Dual Convolutional Neural Networks With Attention Mechanisms Based Method for Predicting Disease-Related lncRNA Genes - Xuan, Ping; Cao, Yangkun; Zhang, Tiangang; Kong, Rui; Zhang, Zhaogong (Frontiers in Genetics, 2019)
A great many of studies indicated that aberrant expression of long non-coding RNA genes (lncRNAs) is closely related with 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 the 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 the 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.