Inferring the Disease-Associated miRNAs Based on Network Representation Learning and Convolutional Neural Networks

• Published in: International journal of molecular sciences 2019-07, Vol.20 (15), p.3648
• Main Authors: Xuan, Ping, Sun, Hao, Wang, Xiao, Zhang, Tiangang, Pan, Shuxiang
• Format: Article
• Language: English
• Subjects: Algorithms; Computational Biology - methods; convolutional neural network; Databases, Genetic; Deep Learning; Disease; disease-associated miRNAs; Etiology; Gene expression; Genetic Predisposition to Disease; Humans; Methods; MicroRNAs; MicroRNAs - genetics; Models, Biological; network representation learning; Neural networks; Neural Networks, Computer; non-negative matrix factorization; Performance evaluation; ROC Curve
• ISSN: 1422-0067; 1661-6596; 1422-0067
• DOI: 10.3390/ijms20153648

Identification of disease-associated miRNAs (disease miRNAs) are critical for understanding etiology and pathogenesis. Most previous methods focus on integrating similarities and associating information contained in heterogeneous miRNA-disease networks. However, these methods establish only shallow prediction models that fail to capture complex relationships among miRNA similarities, disease similarities, and miRNA-disease associations. We propose a prediction method on the basis of network representation learning and convolutional neural networks to predict disease miRNAs, called CNNMDA. CNNMDA deeply integrates the similarity information of miRNAs and diseases, miRNA-disease associations, and representations of miRNAs and diseases in low-dimensional feature space. The new framework based on deep learning was built to learn the original and global representation of a miRNA-disease pair. First, diverse biological premises about miRNAs and diseases were combined to construct the embedding layer in the left part of the framework, from a biological perspective. Second, the various connection edges in the miRNA-disease network, such as similarity and association connections, were dependent on each other. Therefore, it was necessary to learn the low-dimensional representations of the miRNA and disease nodes based on the entire network. The right part of the framework learnt the low-dimensional representation of each miRNA and disease node based on non-negative matrix factorization, and these representations were used to establish the corresponding embedding layer. Finally, the left and right embedding layers went through convolutional modules to deeply learn the complex and non-linear relationships among the similarities and associations between miRNAs and diseases. Experimental results based on cross validation indicated that CNNMDA yields superior performance compared to several state-of-the-art methods. Furthermore, case studies on lung, breast, and pancreatic neoplasms demonstrated the powerful ability of CNNMDA to discover potential disease miRNAs.