Long non-coding RNA (LncRNA) and microRNA (miRNA) are both non-coding RNAs that play significant regulatory roles in many life processes. There is cumulating evidence showing that the interaction patterns between lncRNAs and miRNAs are highly related to cancer development, gene regulation, cellular metabolic process, etc. Contemporaneously, with the rapid development of RNA sequence technology, numerous novel lncRNAs and miRNAs have been found, which might help to explore novel regulated patterns. However, the increasing unknown interactions between lncRNAs and miRNAs may hinder finding the novel regulated pattern, and wet experiments to identify the potential interaction are costly and time-consuming. Furthermore, few computational tools are available for predicting lncRNA–miRNA interaction based on a sequential level. In this paper, we propose a hybrid sequence feature-based model, LncMirNet (lncRNA–miRNA interactions network), to predict lncRNA–miRNA interactions via deep convolutional neural networks (CNN). First, four categories of sequence-based features are introduced to encode lncRNA/miRNA sequences including k-mer (k = 1, 2, 3, 4), composition transition distribution (CTD), doc2vec, and graph embedding features. Then, to fit the CNN learning pattern, a histogram-dd method is incorporated to fuse multiple types of features into a matrix. Finally, LncMirNet attained excellent performance in comparison with six other state-of-the-art methods on a real dataset collected from lncRNASNP2 via five-fold cross validation. LncMirNet increased accuracy and area under curve (AUC) by more than 3%, respectively, over that of the other tools, and improved the Matthews correlation coefficient (MCC) by more than 6%. These results show that LncMirNet can obtain high confidence in predicting potential interactions between lncRNAs and miRNAs.

中文翻译：

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LncMirNet：基于核糖核酸序列深度学习预测 LncRNA-miRNA 相互作用


长链非编码RNA（LncRNA）和微小RNA（miRNA）都是非编码RNA，在许多生命过程中发挥着重要的调节作用。越来越多的证据表明，lncRNA和miRNA之间的相互作用模式与癌症的发生、基因调控、细胞代谢过程等高度相关。同时，随着RNA测序技术的快速发展，大量新的lncRNA和miRNA被发现，可能有助于探索新颖的监管模式。然而，lncRNA 和 miRNA 之间越来越多的未知相互作用可能会阻碍发现新的调控模式，而识别潜在相互作用的湿实验既昂贵又耗时。此外，很少有计算工具可用于基于顺序水平预测 lncRNA-miRNA 相互作用。在本文中，我们提出了一种基于混合序列特征的模型 LncMirNet（lncRNA-miRNA 相互作用网络），通过深度卷积神经网络（CNN）预测 lncRNA-miRNA 相互作用。首先，引入四类基于序列的特征来编码 lncRNA/miRNA 序列，包括 k-mer (k = 1, 2, 3, 4)、成分转换分布 (CTD)、doc2vec 和图嵌入特征。然后，为了适应 CNN 学习模式，采用直方图-dd 方法将多种类型的特征融合到矩阵中。最后，通过五倍交叉验证，在从 lncRNASNP2 收集的真实数据集上，与其他六种最先进的方法相比，LncMirNet 获得了优异的性能。与其他工具相比，LncMirNet 的准确度和曲线下面积 (AUC) 分别提高了 3% 以上，马修斯相关系数 (MCC) 提高了 6% 以上。 这些结果表明，LncMirNet 在预测 lncRNA 和 miRNA 之间的潜在相互作用方面可以获得高置信度。