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Updated: Mar 21, 2026

Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization
Published on: October 3, 2025
Differential distribution improves gene selection stability and has competitive classification performance for
Dario Strbenac1, Graham J Mann2, Jean Y H Yang1
1School of Mathematics and Statistics, University of Sydney, NSW 2006, Australia.
This study introduces differential distribution (DD) to identify gene biomarkers by combining differential expression (DE) and differential variability (DV). DD improves gene selection stability and uncovers complementary biomarkers for better cancer prediction and treatment.
Area of Science:
- Genomics
- Bioinformatics
- Cancer Research
Background:
- Differential expression (DE) is a standard method for identifying gene biomarkers.
- Deregulation of transcription factors or epigenetic signals can alter gene expression variability (DV).
- Relying solely on DE or DV may miss crucial biomarkers.
Purpose of the Study:
- To develop a novel approach, differential distribution (DD), for assessing gene importance.
- To integrate information from both DE and DV into a unified metric.
- To improve biomarker discovery for enhanced prediction and treatment in cancer.
Main Methods:
- Developed a new metric, differential distribution (DD), combining DE and DV.
- Evaluated feature ranking and selection stability using DD.
- Compared DD performance against DE and DV alone.
Main Results:
- DD demonstrated 2-3 times better performance in feature ranking and selection stability compared to DE or DV alone.
- DD achieved equivalent error rates to DE and DV.
- DD identified a complementary set of genes compared to DE and DV.
Conclusions:
- Differential distribution (DD) offers a more robust method for gene biomarker assessment.
- DD enhances the discovery of novel cancer biomarkers.
- This approach has the potential to improve diagnostic and therapeutic strategies.
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