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Related Experiment Video

Updated: Feb 10, 2026

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

Detecting complexes from edge-weighted PPI networks via genes expression analysis.

Zehua Zhang^1,2, Jian Song^1,2,3, Jijun Tang^1,2,4

¹School of Computer Science and Technology, Tianjin University, Tianjin, People's Republic of China.

BMC Systems Biology

|May 11, 2018

Summary

This study introduces a new method for identifying protein complexes in protein-protein interaction (PPI) networks using co-expression data. The approach enhances complex prediction accuracy compared to existing methods.

Keywords:

Complex detection Edge-weighting scheme Gene expression analysis Graph information PPI networks

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Area of Science:

Bioinformatics
Systems Biology
Computational Biology

Background:

Protein complexes are crucial for cellular functions and biological processes.
Accurate identification of protein complexes in protein-protein interaction (PPI) networks is essential for understanding cellular organization.
Identifying protein complexes aids in elucidating protein functions.

Purpose of the Study:

To propose a novel method for identifying protein complexes within PPI networks.
To leverage co-expression information to improve the accuracy of complex identification.
To enhance the prediction of protein complexes by integrating graph and gene expression analysis.

Main Methods:

Utilized the Markov Cluster Algorithm with an edge-weighting scheme for initial complex detection.
Developed significant features, including graph information and gene expression analysis, for complex filtering and modification.
Evaluated the proposed method on two experimental yeast PPI networks (DIP and MIPS).

Main Results:

The method achieved a Precision of 0.6004 and F-Measure of 0.5528 on the DIP network.
On the MIPS network, the method obtained an F-Measure of 0.3774 and S_n of 0.3453.
Demonstrated significant improvements over existing methods, with Precision increased by at least 0.1752 and F-Measure by at least 0.0448.

Conclusions:

The proposed method effectively identifies protein complexes in PPI networks.
The integration of co-expression information and feature-based filtering enhances prediction quality.
The method outperforms several state-of-the-art approaches in identifying protein complexes.