Computational method using heterogeneous graph convolutional network model combined with reinforcement layer for MiRNA–disease association prediction
Abstract Background A large number of evidences from biological experiments have confirmed that miRNAs play an important role in the progression and development of various human complex diseases. However, the traditional experiment methods are expensive and time-consuming. Therefore, it is a challen...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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BMC
2022-07-01
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| Series: | BMC Bioinformatics |
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| Online Access: | https://doi.org/10.1186/s12859-022-04843-3 |
| _version_ | 1818014910526783488 |
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| author | Dan Huang JiYong An Lei Zhang BaiLong Liu |
| author_facet | Dan Huang JiYong An Lei Zhang BaiLong Liu |
| author_sort | Dan Huang |
| collection | DOAJ |
| description | Abstract Background A large number of evidences from biological experiments have confirmed that miRNAs play an important role in the progression and development of various human complex diseases. However, the traditional experiment methods are expensive and time-consuming. Therefore, it is a challenging task that how to develop more accurate and efficient methods for predicting potential associations between miRNA and disease. Results In the study, we developed a computational model that combined heterogeneous graph convolutional network with enhanced layer for miRNA–disease association prediction (HGCNELMDA). The major improvement of our method lies in through restarting the random walk optimized the original features of nodes and adding a reinforcement layer to the hidden layer of graph convolutional network retained similar information between nodes in the feature space. In addition, the proposed approach recalculated the influence of neighborhood nodes on target nodes by introducing the attention mechanism. The reliable performance of the HGCNELMDA was certified by the AUC of 93.47% in global leave-one-out cross-validation (LOOCV), and the average AUCs of 93.01% in fivefold cross-validation. Meanwhile, we compared the HGCNELMDA with the state‑of‑the‑art methods. Comparative results indicated that o the HGCNELMDA is very promising and may provide a cost‑effective alternative for miRNA–disease association prediction. Moreover, we applied HGCNELMDA to 3 different case studies to predict potential miRNAs related to lung cancer, prostate cancer, and pancreatic cancer. Results showed that 48, 50, and 50 of the top 50 predicted miRNAs were supported by experimental association evidence. Therefore, the HGCNELMDA is a reliable method for predicting disease-related miRNAs. Conclusions The results of the HGCNELMDA method in the LOOCV (leave-one-out cross validation, LOOCV) and 5-cross validations were 93.47% and 93.01%, respectively. Compared with other typical methods, the performance of HGCNELMDA is higher. Three cases of lung cancer, prostate cancer, and pancreatic cancer were studied. Among the predicted top 50 candidate miRNAs, 48, 50, and 50 were verified in the biological database HDMMV2.0. Therefore; this further confirms the feasibility and effectiveness of our method. Therefore, this further confirms the feasibility and effectiveness of our method. To facilitate extensive studies for future disease-related miRNAs research, we developed a freely available web server called HGCNELMDA is available at http://124.221.62.44:8080/HGCNELMDA.jsp . |
| first_indexed | 2024-04-14T06:51:14Z |
| format | Article |
| id | doaj.art-3bd0ee618a844ba6a6d42f4f7d4efa16 |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-04-14T06:51:14Z |
| publishDate | 2022-07-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-3bd0ee618a844ba6a6d42f4f7d4efa162022-12-22T02:07:02ZengBMCBMC Bioinformatics1471-21052022-07-0123111910.1186/s12859-022-04843-3Computational method using heterogeneous graph convolutional network model combined with reinforcement layer for MiRNA–disease association predictionDan Huang0JiYong An1Lei Zhang2BaiLong Liu3School of Computer Science and Technology, China University of Mining and TechnologySchool of Computer Science and Technology, China University of Mining and TechnologySchool of Computer Science and Technology, China University of Mining and TechnologySchool of Computer Science and Technology, China University of Mining and TechnologyAbstract Background A large number of evidences from biological experiments have confirmed that miRNAs play an important role in the progression and development of various human complex diseases. However, the traditional experiment methods are expensive and time-consuming. Therefore, it is a challenging task that how to develop more accurate and efficient methods for predicting potential associations between miRNA and disease. Results In the study, we developed a computational model that combined heterogeneous graph convolutional network with enhanced layer for miRNA–disease association prediction (HGCNELMDA). The major improvement of our method lies in through restarting the random walk optimized the original features of nodes and adding a reinforcement layer to the hidden layer of graph convolutional network retained similar information between nodes in the feature space. In addition, the proposed approach recalculated the influence of neighborhood nodes on target nodes by introducing the attention mechanism. The reliable performance of the HGCNELMDA was certified by the AUC of 93.47% in global leave-one-out cross-validation (LOOCV), and the average AUCs of 93.01% in fivefold cross-validation. Meanwhile, we compared the HGCNELMDA with the state‑of‑the‑art methods. Comparative results indicated that o the HGCNELMDA is very promising and may provide a cost‑effective alternative for miRNA–disease association prediction. Moreover, we applied HGCNELMDA to 3 different case studies to predict potential miRNAs related to lung cancer, prostate cancer, and pancreatic cancer. Results showed that 48, 50, and 50 of the top 50 predicted miRNAs were supported by experimental association evidence. Therefore, the HGCNELMDA is a reliable method for predicting disease-related miRNAs. Conclusions The results of the HGCNELMDA method in the LOOCV (leave-one-out cross validation, LOOCV) and 5-cross validations were 93.47% and 93.01%, respectively. Compared with other typical methods, the performance of HGCNELMDA is higher. Three cases of lung cancer, prostate cancer, and pancreatic cancer were studied. Among the predicted top 50 candidate miRNAs, 48, 50, and 50 were verified in the biological database HDMMV2.0. Therefore; this further confirms the feasibility and effectiveness of our method. Therefore, this further confirms the feasibility and effectiveness of our method. To facilitate extensive studies for future disease-related miRNAs research, we developed a freely available web server called HGCNELMDA is available at http://124.221.62.44:8080/HGCNELMDA.jsp .https://doi.org/10.1186/s12859-022-04843-3miRNA and disease interactionsGraph convolutional network |
| spellingShingle | Dan Huang JiYong An Lei Zhang BaiLong Liu Computational method using heterogeneous graph convolutional network model combined with reinforcement layer for MiRNA–disease association prediction BMC Bioinformatics miRNA and disease interactions Graph convolutional network |
| title | Computational method using heterogeneous graph convolutional network model combined with reinforcement layer for MiRNA–disease association prediction |
| title_full | Computational method using heterogeneous graph convolutional network model combined with reinforcement layer for MiRNA–disease association prediction |
| title_fullStr | Computational method using heterogeneous graph convolutional network model combined with reinforcement layer for MiRNA–disease association prediction |
| title_full_unstemmed | Computational method using heterogeneous graph convolutional network model combined with reinforcement layer for MiRNA–disease association prediction |
| title_short | Computational method using heterogeneous graph convolutional network model combined with reinforcement layer for MiRNA–disease association prediction |
| title_sort | computational method using heterogeneous graph convolutional network model combined with reinforcement layer for mirna disease association prediction |
| topic | miRNA and disease interactions Graph convolutional network |
| url | https://doi.org/10.1186/s12859-022-04843-3 |
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