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 Experiment Video

Updated: May 11, 2026

Automated Analysis of C. elegans Fluorescence Images using SegElegans
06:27

Automated Analysis of C. elegans Fluorescence Images using SegElegans

Published on: October 10, 2025

A novel neural network approach to cDNA microarray image segmentation.

Zidong Wang1, Bachar Zineddin, Jinling Liang

  • 1Department of Information Systems and Computing, Brunel University, Uxbridge, Middlesex UB8 3PH, United Kingdom. Zidong.Wang@brunel.ac.uk

Computer Methods and Programs in Biomedicine
|May 15, 2013
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Clinical Efficacy of Laparoscopic Surgery for T4 Colon Cancer Compared with Open Surgery: A Single Center's Experience.

Journal of laparoendoscopic & advanced surgical techniques. Part A·2018
Same author

5-HTT, BMPR2, EDN1, ENG, KCNA5 gene polymorphisms and susceptibility to pulmonary arterial hypertension: A meta-analysis.

Gene·2018
Same author

Effects of Microcystis aeruginosa on the life history traits and SOD activity of Daphnia similoides sinensis.

Environmental science and pollution research international·2018
Same author

Effect of surfactants on the removal of nitrobenzene by Fe-bearing montmorillonite/Fe(II).

Journal of colloid and interface science·2018
Same author

A Novel Tetramethylpyrazine Derivative Protects Against Glutamate-Induced Cytotoxicity Through PGC1α/Nrf2 and PI3K/Akt Signaling Pathways.

Frontiers in neuroscience·2018
Same author

m<sup>6</sup>A mRNA methylation regulates AKT activity to promote the proliferation and tumorigenicity of endometrial cancer.

Nature cell biology·2018
Same journal

Accounting for approximation errors using surrogate-based parameter estimation of cardiac mechanics digital twins.

Computer methods and programs in biomedicine·2026
Same journal

Facial iPPG heatmap patterns based on period-aware autoencoder show association with carotid atherosclerosis towards non-contact hemodynamic assessment.

Computer methods and programs in biomedicine·2026
Same journal

Explainable machine learning models predict liver fibrosis risk and outcome in the general population: Development and multi-cohort external validation.

Computer methods and programs in biomedicine·2026
Same journal

Evaluation of surrogate endpoints for survival outcomes using the surrogate package in R.

Computer methods and programs in biomedicine·2026
Same journal

Relative spectral and frication-based descriptors as numerical indicators of place of articulation shifts in fricatives produced by Polish children.

Computer methods and programs in biomedicine·2026
Same journal

Leaflet resection improves valve expansion and hemodynamic performance in redo TAVI with balloon- and self-expanding transcatheter heart valve configurations.

Computer methods and programs in biomedicine·2026
See all related articles

This study introduces a novel artificial neural network method for segmenting microarray images, improving gene identification accuracy. The technique offers comparable or superior results to existing methods with faster processing times.

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Microarray technology is crucial for understanding DNA and gene expression.
  • Accurate gene identification from microarray images relies on effective segmentation.
  • Image noise and spot variations complicate microarray image segmentation.

Purpose of the Study:

  • To develop an advanced method for microarray image segmentation.
  • To improve the accuracy and efficiency of gene identification in biological experiments.

Main Methods:

  • Utilized a series of artificial neural networks, including multi-layer perceptron (MLP) and Kohonen networks.
  • Applied the proposed method to real-world cDNA microarray images.
  • Performed quantitative comparisons using peak signal-to-noise ratio (PSNR).

Related Experiment Videos

Last Updated: May 11, 2026

Automated Analysis of C. elegans Fluorescence Images using SegElegans
06:27

Automated Analysis of C. elegans Fluorescence Images using SegElegans

Published on: October 10, 2025

Main Results:

  • The proposed artificial neural network method achieved results comparable or superior to commercial software (GenePix®).
  • Demonstrated a faster run time compared to existing segmentation techniques.
  • Successfully identified genes within complex microarray images with high accuracy.

Conclusions:

  • The novel artificial neural network approach provides an effective solution for microarray image segmentation.
  • This method enhances the reliability and speed of extracting biological information from gene expression data.
  • Offers a promising advancement for genomic research and analysis.