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

SARS-CoV-2 Spike Protein's Structural Dynamics Affect the Activity of the Bebtelovimab Antibody.

Journal of chemical information and modeling·2026
Same author

Identifying Sex Differences in Adverse Events Reported on Opioid Drugs in the FDA's Adverse Event Reporting System (FAERS).

Pharmaceuticals (Basel, Switzerland)·2026
Same author

Leveraging machine learning for selective cannabinoid ligand discovery: methods, challenges, and opportunities.

Expert opinion on drug discovery·2026
Same author

Using Machine Learning for Green Substitution of Industrial Chemicals: Integrating Functionality, Hazard, and Life Cycle Impact.

Chemical reviews·2026
Same author

Beyond Competitive Binding: New Biochemical Insights Challenge Endocrine Disrupting Chemical Screening Paradigms.

Environmental science & technology·2025
Same author

Integrating Molecular Dynamics, Molecular Docking, and Machine Learning for Predicting SARS-CoV-2 Papain-like Protease Binders.

Molecules (Basel, Switzerland)·2025

Related Experiment Video

Updated: May 8, 2026

DNA Microarrays: Sample Quality Control, Array Hybridization and Scanning
09:27

DNA Microarrays: Sample Quality Control, Array Hybridization and Scanning

Published on: March 15, 2011

Estimating relative noise to signal in DNA microarray data.

Huixiao Hong1, Qilong Hong, Jie Liu

  • 1Division of Systems Biology, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, Arkansas 72079, USA. Huixiao.Hong@fda.hhs.gov

International Journal of Bioinformatics Research and Applications
|September 5, 2013
PubMed
Summary

A new signal to noise index (SNI) reliably estimates relative noise in microarray data. This method validates findings in existing datasets, improving data analysis accuracy.

More Related Videos

Global Gene Expression Analysis Using a Zebrafish Oligonucleotide Microarray Platform
13:14

Global Gene Expression Analysis Using a Zebrafish Oligonucleotide Microarray Platform

Published on: August 10, 2009

A Rapid High-throughput Method for Mapping Ribonucleoproteins (RNPs) on Human pre-mRNA
13:00

A Rapid High-throughput Method for Mapping Ribonucleoproteins (RNPs) on Human pre-mRNA

Published on: December 2, 2009

Related Experiment Videos

Last Updated: May 8, 2026

DNA Microarrays: Sample Quality Control, Array Hybridization and Scanning
09:27

DNA Microarrays: Sample Quality Control, Array Hybridization and Scanning

Published on: March 15, 2011

Global Gene Expression Analysis Using a Zebrafish Oligonucleotide Microarray Platform
13:14

Global Gene Expression Analysis Using a Zebrafish Oligonucleotide Microarray Platform

Published on: August 10, 2009

A Rapid High-throughput Method for Mapping Ribonucleoproteins (RNPs) on Human pre-mRNA
13:00

A Rapid High-throughput Method for Mapping Ribonucleoproteins (RNPs) on Human pre-mRNA

Published on: December 2, 2009

Area of Science:

  • Bioinformatics
  • Genomics
  • Statistical analysis

Background:

  • Microarray data analysis is crucial for gene expression studies.
  • Accurate estimation of signal-to-noise ratio is essential for reliable results.
  • Existing methods may not fully capture noise levels in complex datasets.

Purpose of the Study:

  • To propose a novel method for estimating noise relative to signal in microarray data.
  • To introduce a quantitative Signal to Noise Index (SNI) for noise measurement.
  • To validate the proposed method's reliability and consistency.

Main Methods:

  • Definition of a Signal to Noise Index (SNI).
  • Conducting simulations to establish the SNI's quantitative relationship with relative noise.
  • Application of the SNI method to two established microarray datasets.

Main Results:

  • The SNI was successfully defined and implemented.
  • Simulations provided a clear quantitative relationship between SNI and relative noise.
  • Estimated relative noise levels for two datasets were consistent with original findings.

Conclusions:

  • The proposed SNI method is a reliable tool for estimating relative noise in microarray data.
  • The method enhances the accuracy and interpretability of gene expression analyses.
  • This approach contributes to more robust bioinformatics pipelines.