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

Identifying Significant Features in Cancer Methylation Data Using Gene Pathway Segmentation.

Cancer informatics·2016

Same author

An algorithm for finding biologically significant features in microarray data based on a priori manifold learning.

PloS one·2014

Same journal

Data Analysis and Classification of Autism Spectrum Disorder Using Principal Component Analysis.

Advances in bioinformatics·2020

Same journal

Peptide-Protein Interaction Studies of Antimicrobial Peptides Targeting Middle East Respiratory Syndrome Coronavirus Spike Protein: An In Silico Approach.

Advances in bioinformatics·2019

Same journal

<i>In Silico</i> Screening of Aptamers Configuration against Hepatitis B Surface Antigen.

Advances in bioinformatics·2019

Same journal

Novel Deleterious nsSNPs within <i>MEFV</i> Gene that Could Be Used as Diagnostic Markers to Predict Hereditary Familial Mediterranean Fever: Using Bioinformatics Analysis.

Advances in bioinformatics·2019

Same journal

Gastroenterology Meets Machine Learning: Status Quo and Quo Vadis.

Advances in bioinformatics·2019

Same journal

Immunoinformatics Approach for Multiepitopes Vaccine Prediction against Glycoprotein B of Avian Infectious Laryngotracheitis Virus.

Advances in bioinformatics·2019

See all related articles

Search research articles

Related Experiment Video

Updated: Apr 7, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

A Review of Feature Selection and Feature Extraction Methods Applied on Microarray Data.

Zena M Hira¹, Duncan F Gillies¹

¹Department of Computing, Imperial College London, London SW7 2AZ, UK.

Advances in Bioinformatics

|July 15, 2015

Summary

This summary is machine-generated.

This study explores dimensionality reduction techniques for high-dimensional microarray data. Comparing feature selection and extraction methods helps improve gene expression data classification accuracy and efficiency.

More Related Videos

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

Related Experiment Videos

Last Updated: Apr 7, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

High-dimensional microarray data presents analysis challenges due to large size and complex gene interrelationships.
Dimensionality reduction is crucial for improving the accuracy and efficiency of biological data classification.

Purpose of the Study:

To summarize and compare various dimensionality reduction methods for high-dimensional microarray data.
To guide the selection of appropriate feature selection and extraction techniques for gene expression analysis.

Main Methods:

Review and summarization of popular feature selection and feature extraction techniques.
Comparative analysis of the advantages and disadvantages of different dimensionality reduction approaches.

Main Results:

Identified numerous feature selection and extraction methods applicable to microarray data.
Detailed comparison highlighting the strengths and weaknesses of each method for specific applications.

Conclusions:

Effective dimensionality reduction enhances classification accuracy by removing redundant and irrelevant features.
Understanding method-specific trade-offs optimizes computational time and resource utilization in gene expression analysis.