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 Experiment Video

Updated: May 12, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

High-Dimensional Structured Feature Screening Using Binary Markov Random Fields.

Jie Liu1, Peggy Peissig, Chunming Zhang

  • 1Department of Computer Sciences Univ. of Wisconsin-Madison.

JMLR Workshop and Conference Proceedings
|April 23, 2013
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

The neutrophil-to-lymphocyte ratio and incident chronic kidney disease in a community-based cohort: a prospective study.

Scientific reports·2026
Same author

Rocuronium Dose and First-Attempt Intubation Success in the Critically Ill: Secondary Analysis of Two Multicenter Trials.

American journal of respiratory and critical care medicine·2026
Same author

United Global Advocacy Drives Updates to World Health Organization Essential Medicines List.

Haemophilia : the official journal of the World Federation of Hemophilia·2026
Same author

Impact of measurable residual disease on outcomes using a modified DFCI protocol for adults with BCR-ABL negative acute lymphoblastic leukemia.

Leukemia research·2026
Same author

Stratifying Risk and Treatment Benefit: A Model Predicting Overall Survival in Men with Metastatic De Novo Hormone-sensitive Prostate Cancer in Trials Investigating Docetaxel (the STOPCAP Collaboration).

European urology focus·2026
Same author

Global research landscape and multisystem health mechanisms of luteolin: a comprehensive bibliometric and network pharmacology study.

Frontiers in nutrition·2026
Same journal

Entropic Graph-based Posterior Regularization.

JMLR workshop and conference proceedings·2024
Same journal

Uncovering Voice Misuse Using Symbolic Mismatch.

JMLR workshop and conference proceedings·2021
Same journal

Dirichlet Process Mixture Model for Correcting Technical Variation in Single-Cell Gene Expression Data.

JMLR workshop and conference proceedings·2018
Same journal

Anytime Exploration for Multi-armed Bandits using Confidence Information.

JMLR workshop and conference proceedings·2018
Same journal

Greedy Direction Method of Multiplier for MAP Inference of Large Output Domain.

JMLR workshop and conference proceedings·2017
Same journal

Scalable Convex Multiple Sequence Alignment via Entropy-Regularized Dual Decomposition.

JMLR workshop and conference proceedings·2017
See all related articles

This study introduces a feature relevance network to identify all relevant features in high-dimensional data by considering feature correlations. The proposed method outperforms common techniques in prediction accuracy and feature recovery.

Area of Science:

  • Statistics
  • Machine Learning
  • Data Mining

Background:

  • High-dimensional data analysis requires effective feature selection.
  • Existing feature screening methods often ignore covariate correlations.
  • Identifying all relevant features is crucial for understanding complex datasets.

Purpose of the Study:

  • To propose a novel feature relevance network for feature selection.
  • To incorporate feature correlation structure into the screening process.
  • To develop an algorithm that identifies all relevant features, unlike sparse methods.

Main Methods:

  • Introduced the concept of a feature relevance network, a binary Markov random field.
  • Represented individual feature relevance using node potentials.

More Related Videos

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets
03:37

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets

Published on: March 1, 2024

Related Experiment Videos

Last Updated: May 12, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets
03:37

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets

Published on: March 1, 2024

  • Modeled covariate correlation structure using edge potentials and performed network inference.

    • Main Results:

    • The proposed algorithm demonstrated superior performance in prediction error compared to common methods.
    • It showed improved recovery of truly relevant features on both synthetic and real-world datasets.
    • The method does not enforce sparsity, offering an alternative to penalized regression approaches.

    Conclusions:

    • The feature relevance network provides an effective framework for feature screening in high-dimensional data.
    • Accounting for feature correlations enhances the identification of relevant variables.
    • This approach offers a valuable alternative for feature selection when all relevant features are of interest.