Dual Convolutional Neural Networks With Attention Mechanisms Based Method for Predicting Disease-Related lncRNA Genes

Abstract

A lot of studies indicated that aberrant expression of long non-coding RNA genes (lncRNAs) is closely related to human diseases. Identifying disease-related lncRNAs (disease lncRNAs) is critical for understanding the pathogenesis and etiology of diseases. Most of the previous methods focus on prioritizing the potential disease lncRNAs based on shallow learning methods. The methods fail to extract the deep and complex feature representations of lncRNA-disease associations. Furthermore, nearly all the methods ignore the discriminative contributions of the similarity, association, and interaction relationships among lncRNAs, disease, and miRNAs for the association prediction. A dual convolutional neural networks with attention mechanisms based method is presented for predicting the candidate disease lncRNAs, and it is referred to as CNNLDA. CNNLDA deeply integrates the multiple source data like the lncRNA similarities, the disease similarities, the lncRNA-disease associations, the lncRNA-miRNA interactions, and the miRNA-disease associations. The diverse biological premises about lncRNAs, miRNAs, and diseases are combined to construct the feature matrix from the biological perspectives. A novel framework based on the dual convolutional neural networks is developed to learn the global and attention representations of the lncRNA-disease associations. The left part of the framework exploits the various information contained by the feature matrix to learn the global representation of lncRNA-disease associations. The different connection relationships among the lncRNA, miRNA, and disease nodes and the different features of these nodes have the discriminative contributions for the association prediction. Hence we present the attention mechanisms from the relationship level and the feature level respectively, and the right part of the framework learns the attention representation of associations. The experimental results based on the cross validation indicate that CNNLDA yields superior performance than several state-of-the-art methods. Case studies on stomach cancer, lung cancer, and colon cancer further demonstrate CNNLDA's ability to discover the potential disease lncRNAs.

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].…”

“…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]. 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].…”

“…Inspired by previous works [44,48], we implemented a random forest and feature selection based LDA prediction model (RFLDA). First, the RFLDA represented lncRNAdisease pairs by a high-dimensional feature vector that integrated the DSS, the LFS, the experiment-supported LDAs and MDAs, and the miRNA-lncRNA interactions.…”