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iRF specifications

Information


Unique identifier OMICS_24054
Name iRF
Alternative name iterative Random Forest
Software type Package/Module
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux, Mac OS, Windows
Programming languages R
License GNU General Public License version 2.0
Computer skills Advanced
Version 2.0.0
Stability Stable
Requirements
methods, RColorBrewer, Matrix, Rcpp, dplyr, data.table, MASS, foreach, doParallel, rgl, R(≥3.1.2), AUC
Source code URL https://cran.r-project.org/src/contrib/iRF_2.0.0.tar.gz
Maintained Yes

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Maintainers


  • person_outline Karl Kumbier <>
  • person_outline Bin Yu <>
  • person_outline Sumanta Basu <>
  • person_outline James Brown <>

Publication for iterative Random Forest

iRF in publications

 (8)
PMCID: 5828575
PMID: 29351989
DOI: 10.1073/pnas.1711236115

[…] m.m.h., princess margaret cancer center; and d.j., oak ridge national laboratory., 1s.b. and k.k. contributed equally to this work., we developed a predictive, stable, and interpretable tool: the iterative random forest algorithm (irf). irf discovers high-order interactions among biomolecules with the same order of computational cost as random forests. we demonstrate the efficacy of irf […]

PMCID: 5392111
PMID: 28469446
DOI: 10.1177/1178630217699399

[…] not used in the training (the validation dataset), the correlation coefficient is actually even better than that for the training dataset. these scatter diagrams show the remarkable ability of the iterative random forest approach to accurately estimate the airborne pollen count., shows the relative importance of the 20 most important variables for estimating pollen count. the random forest […]

PMCID: 5042816
PMID: 27059145
DOI: 10.1002/art.39706

[…] metry data. analyses were performed using graphpad prism 6.0 for windows. heatmaps were generated using an r heatmap package (version 3.0.3). to identify the most important classifying variables, the iterative random forest algorithm described by genuer et al was implemented in randomforest (version 4.6‐10) r package. random forest parameters were set to ntry = (number of variable)∧1/2, ntree = 1 […]

PMCID: 4909287
PMID: 27304923
DOI: 10.1371/journal.pone.0157330

[…] of 0.03 which indicates a statistically significant different between esvm-rfe over svm-rfe., we have also compared the performance of esvm-rfe to random forest based approach such as balanced iterative random forest (birf) []. we have applied birf on the childhood leukaemia dataset using the same training and test samples. similarly to the authors in [], we randomly split the dataset, […]

PMCID: 4368049
PMID: 25861214
DOI: 10.4137/CIN.S22371

[…] of genes that are relevant to a trait of interest is crucial and plays a vital role for building a successful gene expression similarity measurement model., the feature selection algorithm balanced iterative random forest (birf) is initially applied to the training cases to select relevant features. on the basis of the performance of the birf reported in the study by anaissi et al, birf […]


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iRF institution(s)
Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, NY, USA; Statistics Department, University of California, Berkeley, CA, USA; Centre for Computational Biology, School of Biosciences, University of Birmingham, Birmingham, UK; Molecular Ecosystems Biology Department, Lawrence Berkeley National Laboratory, Berkeley, CA, USA; Preminon, LLC, Las Vegas, NV, USA; Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA
iRF funding source(s)
Supported by grants NHGRI U01HG007031, ARO W911NF1710005, ONR N00014- 16-1-2664, DOE DE-AC02-05CH11231, NHGRI R00 HG006698, DOE (SBIR/STTR) Award DE-SC0017069, DOE DE-AC02-05CH11231, NSF DMS-1613002, the Center for Science of Information (CSoI), a US NSF Science and Technology Center, under grant agreement CCF-0939370 and the National Library Of Medicine of the NIH under Award Number T32LM012417.

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