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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...

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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

Navigating the microarray landscape: a comprehensive review of feature selection techniques and their applications.

Fangling Wang¹, Azlan Mohd Zain¹, Yanjie Ren²

¹Faculty of Computing, Universiti Teknologi Malaysia, Skudai, Johor, Malaysia.

Frontiers in Big Data

|July 25, 2025

Summary

This review explores microarray feature selection methods for biomedical research, addressing high-dimensional and noisy data. It offers guidance for selecting techniques to improve personalized medicine, cancer diagnosis, and drug discovery.

Keywords:

cancer classification feature selection gene expression analysis machine learning microarray data

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Microarray data presents challenges due to high dimensionality and noise.
Effective feature selection is crucial for extracting meaningful biological insights.
Existing techniques vary in strengths, limitations, and applicability across diverse research scenarios.

Purpose of the Study:

To systematically review recent advances in microarray feature selection techniques.
To evaluate the applicability of these methods in biomedical research.
To identify research gaps and propose future directions for novel techniques and applications.

Main Methods:

Systematic literature review of microarray feature selection methods.
Analysis of strengths, limitations, and applicability across different biomedical contexts.
Identification of underexplored areas and future research opportunities.

Main Results:

Comprehensive evaluation of various feature selection methods for microarray data.
Theoretical foundation and practical guidance for researchers.
Highlighting the potential of feature selection in personalized medicine, cancer diagnosis, and drug discovery.

Conclusions:

Feature selection is vital for advancing microarray data analysis in biomedicine.
Interdisciplinary collaboration can drive innovation in this field.
This review provides essential theoretical and practical support for researchers and practitioners.