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

An Artificial Intelligence-Driven Digital Health Solution to Support Clinical Management of Patients With Long COVID-19: Protocol for a Prospective Multicenter Observational Study.

JMIR research protocols·2022
Same author

Rationale for Timing of Follow-Up Visits to Assess Gluten-Free Diet in Celiac Disease Patients Based on Data Mining.

Nutrients·2021
Same author

TRIQ: a new method to evaluate triclusters.

BioData mining·2018
See all related articles

Related Experiment Video

Updated: Apr 25, 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

Mining 3D patterns from gene expression temporal data: a new tricluster evaluation measure.

David Gutiérrez-Avilés1, Cristina Rubio-Escudero1

  • 1Department of Computer Science, University of Seville, Avenida Reina Mercedes s/n, 41012 Seville, Spain.

Thescientificworldjournal
|August 22, 2014
PubMed
Summary

This study introduces Mean Square Residue 3D, a novel evaluation measure for triclustering in analyzing complex biological data. It effectively identifies groups of genes with similar patterns across conditions and time points in longitudinal experiments.

More Related Videos

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

6.2K
Mining Spatial Transcriptomics Datasets using DeepSpaceDB
10:16

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Published on: September 5, 2025

1.0K

Related Experiment Videos

Last Updated: Apr 25, 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
Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

6.2K
Mining Spatial Transcriptomics Datasets using DeepSpaceDB
10:16

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Published on: September 5, 2025

1.0K

Area of Science:

  • Biotechnology
  • Bioinformatics
  • Computational Biology

Background:

  • Microarray technology generates large datasets, posing computational challenges for analysis.
  • Clustering and biclustering are used to group genes with similar expression patterns.
  • Triclustering is necessary for analyzing longitudinal microarray data with a time dimension.

Purpose of the Study:

  • To introduce a new evaluation measure for triclustering.
  • To adapt the Mean Square Residue measure for three-dimensional data analysis.
  • To assess the effectiveness of the Mean Square Residue 3D measure.

Main Methods:

  • Developed the Mean Square Residue 3D evaluation measure for triclusters.
  • Applied the measure to both synthetic and real longitudinal microarray data.
  • Utilized Gene Ontology for functional annotation of identified gene groups.

Main Results:

  • The Mean Square Residue 3D measure successfully identified groups of genes with homogeneous patterns.
  • These gene groups exhibited high correlation levels across subsets of conditions and time points.
  • Identified gene clusters showed significant relationships with their functional annotations.

Conclusions:

  • Mean Square Residue 3D is a robust measure for evaluating triclusters in longitudinal microarray data.
  • The measure facilitates the discovery of biologically relevant gene groups with complex expression patterns.
  • This approach enhances the understanding of gene behavior in time-series experiments.