GATNNCDA: A Method Based on Graph Attention Network and Multi-Layer Neural Network for Predicting circRNA-Disease Associations
Circular RNAs (circRNAs) are a new class of endogenous non-coding RNAs with covalent closed loop structure. Researchers have revealed that circRNAs play an important role in human diseases. As experimental identification of interactions between circRNA and disease is time-consuming and expensive, ef...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-08-01
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| Series: | International Journal of Molecular Sciences |
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| Online Access: | https://www.mdpi.com/1422-0067/22/16/8505 |
| _version_ | 1797523632341123072 |
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| author | Cunmei Ji Zhihao Liu Yutian Wang Jiancheng Ni Chunhou Zheng |
| author_facet | Cunmei Ji Zhihao Liu Yutian Wang Jiancheng Ni Chunhou Zheng |
| author_sort | Cunmei Ji |
| collection | DOAJ |
| description | Circular RNAs (circRNAs) are a new class of endogenous non-coding RNAs with covalent closed loop structure. Researchers have revealed that circRNAs play an important role in human diseases. As experimental identification of interactions between circRNA and disease is time-consuming and expensive, effective computational methods are an urgent need for predicting potential circRNA–disease associations. In this study, we proposed a novel computational method named GATNNCDA, which combines Graph Attention Network (GAT) and multi-layer neural network (NN) to infer disease-related circRNAs. Specially, GATNNCDA first integrates disease semantic similarity, circRNA functional similarity and the respective Gaussian Interaction Profile (GIP) kernel similarities. The integrated similarities are used as initial node features, and then GAT is applied for further feature extraction in the heterogeneous circRNA–disease graph. Finally, the NN-based classifier is introduced for prediction. The results of fivefold cross validation demonstrated that GATNNCDA achieved an average AUC of 0.9613 and AUPR of 0.9433 on the CircR2Disease dataset, and outperformed other state-of-the-art methods. In addition, case studies on breast cancer and hepatocellular carcinoma showed that 20 and 18 of the top 20 candidates were respectively confirmed in the validation datasets or published literature. Therefore, GATNNCDA is an effective and reliable tool for discovering circRNA–disease associations. |
| first_indexed | 2024-03-10T08:45:47Z |
| format | Article |
| id | doaj.art-5818f33b48644f1087af4482c32b2794 |
| institution | Directory Open Access Journal |
| issn | 1661-6596 1422-0067 |
| language | English |
| last_indexed | 2024-03-10T08:45:47Z |
| publishDate | 2021-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | International Journal of Molecular Sciences |
| spelling | doaj.art-5818f33b48644f1087af4482c32b27942023-11-22T07:56:09ZengMDPI AGInternational Journal of Molecular Sciences1661-65961422-00672021-08-012216850510.3390/ijms22168505GATNNCDA: A Method Based on Graph Attention Network and Multi-Layer Neural Network for Predicting circRNA-Disease AssociationsCunmei Ji0Zhihao Liu1Yutian Wang2Jiancheng Ni3Chunhou Zheng4School of Cyber Science and Engineering, Qufu Normal University, Qufu 273165, ChinaSchool of Cyber Science and Engineering, Qufu Normal University, Qufu 273165, ChinaSchool of Cyber Science and Engineering, Qufu Normal University, Qufu 273165, ChinaSchool of Cyber Science and Engineering, Qufu Normal University, Qufu 273165, ChinaSchool of Artificial Intelligence, Anhui University, Hefei 230601, ChinaCircular RNAs (circRNAs) are a new class of endogenous non-coding RNAs with covalent closed loop structure. Researchers have revealed that circRNAs play an important role in human diseases. As experimental identification of interactions between circRNA and disease is time-consuming and expensive, effective computational methods are an urgent need for predicting potential circRNA–disease associations. In this study, we proposed a novel computational method named GATNNCDA, which combines Graph Attention Network (GAT) and multi-layer neural network (NN) to infer disease-related circRNAs. Specially, GATNNCDA first integrates disease semantic similarity, circRNA functional similarity and the respective Gaussian Interaction Profile (GIP) kernel similarities. The integrated similarities are used as initial node features, and then GAT is applied for further feature extraction in the heterogeneous circRNA–disease graph. Finally, the NN-based classifier is introduced for prediction. The results of fivefold cross validation demonstrated that GATNNCDA achieved an average AUC of 0.9613 and AUPR of 0.9433 on the CircR2Disease dataset, and outperformed other state-of-the-art methods. In addition, case studies on breast cancer and hepatocellular carcinoma showed that 20 and 18 of the top 20 candidates were respectively confirmed in the validation datasets or published literature. Therefore, GATNNCDA is an effective and reliable tool for discovering circRNA–disease associations.https://www.mdpi.com/1422-0067/22/16/8505circRNA–disease associationsgraph attention networkmulti-layer neural network |
| spellingShingle | Cunmei Ji Zhihao Liu Yutian Wang Jiancheng Ni Chunhou Zheng GATNNCDA: A Method Based on Graph Attention Network and Multi-Layer Neural Network for Predicting circRNA-Disease Associations International Journal of Molecular Sciences circRNA–disease associations graph attention network multi-layer neural network |
| title | GATNNCDA: A Method Based on Graph Attention Network and Multi-Layer Neural Network for Predicting circRNA-Disease Associations |
| title_full | GATNNCDA: A Method Based on Graph Attention Network and Multi-Layer Neural Network for Predicting circRNA-Disease Associations |
| title_fullStr | GATNNCDA: A Method Based on Graph Attention Network and Multi-Layer Neural Network for Predicting circRNA-Disease Associations |
| title_full_unstemmed | GATNNCDA: A Method Based on Graph Attention Network and Multi-Layer Neural Network for Predicting circRNA-Disease Associations |
| title_short | GATNNCDA: A Method Based on Graph Attention Network and Multi-Layer Neural Network for Predicting circRNA-Disease Associations |
| title_sort | gatnncda a method based on graph attention network and multi layer neural network for predicting circrna disease associations |
| topic | circRNA–disease associations graph attention network multi-layer neural network |
| url | https://www.mdpi.com/1422-0067/22/16/8505 |
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