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

Abstract

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.

SMALF: miRNA-disease associations prediction based on stacked autoencoder and XGBoost

“…Zhang et al[ 23 ] obtained two splicing matrices from the similarity matrix and association matrix of disease and miRNA, and then adopted two variational autoencoders to predict the unknown miRNA-disease interaction. Xuan et al [ 24 ] proposed CNNMDA constructed by CNN to train the local and global features acquired from the two embedding layers learn from the association between miRNA and disease respectively to expose the relationship between miRNA and disease. Chen et al [ 25 ] presented a model that can easily extend to higher dimension datasets called LRSSLMDA implemented by Laplacian regulation and L1-norm to optimize the function to get the possible connection between disease and miRNA.…”

DANE-MDA: Predicting microRNA-disease associations via deep attributed network embedding

“…Therefore, some of the recent techniques in graph embedding are used for predicting miRNA-disease associations, such as graph convolutional networks (Kipf and Welling, 2016), matrix factorization (He et al, 2018(He et al, , 2019, and Bayesian learning (Hu et al, 2019). For example, Xuan et al (2019) utilized convolutional neural networks and network representation learning to design a computational model to predict miRNA-disease associations. Zheng et al (2020a) exploited the graph embedding method and random forest classifier to reveal novel miRNA and disease associations.…”

Prediction of miRNA-Disease Association Using Deep Collaborative Filtering

“…For example, Chen et al [ 42 ] proposed a model of restricted Boltzmann machine to predict multiple types of miRNA-disease association (RBMMMDA). Xuan et al [ 43 ] presented the convolutional network-based methods for predicting candidate disease. Zeng et al [ 44 ] developed a neural network model to predict miRNA-disease associations (NNMDA).…”