mRMRe specifications

Information


Unique identifier OMICS_19862
Name mRMRe
Software type Package/Module
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux, Mac OS, Windows
Programming languages C, R
License Artistic License version 2.0
Computer skills Advanced
Version 2.0.7
Stability Stable
Requirements
methods, survival, igraph
Source code URL https://cran.r-project.org/src/contrib/mRMRe_2.0.7.tar.gz
Maintained Yes

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Maintainer


  • person_outline Benjamin Haibe-Kains <>

Publication for mRMRe

mRMRe in publications

 (3)
PMCID: 5522256
PMID: 28611293
DOI: 10.18632/oncotarget.18186

[…] adjacent tissues, we applied mrmr method, which ranks the features according to their relevance to the target phenotypes minus the redundancy between the features []. in our study, we used r package mrmre to implement mrmr []. in mrmre, both relevance and redundancy are quantified by mutual information (mi):, where x and y are two variables to be tested, p(x) and p(y) are the marginal […]

PMCID: 5590809
PMID: 28731408
DOI: 10.7554/eLife.23421.027

[…] a supervised feature selection algorithm followed by a stepwise cox regression approach on dataset1: first, we employed the minimum-redundancy maximum-relevance (mrmr) algorithm implemented in the ‘mrmre’ r package () on all radiomic features with respect to os to select a non-redundant, highly informative ranked set of complementary features. next, we trained cox models incrementally, adding […]

PMCID: 3823927
PMID: 24244287
DOI: 10.1371/journal.pone.0078057

[…] ‘relevance’ to target (phenotype) and ‘redundancy’ between features . both relevance and redundancy are quantified using mutual information (mi). in this study, mrmr was realized using a r package ‘mrmre’ , in which mi is estimated as,(1)where i and ρ represent the mi and the correlation coefficient between variables x and y, respectively., let y and x = {x1, …, xn} be the input variable […]


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mRMRe institution(s)
Bioinformatics and Computational Biology Laboratory, Integrative Systems Biology Axis, Institut de recherches cliniques de Montreal, Montreal, Quebec, Canada; Machine Learning Group, Department of Computer Science, Universite Libre de Bruxelles, Brussels, Belgium
mRMRe funding source(s)
Supported by B.H.K’s start-up funds and Belgian French Community ARC funding.

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