Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Multi-species Conserved Sequences02:51

Multi-species Conserved Sequences

Next-generation sequencing technologies have created large genomic databases of a variety of animals and plants. Ever since the human genome project was completed, scientists studied the genome of primates, mammals, and other phylogenetically distant living beings. Such large-scale  studies have provided new insights into the evolutionary relationship between organisms.
Although the genome of each species varies greatly from each other, a few sequences are highly conserved. Such conserved DNA...
Introduction to R01:11

Introduction to R

R is a powerful software environment for statistical computing and graphics. Originating as an implementation of the S language, developed at Bell Laboratories, R has evolved into a robust, open-source statistical software favored by statisticians and data scientists worldwide. Its comprehensive suite includes data manipulation, calculation, and graphical display capabilities, making it versatile for data analysis and visualization. Its programming language is at the core of R's functionality,...
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
High-Resolution Mass Spectrometry (HRMS)01:15

High-Resolution Mass Spectrometry (HRMS)

The resolution of a mass spectrometer depends on the efficiency of separating ions with different ion masses. The mass of an atom is approximated to the sum of the masses of protons and neutrons inside, considering the masses of protons and neutrons as equal. However, the masses of the proton (1.6726 × 10−24 g) and neutron (1.6749 × 10−24 g) are not truly equal. There is a minor error in the expression of atomic masses relative to the simplest atom of hydrogen. For example, the mass of helium...
Statistical Software for Data Analysis and Clinical Trials01:12

Statistical Software for Data Analysis and Clinical Trials

Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...
Protein Folding Quality Check in the RER01:29

Protein Folding Quality Check in the RER

ER is the primary site for the maturation and folding of soluble and transmembrane secretory proteins. The calnexin cycle is a specific chaperone system that folds and assesses the confirmation of N-glycosylated proteins before they can exit the ER lumen. The primary players of this quality check pipeline are the lectins, ER-resident chaperones, and a glucosyl transferase enzyme. In case the calnexin system in the lumen fails to salvage a misfolded protein, it is transported to the cytoplasm...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Auto-segmentation of organs-of-interest clinical acceptability & reproducibility framework in head and neck cancer.

Physics and imaging in radiation oncology·2026
Same author

Variant-Specific Landscape of Mutual Exclusivity Among BRAF, EGFR, and KRAS Oncogenes Reveals Overlap With Functionally Antagonistic Mutant Pairs.

International journal of cancer·2026
Same author

Making head and neck cancer clinical data Findable-Accessible-Interoperable-Reusable to support multi-institutional collaboration and federated learning.

BJR artificial intelligence·2026
Same author

A practical framework for operationalising responsible and equitable artificial intelligence in health care: tackling bias, inequity, and implementation challenges.

The Lancet. Digital health·2026
Same author

Evaluation of ensilication technology for ambient DNA preservation.

NAR molecular medicine·2026
Same author

Unlocking Health Data for Research: Legal, Technical, and Organisational Lessons from a Belgian Interdisciplinary Case Study.

Journal of healthcare informatics research·2026
Same journal

Cross-Domain Transfer Learning from Peptides to Metabolites Using a Multi-Property Fine-Tuned LLM.

Bioinformatics (Oxford, England)·2026
Same journal

Biomedical Concept Recognition with Error-aware Negative-enhanced Ranking Framework.

Bioinformatics (Oxford, England)·2026
Same journal

TEDLH: Domain HMMs for sensitive detection of remote homologues.

Bioinformatics (Oxford, England)·2026
Same journal

PLNFGL: Joint Estimation of Multi-Condition Gene Networks from Single-cell RNA-seq Data.

Bioinformatics (Oxford, England)·2026
Same journal

MCFST: Spatial domain identification method based on multi-view graph convolutional network and graph fusion network.

Bioinformatics (Oxford, England)·2026
Same journal

SpaBiT: Enhancing Spatial Transcriptomics Resolution via Bidirectional Attention Transformers.

Bioinformatics (Oxford, England)·2026
See all related articles

Related Experiment Video

Updated: May 10, 2026

Genomic MRI - a Public Resource for Studying Sequence Patterns within Genomic DNA
12:36

Genomic MRI - a Public Resource for Studying Sequence Patterns within Genomic DNA

Published on: May 9, 2011

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

Nicolas De Jay1, Simon Papillon-Cavanagh, Catharina Olsen

  • 1Bioinformatics and Computational Biology Laboratory, Integrative Systems Biology Axis, Institut de recherches cliniques de Montréal, Montreal, H2W 1R7, Quebec, Canada.

Bioinformatics (Oxford, England)
|July 5, 2013
PubMed
Summary
This summary is machine-generated.

The mRMRe R package enhances feature selection for genomic data using an ensemble approach. This method improves prediction accuracy and biological interpretation while offering faster run times through parallelization.

More Related Videos

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

Related Experiment Videos

Last Updated: May 10, 2026

Genomic MRI - a Public Resource for Studying Sequence Patterns within Genomic DNA
12:36

Genomic MRI - a Public Resource for Studying Sequence Patterns within Genomic DNA

Published on: May 9, 2011

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • High-throughput genomic data analysis presents challenges in feature selection.
  • Minimum redundancy maximum relevance (mRMR) is a rapid method for identifying relevant and complementary features.
  • The mRMRe R package extends mRMR with an ensemble approach for robust predictor development.

Purpose of the Study:

  • To introduce the mRMRe R package, which enhances the mRMR feature selection technique.
  • To improve feature selection by employing an ensemble strategy for broader feature space exploration.
  • To develop more robust predictive models from genomic data.

Main Methods:

  • Implementation of an ensemble approach to extend the mRMR feature selection method.
  • Parallelization of core package functions in C using the OpenMP API to manage computational complexity.
  • Utilizing the R programming language for bioinformatics analysis.

Main Results:

  • Ensemble mRMR implementations demonstrate superior prediction accuracy compared to the classical mRMR approach.
  • Identification of genes with higher relevance to the biological context, facilitating richer interpretations.
  • Parallelized functions show substantial improvements in execution speed over previous packages.

Conclusions:

  • The mRMRe R package offers a powerful and efficient tool for feature selection in high-throughput genomic data.
  • The ensemble strategy leads to more accurate predictions and enhanced biological insights.
  • Parallelization significantly accelerates computation, making the package practical for large datasets.