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GCN-GENE: A novel method for prediction of coronary heart disease-related genes

Tong Zhang1, Yixuan Lin1, Weimin He1

  • 1Department of Cardiology, The Sixth Affiliated Hospital, School of Medicine, South China University of Technology, Guangdong, China.

Computers in Biology and Medicine
|October 10, 2022
PubMed

Insights

Identifying coronary heart disease (CHD) genes is crucial. A new deep learning method using biological networks accurately predicts CHD-related genes, offering a faster, cost-effective approach for research.

Area of Science:

  • Genomics
  • Computational Biology
  • Cardiovascular Disease Research

Background:

  • Coronary heart disease (CHD) is a leading cause of mortality, often linked to lifestyle and genetics.
  • Epidemiological studies indicate a familial tendency for CHD, yet specific genetic factors remain largely undiscovered.
  • Current methods for identifying CHD-related genes are time-consuming and expensive.

Purpose of the Study:

  • To develop a computational method for large-scale identification of coronary heart disease-related genes.
  • To overcome the limitations of traditional experimental validation methods.
  • To enable targeted biological experiments for CHD gene discovery.

Main Methods:

  • Constructed gene interaction networks.
  • Extracted gene expression levels from various tissues as features.
  • Developed a deep learning model (GCN-GENE) integrating network and expression data for gene identification.

Main Results:

  • The proposed GCN-GENE model achieved an Area Under the Curve (AUC) of 0.75 and an Area Under the Precision-Recall Curve (AUPR) of 0.78.
  • The model demonstrated higher accuracy compared to existing methods.
  • Cross-validation confirmed the reliability of GCN-GENE in predicting CHD-related genes.

Conclusions:

  • Deep learning on biological networks provides an effective computational approach for identifying coronary heart disease-related genes.
  • GCN-GENE offers a reliable and efficient alternative to traditional experimental methods.
  • This method facilitates the discovery of novel genetic factors contributing to coronary heart disease.

Keywords:
Coronary heart diseaseGene expressionGene networkGraph convolution network

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