Prediction of circRNA-Disease Associations Based on the Combination of Multi-Head Graph Attention Network and Graph Convolutional Network

被引:17
作者
Cao, Ruifen [1 ]
He, Chuan [1 ]
Wei, Pijing [2 ,3 ]
Su, Yansen [4 ]
Xia, Junfeng [2 ,3 ]
Zheng, Chunhou [4 ]
机构
[1] Anhui Univ, Sch Comp Sci & Technol, Informat Mat & Intelligent Sensing Lab Anhui Prov, Hefei 230601, Peoples R China
[2] Anhui Univ, Inst Phys Sci, Hefei 230601, Peoples R China
[3] Anhui Univ, Inst Informat Technol, Hefei 230601, Peoples R China
[4] Anhui Univ, Sch Artificial Intelligence, Hefei 230601, Peoples R China
基金
中国国家自然科学基金;
关键词
circular RNAs; circRNA-disease associations; graph attention network; random walk with restart algorithm; graph convolutional network; CIRCULAR RNAS; ONTOLOGY; DATABASE;
D O I
10.3390/biom12070932
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
Circular RNAs (circRNAs) are covalently closed single-stranded RNA molecules, which have many biological functions. Previous experiments have shown that circRNAs are involved in numerous biological processes, especially regulatory functions. It has also been found that circRNAs are associated with complex diseases of human beings. Therefore, predicting the associations of circRNA with disease (called circRNA-disease associations) is useful for disease prevention, diagnosis and treatment. In this work, we propose a novel computational approach called GGCDA based on the Graph Attention Network (GAT) and Graph Convolutional Network (GCN) to predict circRNA-disease associations. Firstly, GGCDA combines circRNA sequence similarity, disease semantic similarity and corresponding Gaussian interaction profile kernel similarity, and then a random walk with restart algorithm (RWR) is used to obtain the preliminary features of circRNA and disease. Secondly, a heterogeneous graph is constructed from the known circRNA-disease association network and the calculated similarity of circRNAs and diseases. Thirdly, the multi-head Graph Attention Network (GAT) is adopted to obtain different weights of circRNA and disease features, and then GCN is employed to aggregate the features of adjacent nodes in the network and the features of the nodes themselves, so as to obtain multi-view circRNA and disease features. Finally, we combined a multi-layer fully connected neural network to predict the associations of circRNAs with diseases. In comparison with state-of-the-art methods, GGCDA can achieve AUC values of 0.9625 and 0.9485 under the results of fivefold cross-validation on two datasets, and AUC of 0.8227 on the independent test set. Case studies further demonstrate that our approach is promising for discovering potential circRNA-disease associations.
引用
收藏
页数:17
相关论文
共 43 条
[1]   GATCDA: Predicting circRNA-Disease Associations Based on Graph Attention Network [J].
Bian, Chen ;
Lei, Xiu-Juan ;
Wu, Fang-Xiang .
CANCERS, 2021, 13 (11)
[2]   Management of hepatoceullular carcinoma [J].
Bruix, J ;
Sherman, M .
HEPATOLOGY, 2005, 42 (05) :1208-1236
[3]   Worldwide Variations in Colorectal Cancer [J].
Center, Melissa M. ;
Jemal, Ahmedin ;
Smith, Robert A. ;
Ward, Elizabeth .
CA-A CANCER JOURNAL FOR CLINICIANS, 2009, 59 (06) :366-378
[4]   MDHGI: Matrix Decomposition and Heterogeneous Graph Inference for miRNA-disease association prediction [J].
Chen, Xing ;
Yin, Jun ;
Qu, Jia ;
Huang, Li .
PLOS COMPUTATIONAL BIOLOGY, 2018, 14 (08)
[5]   MISSPLICING YIELDS CIRCULAR RNA MOLECULES [J].
COCQUERELLE, C ;
MASCREZ, B ;
HETUIN, D ;
BAILLEUL, B .
FASEB JOURNAL, 1993, 7 (01) :155-160
[6]   The RNA Binding Protein Quaking Regulates Formation of circRNAs [J].
Conn, Simon J. ;
Pillman, Katherine A. ;
Toubia, John ;
Conn, Vanessa M. ;
Salmanidis, Marika ;
Phillips, Caroline A. ;
Roslan, Suraya ;
Schreiber, Andreas W. ;
Gregory, Philip A. ;
Goodall, Gregory J. .
CELL, 2015, 160 (06) :1125-1134
[7]   Prediction of CircRNA-Disease Associations Using KATZ Model Based on Heterogeneous Networks [J].
Fan, Chunyan ;
Lei, Xiujuan ;
Wu, Fang-Xiang .
INTERNATIONAL JOURNAL OF BIOLOGICAL SCIENCES, 2018, 14 (14) :1950-1959
[8]   CircR2Disease: a manually curated database for experimentally supported circular RNAs associated with various diseases [J].
Fan, Chunyan ;
Lei, Xiujuan ;
Fang, Zengqiang ;
Jiang, Qinghua ;
Wu, Fang-Xiang .
DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION, 2018,
[9]   circBase: a database for circular RNAs [J].
Glazar, Petar ;
Papavasileiou, Panagiotis ;
Rajewsky, Nikolaus .
RNA, 2014, 20 (11) :1666-1670
[10]  
Kipf T.N., 2017, INT C LEARN REPR ICL