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Protein Networks02:26

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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
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Spectral clustering on protein-protein interaction networks via constructing affinity matrix using attributed graph

Kamal Berahmand1, Elahe Nasiri2, Rojiar Pir Mohammadiani3

  • 1School of Computer Sciences, Science and Engineering Faculty, Queensland University of Technology (QUT), Brisbane, Australia.

Computers in Biology and Medicine
|October 16, 2021
PubMed
Summary

This study introduces a new method, text-associated DeepWalk-Spectral Clustering (TADW-SC), to identify protein complexes by integrating network structure and node features. TADW-SC improves accuracy in protein-protein interaction network analysis.

Keywords:
Affinity matrixGraph embeddingProtein complexes identificationProtein-protein interaction networkSpectral clustering

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Network Science

Background:

  • Protein complex identification is crucial for understanding biological mechanisms.
  • Existing methods often overlook node feature information in protein-protein interaction networks.
  • Both network topology and node features are vital for accurate protein complex identification.

Purpose of the Study:

  • To propose a novel spectral clustering method, text-associated DeepWalk-Spectral Clustering (TADW-SC), for identifying protein complexes in attributed networks.
  • To enhance protein complex identification by integrating network topology with node attribute information.
  • To improve the accuracy of the affinity matrix calculation for spectral clustering.

Main Methods:

  • Utilized text-associated DeepWalk (TADW) to generate embedding vectors for proteins.
  • Calculated the affinity matrix using cosine similarity between TADW embedding vectors.
  • Applied spectral clustering to attributed networks for protein complex identification.

Main Results:

  • The proposed TADW-SC method demonstrates superior performance compared to existing state-of-the-art methods.
  • Achieved high accuracy in identifying protein complexes on both real and synthetic protein network datasets.
  • The integration of node features significantly improved the effectiveness of spectral clustering.

Conclusions:

  • TADW-SC effectively identifies protein complexes by leveraging both structural and attribute information in networks.
  • The method offers a significant advancement in protein complex identification, outperforming current approaches.
  • This approach provides a more comprehensive analysis of protein-protein interaction networks.