GATCDA: Predicting circRNA-Disease Associations Based on Graph Attention Network.
GATCDA: Predicting circRNA-Disease Associations Based on Graph Attention Network.
复制标题
DOI：
10.3390/cancers13112595
复制
发表时间：
2021-05-25
期刊：
影响因子：
5.2
通讯作者：
Wu FX
中科院分区：
文献类型：
--
作者：
Bian C;Lei XJ;Wu FX
CircRNA（circular RNAs）是一类新型的非编码RNA，在细胞生物学过程中起着重要的调控作用。越来越多的生物学实验证明，circRNA可以作为某些癌症的生物标志物和治疗靶点。由于生物学实验的时间和经济成本很高，计算方法已成为预测circRNA与疾病之间关系的更好方法。本研究首次将图形注意力网络应用于预测具有多种相似性数据的circRNA-疾病关联。采用circRNA-miRNA相互作用和疾病-mRNA相互作用来构建特征。本研究所提出的计算方法改善了预测性能。CircRNA（circular RNA）是一类具有闭合环状结构的非编码RNA分子。CircRNA与疾病的发生、发展密切相关。由于生物学实验的耗时性，计算方法已成为预测circRNA与疾病之间相互作用的更好方法。在这项研究中，我们开发了一种新的计算方法，称为GATCDA，利用图形注意力网络（GAT）来预测circRNA与疾病之间的疾病症状相似性，网络相似性和信息熵相似性。GAT通过注意力机制学习图上节点的表示，该机制为邻域中的不同节点分配不同的权重。考虑到circRNA-miRNA-mRNA轴在疾病发生发展中的重要作用，采用circRNA-miRNA相互作用和疾病-mRNA相互作用构建特征，其中mRNA与88%的miRNA相关。如五重交叉验证所示，GATCDA产生的AUC值为0.9011。此外，案例研究表明，GATCDA可以预测未知的circRNA-疾病关联。总之，GATCDA是探索circRNA与疾病之间关联的有用方法。
CircRNAs (circular RNAs), a novel kind of non-coding RNAs, play a regulatory role in cellular processes. A growing number of biological experiments has proved that circRNAs can be used as biomarkers and therapeutic targets of some cancers. As the time and financial costs of biological experiments are high, computational methods have become a better way to predict the associations between circRNAs and diseases. Graph attention network was first applied to predict circRNA-disease associations with multiple similarities of data in this study. The circRNA–miRNA interactions and disease-mRNA interactions were adopted to construct features. The computational method proposed in this study has improved the prediction performance. CircRNAs (circular RNAs) are a class of non-coding RNA molecules with a closed circular structure. CircRNAs are closely related to the occurrence and development of diseases. Due to the time-consuming nature of biological experiments, computational methods have become a better way to predict the interactions between circRNAs and diseases. In this study, we developed a novel computational method called GATCDA utilizing a graph attention network (GAT) to predict circRNA–disease associations with disease symptom similarity, network similarity, and information entropy similarity for both circRNAs and diseases. GAT learns representations for nodes on a graph by an attention mechanism, which assigns different weights to different nodes in a neighborhood. Considering that the circRNA–miRNA–mRNA axis plays an important role in the generation and development of diseases, circRNA–miRNA interactions and disease–mRNA interactions were adopted to construct features, in which mRNAs were related to 88% of miRNAs. As demonstrated by five-fold cross-validation, GATCDA yielded an AUC value of 0.9011. In addition, case studies showed that GATCDA can predict unknown circRNA–disease associations. In conclusion, GATCDA is a useful method for exploring associations between circRNAs and diseases.
登录
查看更多内容
DOI：
10.12659/msm.908837
发表时间：
2018-03-20
期刊：
Medical science monitor : international medical journal of experimental and clinical research
影响因子：
--
作者：
Cai D;Liu Z;Kong G
通讯作者：
Kong G
starBase v2.0：从大规模 CLIP-Seq 数据中解码 miRNA-ceRNA、miRNA-ncRNA 和蛋白质-RNA 相互作用网络
影响因子：
14.9
作者：
Li JH;Liu S;Zhou H;Qu LH;Yang JH
通讯作者：
Yang JH
DOI：
10.1016/j.urolonc.2018.03.019
发表时间：
2019-06
期刊：
Urologic oncology
影响因子：
--
作者：
Kwan ML;Garren B;Nielsen ME;Tang L
通讯作者：
Tang L
影响因子：
3.3
作者：
Wang JH;Wu XJ;Duan YZ;Li F
通讯作者：
Li F
影响因子：
3.1
作者：
Ding, Yulian;Chen, Bolin;Wu, Fang-Xiang
通讯作者：
Wu, Fang-Xiang