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Updated: Apr 17, 2026
07:28
JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
Published on: October 19, 2021
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AI-driven gene-sets, networks, pathways, and interactions analyses of multi-omics data.
Zongliang Yue1, Zeru Zhang1, Quanghuy Thanh1
1Harrison College of Pharmacy, Auburn University, Auburn, AL, United States.
Progress in Molecular Biology and Translational Science
|April 15, 2026
Summary
PAGER 3.0 enhances translational bioinformatics by integrating gene-set and pathway analysis (PAGs) for interpreting large omics datasets. This tool aids in extracting mechanistic insights for systems biology and precision medicine applications.
Area of Science:
- Bioinformatics
- Translational Bioinformatics
- Systems Biology
Background:
- High-throughput omics technologies generate vast biological datasets, posing challenges for mechanistic interpretation.
- Translational bioinformatics requires methods to translate molecular data into biological insights.
Purpose of the Study:
- To introduce PAGER 3.0, an advanced knowledge ecosystem for pathway and gene-set analysis.
- To detail the architecture, analytical workflows, and utility of PAGER 3.0 for interpreting complex biological data.
Main Methods:
- Development of PAGER 3.0 with an expanded collection of Pathways, Annotated gene-lists, and Gene signatures (PAGs).
- Implementation of ontology-aware knowledge navigation and weighted enrichment analytics.
- Integration of structured network-based prioritization tools.
Main Results:
- PAGER 3.0 offers an expanded and curated collection of PAGs.
- The system provides ontology-aware navigation and weighted enrichment analytics.
- Demonstrated utility through a leukemia single-cell transcriptomics case study.
Conclusions:
- PAGER 3.0 significantly advances PAG-based analysis for interpreting large-scale omics data.
- The tool supports applications in systems biology, precision medicine, machine learning, and drug repurposing.
- Facilitates the transformation of molecular data into actionable biological insights.
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