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

Synthetic data generation: challenges and perspectives for gastrointestinal medicine.

Nature reviews. Gastroenterology & hepatology·2026

Same author

Clinical evaluation of medical image synthesis: a case study in wireless capsule endoscopy.

Scientific reports·2025

Same author

E pluribus unum interpretable convolutional neural networks.

Scientific reports·2023

Same author

Interpretable EEG-Based Emotion Recognition Using Fuzzy Cognitive Maps.

Studies in health technology and informatics·2023

Same author

Artificial Intelligence in Cardiology-A Narrative Review of Current Status.

Journal of clinical medicine·2022

Same author

Constructive Fuzzy Cognitive Map for Depression Severity Estimation.

Studies in health technology and informatics·2022

Same journal

Self-supervised isotropic reconstruction for abnormality detection in anisotropic MRI.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society·2026

Same journal

WDBDM: Wavelet-based dual-branch diffusion model for low-dose CT and PET denoising.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society·2026

Same journal

ScribSAM: A robust scribble-supervised framework for spatiotemporal segmentation of breast lesions in ultrasound videos.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society·2026

Same journal

Anatomically and biochemically guided deep image prior for sodium MRI denoising.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society·2026

Same journal

Segment Anything Model for medical image segmentation: A review.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society·2026

Same journal

HiCAF-Net: A Hierarchical Cross-Attention Fusion framework for cross-cancer subtype classification using histopathological and genomic data.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 19, 2026

DNA-barcode-based Multiplex Immunofluorescence Imaging to Analyze FFPE Specimens from Genetically Reprogrammed Murine Melanoma

DNA-barcode-based Multiplex Immunofluorescence Imaging to Analyze FFPE Specimens from Genetically Reprogrammed Murine Melanoma

Published on: June 6, 2025

Unsupervised SVM-based gridding for DNA microarray images.

Dimitris Bariamis¹, Dimitris Maroulis, Dimitris K Iakovidis

¹Dept. of Informatics and Telecommunications, University of Athens, Panepistimiopolis, Illisia, 15784 Athens, Greece. d.bariamis@di.uoa.gr

Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society

|November 3, 2009

Summary

This summary is machine-generated.

This study introduces an unsupervised DNA microarray gridding method using support vector machines (SVMs) for accurate spot separation. The novel approach achieves superior performance, ensuring spots are correctly placed within grid cells.

More Related Videos

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

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

Published on: March 1, 2017

Co-culture of Glioblastoma Stem-like Cells on Patterned Neurons to Study Migration and Cellular Interactions

Co-culture of Glioblastoma Stem-like Cells on Patterned Neurons to Study Migration and Cellular Interactions

Published on: February 24, 2021

Related Experiment Videos

Last Updated: Jun 19, 2026

DNA-barcode-based Multiplex Immunofluorescence Imaging to Analyze FFPE Specimens from Genetically Reprogrammed Murine Melanoma

DNA-barcode-based Multiplex Immunofluorescence Imaging to Analyze FFPE Specimens from Genetically Reprogrammed Murine Melanoma

Published on: June 6, 2025

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

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

Published on: March 1, 2017

Co-culture of Glioblastoma Stem-like Cells on Patterned Neurons to Study Migration and Cellular Interactions

Co-culture of Glioblastoma Stem-like Cells on Patterned Neurons to Study Migration and Cellular Interactions

Published on: February 24, 2021

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

DNA microarrays are crucial for gene expression analysis.
Accurate gridding is essential for reliable data extraction from microarrays.
Existing gridding methods face challenges with image artifacts and noise.

Purpose of the Study:

To develop a novel unsupervised method for DNA microarray gridding.
To improve the accuracy and robustness of spot separation in microarray images.
To leverage support vector machines (SVMs) for optimal grid line estimation.

Main Methods:

Unsupervised gridding based on support vector machines (SVMs).
Image analysis to detect spot positions.
Soft-margin linear SVM classifiers to determine optimal grid lines.
Training SVMs using spot locations as vectors.

Main Results:

The method accurately separates spots into distinct grid cells.
Achieved 96.4% of spots completely within their designated grid cells.
Demonstrated robustness against artifacts, noise, weak signals, and image rotation.

Conclusions:

The proposed SVM-based gridding method offers superior performance compared to state-of-the-art techniques.
This approach enhances the reliability of DNA microarray data analysis.
The method is effective even with challenging image conditions.