MLPMDA: Multi-layer linear projection for predicting miRNA-disease association
The miRNA plays a key role in the biological process and it has close relationship with disease. The wet experiment to test the association between miRNA and disease is time-consuming and costly, so semi-supervised learning based computational methods have been proposed to predict potential miRNAs a...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Guo, Leiming [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021transfer abstract |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Subsurface fluid flow at an active cold seep area in the Qiongdongnan Basin, northern South China Sea - Wang, Jiliang ELSEVIER, 2018, Amsterdam [u.a.] |
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| Übergeordnetes Werk: |
volume:214 ; year:2021 ; day:28 ; month:02 ; pages:0 |
| Links: |
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| DOI / URN: |
10.1016/j.knosys.2020.106718 |
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ELV052936651 |
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| 520 | |a The miRNA plays a key role in the biological process and it has close relationship with disease. The wet experiment to test the association between miRNA and disease is time-consuming and costly, so semi-supervised learning based computational methods have been proposed to predict potential miRNAs associated with disease. However, these methods cannot fully utilize the local structure and global structure information of miRNA-disease association data, and the prediction performance can be improved further. In this work, we propose a novel approach for miRNA-disease association prediction by using multi-layer linear projection (MLPMDA). Firstly, we use the top n neighbors of miRNA and disease to update miRNA-disease association matrix, respectively, to employ the local structure and reduce the sparsity effect. Secondly, we define a computing layer to which a heterogeneous matrix composing of the updated association matrix and integrated miRNA similarity and integrated disease similarity is fed, and output predicted scores for miRNA-disease associations by linear projection method. Thirdly, we design multiple computing layers where the heterogeneous matrix input for the current layer is constructed based on the predicted miRNA-disease association scores from the last layer. Finally, we capture possible missing miRNA-disease associations by integrating prediction scores from each single view. We obtain AUC values 0.9847, 0.9883, 0.9899 for one dataset under 2-fold, 5-fold and 10-fold cross-validations, respectively, and their corresponding AUPR values are 0.7777, 0.7806 and 0.7518, which outperforms seven state-of-the-art methods. At last, we predict the potential miRNAs associated with three diseases, most of which are verified with some evidence. | ||
| 520 | |a The miRNA plays a key role in the biological process and it has close relationship with disease. The wet experiment to test the association between miRNA and disease is time-consuming and costly, so semi-supervised learning based computational methods have been proposed to predict potential miRNAs associated with disease. However, these methods cannot fully utilize the local structure and global structure information of miRNA-disease association data, and the prediction performance can be improved further. In this work, we propose a novel approach for miRNA-disease association prediction by using multi-layer linear projection (MLPMDA). Firstly, we use the top n neighbors of miRNA and disease to update miRNA-disease association matrix, respectively, to employ the local structure and reduce the sparsity effect. Secondly, we define a computing layer to which a heterogeneous matrix composing of the updated association matrix and integrated miRNA similarity and integrated disease similarity is fed, and output predicted scores for miRNA-disease associations by linear projection method. Thirdly, we design multiple computing layers where the heterogeneous matrix input for the current layer is constructed based on the predicted miRNA-disease association scores from the last layer. Finally, we capture possible missing miRNA-disease associations by integrating prediction scores from each single view. We obtain AUC values 0.9847, 0.9883, 0.9899 for one dataset under 2-fold, 5-fold and 10-fold cross-validations, respectively, and their corresponding AUPR values are 0.7777, 0.7806 and 0.7518, which outperforms seven state-of-the-art methods. At last, we predict the potential miRNAs associated with three diseases, most of which are verified with some evidence. | ||
| 650 | 7 | |a MicroRNA |2 Elsevier | |
| 650 | 7 | |a Disease |2 Elsevier | |
| 650 | 7 | |a Multi-layer structure |2 Elsevier | |
| 650 | 7 | |a Linear projection |2 Elsevier | |
| 650 | 7 | |a Association prediction |2 Elsevier | |
| 700 | 1 | |a Shi, Kun |4 oth | |
| 700 | 1 | |a Wang, Lin |4 oth | |
