Fan Yongxian, Cui Juan, Zhu QingQi
School of Computer and Information Security, Guilin University of Electronic Technology Guilin 541004 China
RSC Adv. 2020 Mar 23;10(20):11634-11642. doi: 10.1039/c9ra11043g. eCollection 2020 Mar 19.
LncRNA and miRNA are two non-coding RNA types that are popular in current research. LncRNA interacts with miRNA to regulate gene transcription, further affecting human health and disease. Accurate identification of lncRNA-miRNA interactions contributes to the in-depth study of the biological functions and mechanisms of non-coding RNA. However, relying on biological experiments to obtain interaction information is time-consuming and expensive. Considering the rapid accumulation of gene information and the few computational methods, it is urgent to supplement the effective computational models to predict lncRNA-miRNA interactions. In this work, we propose a heterogeneous graph inference method based on similarity network fusion (SNFHGILMI) to predict potential lncRNA-miRNA interactions. First, we calculated multiple similarity data, including lncRNA sequence similarity, miRNA sequence similarity, lncRNA Gaussian nuclear similarity, and miRNA Gaussian nuclear similarity. Second, the similarity network fusion method was employed to integrate the data and get the similarity network of lncRNA and miRNA. Then, we constructed a bipartite network by combining the known interaction network and similarity network of lncRNA and miRNA. Finally, the heterogeneous graph inference method was introduced to construct a prediction model. On the real dataset, the model SNFHGILMI achieved AUC of 0.9501 and 0.9426 ± 0.0035 based on LOOCV and 5-fold cross validation, respectively. Furthermore, case studies also demonstrate that SNFHGILMI is a high-performance prediction method that can accurately predict new lncRNA-miRNA interactions. The Matlab code and readme file of SNFHGILMI can be downloaded from https://github.com/cj-DaSE/SNFHGILMI.
长链非编码RNA（lncRNA）和微小RNA（miRNA）是当前研究中备受关注的两种非编码RNA类型。lncRNA与miRNA相互作用以调节基因转录，进而影响人类健康和疾病。准确识别lncRNA与miRNA的相互作用有助于深入研究非编码RNA的生物学功能和机制。然而，依靠生物学实验获取相互作用信息既耗时又昂贵。考虑到基因信息的快速积累以及计算方法的匮乏，迫切需要补充有效的计算模型来预测lncRNA与miRNA的相互作用。在这项工作中，我们提出了一种基于相似性网络融合的异质图推理方法（SNFHGILMI）来预测潜在的lncRNA与miRNA相互作用。首先，我们计算了多种相似性数据，包括lncRNA序列相似性、miRNA序列相似性、lncRNA高斯核相似性和miRNA高斯核相似性。其次，采用相似性网络融合方法整合数据，得到lncRNA和miRNA的相似性网络。然后，通过结合lncRNA和miRNA的已知相互作用网络和相似性网络构建二分网络。最后，引入异质图推理方法构建预测模型。在真实数据集上，基于留一法交叉验证（LOOCV）和五折交叉验证，模型SNFHGILMI的曲线下面积（AUC）分别达到了0.9501和0.9426±0.0035。此外，案例研究也表明SNFHGILMI是一种高性能的预测方法，能够准确预测新的lncRNA与miRNA相互作用。SNFHGILMI的Matlab代码和自述文件可从https://github.com/cj-DaSE/SNFHGILMI下载。
