School of Computer Science and Software Engineering, University of Science and Technology Liaoning, Anshan, 114051, China.
School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, 221116, China.
Brief Bioinform. 2022 Nov 19;23(6). doi: 10.1093/bib/bbac463.
Long non-coding RNA (lncRNA) and microRNA (miRNA) are two typical types of non-coding RNAs (ncRNAs), their interaction plays an important regulatory role in many biological processes. Exploring the interactions between unknown lncRNA and miRNA can help us better understand the functional expression between lncRNA and miRNA. At present, the interactions between lncRNA and miRNA are mainly obtained through biological experiments, but such experiments are often time-consuming and labor-intensive, it is necessary to design a computational method that can predict the interactions between lncRNA and miRNA. In this paper, we propose a method based on graph convolutional neural (GCN) network and conditional random field (CRF) for predicting human lncRNA-miRNA interactions, named GCNCRF. First, we construct a heterogeneous network using the known interactions of lncRNA and miRNA in the LncRNASNP2 database, the lncRNA/miRNA integration similarity network, and the lncRNA/miRNA feature matrix. Second, the initial embedding of nodes is obtained using a GCN network. A CRF set in the GCN hidden layer can update the obtained preliminary embeddings so that similar nodes have similar embeddings. At the same time, an attention mechanism is added to the CRF layer to reassign weights to nodes to better grasp the feature information of important nodes and ignore some nodes with less influence. Finally, the final embedding is decoded and scored through the decoding layer. Through a 5-fold cross-validation experiment, GCNCRF has an area under the receiver operating characteristic curve value of 0.947 on the main dataset, which has higher prediction accuracy than the other six state-of-the-art methods.
长链非编码 RNA（lncRNA）和 microRNA（miRNA）是两种典型的非编码 RNA（ncRNA），它们的相互作用在许多生物过程中发挥着重要的调节作用。探索未知 lncRNA 与 miRNA 之间的相互作用可以帮助我们更好地理解 lncRNA 与 miRNA 之间的功能表达。目前，lncRNA 与 miRNA 的相互作用主要是通过生物实验获得的，但这类实验往往费时费力，需要设计一种可以预测 lncRNA 与 miRNA 相互作用的计算方法。本文提出了一种基于图卷积神经网络（GCN）和条件随机场（CRF）的用于预测人 lncRNA-miRNA 相互作用的方法，命名为 GCNCRF。首先，我们使用 LncRNASNP2 数据库中的已知 lncRNA 和 miRNA 相互作用、lncRNA/miRNA 整合相似性网络和 lncRNA/miRNA 特征矩阵构建一个异构网络。其次，使用 GCN 网络获取节点的初始嵌入。CRF 在 GCN 隐藏层中的设置可以更新获得的初步嵌入，使得相似的节点具有相似的嵌入。同时，在 CRF 层中添加注意力机制，对节点重新分配权重，以便更好地捕捉重要节点的特征信息，并忽略一些影响较小的节点。最后，通过解码层对最终嵌入进行解码和评分。通过 5 折交叉验证实验，GCNCRF 在主数据集上的接收器工作特征曲线下面积值为 0.947，预测精度高于其他六种最先进的方法。
