RFECS pipeline

RFECS specifications

Information


Unique identifier OMICS_06230
Name RFECS
Alternative name Random Forest based Enhancer identification from Chromatin States
Software type Package/Module
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux
Computer skills Advanced
Stability Stable
Maintained Yes

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Maintainer


  • person_outline Bing Ren <>

Publication for Random Forest based Enhancer identification from Chromatin States

RFECS IN pipelines

 (6)
2016
PMCID: 5058335
PMID: 27727234
DOI: 10.1038/sdata.2016.90

[…] of the transcription factors cbp, npas4 and creb to build the training dataset for the crm detection., active cell type-specific crms were predicted using a modified random forest classifier ‘rfecs’41 and naïve chromatin modification data. since rfecs is a supervised machine learner it requires a training dataset to learn the parameters for future classification. due to the lack […]

2016
PMCID: 5058335
PMID: 27727234
DOI: 10.1038/sdata.2016.90

[…] build the training dataset for the crm detection., active cell type-specific crms were predicted using a modified random forest classifier ‘rfecs’41 and naïve chromatin modification data. since rfecs is a supervised machine learner it requires a training dataset to learn the parameters for future classification. due to the lack of genome-wide crm, transcription factor binding, and chromatin […]

2016
PMCID: 5058335
PMID: 27727234
DOI: 10.1038/sdata.2016.90

[…] creation of the training dataset and subsequently explain the training, prediction, and validation of the machine learner. training dataset. in order to build positive and negative crm sets for rfecs training (parameter estimation) published cell culture data for tfs (cbp, npas4 and creb) and chromatin modifications (h3k4me1, h3k4me3, h3k27ac and h3k27me3) was used. a high confidence […]

2016
PMCID: 5058335
PMID: 27727234
DOI: 10.1038/sdata.2016.90

[…] able efficiently learn negative regions with little to no hptm signal and very high promoter hptm signal. , active cell type-specific crms were predicted using a modified random forest classifier ‘rfecs’41 and naïve chromatin modification data. since rfecs is a supervised machine learner it requires a training dataset to learn the parameters for future classification. due to the lack […]

2016
PMCID: 5058335
PMID: 27727234
DOI: 10.1038/sdata.2016.90

[…] no hptm signal and very high promoter hptm signal. , active cell type-specific crms were predicted using a modified random forest classifier ‘rfecs’41 and naïve chromatin modification data. since rfecs is a supervised machine learner it requires a training dataset to learn the parameters for future classification. due to the lack of genome-wide crm, transcription factor binding, and chromatin […]

RFECS institution(s)
Ludwig Institute for Cancer Research, University of California at San Diego, La Jolla, CA, USA

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