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

Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

712
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
712

You might also read

Related Articles

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

Sort by
Same author

Biomarker dynamics affecting neoadjuvant therapy response and outcome of HER2-positive breast cancer subtype.

Scientific reports·2023
Same author

Prognostic Role of Androgen Receptor Expression in HER2+ Breast Carcinoma Subtypes.

Biomedicines·2022
Same author

Cost-sensitive learning strategies for high-dimensional and imbalanced data: a comparative study.

PeerJ. Computer science·2022
See all related articles

Related Experiment Video

Updated: May 5, 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

7.0K

A comparative analysis of biomarker selection techniques.

Nicoletta Dessì1, Emanuele Pascariello, Barbara Pes

  • 1Dipartimento di Matematica e Informatica, Università degli Studi di Cagliari, Via Ospedale 72, 09124 Cagliari, Italy.

Biomed Research International
|December 11, 2013
PubMed
Summary

Comparing feature selection methods for biomarker discovery is crucial. This study introduces a methodology to systematically evaluate differences in gene sets, predictive performance, and stability, offering insights into biomarker discovery technique agreement.

More Related Videos

Dried Blood Spot Collection of Health Biomarkers to Maximize Participation in Population Studies
07:20

Dried Blood Spot Collection of Health Biomarkers to Maximize Participation in Population Studies

Published on: January 28, 2014

40.0K
Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
07:41

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

Published on: May 17, 2019

8.6K

Related Experiment Videos

Last Updated: May 5, 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

7.0K
Dried Blood Spot Collection of Health Biomarkers to Maximize Participation in Population Studies
07:20

Dried Blood Spot Collection of Health Biomarkers to Maximize Participation in Population Studies

Published on: January 28, 2014

40.0K
Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
07:41

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

Published on: May 17, 2019

8.6K

Area of Science:

  • Genomics
  • Bioinformatics
  • Biostatistics

Background:

  • Feature selection is vital for identifying biomarkers from high-dimensional genomics data.
  • Different feature selection techniques yield varying biomarker sets, but systematic comparisons are scarce.

Purpose of the Study:

  • To propose a general methodology for comparing outcomes of different feature selection techniques in biomarker discovery.
  • To quantify differences in selected gene sets, predictive performance, and stability.

Main Methods:

  • Developed a two-dimensional comparison framework: gene set similarity/dissimilarity and performance/stability evaluation.
  • Applied the methodology to three DNA microarray datasets.
  • Compared eight distinct feature selection methods representing various classes.

Main Results:

  • The proposed approach effectively highlights similarities and differences among feature selection techniques.
  • Analysis revealed varying degrees of agreement and divergence in biomarker sets generated by different methods.
  • Implications for predictive performance and stability were systematically assessed.

Conclusions:

  • The developed methodology provides valuable insights into the concordance of biomarker discovery techniques.
  • Facilitates informed selection of appropriate feature selection methods based on desired outcomes.
  • Enhances understanding of the impact of feature selection choices on biomarker reliability.