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10.1016/j.knosys.2020.106718 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001285.pica (DE-627)ELV052936651 (ELSEVIER)S0950-7051(20)30847-9 DE-627 ger DE-627 rakwb eng 550 VZ 38.00 bkl Guo, Leiming verfasserin aut MLPMDA: Multi-layer linear projection for predicting miRNA-disease association 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The miRNA plays a key role in the biological process and it has close relationship with disease. The wet experiment to test the association between miRNA and disease is time-consuming and costly, so semi-supervised learning based computational methods have been proposed to predict potential miRNAs associated with disease. However, these methods cannot fully utilize the local structure and global structure information of miRNA-disease association data, and the prediction performance can be improved further. In this work, we propose a novel approach for miRNA-disease association prediction by using multi-layer linear projection (MLPMDA). Firstly, we use the top n neighbors of miRNA and disease to update miRNA-disease association matrix, respectively, to employ the local structure and reduce the sparsity effect. Secondly, we define a computing layer to which a heterogeneous matrix composing of the updated association matrix and integrated miRNA similarity and integrated disease similarity is fed, and output predicted scores for miRNA-disease associations by linear projection method. Thirdly, we design multiple computing layers where the heterogeneous matrix input for the current layer is constructed based on the predicted miRNA-disease association scores from the last layer. Finally, we capture possible missing miRNA-disease associations by integrating prediction scores from each single view. We obtain AUC values 0.9847, 0.9883, 0.9899 for one dataset under 2-fold, 5-fold and 10-fold cross-validations, respectively, and their corresponding AUPR values are 0.7777, 0.7806 and 0.7518, which outperforms seven state-of-the-art methods. At last, we predict the potential miRNAs associated with three diseases, most of which are verified with some evidence. The miRNA plays a key role in the biological process and it has close relationship with disease. The wet experiment to test the association between miRNA and disease is time-consuming and costly, so semi-supervised learning based computational methods have been proposed to predict potential miRNAs associated with disease. However, these methods cannot fully utilize the local structure and global structure information of miRNA-disease association data, and the prediction performance can be improved further. In this work, we propose a novel approach for miRNA-disease association prediction by using multi-layer linear projection (MLPMDA). Firstly, we use the top n neighbors of miRNA and disease to update miRNA-disease association matrix, respectively, to employ the local structure and reduce the sparsity effect. Secondly, we define a computing layer to which a heterogeneous matrix composing of the updated association matrix and integrated miRNA similarity and integrated disease similarity is fed, and output predicted scores for miRNA-disease associations by linear projection method. Thirdly, we design multiple computing layers where the heterogeneous matrix input for the current layer is constructed based on the predicted miRNA-disease association scores from the last layer. Finally, we capture possible missing miRNA-disease associations by integrating prediction scores from each single view. We obtain AUC values 0.9847, 0.9883, 0.9899 for one dataset under 2-fold, 5-fold and 10-fold cross-validations, respectively, and their corresponding AUPR values are 0.7777, 0.7806 and 0.7518, which outperforms seven state-of-the-art methods. At last, we predict the potential miRNAs associated with three diseases, most of which are verified with some evidence. MicroRNA Elsevier Disease Elsevier Multi-layer structure Elsevier Linear projection Elsevier Association prediction Elsevier Shi, Kun oth Wang, Lin oth Enthalten in Elsevier Science Wang, Jiliang ELSEVIER Subsurface fluid flow at an active cold seep area in the Qiongdongnan Basin, northern South China Sea 2018 Amsterdam [u.a.] (DE-627)ELV001104926 volume:214 year:2021 day:28 month:02 pages:0 https://doi.org/10.1016/j.knosys.2020.106718 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-GGO 38.00 Geowissenschaften: Allgemeines VZ AR 214 2021 28 0228 0 |
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10.1016/j.knosys.2020.106718 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001285.pica (DE-627)ELV052936651 (ELSEVIER)S0950-7051(20)30847-9 DE-627 ger DE-627 rakwb eng 550 VZ 38.00 bkl Guo, Leiming verfasserin aut MLPMDA: Multi-layer linear projection for predicting miRNA-disease association 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The miRNA plays a key role in the biological process and it has close relationship with disease. The wet experiment to test the association between miRNA and disease is time-consuming and costly, so semi-supervised learning based computational methods have been proposed to predict potential miRNAs associated with disease. However, these methods cannot fully utilize the local structure and global structure information of miRNA-disease association data, and the prediction performance can be improved further. In this work, we propose a novel approach for miRNA-disease association prediction by using multi-layer linear projection (MLPMDA). Firstly, we use the top n neighbors of miRNA and disease to update miRNA-disease association matrix, respectively, to employ the local structure and reduce the sparsity effect. Secondly, we define a computing layer to which a heterogeneous matrix composing of the updated