Zhao Yi-Xin, Yu Chang-Qing, Li Li-Ping, Wang Deng-Wu, Song Hui-Fan, Wei Yu
School of information Engineering, Xijing University, Xi'an, China.
College of Grassland and Environment Sciences, Xinjiang Agricultural University, Ürümqi, China.
Front Genet. 2024 May 10;15:1399810. doi: 10.3389/fgene.2024.1399810. eCollection 2024.
Increasing research findings suggest that circular RNA (circRNA) exerts a crucial function in the pathogenesis of complex human diseases by binding to miRNA. Identifying their potential interactions is of paramount importance for the diagnosis and treatment of diseases. However, long cycles, small scales, and time-consuming processes characterize previous biological wet experiments. Consequently, the use of an efficient computational model to forecast the interactions between circRNA and miRNA is gradually becoming mainstream. In this study, we present a new prediction model named BJLD-CMI. The model extracts circRNA sequence features and miRNA sequence features by applying Jaccard and Bert's method and organically integrates them to obtain CMI attribute features, and then uses the graph embedding method Line to extract CMI behavioral features based on the known circRNA-miRNA correlation graph information. And then we predict the potential circRNA-miRNA interactions by fusing the multi-angle feature information such as attribute and behavior through Autoencoder in Autoencoder Networks. BJLD-CMI attained 94.95% and 90.69% of the area under the ROC curve on the CMI-9589 and CMI-9905 datasets. When compared with existing models, the results indicate that BJLD-CMI exhibits the best overall competence. During the case study experiment, we conducted a PubMed literature search to confirm that out of the top 10 predicted CMIs, seven pairs did indeed exist. These results suggest that BJLD-CMI is an effective method for predicting interactions between circRNAs and miRNAs. It provides a valuable candidate for biological wet experiments and can reduce the burden of researchers.
越来越多的研究结果表明，环状RNA（circRNA）通过与微小RNA（miRNA）结合，在复杂人类疾病的发病机制中发挥关键作用。识别它们之间的潜在相互作用对于疾病的诊断和治疗至关重要。然而，以往的生物湿实验存在周期长、规模小和过程耗时等特点。因此，使用高效的计算模型来预测circRNA与miRNA之间的相互作用正逐渐成为主流。在本研究中，我们提出了一种名为BJLD-CMI的新预测模型。该模型通过应用杰卡德（Jaccard）方法和伯特（Bert）方法提取circRNA序列特征和miRNA序列特征，并将它们有机整合以获得CMI属性特征，然后基于已知的circRNA-miRNA相关图信息，使用图嵌入方法Line提取CMI行为特征。接着，我们通过自动编码器网络中的自动编码器融合属性和行为等多角度特征信息，来预测潜在的circRNA-miRNA相互作用。BJLD-CMI在CMI-9589和CMI-9905数据集上的ROC曲线下面积分别达到了94.95%和90.69%。与现有模型相比，结果表明BJLD-CMI展现出最佳的整体性能。在案例研究实验中，我们进行了PubMed文献检索，以确认在预测的前10个CMI中，确实存在7对。这些结果表明，BJLD-CMI是预测circRNA与miRNA相互作用的有效方法。它为生物湿实验提供了有价值的候选对象，并可以减轻研究人员的负担。
