Graph Convolutional Network and Convolutional Neural Network Based Method for Predicting lncRNA-Disease Associations

Abstract

Aberrant expressions of long non-coding RNAs (lncRNAs) are often associated with diseases and identification of disease-related lncRNAs is helpful for elucidating complex pathogenesis. Recent methods for predicting associations between lncRNAs and diseases integrate their pertinent heterogeneous data. However, they failed to deeply integrate topological information of heterogeneous network comprising lncRNAs, diseases, and miRNAs. We proposed a novel method based on the graph convolutional network and convolutional neural network, referred to as GCNLDA, to infer disease-related lncRNA candidates. The heterogeneous network containing the lncRNA, disease, and miRNA nodes, is constructed firstly. The embedding matrix of a lncRNA-disease node pair was constructed according to various biological premises about lncRNAs, diseases, and miRNAs. A new framework based on a graph convolutional network and a convolutional neural network was developed to learn network and local representations of the lncRNA-disease pair. On the left side of the framework, the autoencoder based on graph convolution deeply integrated topological information within the heterogeneous lncRNA-disease-miRNA network. Moreover, as different node features have discriminative contributions to the association prediction, an attention mechanism at node feature level is constructed. The left side learnt the network representation of the lncRNA-disease pair. The convolutional neural networks on the right side of the framework learnt the local representation of the lncRNA-disease pair by focusing on the similarities, associations, and interactions that are only related to the pair. Compared to several state-of-the-art prediction methods, GCNLDA had superior performance. Case studies on stomach cancer, osteosarcoma, and lung cancer confirmed that GCNLDA effectively discovers the potential lncRNA-disease associations.

A random forest based computational model for predicting novel lncRNA-disease associations

“…To show the prediction ability of the RFLDA, we compare it with several excellent LDA prediction models, such as SIMCLDA [33], Ping's method [18], MFLDA [31], LDAP [39], CNNLDA [44], and GCNLDA [45]. The AUCs and AUPRs of all LDA prediction models are shown in Table 1.…”

“…The comparison results indicate that the RFLDA has excellent ability of LDA prediction. It should be noted that the AUCs and AUPRs of other six models except RFLDA in Table 1 are derived from Xuan et al's work [44,45].…”

“…Guo et al implemented a random forest classifier based model for inferring novel associations among various bimolecular by constructing a molecular association network based on the known associations among diseases, proteins, miRNAs, lncRNAs and drugs [43]. Latterly, Xuan et al proposed a series of convolutional neural network based LDA prediction models, including CNNLDA [44], GCNLDA [45], CNNDLP [46] and LDAPred [47]. CNNLDA learned the global and attention characteristics of lncRNA-disease pairs using convolutional neural networks by integrating the DSS, the LFS, and the lncRNA-disease, miRNAdisease and lncRNA-miRNA associations [44].…”

“…CNNLDA learned the global and attention characteristics of lncRNA-disease pairs using convolutional neural networks by integrating the DSS, the LFS, and the lncRNA-disease, miRNAdisease and lncRNA-miRNA associations [44]. GCNLDA learned the local and network characteristics of lncRNA-disease pairs using convolutional neural network, graph convolutional network and convolutional auto-encoder by combining multiple associations among diseases, miRNAs and lncRNAs [45]. CNNDLP learned the network and attention characteristics of lncRNA-disease pairs using convolutional neural network and convolutional auto-encoder by integrating various associations, interactions and similarities among miRNAs, lncRNAs and diseases [46].…”

Identifying Cancer-Related lncRNAs Based on a Convolutional Neural Network

“…Sumathipala et al ( 2019 ) used a multilevel network topology which includes lncRNA–protein, protein–protein interaction, protein–disease relationship to use network diffusion algorithm to predict disease-related lncRNAs. The graph convolutional network (GCN) and CNN were used on a lncRNA–miRNA–disease network by Xuan et al ( 2019b ). Deng et al ( 2019 ) built lncRNA similarity network, disease similarity network, miRNA similarity network, and their associations.…”