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

机译：基于注意力机制的双卷积神经网络预测疾病相关lncRNA基因的方法

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摘要

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.
机译：大量研究表明，长的非编码RNA基因（lncRNA）的异常表达与人类疾病密切相关。识别与疾病相关的lncRNA（疾病lncRNA）对于理解疾病的发病机理和病因至关重要。以前的大多数方法都集中在基于浅层学习方法的潜在疾病lncRNA的优先级排序上。该方法无法提取lncRNA-疾病关联的深层和复杂的特征表示。此外，几乎所有方法都忽略了lncRNA，疾病和miRNA之间的相似性，关联性和相互作用关系对关联预测的区别性贡献。提出了一种基于注意力机制的双卷积神经网络预测候选疾病lncRNA的方法，被称为CNNLDA。 CNNLDA对lncRNA相似性，疾病相似性，lncRNA-疾病关联，lncRNA-miRNA相互作用以及miRNA-疾病关联等多个源数据进行了深度整合。从生物学的角度出发，结合有关lncRNA，miRNA和疾病的多种生物学前提来构建特征矩阵。开发了一种基于双卷积神经网络的新颖框架，以学习lncRNA-疾病关联的全局和关注表示。框架的左侧部分利用特征矩阵包含的各种信息来学习lncRNA-疾病关联的整体表示。 lncRNA，miRNA和疾病节点之间的不同连接关系以及这些节点的不同特征对关联预测具有区别作用。因此，我们分别从关系级别和特征级别介绍了注意机制，框架的右部分学习了关联的注意表示。基于交叉验证的实验结果表明，CNNLDA的性能优于几种最先进的方法。关于胃癌，肺癌和结肠癌的案例研究进一步证明了CNNLDA具有发现潜在疾病lncRNA的能力。

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