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

Friedreich's ataxia patient pathway in Europe.

Frontiers in health services·2026

Same author

Transcriptomic rewiring of the JAK-STAT pathway in circulating CD4<sup>+</sup>CLA<sup>+</sup> and CD4<sup>+</sup> naïve T cells from patients with atopic dermatitis and psoriasis.

Frontiers in immunology·2026

Same author

Genomic and functional insights into the thermophilic strain Geobacillus sp. Geo 8.1: a source of thermostable xylanase for sustainable bioprocesses.

World journal of microbiology & biotechnology·2026

Same author

MUUMI: an R package for statistical and network-based meta-analysis for multi-omics data integration.

BMC bioinformatics·2026

Same author

Extracellular Matrix Origin Directs Morphogenesis and Gene Regulation in Bioengineered Human Skin.

Advanced healthcare materials·2026

Same author

Review on Predictive Models and Integration Strategies for Holistic Impact Assessment of Chemicals and Materials.

Environmental science & technology·2026

Same journal

Isolation of Mesenchymal Stem Cell-Derived Extracellular Vesicles.

Methods in molecular biology (Clifton, N.J.)·2026

Same journal

Modeling Melanoma Immune Surveillance by CAR-T Cells in Human Skin Organoids.

Methods in molecular biology (Clifton, N.J.)·2026

Same journal

Stepwise Optimization of a Matrigel-Based In Vitro Angiogenesis Assay for Reproducible and Quantifiable 2D-Tube Formation Using HUVECs.

Methods in molecular biology (Clifton, N.J.)·2026

Same journal

Quantifying Mechanical Properties of Fresh Ovarian Tissue with Optical Brillouin Microscopy.

Methods in molecular biology (Clifton, N.J.)·2026

Same journal

3D Chromatin Architecture During Early Development: New Methods and New Findings.

Methods in molecular biology (Clifton, N.J.)·2026

Same journal

Metabolic Plasticity in Embryogenesis Throughout the Lens of NAD<sup></sup>.

Methods in molecular biology (Clifton, N.J.)·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Oct 10, 2025

Microarray Analysis for Saccharomyces cerevisiae

Microarray Analysis for Saccharomyces cerevisiae

Published on: April 7, 2011

Network Analysis of Microarray Data.

Alisa Pavel^1,2,3, Angela Serra^1,2,3, Luca Cattelani^1,2,3

¹Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland.

Methods in Molecular Biology (Clifton, N.J.)

|December 13, 2021

Summary

This summary is machine-generated.

Network analysis reveals gene associations from DNA microarray data, moving beyond individual gene expression to understand biological systems and diseases. This approach aids in gene prioritization and patient stratification.

Keywords:

Coexpression Differential coexpression Microarray Multilayer networks Pathways

More Related Videos

Using Microarrays to Interrogate Microenvironmental Impact on Cellular Phenotypes in Cancer

Using Microarrays to Interrogate Microenvironmental Impact on Cellular Phenotypes in Cancer

Published on: May 21, 2019

DNA Microarrays: Sample Quality Control, Array Hybridization and Scanning

DNA Microarrays: Sample Quality Control, Array Hybridization and Scanning

Published on: March 15, 2011

Related Experiment Videos

Last Updated: Oct 10, 2025

Microarray Analysis for Saccharomyces cerevisiae

Microarray Analysis for Saccharomyces cerevisiae

Published on: April 7, 2011

Using Microarrays to Interrogate Microenvironmental Impact on Cellular Phenotypes in Cancer

Using Microarrays to Interrogate Microenvironmental Impact on Cellular Phenotypes in Cancer

Published on: May 21, 2019

DNA Microarrays: Sample Quality Control, Array Hybridization and Scanning

DNA Microarrays: Sample Quality Control, Array Hybridization and Scanning

Published on: March 15, 2011

Area of Science:

Bioinformatics
Systems Biology
Genomics

Background:

DNA microarrays are crucial for gene expression studies.
Classical analysis focuses on differentially expressed genes, overlooking gene interactions.
Genes function in networks, necessitating holistic analytical approaches.

Purpose of the Study:

To provide an overview of methods and tools for creating gene networks from microarray data.
To describe network analysis techniques for single or multiple networks.
To highlight applications in disease gene prioritization and patient stratification.

Main Methods:

Network construction from microarray data.
Analysis of single and multiple gene networks.
Application of topological metrics, functional group identification, data integration, pathway analysis, and graphical models.

Main Results:

Network analysis reveals complex gene association patterns.
Differential coexpression analysis identifies system-specific interactions.
Methods cover network creation, analysis, and data integration strategies.

Conclusions:

Network analysis offers a powerful framework for interpreting gene expression data.
Understanding gene networks is essential for advancing biological system research.
This approach enhances disease gene discovery and clinical applications.