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

Evaluating COVID-19 vaccine allocation policies using Bayesian m-top exploration.

Scientific reports·2026
Same author

Benchmarking knowledge graph embedding models for the prediction of oligogenic combinations.

Briefings in bioinformatics·2026
Same author

Short-term atrial fibrillation onset prediction using machine learning.

European heart journal. Digital health·2025
Same author

Autonomic Nervous System Activity before Atrial Fibrillation Onset as Assessed by Heart Rate Variability.

Reviews in cardiovascular medicine·2025
Same author

A shared robot control system combining augmented reality and motor imagery brain-computer interfaces with eye tracking.

Journal of neural engineering·2024
Same author

User Evaluation of a Shared Robot Control System Combining BCI and Eye Tracking in a Portable Augmented Reality User Interface.

Sensors (Basel, Switzerland)·2024
Same journal

circ2DGNN: circRNA-Disease Association Prediction via Transformer-Based Graph Neural Network.

IEEE/ACM transactions on computational biology and bioinformatics·2024
Same journal

Hierarchical Hypergraph Learning in Association- Weighted Heterogeneous Network for miRNA- Disease Association Identification.

IEEE/ACM transactions on computational biology and bioinformatics·2024
Same journal

Discriminative Domain Adaption Network for Simultaneously Removing Batch Effects and Annotating Cell Types in Single-Cell RNA-Seq.

IEEE/ACM transactions on computational biology and bioinformatics·2024
Same journal

MLW-BFECF: A Multi-Weighted Dynamic Cascade Forest Based on Bilinear Feature Extraction for Predicting the Stage of Kidney Renal Clear Cell Carcinoma on Multi-Modal Gene Data.

IEEE/ACM transactions on computational biology and bioinformatics·2024
Same journal

An End-to-End Knowledge Graph Fused Graph Neural Network for Accurate Protein-Protein Interactions Prediction.

IEEE/ACM transactions on computational biology and bioinformatics·2024
Same journal

Generative Biomedical Event Extraction With Constrained Decoding Strategy.

IEEE/ACM transactions on computational biology and bioinformatics·2024
See all related articles

Related Experiment Video

Updated: May 24, 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

A survey on filter techniques for feature selection in gene expression microarray analysis.

Cosmin Lazar1, Jonatan Taminau, Stijn Meganck

  • 1Computational Modeling Group, Department of Computer Science, Vrije Universiteit Brussel, Pleinlaan 2, Brussels 1050, Belgium. vlazar@vub.ac.be

IEEE/ACM Transactions on Computational Biology and Bioinformatics
|February 22, 2012
PubMed
Summary
This summary is machine-generated.

This survey focuses on filter feature selection methods for discovering informative genes in gene expression microarray analysis. It unifies and details these methods, aiding in gene prioritization and biomarker discovery.

More Related Videos

Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples
07:30

Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples

Published on: June 8, 2020

Bacterial Gene Expression Analysis Using Microarrays
29:41

Bacterial Gene Expression Analysis Using Microarrays

Published on: May 28, 2007

Related Experiment Videos

Last Updated: May 24, 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

Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples
07:30

Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples

Published on: June 8, 2020

Bacterial Gene Expression Analysis Using Microarrays
29:41

Bacterial Gene Expression Analysis Using Microarrays

Published on: May 28, 2007

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • High-dimensional data analysis is crucial in fields like bioinformatics.
  • Feature selection (FS) methods are essential for handling datasets with numerous variables.
  • Existing FS methods are broadly categorized into filters, wrappers, and embedded techniques.

Purpose of the Study:

  • To survey filter feature selection methods specifically for gene expression microarray (GEM) analysis.
  • To provide a unified framework for understanding informative feature discovery, also known as differentially expressed genes (DEGs) discovery, gene prioritization, or biomarker discovery.
  • To standardize notation for clear technical detail and comparison of methods.

Main Methods:

  • Focus on filter methods within the broader feature selection landscape.
  • Analysis of methods used for informative feature discovery in gene expression data.
  • Development of a unified framework with standardized notations.

Main Results:

  • Categorization of feature selection methods into filters, wrappers, embedded, and ensemble techniques.
  • Detailed examination of filter methods for gene expression microarray analysis.
  • Highlighting commonalities and specificities of various filter feature selection approaches.

Conclusions:

  • Filter methods offer a robust approach for identifying key features in high-dimensional gene expression data.
  • A unified framework enhances the understanding and application of these methods.
  • Standardized notation facilitates reproducible research and comparative analysis in bioinformatics.