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A graph regularized generalized matrix factorization model for predicting links in biomedical bipartite networks.

Zi-Chao Zhang1,2, Xiao-Fei Zhang3, Min Wu4

  • 1Guangdong Key Laboratory of Intelligent Information Processing, Key Laboratory of Media Security, Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ), Shenzhen University, Shenzhen 518060, China.

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This study introduces Graph Regularized Generalized Matrix Factorization (GRGMF) for predicting links in biomedical networks. GRGMF effectively identifies potential relationships even for new nodes, aiding disease treatment and drug discovery.

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Area of Science:

  • Biomedical Informatics
  • Network Science
  • Computational Biology

Background:

  • Predicting links in biomedical bipartite networks is crucial for understanding complex diseases and discovering drug targets.
  • Existing computational methods often struggle with new nodes lacking prior link information.

Purpose of the Study:

  • To propose a novel link prediction method, Graph Regularized Generalized Matrix Factorization (GRGMF), for biomedical bipartite networks.
  • To address the limitations of existing methods in handling nodes with no known links.

Main Methods:

  • Formulated a generalized matrix factorization model to capture latent patterns in observed links.
  • Incorporated adaptive neighborhood information into node representation learning.
  • Introduced graph regularization terms using external affinity data to enhance latent representations.

Main Results:

  • GRGMF demonstrated competitive performance across six real-world biomedical datasets.
  • The method effectively predicts potential links, including for nodes with limited initial data.
  • Experimental results validate the effectiveness of GRGMF in biomedical link prediction.

Conclusions:

  • GRGMF offers a robust approach for link prediction in biomedical bipartite networks.
  • The method enhances the discovery of potential disease associations and drug targets.
  • GRGMF provides a valuable tool for advancing biomedical research and therapeutic development.