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A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
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Computational method using heterogeneous graph convolutional network model combined with reinforcement layer for

Dan Huang1, JiYong An2, Lei Zhang3

  • 1School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, 21116, Jiangsu, China.

BMC Bioinformatics
|July 25, 2022
PubMed
Summary

This study introduces HGCNELMDA, a computational model for predicting microRNA (miRNA)-disease associations. The model achieves high accuracy, offering a cost-effective alternative to traditional methods for identifying disease-related miRNAs.

Keywords:
Graph convolutional networkmiRNA and disease interactions

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • MicroRNAs (miRNAs) are crucial in human complex diseases.
  • Traditional experimental methods for miRNA-disease association are costly and time-consuming.
  • Developing efficient computational methods for miRNA-disease prediction is essential.

Purpose of the Study:

  • To develop an accurate and efficient computational model for predicting miRNA-disease associations.
  • To improve upon existing methods for identifying potential links between miRNAs and diseases.

Main Methods:

  • Developed HGCNELMDA, a heterogeneous graph convolutional network with an enhanced layer.
  • Incorporated random walk with restart for feature optimization.
  • Utilized a reinforcement layer and attention mechanism to retain node information and recalculate neighborhood influence.

Main Results:

  • Achieved 93.47% AUC in global leave-one-out cross-validation (LOOCV) and 93.01% average AUC in fivefold cross-validation.
  • Demonstrated superior performance compared to state-of-the-art methods.
  • Identified experimentally validated miRNAs for lung, prostate, and pancreatic cancers with high precision (48-50 out of top 50).

Conclusions:

  • HGCNELMDA is a reliable and effective method for predicting disease-related miRNAs.
  • The model offers a cost-effective alternative for miRNA-disease association studies.
  • A freely available web server (http://124.221.62.44:8080/HGCNELMDA.jsp) has been developed to support future research.