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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: May 27, 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

Biclustering of time series microarray data.

Jia Meng¹, Yufei Huang

¹Department of Electrical and Computer Engineering, University of Texas at San Antonio, San Antonio, TX, USA.

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

|December 2, 2011

Summary

This summary is machine-generated.

This study reviews biclustering algorithms for analyzing gene expression data. It introduces the enrichment constraint time-dependent iterative signature algorithm (ECTDISA) for identifying temporal transcription modules in time series microarray data.

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Analysis of Multidimensional Microscopy Data Using Cell-ACDC

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Published on: November 7, 2025

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Clustering is a key technique in microarray data analysis.
Biclustering offers advanced methods for exploring complex biological datasets.
Time series analysis is crucial for understanding dynamic biological processes.

Purpose of the Study:

To review biclustering concepts and algorithms for time series microarray data.
To introduce and detail the enrichment constraint time-dependent iterative signature algorithm (ECTDISA).
To demonstrate the application of ECTDISA in identifying biologically relevant temporal transcription modules.

Main Methods:

Review of biclustering algorithms.
Focus on the iterative signature algorithm (ISA).
Detailed explanation of the enrichment constraint time-dependent ISA (ECTDISA).

Main Results:

ECTDISA effectively identifies biologically meaningful temporal transcription modules.
The algorithm is applied to time series microarray data from Kaposi's Sarcoma-associated Herpesvirus (KSHV) infection.
Demonstrates the utility of biclustering in uncovering dynamic gene expression patterns.

Conclusions:

Biclustering, particularly ECTDISA, is a powerful approach for time series microarray data analysis.
ECTDISA facilitates the discovery of coordinated gene expression patterns over time.
This method aids in understanding complex biological systems, such as viral infections.