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MLNGCF: circRNA-disease associations prediction with multilayer attention neural graph-based collaborative filtering
Qunzhuo Wu1, Zhaohong Deng1, Wei Zhang1
1School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, China.
Bioinformatics (Oxford, England)
|August 10, 2023
Summary
This study introduces MLNGCF, a novel computational method for predicting circular RNA (circRNA)-disease associations. MLNGCF effectively identifies latent features, outperforming existing methods in predicting these crucial biological interactions.
Area of Science:
- Bioinformatics
- Computational Biology
- Genomics
Background:
- Circular RNAs (circRNAs) are key regulators in biological processes, with abnormal expression linked to various diseases.
- Investigating circRNA-disease associations is vital for understanding disease mechanisms.
- Traditional wet-lab validation of these associations is costly and time-consuming, necessitating computational approaches.
Purpose of the Study:
- To develop an advanced computational method for predicting circRNA-disease associations.
- To address limitations in current methods regarding the consideration of latent features in circRNA-disease interactions.
Main Methods:
- A multilayer attention neural graph-based collaborative filtering (MLNGCF) model was proposed.
- Autoencoders were used to enhance initial features of circRNAs and diseases.
- A multilayer cooperative attention mechanism was applied to a central network for high-order feature extraction.
- Neural network-based collaborative filtering was employed for prediction and model parameter updates.
Main Results:
- The proposed MLNGCF method demonstrated superior performance compared to state-of-the-art approaches.
- Experimental results on benchmark datasets validated the effectiveness of MLNGCF.
- Case studies confirmed the biological relevance of the predicted circRNA-disease associations through literature support.
Conclusions:
- MLNGCF offers a powerful and accurate computational tool for predicting circRNA-disease associations.
- The method's ability to capture latent features enhances the prediction of these critical biological links.
- The open availability of source codes and datasets facilitates further research and application.
