Using Graph Attention Network and Graph Convolutional Network to Explore Human CircRNA–Disease Associations Based on Multi-Source Data
Cumulative research studies have verified that multiple circRNAs are closely associated with the pathogenic mechanism and cellular level. Exploring human circRNA–disease relationships is significant to decipher pathogenic mechanisms and provide treatment plans. At present, several computational mode...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-02-01
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| Series: | Frontiers in Genetics |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fgene.2022.829937/full |
| _version_ | 1828438269832986624 |
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| author | Guanghui Li Diancheng Wang Yuejin Zhang Cheng Liang Qiu Xiao Jiawei Luo |
| author_facet | Guanghui Li Diancheng Wang Yuejin Zhang Cheng Liang Qiu Xiao Jiawei Luo |
| author_sort | Guanghui Li |
| collection | DOAJ |
| description | Cumulative research studies have verified that multiple circRNAs are closely associated with the pathogenic mechanism and cellular level. Exploring human circRNA–disease relationships is significant to decipher pathogenic mechanisms and provide treatment plans. At present, several computational models are designed to infer potential relationships between diseases and circRNAs. However, the majority of existing approaches could not effectively utilize the multisource data and achieve poor performance in sparse networks. In this study, we develop an advanced method, GATGCN, using graph attention network (GAT) and graph convolutional network (GCN) to detect potential circRNA–disease relationships. First, several sources of biomedical information are fused via the centered kernel alignment model (CKA), which calculates the corresponding weight of different kernels. Second, we adopt the graph attention network to learn latent representation of diseases and circRNAs. Third, the graph convolutional network is deployed to effectively extract features of associations by aggregating feature vectors of neighbors. Meanwhile, GATGCN achieves the prominent AUC of 0.951 under leave-one-out cross-validation and AUC of 0.932 under 5-fold cross-validation. Furthermore, case studies on lung cancer, diabetes retinopathy, and prostate cancer verify the reliability of GATGCN for detecting latent circRNA–disease pairs. |
| first_indexed | 2024-12-10T20:01:43Z |
| format | Article |
| id | doaj.art-f0056568cb504943b91a6d2ad906e44b |
| institution | Directory Open Access Journal |
| issn | 1664-8021 |
| language | English |
| last_indexed | 2024-12-10T20:01:43Z |
| publishDate | 2022-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Genetics |
| spelling | doaj.art-f0056568cb504943b91a6d2ad906e44b2022-12-22T01:35:31ZengFrontiers Media S.A.Frontiers in Genetics1664-80212022-02-011310.3389/fgene.2022.829937829937Using Graph Attention Network and Graph Convolutional Network to Explore Human CircRNA–Disease Associations Based on Multi-Source DataGuanghui Li0Diancheng Wang1Yuejin Zhang2Cheng Liang3Qiu Xiao4Jiawei Luo5School of Information Engineering, East China Jiaotong University, Nanchang, ChinaSchool of Information Engineering, East China Jiaotong University, Nanchang, ChinaSchool of Information Engineering, East China Jiaotong University, Nanchang, ChinaSchool of Information Science and Engineering, Shandong Normal University, Jinan, ChinaCollege of Information Science and Engineering, Hunan Normal University, Changsha, ChinaCollege of Computer Science and Electronic Engineering, Hunan University, Changsha, ChinaCumulative research studies have verified that multiple circRNAs are closely associated with the pathogenic mechanism and cellular level. Exploring human circRNA–disease relationships is significant to decipher pathogenic mechanisms and provide treatment plans. At present, several computational models are designed to infer potential relationships between diseases and circRNAs. However, the majority of existing approaches could not effectively utilize the multisource data and achieve poor performance in sparse networks. In this study, we develop an advanced method, GATGCN, using graph attention network (GAT) and graph convolutional network (GCN) to detect potential circRNA–disease relationships. First, several sources of biomedical information are fused via the centered kernel alignment model (CKA), which calculates the corresponding weight of different kernels. Second, we adopt the graph attention network to learn latent representation of diseases and circRNAs. Third, the graph convolutional network is deployed to effectively extract features of associations by aggregating feature vectors of neighbors. Meanwhile, GATGCN achieves the prominent AUC of 0.951 under leave-one-out cross-validation and AUC of 0.932 under 5-fold cross-validation. Furthermore, case studies on lung cancer, diabetes retinopathy, and prostate cancer verify the reliability of GATGCN for detecting latent circRNA–disease pairs.https://www.frontiersin.org/articles/10.3389/fgene.2022.829937/fullcircRNA-disease associationsdeep learninggraph attention networkgraph convolutional networkcentered kernel alignment |
| spellingShingle | Guanghui Li Diancheng Wang Yuejin Zhang Cheng Liang Qiu Xiao Jiawei Luo Using Graph Attention Network and Graph Convolutional Network to Explore Human CircRNA–Disease Associations Based on Multi-Source Data Frontiers in Genetics circRNA-disease associations deep learning graph attention network graph convolutional network centered kernel alignment |
| title | Using Graph Attention Network and Graph Convolutional Network to Explore Human CircRNA–Disease Associations Based on Multi-Source Data |
| title_full | Using Graph Attention Network and Graph Convolutional Network to Explore Human CircRNA–Disease Associations Based on Multi-Source Data |
| title_fullStr | Using Graph Attention Network and Graph Convolutional Network to Explore Human CircRNA–Disease Associations Based on Multi-Source Data |
| title_full_unstemmed | Using Graph Attention Network and Graph Convolutional Network to Explore Human CircRNA–Disease Associations Based on Multi-Source Data |
| title_short | Using Graph Attention Network and Graph Convolutional Network to Explore Human CircRNA–Disease Associations Based on Multi-Source Data |
| title_sort | using graph attention network and graph convolutional network to explore human circrna disease associations based on multi source data |
| topic | circRNA-disease associations deep learning graph attention network graph convolutional network centered kernel alignment |
| url | https://www.frontiersin.org/articles/10.3389/fgene.2022.829937/full |
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