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

DNA Microarrays02:34

DNA Microarrays

23.0K
Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...
23.0K

You might also read

Related Articles

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

Sort by
Same author

Improving classification on imbalanced genomic data via KDE-based synthetic sampling.

BioData mining·2025
Same author

Classification performance assessment for imbalanced multiclass data.

Scientific reports·2024
Same author

Advance Monitoring of COVID-19 Incidence Based on Taxi Mobility: The Infection Ratio Measure.

Healthcare (Basel, Switzerland)·2024
Same author

Addressing energy challenges in Iraq: Forecasting power supply and demand using artificial intelligence models.

Heliyon·2024
Same author

A new challenge for data analytics: transposons.

BioData mining·2022
Same author

A data mining based clinical decision support system for survival in lung cancer.

Reports of practical oncology and radiotherapy : journal of Greatpoland Cancer Center in Poznan and Polish Society of Radiation Oncology·2022

Related Experiment Video

Updated: Apr 7, 2026

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
05:22

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

4.1K

Biclustering on expression data: A review.

Beatriz Pontes1, Raúl Giráldez2, Jesús S Aguilar-Ruiz2

  • 1Department of Languages and Computer Systems, University of Seville, Seville, Spain.

Journal of Biomedical Informatics
|July 11, 2015
PubMed
Summary

This survey categorizes biclustering algorithms for gene expression data. It distinguishes between methods using evaluation measures and those that do not, aiding in the discovery of related gene sets.

Keywords:
Biclustering techniquesGene expression dataMicroarray analysis

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

8.1K
Bacterial Gene Expression Analysis Using Microarrays
29:41

Bacterial Gene Expression Analysis Using Microarrays

Published on: May 28, 2007

9.4K

Related Experiment Videos

Last Updated: Apr 7, 2026

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
05:22

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

4.1K
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

8.1K
Bacterial Gene Expression Analysis Using Microarrays
29:41

Bacterial Gene Expression Analysis Using Microarrays

Published on: May 28, 2007

9.4K

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Biclustering is vital for analyzing gene expression data, identifying gene sets with similar functions across experimental conditions.
  • Effective biclustering relies on robust evaluation measures and heuristics to guide the search for meaningful patterns.
  • Existing biclustering tools employ diverse strategies, not all of which are based on explicit evaluation metrics.

Purpose of the Study:

  • To conduct an extensive survey of biclustering approaches used in gene expression data analysis.
  • To classify biclustering algorithms based on their use of evaluation metrics during the search process.
  • To categorize algorithms by the meta-heuristics employed.

Main Methods:

  • Classification of biclustering algorithms into two main categories: metric-based and non-metric-based.
  • Analysis of the underlying search strategies and algorithmic concepts for each category.
  • Further classification within each category based on the type of meta-heuristics utilized.

Main Results:

  • Identification of two primary classes of biclustering algorithms: those employing evaluation measures and those that do not.
  • Detailed review of various biclustering tools and their distinct search methodologies.
  • A structured classification framework for understanding the landscape of biclustering techniques.

Conclusions:

  • The survey provides a comprehensive overview and classification of biclustering methods.
  • Understanding these classifications aids researchers in selecting appropriate tools for gene expression data analysis.
  • This work facilitates the discovery of functionally related gene sets and experimental conditions.