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CRPGCN: predicting circRNA-disease associations using graph convolutional network based on heterogeneous network
Zhihao Ma1, Zhufang Kuang2, Lei Deng3
1School of Computer and Information Engineering, Central South University of Forestry and Technology, Changsha, China.
Insights
This study introduces a novel computational method, CRPGCN, to predict associations between circular RNAs (circRNAs) and diseases. The algorithm effectively identifies potential disease-related circRNAs, aiding in diagnosis and treatment strategies.
Area of Science:
- Genomics and Bioinformatics
- Computational Biology
- Disease Biomarker Discovery
Background:
- Circular RNAs (circRNAs) show potential as biomarkers for disease diagnosis and treatment.
- The functional roles and disease associations of most circRNAs remain largely unknown.
- Current experimental methods for predicting circRNA-disease associations are often costly and time-consuming.
Purpose of the Study:
- To develop an efficient computational method for predicting circRNA-disease associations.
- To overcome limitations of existing methods that use insufficient circRNA attribute information.
- To provide a more accurate and cost-effective approach for identifying potential circRNA-disease links.
Main Methods:
- A novel algorithm, CRPGCN, integrating Graph Convolutional Network (GCN), Random Walk with Restart (RWR), and Principal Component Analysis (PCA) was developed.
- RWR was employed to enhance similarity associations between neighboring nodes.
- PCA was utilized for dimensionality reduction and feature extraction, improving the proximity of related circRNAs and diseases.
Main Results:
- The CRPGCN algorithm successfully learned features between circRNAs and diseases using heterogeneous adjacency and feature matrices.
- Cross-validation studies demonstrated the algorithm's predictive power.
- The method achieved high Area Under the ROC Curve (AUC) values: 0.9490 (2-fold), 0.9720 (5-fold), and 0.9722 (10-fold).
Conclusions:
- The proposed CRPGCN method is effective in predicting associations between circRNAs and diseases.
- CRPGCN offers a valuable computational tool for advancing research in circRNA-mediated diseases.
- This approach contributes to a more efficient discovery of disease biomarkers and therapeutic targets.
Background:
The existing studies show that circRNAs can be used as a biomarker of diseases and play a prominent role in the treatment and diagnosis of diseases. However, the relationships between the vast majority of circRNAs and diseases are still unclear, and more experiments are needed to study the mechanism of circRNAs. Nowadays, some scholars use the attributes between circRNAs and diseases to study and predict their associations. Nonetheless, most of the existing experimental methods use less information about the attributes of circRNAs, which has a certain impact on the accuracy of the final prediction results. On the other hand, some scholars also apply experimental methods to predict the associations between circRNAs and diseases. But such methods are usually expensive and time-consuming. Based on the above shortcomings, follow-up research is needed to propose a more efficient calculation-based method to predict the associations between circRNAs and diseases.
Results:
In this study, a novel algorithm (method) is proposed, which is based on the Graph Convolutional Network (GCN) constructed with Random Walk with Restart (RWR) and Principal Component Analysis (PCA) to predict the associations between circRNAs and diseases (CRPGCN). In the construction of CRPGCN, the RWR algorithm is used to improve the similarity associations of the computed nodes with their neighbours. After that, the PCA method is used to dimensionality reduction and extract features, it makes the connection between circRNAs with higher similarity and diseases closer. Finally, The GCN algorithm is used to learn the features between circRNAs and diseases and calculate the final similarity scores, and the learning datas are constructed from the adjacency matrix, similarity matrix and feature matrix as a heterogeneous adjacency matrix and a heterogeneous feature matrix.
Conclusions:
After 2-fold cross-validation, 5-fold cross-validation and 10-fold cross-validation, the area under the ROC curve of the CRPGCN is 0.9490, 0.9720 and 0.9722, respectively. The CRPGCN method has a valuable effect in predict the associations between circRNAs and diseases.
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