association matrix and integrated miRNA similarity and integrated disease similarity is fed, and output predicted scores for miRNA-disease associations by linear projection method. Thirdly, we design multiple computing layers where the heterogeneous matrix input for the current layer is constructed based on the predicted miRNA-disease association scores from the last layer. Finally, we capture possible missing miRNA-disease associations by integrating prediction scores from each single view. We obtain AUC values 0.9847, 0.9883, 0.9899 for one dataset under 2-fold, 5-fold and 10-fold cross-validations, respectively, and their corresponding AUPR values are 0.7777, 0.7806 and 0.7518, which outperforms seven state-of-the-art methods. At last, we predict the potential miRNAs associated with three diseases, most of which are verified with some evidence. The miRNA plays a key role in the biological process and it has close relationship with disease. The wet experiment to test the association between miRNA and disease is time-consuming and costly, so semi-supervised learning based computational methods have been proposed to predict potential miRNAs associated with disease. However, these methods cannot fully utilize the local structure and global structure information of miRNA-disease association data, and the prediction performance can be improved further. In this work, we propose a novel approach for miRNA-disease association prediction by using multi-layer linear projection (MLPMDA). Firstly, we use the top n neighbors of miRNA and disease to update miRNA-disease association matrix, respectively, to employ the local structure and reduce the sparsity effect. Secondly, we define a computing layer to which a heterogeneous matrix composing of the updated association matrix and integrated miRNA similarity and integrated disease similarity is fed, and output predicted scores for miRNA-disease associations by linear projection method. Thirdly, we design multiple computing layers where the heterogeneous matrix input for the current layer is constructed based on the predicted miRNA-disease association scores from the last layer. Finally, we capture possible missing miRNA-disease associations by integrating prediction scores from each single view. We obtain AUC values 0.9847, 0.9883, 0.9899 for one dataset under 2-fold, 5-fold and 10-fold cross-validations, respectively, and their corresponding AUPR values are 0.7777, 0.7806 and 0.7518, which outperforms seven state-of-the-art methods. At last, we predict the potential miRNAs associated with three diseases, most of which are verified with some evidence. MicroRNA Elsevier Disease Elsevier Multi-layer structure Elsevier Linear projection Elsevier Association prediction Elsevier Shi, Kun oth Wang, Lin oth Enthalten in Elsevier Science Wang, Jiliang ELSEVIER Subsurface fluid flow at an active cold seep area in the Qiongdongnan Basin, northern South China Sea 2018 Amsterdam [u.a.] (DE-627)ELV001104926 volume:214 year:2021 day:28 month:02 pages:0 https://doi.org/10.1016/j.knosys.2020.106718 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-GGO 38.00 Geowissenschaften: Allgemeines VZ AR 214 2021 28 0228 0 |
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10.1016/j.knosys.2020.106718 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001285.pica (DE-627)ELV052936651 (ELSEVIER)S0950-7051(20)30847-9 DE-627 ger DE-627 rakwb eng 550 VZ 38.00 bkl Guo, Leiming verfasserin aut MLPMDA: Multi-layer linear projection for predicting miRNA-disease association 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The miRNA plays a key role in the biological process and it has close relationship with disease. The wet experiment to test the association between miRNA and disease is time-consuming and costly, so semi-supervised learning based computational methods have been proposed to predict potential miRNAs associated with disease. However, these methods cannot fully utilize the local structure and global structure information of miRNA-disease association data, and the prediction performance can be improved further. In this work, we propose a novel approach for miRNA-disease association prediction by using multi-layer linear projection (MLPMDA). Firstly, we use the top n neighbors of miRNA and disease to update miRNA-disease association matrix, respectively, to employ the local structure and reduce the sparsity effect. Secondly, we define a computing layer to which a heterogeneous matrix composing of the updated association matrix and integrated miRNA similarity and integrated disease similarity is fed, and output predicted scores for miRNA-disease associations by linear projection method. Thirdly, we design multiple computing layers where the heterogeneous matrix input for the current layer is constructed based on the predicted miRNA-disease association scores from the last layer. Finally, we capture possible missing miRNA-disease associations by integrating prediction scores from each single view. We obtain AUC values 0.9847, 0.9883, 0.9899 for one dataset under 2-fold, 5-fold and 10-fold cross-validations, respectively, and their corresponding AUPR values are 0.7777, 0.7806 and 0.7518, which outperforms seven state-of-the-art methods. At last, we predict the potential miRNAs associated with three diseases, most of which are verified with some evidence. The miRNA plays a key role in the