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Related Experiment Video

Updated: Jan 31, 2026

TurboID-Based Proximity Labeling for In Planta Identification of Protein-Protein Interaction Networks

TurboID-Based Proximity Labeling for In Planta Identification of Protein-Protein Interaction Networks

Published on: May 17, 2020

Protein complexes identification based on go attributed network embedding.

Bo Xu^1,2, Kun Li³, Wei Zheng^4,5

¹School of Software Technology, Dalian University of Technology, No.321 Tuqiang Road, Economic Development Zone, Dalian, 116024, China. boxu@dlut.edu.cn.

BMC Bioinformatics

|December 22, 2018

Summary

We developed GANE, a novel method for identifying protein complexes using Gene Ontology (GO) attributed network embedding. GANE effectively integrates diverse biological data for improved protein complex prediction in proteomics research.

Keywords:

Network embedding Protein complexes identification Protein-protein interaction network

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Last Updated: Jan 31, 2026

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Identification of protein complexes with quantitative proteomics in S. cerevisiae

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Area of Science:

Proteomics
Computational Biology
Bioinformatics

Background:

Protein complex identification is crucial in proteomics.
Integrating diverse biological evidence into computational methods remains challenging.
Attributed network embedding offers a unified framework for integrating biological data.

Purpose of the Study:

To propose GANE, a novel method for protein complex prediction.
To leverage Gene Ontology (GO) attributed network embedding for enhanced accuracy.
To provide a unified framework for integrating multiple biological evidences.

Main Methods:

Learned protein vector representations from a GO attributed protein-protein interaction (PPI) network.
Constructed a weighted adjacency matrix based on vector similarity.
Employed clique mining and density-based ranking to identify seed cores.
Defined protein complexes as seed cores with their attached proteins.

Main Results:

GANE demonstrated superior performance compared to six existing methods on five yeast PPI networks.
Experimental results showed improved accuracy across various evaluation metrics.
The method effectively integrates topological and attributed information for complex prediction.

Conclusions:

GANE offers a robust framework for integrating diverse biological information in protein complex identification.
The learned protein vector representations have potential applications in other areas like PPI and disease gene prediction.