COGRIM statistics

To access cutting-edge analytics on consensus tools, life science contexts and associated fields, you will need to subscribe to our premium service.


Citations per year

Citations chart

Popular tool citations

chevron_left Gene regulatory network inference chevron_right
Popular tools chart

Tool usage distribution map

Tool usage distribution map

Associated diseases

Associated diseases

COGRIM specifications


Unique identifier OMICS_22849
Alternative name Clustering Of Genes into Regulons using Integrated Modeling
Software type Package/Module
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux, Mac OS, Windows
Programming languages R
Computer skills Advanced
Stability Stable
Maintained Yes



Add your version


  • person_outline Christian Stoeckert <>

Publication for Clustering Of Genes into Regulons using Integrated Modeling

COGRIM in publications

PMCID: 3777940
PMID: 24069219
DOI: 10.1371/journal.pone.0073656

[…] over-expressed gene clusters in hnscc . in a systems biology study, we have also identified 748 potential nf-κb target genes that are functionally associated with hnscc by using an integrative model cogrim . nf-κb and related signaling pathways have served as potential biomarkers and therapeutic targets for hnscc and other human cancers , , . together with investigations […]

PMCID: 4289631
PMID: 25587491
DOI: 10.4172/2157-2518.S7-004

[…] validated these transcription factors, nf-κb, tp53, ap1, stat3 and egr1, in modulation of gene expression in hnscc cells [,,,,]. following this study, we applied a statistical method called cogrim (based on bayesian hierarchical model with gibbs sampling) that was able to integrate heterogeneous data to capture transcription factor-gene associations []. we identified three sets of nf-κb […]

PMCID: 3155518
PMID: 21857910
DOI: 10.1371/journal.pone.0021969

[…] in the literature to integrate gene expression data with other types of genome-wide data to identify targets of transcription factors and to discover network modules. gram , remodiscovery and cogrim , to name a few, are among those which combine gene expression data, chip-chip and motif data to discover regulatory modules. for example, remodiscovery algorithm first detects large modules […]

PMCID: 3072956
PMID: 21426557
DOI: 10.1186/1471-2105-12-82

[…] since there is no ground truth of target genes available for this experiment, we used the functional enrichment of regulatory modules to compare the performance of msd with that of another method, cogrim []. cogrim is derived from a bayesian hierarchical model and implemented using the gibbs sampling technique. cogrim can help infer the activation or inhibition of tfs acting on their target […]

PMCID: 2519161
PMID: 18586698
DOI: 10.1093/bioinformatics/btn332

[…] be considerably obscured by spurious interactions when the number of observations is small or the quality of the data is poor (husmeier, ). several approaches, including gram (bar-joseph et al., ), cogrim (chen et al., ) and remodiscovery (lemmens et al., ), have been developed to infer transcriptional regulatory networks by integrating gene expression data with transcription factor (tf) […]

To access a full list of publications, you will need to upgrade to our premium service.

COGRIM institution(s)
Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA; Center for Bioinformatics, University of Pennsylvania, Philadelphia, PA, USA; Department of Statistics, The Wharton School, University of Pennsylvania, Philadelphia, PA, USA; Department of Genetics, School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
COGRIM funding source(s)
Supported in part by NIH grant U01-DK56947 and a grant from the University of Pennsylvania Research Foundation.

COGRIM reviews

star_border star_border star_border star_border star_border
star star star star star

Be the first to review COGRIM