varSelRF statistics

info info

Citations per year

info

Popular tool citations

chevron_left Gene expression classification chevron_right
info

Tool usage distribution map

Tool usage distribution map
info info

Associated diseases

Associated diseases
Want to access the full stats & trends on this tool?

Protocols

varSelRF specifications

Information


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
Requirements
parallel, randomforest, R(≥2.0.0)
Source code URL https://cran.r-project.org/src/contrib/varSelRF_0.7-8.tar.gz
Maintained Yes

Download


download.png

Versioning


No version available

Documentation


Maintainer


  • person_outline Ramón Diaz-Uriarte

Publication for varSelRF

varSelRF citations

 (42)
library_books

Rapid and easy detection of low level resistance to vancomycin in methicillin resistant Staphylococcus aureus by matrix assisted laser desorption ionization time of flight mass spectrometry

2018
PLoS One
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 […]

library_books

Signaling protein signature predicts clinical outcome of non small cell lung cancer

2018
BMC Cancer
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 […]

library_books

Predicting Lameness in Sheep Activity Using Tri Axial Acceleration Signals

2018
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 […]

library_books

A network based predictive gene expression signature for adjuvant chemotherapy benefit in stage II colorectal cancer

2017
BMC Cancer
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 […]

library_books

Leaf herbivory imposes fitness costs mediated by hummingbird and insect pollinators

2017
PLoS One
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 […]

library_books

Maize Cropping Systems Mapping Using RapidEye Observations in Agro Ecological Landscapes in Kenya

2017
PMCID: 5713137
PMID: 29099780
DOI: 10.3390/s17112537

[…] that achieved significant accuracies from the important variables returned by the rf classification model using the oob error rate, we used the rf backward feature elimination method using the “varselrf” package [] in the r statistical software [] for level 2 of distinguishing the two maize cropping systems. to select the most relevant spectral variables without any over-fitting, a .632+ […]


Want to access the full list of citations?
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.

varSelRF reviews

star_border star_border star_border star_border star_border
star star star star star

Be the first to review varSelRF