Liu Qingyu, Yu Junjie, Cai Yanning, Zhang Guishan, Dai Xianhua
School of Electronics and Information Technology, Sun Yat-Sen University, Guangzhou, China.
Macquarie Business School, Macquarie University, Sydney, NSW, Australia.
Front Genet. 2022 Feb 22;13:832244. doi: 10.3389/fgene.2022.832244. eCollection 2022.
Emerging evidence indicates that circRNA can regulate various diseases. However, the mechanisms of circRNA in these diseases have not been fully understood. Therefore, detecting potential circRNA-disease associations has far-reaching significance for pathological development and treatment of these diseases. In recent years, deep learning models are used in association analysis of circRNA-disease, but a lack of circRNA-disease association data limits further improvement. Therefore, there is an urgent need to mine more semantic information from data. In this paper, we propose a novel method called Semantic Association Analysis by Embedding and Deep learning (SAAED), which consists of two parts, a neural network embedding model called Entity Relation Network (ERN) and a Pseudo-Siamese network (PSN) for analysis. ERN can fuse multiple sources of data and express the information with low-dimensional embedding vectors. PSN can extract the feature between circRNA and disease for the association analysis. CircRNA-disease, circRNA-miRNA, disease-gene, disease-miRNA, disease-lncRNA, and disease-drug association information are used in this paper. More association data can be introduced for analysis without restriction. Based on the CircR2Disease benchmark dataset for evaluation, a fivefold cross-validation experiment showed an AUC of 98.92%, an accuracy of 95.39%, and a sensitivity of 93.06%. Compared with other state-of-the-art models, SAAED achieves the best overall performance. SAAED can expand the expression of the biological related information and is an efficient method for predicting potential circRNA-disease association.
新出现的证据表明，环状RNA可调控多种疾病。然而，环状RNA在这些疾病中的作用机制尚未完全明确。因此，检测潜在的环状RNA与疾病的关联对这些疾病的病理发展和治疗具有深远意义。近年来，深度学习模型被用于环状RNA与疾病的关联分析，但环状RNA与疾病的关联数据匮乏限制了其进一步发展。因此，迫切需要从数据中挖掘更多语义信息。本文提出了一种名为嵌入与深度学习语义关联分析（SAAED）的新方法，该方法由两部分组成，一个名为实体关系网络（ERN）的神经网络嵌入模型和一个用于分析的伪孪生网络（PSN）。ERN可以融合多源数据并用低维嵌入向量表达信息。PSN可以提取环状RNA与疾病之间的特征用于关联分析。本文使用了环状RNA与疾病、环状RNA与微小RNA、疾病与基因、疾病与微小RNA、疾病与长链非编码RNA以及疾病与药物的关联信息。可以不受限制地引入更多关联数据进行分析。基于CircR2Disease基准数据集进行评估，五折交叉验证实验显示曲线下面积（AUC）为98.92%，准确率为95.39%，灵敏度为93.06%。与其他现有最佳模型相比，SAAED取得了最佳的整体性能。SAAED可以扩展生物相关信息的表达，是预测潜在环状RNA与疾病关联的有效方法。