biological process and it has close relationship with disease. The wet experiment to test the association between miRNA and disease is time-consuming and costly, so semi-supervised learning based computational methods have been proposed to predict potential miRNAs associated with disease. However, these methods cannot fully utilize the local structure and global structure information of miRNA-disease association data, and the prediction performance can be improved further. In this work, we propose a novel approach for miRNA-disease association prediction by using multi-layer linear projection (MLPMDA). Firstly, we use the top n neighbors of miRNA and disease to update miRNA-disease association matrix, respectively, to employ the local structure and reduce the sparsity effect. Secondly, we define a computing layer to which a heterogeneous matrix composing of the updated association matrix and integrated miRNA similarity and integrated disease similarity is fed, and output predicted scores for miRNA-disease associations by linear projection method. Thirdly, we design multiple computing layers where the heterogeneous matrix input for the current layer is constructed based on the predicted miRNA-disease association scores from the last layer. Finally, we capture possible missing miRNA-disease associations by integrating prediction scores from each single view. We obtain AUC values 0.9847, 0.9883, 0.9899 for one dataset under 2-fold, 5-fold and 10-fold cross-validations, respectively, and their corresponding AUPR values are 0.7777, 0.7806 and 0.7518, which outperforms seven state-of-the-art methods. At last, we predict the potential miRNAs associated with three diseases, most of which are verified with some evidence. MicroRNA Elsevier Disease Elsevier Multi-layer structure Elsevier Linear projection Elsevier Association prediction Elsevier Shi, Kun oth Wang, Lin oth Enthalten in Elsevier Science Wang, Jiliang ELSEVIER Subsurface fluid flow at an active cold seep area in the Qiongdongnan Basin, northern South China Sea 2018 Amsterdam [u.a.] (DE-627)ELV001104926 volume:214 year:2021 day:28 month:02 pages:0 https://doi.org/10.1016/j.knosys.2020.106718 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-GGO 38.00 Geowissenschaften: Allgemeines VZ AR 214 2021 28 0228 0 |
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10.1016/j.knosys.2020.106718 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001285.pica (DE-627)ELV052936651 (ELSEVIER)S0950-7051(20)30847-9 DE-627 ger DE-627 rakwb eng 550 VZ 38.00 bkl Guo, Leiming verfasserin aut MLPMDA: Multi-layer linear projection for predicting miRNA-disease association 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The miRNA plays a key role in the biological process and it has close relationship with disease. The wet experiment to test the association between miRNA and disease is time-consuming and costly, so semi-supervised learning based computational methods have been proposed to predict potential miRNAs associated with disease. However, these methods cannot fully utilize the local structure and global structure information of miRNA-disease association data, and the prediction performance can be improved further. In this work, we propose a novel approach for miRNA-disease association prediction by using multi-layer linear projection (MLPMDA). Firstly, we use the top n neighbors of miRNA and disease to update miRNA-disease association matrix, respectively, to employ the local structure and reduce the sparsity effect. Secondly, we define a computing layer to which a heterogeneous matrix composing of the updated association matrix and integrated miRNA similarity and integrated disease similarity is fed, and output predicted scores for miRNA-disease associations by linear projection method. Thirdly, we design multiple computing layers where the heterogeneous matrix input for the current layer is constructed based on the predicted miRNA-disease association scores from the last layer. Finally, we capture possible missing miRNA-disease associations by integrating prediction scores from each single view. We obtain AUC values 0.9847, 0.9883, 0.9899 for one dataset under 2-fold, 5-fold and 10-fold cross-validations, respectively, and their corresponding AUPR values are 0.7777, 0.7806 and 0.7518, which outperforms seven state-of-the-art methods. At last, we predict the potential miRNAs associated with three diseases, most of which are verified with some evidence. The miRNA plays a key role in the biological process and it has close relationship with disease. The wet experiment to test the association between miRNA and disease is time-consuming and costly, so semi-supervised learning based computational methods have been proposed to predict potential miRNAs associated with disease. However, these methods cannot fully utilize the local structure and global structure information of miRNA-disease association data, and the prediction performance can be improved further. In this work, we propose a novel approach for miRNA-disease association prediction by using multi-layer linear projection (MLPMDA). Firstly, we use the top n neighbors of miRNA and disease to update miRNA-disease association matrix, respectively, to employ the local structure and reduce the sparsity effect. Secondly, we define a computing layer to which a heterogeneous matrix composing of the updated association