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Related Concept Videos

DNA Microarrays02:34

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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Related Experiment Video

Updated: Jun 7, 2026

Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization
03:08

Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization

Published on: October 3, 2025

Intertwining threshold settings, biological data and database knowledge to optimize the selection of differentially

Paul Chuchana1, Philippe Holzmuller, Frederic Vezilier

  • 1INSERM, Unité 844 - Montpellier, France. paul.chuchana@inserm.fr

Plos One
|October 27, 2010
PubMed
Summary

This study introduces a novel statistical method for analyzing microarray data, improving the identification of differentially expressed genes by integrating biological parameters. The approach enhances transcriptomic analysis accuracy for better biological insights.

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High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions
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Last Updated: Jun 7, 2026

Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization
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Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization

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High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions
14:58

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

Published on: March 5, 2022

Area of Science:

  • Bioinformatics
  • Transcriptomics
  • Systems Biology

Background:

  • Existing microarray analysis tools lack integration of deregulated genes into metabolic pathways.
  • Objective criteria for determining differential gene expression based on biological functions are absent.

Purpose of the Study:

  • To develop a new statistical approach for transcriptomic analysis of microarrays.
  • To improve the selection of differentially expressed genes by incorporating biological parameters.

Main Methods:

  • An iterative statistical method was developed to optimize gene selection.
  • Gene selection stringency was linked to p-value and occurrence rate parameters.
  • The method integrates stringency, biological data, and a knowledge database to visualize molecular interactions.

Main Results:

  • The approach successfully identified optimal thresholds for selecting differentially expressed genes.
  • Network and pathway analyses were used to highlight molecular interactions.
  • Application to human macrophage response to lipopolysaccharide confirmed method's accuracy.

Conclusions:

  • The proposed method accurately determines the best threshold for identifying truly differentially expressed genes.
  • This approach enhances the biological interpretation of microarray data.
  • It provides a more objective criterion for differential gene expression analysis.