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

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

Tangential flow filtration for isolating exomeres and other nanoscale extracellular particles.

Nanoscale·2026
Same author

Generation of Cellular Biofactories for the Scalable Production of Surface-Engineered Extracellular Vesicles via CRISPR Genome Editing.

ACS biomaterials science & engineering·2026
Same author

NRSF regulation of OPRM1 through histone acetylation.

Clinical epigenetics·2026
Same author

Endoplasmic reticulum stress induced autophagy alters cellular processing of cationic lipid delivered siRNAs.

Drug delivery and translational research·2026
Same author

Using electronic health records to assess the relationship between colonization pressure and nosocomial pathogen acquisition.

Nature communications·2026
Same author

Longitudinal changes in vibration-controlled transient elastography in pediatric fatty liver disease.

Journal of pediatric gastroenterology and nutrition·2026
Same journal

Correction to: A quantitative systems pharmacology (QSP) model for Pneumocystis treatment in mice.

BMC systems biology·2019
Same journal

Predicting disease-related phenotypes using an integrated phenotype similarity measurement based on HPO.

BMC systems biology·2019
Same journal

Fusing gene expressions and transitive protein-protein interactions for inference of gene regulatory networks.

BMC systems biology·2019
Same journal

A fast and efficient count-based matrix factorization method for detecting cell types from single-cell RNAseq data.

BMC systems biology·2019
Same journal

GNE: a deep learning framework for gene network inference by aggregating biological information.

BMC systems biology·2019
Same journal

FCMDAP: using miRNA family and cluster information to improve the prediction accuracy of disease related miRNAs.

BMC systems biology·2019
See all related articles

Related Experiment Video

Updated: Jul 3, 2026

Demonstrating a Multi-drug Resistant Mycobacterium tuberculosis Amplification Microarray
07:35

Demonstrating a Multi-drug Resistant Mycobacterium tuberculosis Amplification Microarray

Published on: April 25, 2014

Short time-series microarray analysis: methods and challenges.

Xuewei Wang1, Ming Wu, Zheng Li

  • 1Department of Chemical Engineering and Material Science, Michigan State University, East Lansing, MI 48824, USA. xwang@egr.msu.edu

BMC Systems Biology
|July 9, 2008
PubMed
Summary
This summary is machine-generated.

Analyzing short time-series gene expression data from microarrays is challenging due to limited sampling. Recent advances offer promise, but further computational method development is needed for full data potential.

More Related Videos

Sample Preparation to Bioinformatics Analysis of DNA Methylation: Association Strategy for Obesity and Related Trait Studies
14:56

Sample Preparation to Bioinformatics Analysis of DNA Methylation: Association Strategy for Obesity and Related Trait Studies

Published on: May 6, 2022

A High-throughput Cell Microarray Platform for Correlative Analysis of Cell Differentiation and Traction Forces
12:04

A High-throughput Cell Microarray Platform for Correlative Analysis of Cell Differentiation and Traction Forces

Published on: March 1, 2017

Related Experiment Videos

Last Updated: Jul 3, 2026

Demonstrating a Multi-drug Resistant Mycobacterium tuberculosis Amplification Microarray
07:35

Demonstrating a Multi-drug Resistant Mycobacterium tuberculosis Amplification Microarray

Published on: April 25, 2014

Sample Preparation to Bioinformatics Analysis of DNA Methylation: Association Strategy for Obesity and Related Trait Studies
14:56

Sample Preparation to Bioinformatics Analysis of DNA Methylation: Association Strategy for Obesity and Related Trait Studies

Published on: May 6, 2022

A High-throughput Cell Microarray Platform for Correlative Analysis of Cell Differentiation and Traction Forces
12:04

A High-throughput Cell Microarray Platform for Correlative Analysis of Cell Differentiation and Traction Forces

Published on: March 1, 2017

Area of Science:

  • Systems biology
  • Gene expression analysis
  • Bioinformatics

Background:

  • Steady-state gene expression analysis is routine.
  • Time-series microarrays are increasingly used to study dynamic biological systems.
  • Short time-series data from microarrays present analytical challenges due to limited sampling.

Discussion:

  • Traditional methods struggle with limited data points in short time-series.
  • Recent advances focus on simplification-based approaches.
  • Integrating multi-source information shows promise for enhanced analysis.

Key Insights:

  • Extracting meaningful biological insights from short time-series microarray data requires specialized methods.
  • Simplification and data integration are key strategies for overcoming sampling limitations.
  • Despite progress, significant challenges remain in fully exploiting this data.

Outlook:

  • Further research into computational methods is essential.
  • Development of practical solutions is needed to unlock the potential of short time-series data.
  • Continued innovation in bioinformatics will drive advancements in systems biology.