MVNMDA: A Multi-View Network Combing Semantic and Global Features for Predicting miRNA–Disease Association
A growing body of experimental evidence suggests that microRNAs (miRNAs) are closely associated with specific human diseases and play critical roles in their development and progression. Therefore, identifying miRNA related to specific diseases is of great significance for disease screening and trea...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-12-01
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| Series: | Molecules |
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| Online Access: | https://www.mdpi.com/1420-3049/29/1/230 |
| _version_ | 1797358408320417792 |
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| author | Chen Yang Zhen Wang Shanwen Zhang Xiaoqian Li Xuqi Wang Jiashan Liu Ruixiang Li Sihao Zeng |
| author_facet | Chen Yang Zhen Wang Shanwen Zhang Xiaoqian Li Xuqi Wang Jiashan Liu Ruixiang Li Sihao Zeng |
| author_sort | Chen Yang |
| collection | DOAJ |
| description | A growing body of experimental evidence suggests that microRNAs (miRNAs) are closely associated with specific human diseases and play critical roles in their development and progression. Therefore, identifying miRNA related to specific diseases is of great significance for disease screening and treatment. In the early stages, the identification of associations between miRNAs and diseases demanded laborious and time-consuming biological experiments that often carried a substantial risk of failure. With the exponential growth in the number of potential miRNA-disease association combinations, traditional biological experimental methods face difficulties in processing massive amounts of data. Hence, developing more efficient computational methods to predict possible miRNA-disease associations and prioritize them is particularly necessary. In recent years, numerous deep learning-based computational methods have been developed and have demonstrated excellent performance. However, most of these methods rely on external databases or tools to compute various auxiliary information. Unfortunately, these external databases or tools often cover only a limited portion of miRNAs and diseases, resulting in many miRNAs and diseases being unable to match with these computational methods. Therefore, there are certain limitations associated with the practical application of these methods. To overcome the above limitations, this study proposes a multi-view computational model called MVNMDA, which predicts potential miRNA-disease associations by integrating features of miRNA and diseases from local views, global views, and semantic views. Specifically, MVNMDA utilizes known association information to construct node initial features. Then, multiple networks are constructed based on known association to extract low-dimensional feature embedding of all nodes. Finally, a cascaded attention classifier is proposed to fuse features from coarse to fine, suppressing noise within the features and making precise predictions. To validate the effectiveness of the proposed method, extensive experiments were conducted on the HMDD v2.0 and HMDD v3.2 datasets. The experimental results demonstrate that MVNMDA achieves better performance compared to other computational methods. Additionally, the case study results further demonstrate the reliable predictive performance of MVNMDA. |
| first_indexed | 2024-03-08T15:01:33Z |
| format | Article |
| id | doaj.art-9dcf0b3380a2404e8ef24e60910a4a83 |
| institution | Directory Open Access Journal |
| issn | 1420-3049 |
| language | English |
| last_indexed | 2024-03-08T15:01:33Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Molecules |
| spelling | doaj.art-9dcf0b3380a2404e8ef24e60910a4a832024-01-10T15:04:34ZengMDPI AGMolecules1420-30492023-12-0129123010.3390/molecules29010230MVNMDA: A Multi-View Network Combing Semantic and Global Features for Predicting miRNA–Disease AssociationChen Yang0Zhen Wang1Shanwen Zhang2Xiaoqian Li3Xuqi Wang4Jiashan Liu5Ruixiang Li6Sihao Zeng7School of Electronic Infomation, Xijing University, Xi’an 710123, ChinaSchool of Electronic Infomation, Xijing University, Xi’an 710123, ChinaSchool of Electronic Infomation, Xijing University, Xi’an 710123, ChinaSchool of Electronic Infomation, Xijing University, Xi’an 710123, ChinaSchool of Electronic Infomation, Xijing University, Xi’an 710123, ChinaSchool of Electronic Infomation, Xijing University, Xi’an 710123, ChinaSchool of Electronic Infomation, Xijing University, Xi’an 710123, ChinaSchool of Electronic Infomation, Xijing University, Xi’an 710123, ChinaA growing body of experimental evidence suggests that microRNAs (miRNAs) are closely associated with specific human diseases and play critical roles in their development and progression. Therefore, identifying miRNA related to specific diseases is of great significance for disease screening and treatment. In the early stages, the identification of associations between miRNAs and diseases demanded laborious and time-consuming biological experiments that often carried a substantial risk of failure. With the exponential growth in the number of potential miRNA-disease association combinations, traditional biological experimental methods face difficulties in processing massive amounts of data. Hence, developing more efficient computational methods to predict possible miRNA-disease associations and prioritize them is particularly necessary. In recent years, numerous deep learning-based computational methods have been developed and have demonstrated excellent performance. However, most of these methods rely on external databases or tools to compute various auxiliary information. Unfortunately, these external databases or tools often cover only a limited portion of miRNAs and diseases, resulting in many miRNAs and diseases being unable to match with these computational methods. Therefore, there are certain limitations associated with the practical application of these methods. To overcome the above limitations, this study proposes a multi-view computational model called MVNMDA, which predicts potential miRNA-disease associations by integrating features of miRNA and diseases from local views, global views, and semantic views. Specifically, MVNMDA utilizes known association information to construct node initial features. Then, multiple networks are constructed based on known association to extract low-dimensional feature embedding of all nodes. Finally, a cascaded attention classifier is proposed to fuse features from coarse to fine, suppressing noise within the features and making precise predictions. To validate the effectiveness of the proposed method, extensive experiments were conducted on the HMDD v2.0 and HMDD v3.2 datasets. The experimental results demonstrate that MVNMDA achieves better performance compared to other computational methods. Additionally, the case study results further demonstrate the reliable predictive performance of MVNMDA.https://www.mdpi.com/1420-3049/29/1/230miRNA–disease associationmulti-view networkcascade attentionmeta-path |
| spellingShingle | Chen Yang Zhen Wang Shanwen Zhang Xiaoqian Li Xuqi Wang Jiashan Liu Ruixiang Li Sihao Zeng MVNMDA: A Multi-View Network Combing Semantic and Global Features for Predicting miRNA–Disease Association Molecules miRNA–disease association multi-view network cascade attention meta-path |
| title | MVNMDA: A Multi-View Network Combing Semantic and Global Features for Predicting miRNA–Disease Association |
| title_full | MVNMDA: A Multi-View Network Combing Semantic and Global Features for Predicting miRNA–Disease Association |
| title_fullStr | MVNMDA: A Multi-View Network Combing Semantic and Global Features for Predicting miRNA–Disease Association |
| title_full_unstemmed | MVNMDA: A Multi-View Network Combing Semantic and Global Features for Predicting miRNA–Disease Association |
| title_short | MVNMDA: A Multi-View Network Combing Semantic and Global Features for Predicting miRNA–Disease Association |
| title_sort | mvnmda a multi view network combing semantic and global features for predicting mirna disease association |
| topic | miRNA–disease association multi-view network cascade attention meta-path |
| url | https://www.mdpi.com/1420-3049/29/1/230 |
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