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Updated: Oct 29, 2025

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

Biological Network Mining.

Zongliang Yue¹, Da Yan², Guimu Guo¹

¹The University of Alabama at Birmingham, Birmingham, AL, USA.

Methods in Molecular Biology (Clifton, N.J.)

|July 12, 2021

Summary

This summary is machine-generated.

This study presents a pipeline for identifying key biomedical entities and their associations within biological networks. The tools BEERE, WIPER, and PAGER 2.0 aid in network analysis and function interpretation.

Keywords:

Gene Microarray Network mining Protein Protein–protein interaction (PPI)

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Area of Science:

Bioinformatics
Systems Biology
Computational Biology

Background:

Biological networks are crucial for understanding complex biological systems.
Identifying significant biomedical entities and their interactions is essential for elucidating biological functions.
Existing methods may lack comprehensive approaches for prioritizing entities and associations within networks.

Purpose of the Study:

To introduce an integrated pipeline for mining significant biomedical entities and their associations in biological networks.
To prioritize bioentities and their relationships to reveal underlying biological functions.
To present three complementary tools: BEERE, WIPER, and PAGER 2.0 for network analysis and functional interpretation.

Main Methods:

Development of a computational pipeline integrating network analysis and functional enrichment.
Utilizing the BEERE tool for Biomedical Entity Expansion, Ranking and Explorations.
Employing the WIPER tool for entity-to-entity association ranking.
Leveraging the PAGER 2.0 service for gene enrichment analysis.

Main Results:

The pipeline effectively identifies and prioritizes significant bioentities within biological networks.
The integrated approach enhances the understanding of biological functions by analyzing entity associations.
Demonstration of the utility of BEERE, WIPER, and PAGER 2.0 in network analysis and function interpretation.

Conclusions:

The proposed pipeline offers a robust framework for dissecting biological networks.
The combination of BEERE, WIPER, and PAGER 2.0 provides a powerful toolkit for biomedical research.
This approach facilitates the discovery of novel biological insights through network analysis.