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

Breast milk exosomes: Implications for Brain function and Oncogenesis.

Neuroscience and biobehavioral reviews·2026

Same author

Novel Disease-Specific Panel of Salivary microRNAs for the Detection of Oral Squamous Cell Carcinoma from Early Invasion to Stage IV Disease.

International journal of molecular sciences·2026

Same author

DNA-PK interacts with cyclic dinucleotides and inhibits type I interferon responses.

The Journal of experimental medicine·2026

Same author

Stress-related disorders and nonpeptidic CRH antagonists.

Hormones (Athens, Greece)·2026

Same author

Clinical, Molecular and Bioinformatic Study of Common Thrombophilia Mutation Factor V Leiden.

Advances in experimental medicine and biology·2026

Same author

Polyphenols from Olive Oil: A Promising Therapeutic Approach for Neurodegenerative Diseases.

Advances in experimental medicine and biology·2026

Same journal

Correction to: 'Stokes settling and particle-laden plumes: implications for deep-sea mining and volcanic eruption plumes' (2020), by Mingotti et al.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences·2026

Same journal

A stable hothouse triggered by a tipping mechanism.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences·2026

Same journal

Beyond distance: quantifying point cloud dynamics with persistent homology and dynamic optimal transport.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences·2026

Same journal

Global stability of the Atlantic overturning circulation: edge state, long transients and boundary crisis under CO2 forcing.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences·2026

Same journal

Morse index classification and landscape of Kuramoto system for Hebbian-based binary pattern recognition.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences·2026

Same journal

Interpretable and equation-free response theory for complex systems.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences·2026

See all related articles

Search research articles

Related Experiment Video

Updated: May 1, 2026

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

On integrating multi-experiment microarray data.

Georgia Tsiliki¹, Dimitrios Vlachakis, Sophia Kossida

¹Bioinformatics and Medical Informatics Group, Biomedical Research Foundation, Academy of Athens, , 4 Soranou Ephessiou 115 27, Greece.

Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences

|April 23, 2014

Summary

This summary is machine-generated.

This study introduces a novel method for integrating microarray gene-expression data from different studies. The technique enhances accuracy and addresses sample size limitations in cancer research.

Keywords:

composite likelihood gene expression integrative genomics partition modelling

More Related Videos

The Terroir Concept Interpreted through Grape Berry Metabolomics and Transcriptomics

The Terroir Concept Interpreted through Grape Berry Metabolomics and Transcriptomics

Published on: October 5, 2016

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

Published on: March 12, 2021

Related Experiment Videos

Last Updated: May 1, 2026

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

The Terroir Concept Interpreted through Grape Berry Metabolomics and Transcriptomics

The Terroir Concept Interpreted through Grape Berry Metabolomics and Transcriptomics

Published on: October 5, 2016

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

Published on: March 12, 2021

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Microarray technology is widely used for cancer prognosis and diagnosis.
Reproducibility and data integration across different microarray platforms are crucial for robust findings.
Combining datasets can overcome limitations of individual studies and improve statistical power.

Purpose of the Study:

To develop a novel technique for integrating microarray gene-expression data from diverse studies.
To enable robust co-expression profiling across genes and biological samples using Bayesian partition modeling.
To validate gene signatures across multiple experiments and improve the reliability of genomic data analysis.

Main Methods:

Developed a novel integration technique for microarray gene-expression data.
Employed a two-way Bayesian partition modeling approach.
Transformed disparate gene-expression data onto a common probability scale for inter-study validation.

Main Results:

The model was evaluated using artificial data and demonstrated effectiveness.
Applied to six public cancer gene-expression datasets, outperforming existing integrative methods.
Successfully addressed the limited sample size problem inherent in many genomic studies.
Achieved high accuracy in integrating multi-experiment microarray data.

Conclusions:

The proposed framework offers a powerful approach for integrating heterogeneous microarray datasets.
This method enhances the reliability and accuracy of gene-expression analysis in cancer research.
The technique facilitates the discovery of robust gene signatures by leveraging multi-experiment data.