PERGAT: Pretrained Embeddings of Graph Neural Networks for miRNA-Cancer Association Prediction

MicroRNAs (miRNAs) play a crucial role in the regulation of gene expression and have been implicated in the pathogenesis of various cancers. Predicting miRNA-cancer associations is essential for understanding cancer mechanisms and developing therapeutic strategies. In this work, we introduce a computational method named PERGAT (Pretrained Embeddings based on the Residual Graph Attention Network) for predicting miRNA-cancer associations. Extensive experimental results demonstrate the superior performance of PERGAT compared to other state-of-the-art methods. We achieved the AUC as high as 0.9641 in terms of the area under the Receiver Operating Characteristic (ROC) curves, and the area under the precision-recall curves as 0.9377. Our approach leverages the structure of miRNA-disease networks to capture complex relationships efficiently and improves prediction accuracy. Additionally, PERGAT exhibits exceptional performance in three cancer case studies, underscoring its reliability for studying miRNA-disease associations. The data and source codes are available at https://github.com/ericsaliway/PERGAT.