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...
RNA-seq03:21

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while microarray-based...

You might also read

Related Articles

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

Sort by
Same author

StrainMake: reproducible hybrid metagenomics with MAG recovery and strain-level resolution.

Bioinformatics (Oxford, England)·2026
Same author

Learning inherent genetic patterns and trait associations with deep generative models for discrete genotype simulation.

GigaScience·2026
Same author

A gut microbiome-kidney-heart axis predictive of future cardiovascular diseases.

Nature communications·2026
Same author

PlantSAM: An object detection-driven segmentation pipeline for herbarium specimens.

Applications in plant sciences·2025
Same author

Prominent mediatory role of gut microbiome in the effect of lifestyle on host metabolic phenotypes.

Gut microbes·2025
Same author

Self-supervised representation learning on gene expression data.

Bioinformatics (Oxford, England)·2025

Related Experiment Video

Updated: Jul 11, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Feature construction from synergic pairs to improve microarray-based classification.

Blaise Hanczar1, Jean-Daniel Zucker, Corneliu Henegar

  • 1Laboratoire d'Informatique Médicale et Bioinformatique (Lim & Bio), Université Paris 13, 93017 Bobigny, France. hanczar_blaise@yahoo.fr

Bioinformatics (Oxford, England)
|October 11, 2007
PubMed
Summary

This study introduces FeatKNN, a novel dimension reduction method for microarray data analysis. By quantifying gene expression interactions, FeatKNN significantly enhances classification accuracy, uncovering biological mechanisms.

More Related Videos

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets
03:37

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets

Published on: March 1, 2024

The Terroir Concept Interpreted through Grape Berry Metabolomics and Transcriptomics
13:02

The Terroir Concept Interpreted through Grape Berry Metabolomics and Transcriptomics

Published on: October 5, 2016

Related Experiment Videos

Last Updated: Jul 11, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets
03:37

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets

Published on: March 1, 2024

The Terroir Concept Interpreted through Grape Berry Metabolomics and Transcriptomics
13:02

The Terroir Concept Interpreted through Grape Berry Metabolomics and Transcriptomics

Published on: October 5, 2016

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Gene Expression Analysis

Background:

  • Microarray experiments generate high-dimensional data, posing challenges for classification due to a large number of genes (attributes) and few samples.
  • Existing dimension reduction techniques often overlook the crucial aspect of transcriptional interactions between genes.

Purpose of the Study:

  • To develop and validate a new dimension reduction and feature construction method for improving microarray-based classification accuracy.
  • To assess the utility of quantifying gene expression interactions for enhanced data analysis.

Main Methods:

  • Utilized a mutual information measure to identify elementary information constituents in gene expression profiles.
  • Developed FeatKNN, a method that exploits information from synergic gene pairs for classification.
  • Employed a heuristic search to select informative gene pairs and constructed new features based on KNN classifier margins.

Main Results:

  • Demonstrated that interactional information is as significant as individual gene expression profiles for classification.
  • Achieved significant accuracy improvements on public microarray datasets using FeatKNN with various classifiers.
  • Showcased the potential of synergic gene pairs to reveal biological mechanisms underlying cellular processes.

Conclusions:

  • FeatKNN effectively improves microarray classification accuracy by incorporating gene interaction information.
  • The method offers a valuable approach for uncovering biologically relevant gene interactions and mechanisms.