EOESGC: predicting miRNA-disease associations based on embedding of embedding and simplified graph convolutional network

• Published in: BMC medical informatics and decision making Vol. 21; no. 1; p. 319
• Main Authors: Pang, Shanchen, Zhuang, Yu, Wang, Xinzeng, Wang, Fuyu, Qiao, Sibo
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 16.11.2021; BioMed Central; BMC
• Subjects: Algorithms; Alzheimer's disease; Artificial neural networks; Breast cancer; Breast Neoplasms; Complexity; Computational Biology; Computer applications; Convolution; Convolutional codes; Coupled heterogeneous graph; Deep learning; Diagnosis; Drug development; Embedding; Embedding of embedding; Feature selection; Female; Gene expression; Genetic aspects; Graph theory; Health aspects; Health informatics; Humans; Lung cancer; Lung Neoplasms; Machine learning; Methods; MicroRNA; MicroRNAs - genetics; miRNA; miRNA-disease associations; Neural networks; Nodes; Noise; Pathogenesis; Similarity; Simplified graph convolutional network; Training; China; Coupled heterogeneous graph; miRNA-disease associations; Simplified graph convolutional network; Embedding of embedding
• ISSN: 1472-6947; 1472-6947
• DOI: 10.1186/s12911-021-01671-y

A large number of biological studies have shown that miRNAs are inextricably linked to many complex diseases. Studying the miRNA-disease associations could provide us a root cause understanding of the underlying pathogenesis in which promotes the progress of drug development. However, traditional biological experiments are very time-consuming and costly. Therefore, we come up with an efficient models to solve this challenge. In this work, we propose a deep learning model called EOESGC to predict potential miRNA-disease associations based on embedding of embedding and simplified convolutional network. Firstly, integrated disease similarity, integrated miRNA similarity, and miRNA-disease association network are used to construct a coupled heterogeneous graph, and the edges with low similarity are removed to simplify the graph structure and ensure the effectiveness of edges. Secondly, the Embedding of embedding model (EOE) is used to learn edge information in the coupled heterogeneous graph. The training rule of the model is that the associated nodes are close to each other and the unassociated nodes are far away from each other. Based on this rule, edge information learned is added into node embedding as supplementary information to enrich node information. Then, node embedding of EOE model training as a new feature of miRNA and disease, and information aggregation is performed by simplified graph convolution model, in which each level of convolution can aggregate multi-hop neighbor information. In this step, we only use the miRNA-disease association network to further simplify the graph structure, thus reducing the computational complexity. Finally, feature embeddings of both miRNA and disease are spliced into the MLP for prediction. On the EOESGC evaluation part, the AUC, AUPR, and F1-score of our model are 0.9658, 0.8543 and 0.8644 by 5-fold cross-validation respectively. Compared with the latest published models, our model shows better results. In addition, we predict the top 20 potential miRNAs for breast cancer and lung cancer, most of which are validated in the dbDEMC and HMDD3.2 databases. The comprehensive experimental results show that EOESGC can effectively identify the potential miRNA-disease associations.