Predicting miRNA-Disease Associations Based on Heterogeneous Graph Attention Networks - Ji, Cunmei; Wang, Yutian; Ni, Jiancheng; Zheng, Chunhou; Su, Yansen (Frontiers in Genetics, 2021)
In recent years, more and more evidence has shown that microRNAs (miRNAs) play an important role in the regulation of post-transcriptional gene expression, and are closely related to human diseases. Many studies have also revealed that miRNAs can be served as promising biomarkers for the potential diagnosistic and treatment of human diseases. The interaction between miRNA and human disease has rarely been demonstrated, and the underlying mechanism of miRNAs is not clear. Therefore, researchers have paid attention to the computational approaches, which can not only save time and money, but also improve the efficiency and accuracy of biological experiments. In this work, we proposed a Heterogeneous Graph Attention Networks (GAT) based method for miRNA-disease associations prediction, named HGATMDA. We constructed a heterogeneous graph for miRNAs and diseases, introduced weighted DeepWalk and GAT methods to extract features of miRNAs and diseases from the graph. Moreover, a fully-connected neural networks is used to predict correlation scores between miRNA-disease pairs. Experimental results under 5-fold cross validation (5-fold CV) showed that HGATMDA achieves better prediction performance than other state-of-the-art methods. In addition, we performed three case studies on breast neoplasms, lung neoplasms and kidney neoplasms. The results showed that for above three diseases, 50 out of top 50 candidates were confirmed by the validation datasets. Therefore, HGATMDA is suitable as an effective tool for further biological studies to identity potential diseases-related miRNAs.