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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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FCGCNMDA: predicting miRNA-disease associations by applying fully connected graph convolutional networks.

Jiashu Li1,2, Zhengwei Li3,4,5, Ru Nie6,7

  • 1School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, 221116, China.

Molecular Genetics and Genomics : MGG
|June 6, 2020
PubMed
Summary

This study introduces FCGCNMDA, a computational method using graph convolutional networks to predict microRNA-disease associations. It efficiently identifies potential biomarkers for complex diseases, aiding bioinformatics and medicine.

Keywords:
Deep learningFully connected graphGraph convolutional networksmiRNA-disease association

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Area of Science:

  • Bioinformatics
  • Genomics
  • Computational Biology

Background:

  • MicroRNAs (miRNAs) play a role in complex disease development.
  • Identifying miRNA-disease associations is crucial for clinical applications.
  • Traditional experimental methods for identifying miRNA-disease links are costly and time-consuming.

Purpose of the Study:

  • To develop an efficient computational method for predicting latent miRNA-disease associations.
  • To identify novel miRNA biomarkers for disease diagnosis, treatment, and prognosis.

Main Methods:

  • Utilized fully connected graph convolutional networks (FCGCN).
  • Constructed a fully connected graph representing miRNA-disease correlations.
  • Employed a two-layer GCN for training and prediction of unknown miRNA-disease pairs.

Main Results:

  • Achieved high performance with AUC of [Formula: see text] and AUPRC of [Formula: see text] on HMDD v2.0 dataset.
  • Demonstrated high validation rates (98%) for top predicted miRNAs in case studies (Lymphoma, Breast Neoplasms, Prostate Neoplasms).

Conclusions:

  • FCGCNMDA is a reliable computational method for predicting potential miRNA-disease associations.
  • The method aids in discovering novel miRNA biomarkers, advancing bioinformatics and medical research.