circ2DGNN: circRNA-disease Association Prediction via Transformer-based Graph Neural Network.

Abstract

Investigating the associations between circRNA and diseases is vital for comprehending the underlying mechanisms of diseases and formulating effective therapies. Computational prediction methods often rely solely on known circRNA-disease data, indirectly incorporating other biomolecules' effects by computing circRNA and disease similarities based on these molecules. However, this approach is limited, as other biomolecules also play significant roles in circRNA-disease interactions. To address this, we construct a comprehensive heterogeneous network incorporating data on human circRNAs, diseases, and other biomolecule interactions to develop a novel computational model, circ2DGNN, which is built upon a heterogeneous graph neural network. circ2DGNN directly takes heterogeneous networks as inputs and obtains the embedded representation of each node for downstream link prediction through graph representation learning. circ2DGNN employs a Transformer-like architecture, which can compute heterogeneous attention score for each edge, and perform message propagation and aggregation, using a residual connection to enhance the representation vector. It uniquely applies the same parameter matrix only to identical meta-relationships, reflecting diverse parameter spaces for different relationship types. After fine-tuning hyperparameters via five-fold cross-validation, evaluation conducted on a test dataset shows circ2DGNN outperforms existing state-of-the-art(SOTA) methods.