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GGAECDA: Predicting circRNA-disease associations using graph autoencoder based on graph representation learning
Guanghui Li1, Yawei Lin1, Jiawei Luo2
1School of Information Engineering, East China Jiaotong University, Nanchang, China.
Insights
This study introduces GGAECDA, a novel deep learning model for predicting circular RNA (circRNA) and disease associations. GGAECDA efficiently identifies potential disease biomarkers by integrating graph attention networks and random walks with restart, improving upon traditional methods.
Area of Science:
- Biochemistry and Molecular Biology
- Computational Biology and Bioinformatics
- Genomics and Genetics
Background:
- Circular RNAs (circRNAs) are crucial regulators in biological processes and potential disease biomarkers.
- Traditional methods for identifying circRNA-disease associations are resource-intensive and time-consuming.
- Accurate prediction of circRNA-disease associations is vital for understanding disease mechanisms and developing diagnostics.
Purpose of the Study:
- To develop an advanced deep learning model for predicting circRNA-disease associations.
- To overcome the limitations of traditional experimental methods in identifying novel circRNA-disease interactions.
- To provide a reliable computational tool for guiding research on circRNA functions in diseases.
Main Methods:
- A novel deep learning model, GGAECDA, was developed using a graph autoencoder (GAE) framework.
- The model integrates Graph Attention Network (GAT) for learning low-order neighbor information and Random Walk with Restart (RWR) for high-order neighbor information.
- Feature representations from GAT and RWR were combined and processed through co-trained GAEs to predict circRNA-disease associations.
Main Results:
- The GGAECDA model achieved an average AUC of 0.9359 in five-fold cross-validation.
- Case studies validated the model's capability to identify potential candidate circRNAs for human diseases.
- The model effectively mines low-dimensional representations from circRNA and disease similarity networks.
Conclusions:
- GGAECDA is a powerful and reliable computational tool for predicting circRNA-disease associations.
- The model offers a more efficient alternative to traditional experimental approaches.
- GGAECDA can guide future research into the roles of circRNAs in various pathological conditions.
Abstract:
Numerous studies have shown that circular RNAs (circRNAs) can serve as ideal disease markers as they are involved in most cellular activities of organisms and are key regulators in various pathological processes. Therefore, the association analysis of circRNAs and diseases can explore the role of circRNAs in diseases and provide help for practical medical research. However, the traditional biotechnology are not convenient for identifying unconfirmed interactions between circRNAs and diseases, which need too many resources and long experimental period. In this work, a new deep learning model is advanced, which is based on graph autoencoder (GAE) constructed with graph attention network (GAT) and random walk with restart (RWR) for predicting circRNA-disease associations (GGAECDA). In detail, GAT is designed to learn the hidden representations of circRNAs and diseases through using low-order neighbor information from circRNA similarity network and disease similarity network respectively, while RWR is employed to learn the latent features of circRNAs and diseases via using high-order neighbor information from the same two networks respectively. After that, these two parts of features of circRNAs and diseases are combined to form new feature representations of circRNAs and diseases respectively. Finally, two GAEs are constructed for co-training to fully integrate information from circRNA space and disease space and calculate potential association prediction scores. Unlike previous models, GGAECDA deeply mines low-dimensional representations from node similarity network through using GAT and RWR. The average AUC value obtained from GGAECDA with a five-fold cross-validation result is 0.9359. Furthermore, case studies demonstrate the ability of GGAECDA to detect potential candidate circRNAs for human diseases. The above results show that the GGAECDA model can be used as a reliable tool to guide subsequent studies on the regulatory functions of circRNAs.
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