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

You might also read

Related Articles

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

Sort by
Same author

Repeated social defeat causes bone loss from mouse femur.

Brain, behavior, & immunity - health·2026
Same author

Phenylacetic Acid, a Gut Microbially Produced Metabolite, Reduces Atherosclerosis Burden and Impacts Host Lipid Homeostasis.

Journal of the American Heart Association·2026
Same author

Sustained clinical benefit of idursulfase beta in mucopolysaccharidosis II: two-year experience from a phase 3 extension study including patients switched from idursulfase.

Orphanet journal of rare diseases·2026
Same author

Dynamic Ion Migration in 2D Halide Perovskites.

ACS nano·2026
Same author

Extracellular Vesicle-embedded alginate hydrogel patch for accelerated wound healing.

Materials today. Bio·2026
Same author

Deep Learning-Based Multiclass Classification of Mitral Valve Etiologies Using Limited B-Mode and Color Doppler Echocardiography: Internal and External Validation.

Journal of the American Society of Echocardiography : official publication of the American Society of Echocardiography·2026

Related Experiment Video

Updated: Dec 31, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.6K

Integrative Deep Learning for Identifying Differentially Expressed (DE) Biomarkers.

Jayeon Lim1, SoYoun Bang2, Jiyeon Kim3

  • 1Department of Applied Statistics, Konkuk University, Seoul, Republic of Korea.

Computational and Mathematical Methods in Medicine
|January 10, 2020
PubMed
Summary

This study introduces a novel deep learning method for analyzing genetic data and discovering disease biomarkers. The proposed approach demonstrates superior robustness in simulations and identifies significant pathways in breast cancer data.

More Related Videos

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

7.9K
Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization
03:08

Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization

Published on: October 3, 2025

839

Related Experiment Videos

Last Updated: Dec 31, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.6K
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

7.9K
Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization
03:08

Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization

Published on: October 3, 2025

839

Area of Science:

  • Genomics
  • Bioinformatics
  • Machine Learning

Background:

  • Increasing volumes of genetic data necessitate advanced analytical methods.
  • Machine learning (ML) offers powerful tools for processing and interpreting complex genetic information.
  • Existing ML and statistical models require refinement for effective biomarker discovery.

Purpose of the Study:

  • To develop an enhanced deep learning structure for effective genetic data analysis.
  • To identify significant disease biomarkers using an integrated deep learning layer.
  • To evaluate the proposed method's performance against existing techniques.

Main Methods:

  • Proposed an integrated layer within a deep learning framework for genetic data analysis.
  • Utilized a lasso penalty objective function for parameter estimation.
  • Employed the Youden J index for model comparison.
  • Conducted simulation studies and analyzed real-world breast cancer data (TCGA).

Main Results:

  • The proposed deep learning method showed greater robustness to data variance compared to metalogistic regression and meta-SVM.
  • Analysis of TCGA breast cancer data revealed significantly enriched pathways related to the disease.
  • Gene set enrichment analysis highlighted the utility of the proposed method for omics data.

Conclusions:

  • The integrated deep learning approach offers an effective strategy for genetic data analysis and biomarker discovery.
  • The method's robustness and ability to identify relevant biological pathways suggest its potential clinical utility.
  • This work is expected to advance the discovery of novel biomarkers for various diseases.