varSelRF protocols

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varSelRF statistics

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varSelRF specifications


Unique identifier OMICS_14607
Name varSelRF
Software type Package/Module
Interface Command line interface
Restrictions to use None
Input data The gene expression data, the class labels.
Output data The bootstrapped estimates of prediction error rate, assessments of the stability of the solutions, clickable tables link to additional information for each gene (GO terms, PubMed citations, KEGG pathways).
Operating system Unix/Linux
Programming languages Python, R
License GNU General Public License version 3.0, GNU General Public License version 2.0
Computer skills Advanced
Version 0.7-8
Stability Stable
parallel, randomforest, R(≥2.0.0)
Source code URL
Maintained No



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Publication for varSelRF

varSelRF in pipeline

PMCID: 4172658
PMID: 25247789
DOI: 10.1371/journal.pone.0107801

[…] algorithm implemented in the r sva package , . finally, the genes with low variability across the samples were eliminated., depicts the general schema used to evaluate and compare our method with varselrf and boruta. after having randomly selected and left aside the 30% of the data, the remaining 70% of the data was used to generate the sets of selected, relevant features (or genes). since […]

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varSelRF in publications

PMCID: 5844673
PMID: 29522576
DOI: 10.1371/journal.pone.0194212

[…] was constructed from the matrix after variable selection; in this procedure, a combination of peaks yielding the smallest out-of-bag error rate was selected by an algorithm implemented in the varselrf package []. this algorithm iteratively fitted random forests to the data, building a new forest after discarding a fraction of peaks with the smallest variable importance. the graphical user […]

PMCID: 5840771
PMID: 29510676
DOI: 10.1186/s12885-018-4104-4

[…] assigned as training sets by computer-generated random numbers. random forest algorithm was used to identify protein signatures in the training sets []. the procedure was implemented using the r varselrf package with parameters “ntree = 5000, ntreeiterat = 2000, vars.drop.frac = 0.2”, which was built upon the randomforest package [, ]. the set of proteins with the smallest out-of-bag error […]

PMCID: 5789307
PMID: 29324700
DOI: 10.3390/ani8010012

[…] systematically assess the usefulness and identify the most important features for discriminating different activities, rf ranking of importance was performed. the r libraries ‘randomforest’ [] and ‘varselrf’ [] were used to identify the relative importance of the fourteen features based on their gini index, which is used to measure the error across the rf ensemble of trees. within […]

PMCID: 5729289
PMID: 29237416
DOI: 10.1186/s12885-017-3821-4

[…] linkage., the sub-modules with the most importance and optimal predictive performance for the identified sub-groups were defined by the random forest feature selection algorithm using r package “varselrf” [], with the following parameters: 5000 trees in the first forest, 3000 trees in the iterative forests, and excluding 20% of variables at each iteration. the final solution was selected […]

PMCID: 5718403
PMID: 29211805
DOI: 10.1371/journal.pone.0188408

[…] predictor variables. tentative identification of the peaks was made using the nist mass spectrum data base (). we conducted the rf analysis in several stages using the packages randomforest [] and varselrf [] in r version 3.1.2 []. first, we conducted an rf classification analysis for all six groups (locally-induced, systemically-induced, and control for both leaves and flowers) and visually […]

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varSelRF institution(s)
Statistical Computing Team, Structural Biology and Biocomputing Programme, Spanish National Cancer Center (CNIO), Madrid, Spain
varSelRF funding source(s)
This work was supported by Fundación de Investigación Médica Mutua Madrileña and Project TIC2003-09331-C02-02 of the Spanish Ministry of Education and Science (MEC) and partially supported by the Ramón y Cajal programme of the Spanish MEC.

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