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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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Updated: Oct 10, 2025

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

Supervised Methods for Biomarker Detection from Microarray Experiments.

Angela Serra^1,2,3, Luca Cattelani^1,2,3, Michele Fratello^1,2,3

¹Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland.

Methods in Molecular Biology (Clifton, N.J.)

|December 13, 2021

Summary

This summary is machine-generated.

This chapter details biomarker discovery using microarray data for disease prediction and drug evaluation. It covers methodologies, challenges, validation, and multiomics approaches for identifying biological indicators.

Keywords:

Biological validation Biomarker Classifier Data unbalancing Feature selection Hyperparameter estimation Microarray Model selection Multiomics Validation metrics

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Last Updated: Oct 10, 2025

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Area of Science:

Biotechnology and Bioinformatics
Molecular Biology
Computational Biology

Background:

Biomarkers are crucial indicators of biological system states.
Microarray technology is widely applied for biomarker identification and predictive modeling.
Understanding signaling pathways and biological interactions relies on activation biomarkers.

Purpose of the Study:

To describe methodologies for biomarker discovery and predictive modeling from microarray data.
To address challenges in biomarker detection from biological and computational perspectives.
To discuss biomarker validation and explore multiomics strategies.

Main Methods:

Utilizing microarray data for high-throughput gene expression analysis.
Applying computational algorithms for biomarker identification and predictive model construction.
Reviewing established and emerging techniques in biomarker validation.

Main Results:

Outlines key methodologies for identifying reliable biomarkers.
Highlights computational and biological challenges in biomarker discovery.
Provides insights into integrating multiomics data for enhanced biomarker detection.

Conclusions:

Biomarker discovery from microarray data requires careful biological and computational consideration.
Validation is essential for reliable biomarker application in clinical settings.
Multiomics strategies offer promising avenues for comprehensive biomarker detection.