A Novel Computational Model for Predicting microRNA–Disease Associations Based on Heterogeneous Graph Convolutional Networks

• Published in: Cells (Basel, Switzerland) Vol. 8; no. 9; p. 977
• Main Authors: Li, Chunyan, Liu, Hongju, Hu, Qian, Que, Jinlong, Yao, Junfeng
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 26.08.2019; MDPI
• Subjects: Algorithms; Area Under Curve; Computational Biology - methods; Computer applications; Computer Simulation; convolution network; cross validation; Databases, Genetic; Deep learning; Disease; Drug development; Genes; Genetic Association Studies; Genetic Predisposition to Disease; graph; heterogeneous; Humans; Machine learning; Medical Subject Headings-MeSH; microRNA; MicroRNAs; MicroRNAs - genetics; miRNA; negative sampling; Neural networks; Propagation; Protein Interaction Maps; Proteins; convolution network; disease; cross validation; microRNA; negative sampling; heterogeneous; graph
• ISSN: 2073-4409; 2073-4409
• DOI: 10.3390/cells8090977

Identifying the interactions between disease and microRNA (miRNA) can accelerate drugs development, individualized diagnosis, and treatment for various human diseases. However, experimental methods are time-consuming and costly. So computational approaches to predict latent miRNA–disease interactions are eliciting increased attention. But most previous studies have mainly focused on designing complicated similarity-based methods to predict latent interactions between miRNAs and diseases. In this study, we propose a novel computational model, termed heterogeneous graph convolutional network for miRNA–disease associations (HGCNMDA), which is based on known human protein–protein interaction (PPI) and integrates four biological networks: miRNA–disease, miRNA–gene, disease–gene, and PPI network. HGCNMDA achieved reliable performance using leave-one-out cross-validation (LOOCV). HGCNMDA is then compared to three state-of-the-art algorithms based on five-fold cross-validation. HGCNMDA achieves an AUC of 0.9626 and an average precision of 0.9660, respectively, which is ahead of other competitive algorithms. We further analyze the top-10 unknown interactions between miRNA and disease. In summary, HGCNMDA is a useful computational model for predicting miRNA–disease interactions.