Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Receiver Operating Characteristic Plot

Receiver Operating Characteristic Plot

A ROC (Receiver Operating Characteristic) plot is a graphical tool used to assess the performance of a binary classification model by illustrating the trade-off between sensitivity (true positive rate) and specificity (false positive rate). By plotting sensitivity against 1 - specificity across various threshold settings, the ROC curve shows how well the model distinguishes between classes, with a curve closer to the top-left corner indicating a more accurate model. The area under the ROC curve...

You might also read

Related Articles

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

Sort by

Same author

IL-18 derived from microwave-ablated osteosarcoma facilitates antitumor response.

Journal for immunotherapy of cancer·2026

Same author

ClBCM: an EMS mutated gene regulating chlorophyll synthesis and adaptation to diverse stresses in watermelon.

Molecular horticulture·2026

Same author

<i>NRAS</i> mRNA-Degrading Bifunctional Small Molecules Induce Diverse Cellular Morphological Changes in Cancer Cells.

JACS Au·2026

Same author

Cadonilimab induction and consolidation for unresectable stage III non-small cell lung cancer patients receiving concurrent chemoradiation: safety run-in results of a prospective, phase II trial.

Translational lung cancer research·2026

Same author

Application and Research Prospects of CRISPR/Cas Gene Editing Technology in Lactic Acid Bacteria.

Microorganisms·2026

Same author

Comparative population genomics reveal the genetic features associated with the plant host adaptation of Clostridium butyricum.

BMC genomics·2026

Search research articles

Related Experiment Video

Updated: Sep 23, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Biomarker interaction selection and disease detection based on multivariate gain ratio.

Xiao Chu¹, Mao Jiang², Zhuo-Jun Liu³

¹Academy of Mathematics and Systems Science Chinese Academy of Sciences, University of Chinese Academy of Sciences, Beijing, China. chuxiao18@mails.ucas.ac.cn.

BMC Bioinformatics

|May 13, 2022

Summary

We developed Multivariate Gain Ratio (MGR) to improve disease detection by analyzing biomarker interactions. MGR offers a more accurate and credible approach than existing methods, especially for complex datasets.

Keywords:

Biomarker interaction Disease detection Multivariate gain ratio

More Related Videos

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Basics of Multivariate Analysis in Neuroimaging Data

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

Related Experiment Videos

Last Updated: Sep 23, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Basics of Multivariate Analysis in Neuroimaging Data

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

Area of Science:

Biotechnology
Computational Biology
Bioinformatics

Background:

Disease detection is crucial in biotherapy, often relying on single biomarkers.
Biomarker interactions, not just individual markers, significantly influence disease status.
Existing methods like I-score have limitations in accurately assessing multivariate biomarker interactions.

Purpose of the Study:

To introduce a novel influence measure, Multivariate Gain Ratio (MGR), for evaluating biomarker interactions.
To address the deviations in existing methods when assessing interactions with fewer variables.
To develop a robust method for selecting key biomarker interactions for disease detection models.

Main Methods:

Proposed Multivariate Gain Ratio (MGR) based on Gain Ratio (GR).
Developed a preprocessing verification algorithm for selecting appropriate preprocessing methods.
Implemented an algorithm for selecting key biomarker interactions and constructing disease detection models.

Main Results:

MGR demonstrated higher credibility than I-score for interactions involving fewer variables.
Achieved superior average accuracy ([Formula: see text]) compared to I-score ([Formula: see text]) on the Breast Cancer Wisconsin (Diagnostic) Dataset.
MGR effectively identified key biomarkers and reduced dimensionality compared to using all predictor variables.
Showcased effectiveness on the Leukemia Dataset with an accuracy of [Formula: see text] versus I-score's [Formula: see text].

Conclusions:

MGR is effective for selecting important biomarkers and their interactions, even in high-dimensional data.
The predictive ability of MGR-selected interactions surpasses I-score when interactions involve fewer variables.
MGR is broadly applicable to diverse biomarker datasets, including cell nuclei, gene, SNPs, and protein data.