matrix and integrated miRNA similarity and integrated disease similarity is fed, and output predicted scores for miRNA-disease associations by linear projection method. Thirdly, we design multiple computing layers where the heterogeneous matrix input for the current layer is constructed based on the predicted miRNA-disease association scores from the last layer. Finally, we capture possible missing miRNA-disease associations by integrating prediction scores from each single view. We obtain AUC values 0.9847, 0.9883, 0.9899 for one dataset under 2-fold, 5-fold and 10-fold cross-validations, respectively, and their corresponding AUPR values are 0.7777, 0.7806 and 0.7518, which outperforms seven state-of-the-art methods. At last, we predict the potential miRNAs associated with three diseases, most of which are verified with some evidence. MicroRNA Elsevier Disease Elsevier Multi-layer structure Elsevier Linear projection Elsevier Association prediction Elsevier Shi, Kun oth Wang, Lin oth Enthalten in Elsevier Science Wang, Jiliang ELSEVIER Subsurface fluid flow at an active cold seep area in the Qiongdongnan Basin, northern South China Sea 2018 Amsterdam [u.a.] (DE-627)ELV001104926 volume:214 year:2021 day:28 month:02 pages:0 https://doi.org/10.1016/j.knosys.2020.106718 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-GGO 38.00 Geowissenschaften: Allgemeines VZ AR 214 2021 28 0228 0 |
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10.1016/j.knosys.2020.106718 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001285.pica (DE-627)ELV052936651 (ELSEVIER)S0950-7051(20)30847-9 DE-627 ger DE-627 rakwb eng 550 VZ 38.00 bkl Guo, Leiming verfasserin aut MLPMDA: Multi-layer linear projection for predicting miRNA-disease association 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The miRNA plays a key role in the biological process and it has close relationship with disease. The wet experiment to test the association between miRNA and disease is time-consuming and costly, so semi-supervised learning based computational methods have been proposed to predict potential miRNAs associated with disease. However, these methods cannot fully utilize the local structure and global structure information of miRNA-disease association data, and the prediction performance can be improved further. In this work, we propose a novel approach for miRNA-disease association prediction by using multi-layer linear projection (MLPMDA). Firstly, we use the top n neighbors of miRNA and disease to update miRNA-disease association matrix, respectively, to employ the local structure and reduce the sparsity effect. Secondly, we define a computing layer to which a heterogeneous matrix composing of the updated association matrix and integrated miRNA similarity and integrated disease similarity is fed, and output predicted scores for miRNA-disease associations by linear projection method. Thirdly, we design multiple computing layers where the heterogeneous matrix input for the current layer is constructed based on the predicted miRNA-disease association scores from the last layer. Finally, we capture possible missing miRNA-disease associations by integrating prediction scores from each single view. We obtain AUC values 0.9847, 0.9883, 0.9899 for one dataset under 2-fold, 5-fold and 10-fold cross-validations, respectively, and their corresponding AUPR values are 0.7777, 0.7806 and 0.7518, which outperforms seven state-of-the-art methods. At last, we predict the potential miRNAs associated with three diseases, most of which are verified with some evidence. The miRNA plays a key role in the biological process and it has close relationship with disease. The wet experiment to test the association between miRNA and disease is time-consuming and costly, so semi-supervised learning based computational methods have been proposed to predict potential miRNAs associated with disease. However, these methods cannot fully utilize the local structure and global structure information of miRNA-disease association data, and the prediction performance can be improved further. In this work, we propose a novel approach for miRNA-disease association prediction by using multi-layer linear projection (MLPMDA). Firstly, we use the top n neighbors of miRNA and disease to update miRNA-disease association matrix, respectively, to employ the local structure and reduce the sparsity effect. Secondly, we define a computing layer to which a heterogeneous matrix composing of the updated association matrix and integrated miRNA similarity and integrated disease similarity is fed, and output predicted scores for miRNA-disease associations by linear projection method. Thirdly, we design multiple computing layers where the heterogeneous matrix input for the current layer is constructed based on the predicted miRNA-disease association scores from the last layer. Finally, we capture possible missing miRNA-disease associations by integrating prediction scores from each single view. We obtain AUC values 0.9847, 0.9883, 0.9899 for one dataset under 2-fold, 5-fold and 10-fold cross-validations, respectively, and their corresponding AUPR values are 0.7777, 0.7806 and 0.7518, which outperforms seven state-of-the-art methods. At last, we predict the potential miRNAs associated with three diseases, most of which are verified with some evidence. MicroRNA Elsevier Disease Elsevier Multi-layer structure Elsevier Linear projection Elsevier Association prediction Elsevier Shi, Kun oth Wang, Lin oth Enthalten in Elsevier Science Wang, Jiliang ELSEVIER Subsurface fluid flow at an active cold seep area in the Qiongdongnan Basin, northern South China Sea 2018 Amsterdam [u.a.] (DE-627)ELV001104926 volume:214 year:2021 day:28 month:02 pages:0 https://doi.org/10.1016/j.knosys.2020.106718 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-GGO 38.00 Geowissenschaften: Allgemeines VZ AR 214 2021 28 0228 0 |
