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Identifying protein complexes based on node embeddings obtained from protein-protein interaction networks.

Xiaoxia Liu1, Zhihao Yang2, Shengtian Sang1

  • 1College of Computer Science and Technology, Dalian University of Technology, Dalian, 116024, Liaoning, People's Republic of China.

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|September 23, 2018
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Summary

This study introduces a novel supervised learning method using network node embeddings to identify protein complexes. The approach improves detection accuracy on protein-protein interaction networks, advancing cell system analysis.

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Node embeddingsProtein complex detectionRandom forestSupervised learning method

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Area of Science:

  • Computational Biology
  • Systems Biology
  • Bioinformatics

Background:

  • Protein complexes are crucial for cellular functions.
  • Existing computational methods for protein complex identification often rely on dense subgraphs in protein-protein interaction (PPI) networks.
  • These subgraph-based methods have limitations in detecting complexes that are not densely connected.

Purpose of the Study:

  • To develop an improved computational method for identifying protein complexes.
  • To overcome the limitations of traditional subgraph-based approaches in PPI network analysis.
  • To leverage known complex information to guide the discovery of new protein complexes.

Main Methods:

  • A supervised learning approach utilizing network node embeddings derived from human PPI networks.
  • Weighting protein interactions based on the similarity of their node embeddings.
  • Employing a random forest model for filtering candidate complexes to refine predictions.

Main Results:

  • The proposed method effectively identifies protein complexes from PPI networks.
  • Experimental validation on human and yeast PPI networks demonstrates improved performance in protein complex detection.
  • The node embedding-based approach enhances the accuracy of identifying true protein complexes.

Conclusions:

  • A novel and effective method for protein complex identification from PPI networks has been developed.
  • This method shows significant potential for advancing the field of protein complex detection.
  • The approach is applicable to both human and yeast protein interaction data.