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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: Aug 12, 2025

Sample Preparation to Bioinformatics Analysis of DNA Methylation: Association Strategy for Obesity and Related Trait Studies

Sample Preparation to Bioinformatics Analysis of DNA Methylation: Association Strategy for Obesity and Related Trait Studies

Published on: May 6, 2022

Group-shrinkage feature selection with a spatial network for mining DNA methylation data.

Xinlu Tang¹, Zhanfeng Mo², Cheng Chang³

¹Medical Image and Health Informatics Lab, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.

Computers in Biology and Medicine

|January 27, 2023

Summary

This summary is machine-generated.

This study introduces a novel group-shrinkage algorithm for DNA methylation biomarker discovery, effectively identifying clustered sites while excluding isolated ones. This method enhances biomarker candidate identification and clinical application by integrating biological knowledge.

Keywords:

DNA methylation Feature selection Group-shrinkage Spatial network

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Last Updated: Aug 12, 2025

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Genome-Wide Analysis of DNA Methylation in Gastrointestinal Cancer

Genome-Wide Analysis of DNA Methylation in Gastrointestinal Cancer

Published on: September 18, 2020

Area of Science:

Genomics
Epigenetics
Biomarker Discovery

Background:

DNA methylation data analysis is crucial for identifying disease biomarkers and understanding pathogenesis.
Current methods using spatial correlation, group regularization, and network constraints have limitations, including the inability to exclude isolated differential sites.

Purpose of the Study:

To develop a group-shrinkage feature selection algorithm to identify clustered DNA methylation sites and exclude isolated ones.
To enhance biomarker discovery by incorporating spatial correlation and biological prior knowledge.

Main Methods:

A network-guided group-shrinkage strategy was developed, utilizing a spatial network constructed from DNA methylation site correlations.
The method penalizes weakly-correlated isolated methylation sites through network structure constraints, accounting for uneven site distribution.

Main Results:

The proposed method effectively rejects isolated methylation sites, outperforming existing advanced regularization methods.
Experimental simulations and applications demonstrated the method's efficiency and clinical value for biomarker candidate discovery.

Conclusions:

The developed algorithm provides an efficient and reliable method for DNA methylation biomarker discovery.
Integrating biological prior knowledge into feature selection enhances reliability and facilitates clinical application, promoting further research in this area.