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

Multiple Comparison Tests01:13

Multiple Comparison Tests

Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...

You might also read

Related Articles

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

Sort by
Same author

Randomized, Placebo-Controlled, Multicenter Clinical Study on the Efficacy and Safety of Lidocaine Patches in Chinese Patients with Postherpetic Neuralgia.

Dermatology and therapy·2023
Same author

Rare tumors: a blue ocean of investigation.

Frontiers of medicine·2023
Same author

Treatment of primary cutaneous lichenoid amyloidosis with abrocitinib: A pilot study in two cases.

International journal of dermatology·2023
Same author

Adverse events of PD-(L)1 inhibitors plus anti-VEGF(R) agents compared with PD-(L)1 inhibitors alone for cancer patients: a systematic review and meta-analysis.

Frontiers in pharmacology·2023
Same author

CircHIPK2 facilitates phenotypic switching of vascular smooth muscle cells in hypertension.

Journal of human hypertension·2023
Same author

Clinical Features and Skin Microbiome of Tinea Scrotum: An Observational Study of 113 Cases in China.

Mycopathologia·2023
Same journal

SNPio: a Python interface for population genomic data processing.

BMC bioinformatics·2026
Same journal

SpaHNR: a spatial domain identification method via sparse attention-based hierarchical node representation and multi-view contrastive learning.

BMC bioinformatics·2026
Same journal

OpenIMC: an open-source platform for analyzing single-cell and spatial proteomics by imaging mass cytometry.

BMC bioinformatics·2026
Same journal

NAP: an open source pipeline for cross-domain microbiome profiling using Nanopore sequencing-derived amplicon data.

BMC bioinformatics·2026
Same journal

SurvGME: an R package for survival analysis with graphical and measurement error models.

BMC bioinformatics·2026
Same journal

SimMapNet: a Bayesian framework for gene regulatory network inference using gene ontology similarities as external hint.

BMC bioinformatics·2026
See all related articles

Related Experiment Video

Updated: May 26, 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

Selecting a single model or combining multiple models for microarray-based classifier development?--a comparative

Minjun Chen1, Leming Shi, Reagan Kelly

  • 1Center for Bioinformatics, Division of Systems Biology, National Center for Toxicological Research, US Food & Drug Administration, 3900 NCTR Rd, Jefferson, Arkansas, USA.

BMC Bioinformatics
|December 15, 2011
PubMed
Summary
This summary is machine-generated.

Developing reliable genomic biomarkers using microarrays remains challenging. An ensemble method, combining top models, achieved comparable or superior predictive performance in external validation compared to single models, offering a valuable supplement for biomarker development.

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 26, 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

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:

  • Genomics
  • Bioinformatics
  • Biostatistics

Background:

  • Genomic biomarkers are crucial for preclinical and clinical applications, with microarray-based predictive model development being a key research area.
  • Developing reliable microarray-based predictive models for unknown samples presents significant challenges, lacking clear guidelines for optimal model selection.
  • The MicroArray Quality Control (MAQC)-II project involved 36 teams generating numerous models for 13 endpoints, establishing benchmarks with nominated and candidate models.

Purpose of the Study:

  • To evaluate a simple ensemble method for developing microarray-based genomic biomarkers.
  • To compare the predictive performance of ensemble models against nominated and candidate models from the MAQC-II project using external validation.

Main Methods:

  • An ensemble model was developed by combining top-performing cross-validated models for each MAQC-II endpoint.
  • Ensemble models were compared against MAQC-II nominated and candidate models through blinded external validation.

Main Results:

  • Ensemble models achieved equal or better predictive performance than NCTR nominated models for 10 of 13 endpoints.
  • The performance of ensemble models was comparable to the MAQC-II candidate models.
  • Ensemble models generally outperformed nominated models from other MAQC-II analysis teams.

Conclusions:

  • Ensemble methods can enhance average predictive performance in external validation sets compared to single, optimized models.
  • Utilizing an ensemble approach is a valuable addition to established practices for developing robust microarray-based genomic biomarkers.