Zhang Li, Liu Ting, Chen Haoyu, Zhao Qi, Liu Hongsheng
School of Life Science, Liaoning University, Shenyang, 110036, China; Research Center for Computer Simulating and Information Processing of Bio-macromolecules of Shenyang, Liaoning University, Shenyang, 110036, China; Technology Innovation Center for Computer Simulating and Information Processing of Bio-macromolecules of Shenyang, Shenyang, 110036, China.
School of Life Science, Liaoning University, Shenyang, 110036, China; China Medical University, The Queen's University of Belfast Joint College, Shenyang, 110122, China.
Genomics. 2021 May;113(3):874-880. doi: 10.1016/j.ygeno.2021.02.002. Epub 2021 Feb 12.
In the development and treatment of many human diseases, the regulatory roles between lncRNAs and miRNAs are important, but much remains unknown about them; moreover, experimental methods for analyzing them are expensive and time-consuming. In this work, we applied a semi-supervised interactome network-based approach to explore and forecast the latent interaction between lncRNAs and miRNAs. We constructed graphs according to the similarity of each of lncRNAs and miRNAs and determined the number of graphlet interaction isomers between nodes in these two graphs. According to the two graphs and the known interactive relationship, we calculated a score for lncRNA-miRNA pairs, as the prediction result. The results showed that the model (LMI-INGI) was reliable in fivefold cross-validation (AUC = 0.8957, PRE = 0.6815, REC = 0.8842, F1 score = 0.7452, AUPR = 0.9213). We also tested the model with data based on the similarity of expression profile and similarity of function for verifying the applicability of LMI-INGI, and the resulting AUC value was 0.9197 and 0.9006, respectively. Compared with the other four algorithms and variable similarity tests, our model successfully demonstrated superiority and good generalizability. LMI-INGI would be helpful in forecasting interactions between lncRNAs and miRNAs.
在许多人类疾病的发展和治疗过程中，lncRNA与miRNA之间的调控作用至关重要，但人们对它们仍知之甚少；此外，用于分析它们的实验方法既昂贵又耗时。在这项工作中，我们应用了一种基于半监督相互作用组网络的方法来探索和预测lncRNA与miRNA之间的潜在相互作用。我们根据lncRNA和miRNA各自的相似性构建图，并确定这两个图中节点之间的图let相互作用异构体的数量。根据这两个图以及已知的相互作用关系，我们计算lncRNA-miRNA对的得分，作为预测结果。结果表明，该模型（LMI-INGI）在五折交叉验证中是可靠的（AUC = 0.8957，PRE = 0.6815，REC = 0.8842，F1得分 = 0.7452，AUPR = 0.9213）。我们还使用基于表达谱相似性和功能相似性的数据对该模型进行了测试，以验证LMI-INGI的适用性，得到的AUC值分别为0.9197和0.9006。与其他四种算法和可变相似性测试相比，我们的模型成功地展示了优越性和良好的通用性。LMI-INGI将有助于预测lncRNA与miRNA之间的相互作用。
