Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

DNA Microarrays

DNA Microarrays

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...

You might also read

Related Articles

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

Sort by

Same author

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

Heliyon·2024

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

Same author

Biclustering on expression data: A review.

Journal of biomedical informatics·2015

Same author

Ethnic difference in risk of toxicity in prostate cancer patients treated with dynamic arc radiation therapy.

Tumori·2015

Same author

Configurable pattern-based evolutionary biclustering of gene expression data.

Algorithms for molecular biology : AMB·2013

Same author

An effective measure for assessing the quality of biclusters.

Computers in biology and medicine·2011

Search research articles

Related Experiment Video

Updated: Apr 16, 2026

Low-input Nucleus Isolation and Multiplexing with Barcoded Antibodies of Mouse Sympathetic Ganglia for Single-nucleus RNA Sequencing

Low-input Nucleus Isolation and Multiplexing with Barcoded Antibodies of Mouse Sympathetic Ganglia for Single-nucleus RNA Sequencing

Published on: March 23, 2022

Quality measures for gene expression biclusters.

Beatriz Pontes¹, Ral Girldez², Jess S Aguilar-Ruiz²

¹Department of Computer Languages, University of Seville, Seville, Spain.

|March 13, 2015

Summary

This study analyzes biclustering quality measures for gene expression data. It compares various metrics to evaluate their effectiveness in identifying co-expressed gene groups under different conditions.

More Related Videos

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes

Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes

Published on: November 7, 2025

Related Experiment Videos

Last Updated: Apr 16, 2026

Low-input Nucleus Isolation and Multiplexing with Barcoded Antibodies of Mouse Sympathetic Ganglia for Single-nucleus RNA Sequencing

Low-input Nucleus Isolation and Multiplexing with Barcoded Antibodies of Mouse Sympathetic Ganglia for Single-nucleus RNA Sequencing

Published on: March 23, 2022

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes

Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes

Published on: November 7, 2025

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Biclustering is crucial for analyzing gene expression data, aiming to identify functionally related gene sets across experimental conditions.
Recognizing co-expressed or co-regulated genes with similar expression patterns is a primary objective in this field.
Heuristic searches and quality metrics are commonly employed due to the complexity of biclustering gene expression data.

Purpose of the Study:

To analyze existing quality measures for gene expression biclusters.
To present a comparative study of these quality metrics.
To evaluate their capability in recognizing diverse expression patterns within biclusters.

Main Methods:

Literature review and analysis of existing biclustering quality measures.
Comparative evaluation of selected quality metrics.
Assessment of metric performance on recognizing different gene expression patterns.

Main Results:

Identification of key characteristics and limitations of various biclustering quality measures.
Comparative performance data highlighting the strengths and weaknesses of different metrics.
Insights into which quality measures are most effective for specific types of expression patterns.

Conclusions:

The choice of a suitable quality metric is critical for guiding bicluster searches and comparing results.
A comprehensive analysis and comparison of quality measures are essential for advancing biclustering techniques in gene expression studies.
This study provides a foundation for selecting appropriate quality metrics for gene expression biclustering.