GATCDA: Predicting circRNA-Disease Associations Based on Graph Attention Network
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 int...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-05-01
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| Series: | Cancers |
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| Online Access: | https://www.mdpi.com/2072-6694/13/11/2595 |
| _version_ | 1797532685285982208 |
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| author | Chen Bian Xiu-Juan Lei Fang-Xiang Wu |
| author_facet | Chen Bian Xiu-Juan Lei Fang-Xiang Wu |
| author_sort | Chen Bian |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T11:02:47Z |
| format | Article |
| id | doaj.art-c260e461f1a64ea5bc4e1b8ca2d3c767 |
| institution | Directory Open Access Journal |
| issn | 2072-6694 |
| language | English |
| last_indexed | 2024-03-10T11:02:47Z |
| publishDate | 2021-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Cancers |
| spelling | doaj.art-c260e461f1a64ea5bc4e1b8ca2d3c7672023-11-21T21:22:28ZengMDPI AGCancers2072-66942021-05-011311259510.3390/cancers13112595GATCDA: Predicting circRNA-Disease Associations Based on Graph Attention NetworkChen Bian0Xiu-Juan Lei1Fang-Xiang Wu2School of Computer Science, Shaanxi Normal University, Xi’an 710119, ChinaSchool of Computer Science, Shaanxi Normal University, Xi’an 710119, ChinaDivision of Biomedical Engineering, University of Saskatchewan, Saskatoon, SK S7N 5A9, CanadaCircRNAs (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.https://www.mdpi.com/2072-6694/13/11/2595circRNA–disease associationgraph attention networkcircRNA–miRNA–mRNA axis |
| spellingShingle | Chen Bian Xiu-Juan Lei Fang-Xiang Wu GATCDA: Predicting circRNA-Disease Associations Based on Graph Attention Network Cancers circRNA–disease association graph attention network circRNA–miRNA–mRNA axis |
| title | GATCDA: Predicting circRNA-Disease Associations Based on Graph Attention Network |
| title_full | GATCDA: Predicting circRNA-Disease Associations Based on Graph Attention Network |
| title_fullStr | GATCDA: Predicting circRNA-Disease Associations Based on Graph Attention Network |
| title_full_unstemmed | GATCDA: Predicting circRNA-Disease Associations Based on Graph Attention Network |
| title_short | GATCDA: Predicting circRNA-Disease Associations Based on Graph Attention Network |
| title_sort | gatcda predicting circrna disease associations based on graph attention network |
| topic | circRNA–disease association graph attention network circRNA–miRNA–mRNA axis |
| url | https://www.mdpi.com/2072-6694/13/11/2595 |
| work_keys_str_mv | AT chenbian gatcdapredictingcircrnadiseaseassociationsbasedongraphattentionnetwork AT xiujuanlei gatcdapredictingcircrnadiseaseassociationsbasedongraphattentionnetwork AT fangxiangwu gatcdapredictingcircrnadiseaseassociationsbasedongraphattentionnetwork |