miRNA-Disease Association Prediction based on Heterogeneous Graph Transformer with Multi-view similarity and Random Auto-encoder

MicroRNAs (miRNAs) are a class of short non-coding single-stranded RNA molecules that play a key role in gene expression regulation. Understanding the association between miRNAs and diseases is crucial for disease diagnosis and treatment. Although the wet experimental methods can be used to determine the associations, they are both laborious and expensive. In this paper, we propose a novel computational method called TWMHGT for predicting the associations between miRNAs and diseases based a two-way Multi-layer Heterogeneous Graph Transformer (MHGT) framework. For the first way, multi-view similarity of miRNAs and diseases is used as the input encodings for MHGT, and for the second way, random auto-encoders is used to generate the input encodings. In each MHGT way, the encodings of each layer are concatenated to obtain comprehensive embeddings of miRNAs and diseases, thereby integrating multiple high-level information. Then, the attention mechanisms are used to fuse the embeddings generated from the two-way MHGT. During the decoding stage, the fused embeddings are decoded to obtain the predicted association matrix by using matrix multiplication. Our model was benchmarked on two datasets and the 5-fold and 10-fold cross-validation results show that TWMHGT outperforms the state-of-the-art methods in terms of AUC, AUPR, accuracy, sensitivity, and specificity. Furthermore, we conducted case studies on three different diseases to validate the predictive performance of TWMHGT. The results show excellent performance in all cases, indicating the potential of TWMHGT in discovering novel miRNA-disease associations.