GeneSrF specifications

Information


Unique identifier OMICS_14606
Name GeneSrF
Interface Web user 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)
Programming languages Python, R
License Other
Computer skills Basic
Stability Stable
Source code URL https://launchpad.net/genesrf/
Maintained Yes

Documentation


Maintainer


  • person_outline Ramón Diaz-Uriarte <>

Publication for GeneSrF

GeneSrF in publications

 (3)
PMCID: 3562261
PMID: 23148517
DOI: 10.1186/1471-2105-13-298

[…] it is necessary to estimate the number of genes in order to use the svm-rfe which will lead to additional variability on the results., we also compare with the random forest gene selection algorithm genesrf provided by diaz-uriarte []. genesrf was first applied to each dataset to select genes followed by an application of lda, qda, nb, and svm classifiers using the selected genes with loocv. […]

PMCID: 3057009
PMID: 21193901
DOI: 10.1007/s00122-010-1516-1

[…] structure and the amount of genetic differentiation between and within populations was calculated using f statistics (wright ; cockerham , ). in addition for the analysis of metabolite data we used genesrf, a web-based tool and r package to analyze the 5,546 individual lc–ms signals from the 168 accessions that implement individual lcms signals selection and classification using random forests […]

PMCID: 1363357
PMID: 16398926
DOI: 10.1186/1471-2105-7-3

[…] dlda., the microarray and simulated data sets are available from the supplementary material web page []., our procedure is available both as an r package (varselrf) and as a web-based application (genesrf)., project name: varselrf., project home page: , operating system(s): linux and unix, windows, macos., programming language: r., other requirements: linux/unix and lam/mpi for parallelized […]


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GeneSrF institution(s)
Statistical Computing Team, Structural Biology and Biocomputing Programme, Spanish National Cancer Center (CNIO), Madrid, Spain
GeneSrF 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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