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

PerturbPlan: An analytical framework for designing Perturb-seq experiments.

bioRxiv : the preprint server for biology·2026

Same author

A Perturb-seq screen guided by species divergence uncovers pathways for collateral artery formation.

bioRxiv : the preprint server for biology·2026

Same author

Common Coronary Artery Disease Risk Variants in Endothelial Regulatory Elements Modulate Tetraspanin 14 Expression and Notch Signaling.

Arteriosclerosis, thrombosis, and vascular biology·2026

Same author

Genome-scale mapping of variant, enhancer and gene function in primary human CD4+ T cells.

bioRxiv : the preprint server for biology·2026

Same author

Junction-targeting designs limit the application of CRISPR-Cas13d in circular RNA perturbation studies.

Nucleic acids research·2026

Same author

An expanded registry of candidate cis-regulatory elements.

Nature·2026

Same journal

Evaluation of temporal preservation in synthetic longitudinal patient data.

Journal of biomedical informatics·2026

Same journal

ARKE: An ontology-driven framework for automated mapping of local radiology procedure terms to the LOINC-RadLex playbook using large language model.

Journal of biomedical informatics·2026

Same journal

A validation-driven training controller for cross-lingual biomedical NER via reinforcement learning-based adaptive loss weighting.

Journal of biomedical informatics·2026

Same journal

ASP-HR: An Adaptive Spatial Perception and Hierarchical Reasoning mechanism for document-level biomedical relation extraction.

Journal of biomedical informatics·2026

Same journal

Beyond Accuracy: Safety-Centered guidelines for the evaluation of LLM-based therapy recommendation systems for chronic multimorbidity patients.

Journal of biomedical informatics·2026

Same journal

DeepEN: A deep reinforcement learning framework for personalized enteral nutrition in critical care.

Journal of biomedical informatics·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 11, 2026

Microarray-based Identification of Individual HERV Loci Expression: Application to Biomarker Discovery in Prostate Cancer

Microarray-based Identification of Individual HERV Loci Expression: Application to Biomarker Discovery in Prostate Cancer

Published on: November 2, 2013

Independent component analysis: mining microarray data for fundamental human gene expression modules.

Jesse M Engreitz¹, Bernie J Daigle, Jonathan J Marshall

¹Department of Bioengineering, Stanford University, Stanford, CA 94305, USA.

Journal of Biomedical Informatics

|July 13, 2010

Summary

This summary is machine-generated.

Researchers identified 423 fundamental gene modules in human biology using independent component analysis (ICA) on gene expression data. This approach reveals new biological insights and potential drug response pathways.

More Related Videos

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

Published on: March 5, 2022

Related Experiment Videos

Last Updated: Jun 11, 2026

Microarray-based Identification of Individual HERV Loci Expression: Application to Biomarker Discovery in Prostate Cancer

Microarray-based Identification of Individual HERV Loci Expression: Application to Biomarker Discovery in Prostate Cancer

Published on: November 2, 2013

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

Published on: March 5, 2022

Area of Science:

Genomics
Systems Biology
Bioinformatics

Background:

Public gene expression repositories offer vast data for understanding human biology.
Intelligent data mining can extract transcriptional modules from this data.

Purpose of the Study:

To derive fundamental biological components from heterogeneous gene expression data.
To investigate the biological functions of these components using a preclinical anti-cancer drug.

Main Methods:

Utilized independent component analysis (ICA) on a large compendium of human microarray data (9395 arrays).
Modeled cellular gene expression as a combination of functional modules.
Annotated derived components using Gene Ontology (GO).

Main Results:

Derived 423 fundamental components of human biology.
Identified components representing known and potentially novel biological modules.
Investigated the anti-cancer drug parthenolide (PTL) and predicted N-glycan biosynthesis and T-cell receptor signaling pathways involved in its response.

Conclusions:

The derived fundamental gene modules offer pathway-level insights into new gene expression datasets.
This method can uncover novel biological insights and characterize drug mechanisms.