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A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research

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Published on: August 16, 2017

Gene set enrichment analysis: performance evaluation and usage guidelines.

Jui-Hung Hung¹, Tun-Hsiang Yang, Zhenjun Hu

¹Bioinformatics and Integrative Biology, University of Massachusetts Medical School, 364 Plantation Street, Worcester, MA 01605, USA.

Briefings in Bioinformatics

|September 9, 2011

Summary

This summary is machine-generated.

Understanding gene plasticity involves identifying gene sets responding to environmental changes. This study reviews methods for analyzing gene expression data and proposes a voting approach to assess different analytical techniques.

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

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Published on: October 3, 2025

Area of Science:

Molecular Biology
Systems Biology
Bioinformatics

Background:

A key challenge in biology is elucidating the molecular underpinnings of plasticity, which involves identifying gene sets responding to environmental stimuli.
Current approaches often involve identifying common themes or pathways within differentially expressed gene sets.

Purpose of the Study:

To critically review existing methods for analyzing gene sets and their relationships.
To propose best practices for each step of the analysis.
To introduce a novel voting method for evaluating different analytical approaches.

Main Methods:

Critical review of existing computational frameworks for gene set analysis.
Development of best practice guidelines for data analysis.
Proposal of a voting-based ensemble method for assessing analytical performance.

Main Results:

Identified common components across various gene set analysis methods.
Provided recommendations for improving the reliability and reproducibility of results.
Demonstrated the utility of a voting method for method assessment.

Conclusions:

A systematic review and proposed voting method can help standardize and improve the analysis of gene expression data related to plasticity.
The proposed voting approach offers a robust way to evaluate methods in the absence of a gold standard dataset.