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Updated: May 15, 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

Network-based anomaly detection algorithm reveals proteins with major roles in human tissues.

Dima Kagan¹, Juman Jubran², Esti Yeger-Lotem^2,3

¹Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer Sheva 84105, Israel.

|April 8, 2025

Summary

This summary is machine-generated.

We developed a new machine learning method, WGAND, to find unusual proteins in tissue-specific interaction networks. This helps identify proteins critical for specific bodily functions and diseases.

Keywords:

anomaly detection machine learning protein–protein interaction (PPI) networks weighted graphs

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Published on: July 12, 2022

Area of Science:

Systems Biology
Bioinformatics
Network Science

Background:

Protein-protein interactions (PPIs) are crucial for organismal health and understanding cellular processes.
Tissue-specific protein content influences morphology and function, necessitating tissue-specific network analysis.
Weighted PPI networks reveal tissue-specific processes and disease mechanisms, with anomalous nodes potentially indicating key functions.

Purpose of the Study:

To introduce Weighted Graph Anomalous Node Detection (WGAND), a novel machine-learning algorithm for identifying anomalous nodes in weighted graphs.
To test WGAND's ability to detect proteins with key tissue-specific functions by analyzing weighted PPI networks.

Main Methods:

Developed WGAND, a machine-learning algorithm that estimates expected edge weights and uses deviations to detect anomalies in weighted graphs.
Applied WGAND to weighted PPI networks from 17 human tissues.
Evaluated WGAND's performance using ROC curve and precision at K metrics.

Main Results:

WGAND successfully identified anomalous nodes in human tissue-specific PPI networks.
High-ranking anomalous nodes were enriched for proteins involved in tissue-specific diseases and biological processes (e.g., neuron signaling, spermatogenesis).
WGAND demonstrated superior performance compared to other methods in anomaly detection.

Conclusions:

WGAND is a powerful tool for detecting biologically significant anomalous proteins.
The algorithm provides insights into critical tissue-specific processes and diseases, aiding biomarker and therapeutic target discovery.
WGAND is a versatile, open-source tool applicable to any weighted graph across various scientific fields.