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DNA Microarrays

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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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Cis-regulatory sequences are short fragments of non-coding DNA that are present on the same chromosomes as the genes that they regulate. These fragments serve as binding sites for transcriptional regulators, proteins that are responsible for controlling gene transcription and differential gene expression across cell types in eukaryotes. Cis-regulatory sequences can be close to the gene of interest or thousands of bases away in the DNA sequence; however, those sequences that are further away are...

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Related Experiment Video

Updated: Jun 6, 2026

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets

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Cross-platform microarray data normalisation for regulatory network inference.

Alina Sîrbu¹, Heather J Ruskin, Martin Crane

¹Centre for Scientific Computing and Complex Systems Modelling, Dublin City University, Dublin, Ireland. asirbu@computing.dcu.ie

|November 25, 2010

Summary

This summary is machine-generated.

Integrating diverse microarray data is crucial for inferring gene regulatory networks (GRNs). A combined Loess and iterative K-means normalization method best addresses time-series data challenges for GRN modeling.

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Area of Science:

Bioinformatics
Systems Biology
Computational Biology

Background:

Inferring Gene Regulatory Networks (GRNs) from time-course microarray data is challenged by high dimensionality due to short time series and numerous genes.
Integrating diverse microarray data sources is essential but complicated by platform and experimental variations.

Purpose of the Study:

To evaluate different normalization approaches for microarray data integration.
To compare preprocessing techniques for reverse engineering quantitative GRN models.

Main Methods:

Analysis of various normalization methods for microarray data integration.
Introduction of two preprocessing approaches based on existing normalization techniques.
Comprehensive comparison of normalized datasets.

Main Results:

Identification of optimal normalization strategies for time-series microarray data.
Evaluation of data preprocessing impacts on GRN inference.

Conclusions:

A normalization method combining Loess and iterative K-means is identified as superior for time-series data in GRN inference.
This approach enhances the accuracy of quantitative GRN models by addressing data integration challenges.