saez Lab

Julio Saez-Rodriguez's group @ EMBL-EBI and Heidelberg University

Saez Lab

Welcome to Saez Lab!

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.

Resources

Legend:    Image  Home page    Image  R code    Image  Python code    Image  Package    Image  Article    Image  Image  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
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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
Image  Image PYPI  Image Image  Image  Image BIOC  Image  Image  Image  Image Image  Image  Image PYPI  Image  Image BIOC  Image Image  Image  Image  Image  Image  Image  Image
DOT LIANA+ MetalinksDB MetaProViz
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
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MISTy NetworkCommons ocEAn OmniPath
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
Image  Image  Image BIOC  Image Image  Image  Image PYPI  Image Image  Image  Image Image  Image  Image  Image BIOC  Image  Image  Image PYPI  Image  Image  Image PYPI  Image  Image CYTO  Image  Image  Image
PHONEMeS PROGENy
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.

Popular repositories Loading

  1. decoupleR decoupleR Public

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

    R 281 29

  2. liana-py liana-py Public

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

    Python 259 28

  3. liana liana Public

    LIANA: a LIgand-receptor ANalysis frAmework

    R 227 38

  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 156 49

  5. dorothea dorothea Public

    R package to access DoRothEA's regulons

    R 154 29

  6. OmnipathR OmnipathR Public

    R client for the OmniPath web service

    R 151 21

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