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PNAGMDA: A Principal Neighborhood Aggregation Based Graph Neural Network for miRNA-Disease Association Prediction
Abstract:
Increasing research suggests that microRNAs (miRNAs) serve an essential function as biomarkers in various diseases. The variations in miRNA expression can influence their corresponding mRNAs, which, in turn, regulate the expression of target genes. Recently, graph neural networks (GNNs) have been widely utilized to predict miRNA-disease associations. However, a single GNN model is insufficient for fully learning node representations. Furthermore, individual aggregation methods struggle to effectively extract diverse structural information and node weights. To address these challenges, we propose a method that incorporates Principal Neighborhood Aggregation (PNA) and Graph Attention Networks (GAT) for miRNA-disease association prediction. First, we integrated multiple datasets to construct a weighted heterogeneous graph that models miRNA-LncRNA-disease interactions. Subsequently, PNA extracted node representations using multiple aggregators simultaneously. Additionally, features derived from both PNA and GAT were fused using an attention mechanism. These combined representations were then fed into a fully connected neural network for prediction. Experimental results demonstrate that PNAGMDA achieves exceptional performance, with AUC values of 93.82% and 92.77% on HMDD v2.0 and v3.2, respectively. Case studies, along with supplementary findings, confirm PNAGMDA's reliability for miRNA-disease prediction.
Insights
This study introduces PNAGMDA, a novel method combining Principal Neighborhood Aggregation (PNA) and Graph Attention Networks (GAT) to enhance microRNA (miRNA)-disease association prediction. PNAGMDA significantly improves prediction accuracy, offering a reliable tool for identifying disease-related miRNAs.
Area of Science:
- Biomedical Informatics
- Computational Biology
- Genomics
Background:
- MicroRNAs (miRNAs) are crucial biomarkers for various diseases, with expression variations impacting disease pathways.
- Predicting miRNA-disease associations is vital for understanding disease mechanisms and developing targeted therapies.
- Existing graph neural network (GNN) models face limitations in capturing comprehensive node representations and diverse structural information.
Purpose of the Study:
- To develop an advanced computational method for accurate miRNA-disease association prediction.
- To overcome limitations of single GNN models and aggregation methods in learning node representations.
- To leverage integrated datasets and advanced network architectures for improved predictive performance.
Main Methods:
- Constructed a weighted heterogeneous graph integrating miRNA-LncRNA-disease interactions from multiple datasets.
- Employed Principal Neighborhood Aggregation (PNA) for robust node representation extraction using multiple aggregators.
- Fused features from PNA and Graph Attention Networks (GAT) via an attention mechanism for enhanced prediction.
Main Results:
- The proposed PNAGMDA method achieved high performance with Area Under the Curve (AUC) values of 93.82% on HMDD v2.0 and 92.77% on HMDD v3.2.
- Demonstrated superior accuracy in predicting miRNA-disease associations compared to existing methods.
- Case studies validated the reliability and effectiveness of PNAGMDA in identifying disease-associated miRNAs.
Conclusions:
- PNAGMDA offers a powerful and reliable approach for miRNA-disease association prediction.
- The integration of PNA and GAT effectively captures complex relationships within biological networks.
- This method holds significant potential for advancing biomarker discovery and personalized medicine.
