mRMRe

MOTIVATION: Feature selection is one of the main challenges in analyzing
high-throughput genomic data. Minimum redundancy maximum relevance (mRMR) is a
particularly fast feature selection method for finding a set of both relevant and
complementary features. Here we describe the mRMRe R package, in which the mRMR
technique is extended by using an ensemble approach to better explore the feature
space and build more robust predictors. To deal with the computational complexity
of the ensemble approach, the main functions of the package are implemented and
parallelized in C using the openMP Application Programming Interface. RESULTS:
Our ensemble mRMR implementations outperform the classical mRMR approach in terms
of prediction accuracy. They identify genes more relevant to the biological
context and may lead to richer biological interpretations. The parallelized
functions included in the package show significant gains in terms of run-time
speed when compared with previously released packages. AVAILABILITY: The R
package mRMRe is available on Comprehensive R Archive Network and is provided
open source under the Artistic-2.0 License. The code used to generate all the
results reported in this application note is available from Supplementary File 1.
CONTACT: bhaibeka@ircm.qc.ca SUPPLEMENTARY INFORMATION: Supplementary data are
available at Bioinformatics online.

mRMRe: an R package for parallelized mRMR ensemble feature selection.

mRMRe is an R tool for the computation of mutual information matrices using continuous, categorical, and survival variables. The mRMRe algorithm includes a function for feature selection with minimum Redundancy, Maximum Relevance (mRMR), and a novel ensemble mRMR method.

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