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@MCBRLab

MEL Computational Biology Research Lab

MCBRLab utilizes computer science algorithms to solve biology-related problems, develops bioinformatics software, and builds Bio-cloud computing platforms.

Hello 🌎, This is MCBRLab

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.🚀

Saez Lab

Welcome to MCBRLab!

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 have developed a range of tools (Pipelines)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.

Languages and Tools:

Backend:
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Operating Systems:

MCBRLab

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  1. lncRNA-pipeline lncRNA-pipeline Public

    Forked from czheluo/lncRNA-pipeline

    lncRNA analysis pipeline

    Perl

  2. awesome-r-dataviz awesome-r-dataviz Public

    Forked from krzjoa/awesome-r-dataviz

    Curated resources about Data Visualization, Drawing & Publishing in R

    R

  3. gohugo.org gohugo.org Public

    Forked from coderzh/gohugo.org

    Site for http://www.gohugo.org/

    HTML

  4. groovy groovy Public

    Forked from apache/groovy

    Apache Groovy: A powerful multi-faceted programming language for the JVM platform

    Java

  5. hugo-book hugo-book Public

    Forked from alex-shpak/hugo-book

    Hugo documentation theme as simple as plain book

    HTML

  6. scvelo scvelo Public

    Forked from theislab/scvelo

    RNA Velocity generalized through dynamical modeling

    Python

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