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Enthalten in Subsurface fluid flow at an active cold seep area in the Qiongdongnan Basin, northern South China Sea Amsterdam [u.a.] volume:214 year:2021 day:28 month:02 pages:0 |
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mlpmda: multi-layer linear projection for predicting mirna-disease association |
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MLPMDA: Multi-layer linear projection for predicting miRNA-disease association |
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The miRNA plays a key role in the biological process and it has close relationship with disease. The wet experiment to test the association between miRNA and disease is time-consuming and costly, so semi-supervised learning based computational methods have been proposed to predict potential miRNAs associated with disease. However, these methods cannot fully utilize the local structure and global structure information of miRNA-disease association data, and the prediction performance can be improved further. In this work, we propose a novel approach for miRNA-disease association prediction by using multi-layer linear projection (MLPMDA). Firstly, we use the top n neighbors of miRNA and disease to update miRNA-disease association matrix, respectively, to employ the local structure and reduce the sparsity effect. Secondly, we define a computing layer to which a heterogeneous matrix composing of the updated association matrix and integrated miRNA similarity and integrated disease similarity is fed, and output predicted scores for miRNA-disease associations by linear projection method. Thirdly, we design multiple computing layers where the heterogeneous matrix input for the current layer is constructed based on the predicted miRNA-disease association scores from the last layer. Finally, we capture possible missing miRNA-disease associations by integrating prediction scores from each single view. We obtain AUC values 0.9847, 0.9883, 0.9899 for one dataset under 2-fold, 5-fold and 10-fold cross-validations, respectively, and their corresponding AUPR values are 0.7777, 0.7806 and 0.7518, which outperforms seven state-of-the-art methods. At last, we predict the potential miRNAs associated with three diseases, most of which are verified with some evidence. |
| abstractGer |
The miRNA plays a key role in the biological process and it has close relationship with disease. The wet experiment to test the association between miRNA and disease is time-consuming and costly, so semi-supervised learning based computational methods have been proposed to predict potential miRNAs associated with disease. However, these methods cannot fully utilize the local structure and global structure information of miRNA-disease association data, and the prediction performance can be improved further. In this work, we propose a novel approach for miRNA-disease association prediction by using multi-layer linear projection (MLPMDA). Firstly, we use the top n neighbors of miRNA and disease to update miRNA-disease association matrix, respectively, to employ the local structure and reduce the sparsity effect. Secondly, we define a computing layer to which a heterogeneous matrix composing of the updated association matrix and integrated miRNA similarity and integrated disease similarity is fed, and output predicted scores for miRNA-disease associations by linear projection method. Thirdly, we design multiple computing layers where the heterogeneous matrix input for the current layer is constructed based on the predicted miRNA-disease association scores from the last layer. Finally, we capture possible missing miRNA-disease associations by integrating prediction scores from each single view. We obtain AUC values 0.9847, 0.9883, 0.9899 for one dataset under 2-fold, 5-fold and 10-fold cross-validations, respectively, and their corresponding AUPR values are 0.7777, 0.7806 and 0.7518, which outperforms seven state-of-the-art methods. At last, we predict the potential miRNAs associated with three diseases, most of which are verified with some evidence. |
| abstract_unstemmed |
The miRNA plays a key role in the biological process and it has close relationship with disease. The wet experiment to test the association between miRNA and disease is time-consuming and costly, so semi-supervised learning based computational methods have been proposed to predict potential miRNAs associated with disease. However, these methods cannot fully utilize the local structure and global structure information of miRNA-disease association data, and the prediction performance can be improved further. In this work, we propose a novel approach for miRNA-disease association prediction by using multi-layer linear projection (MLPMDA). Firstly, we use the top n neighbors of miRNA and disease to update miRNA-disease association matrix, respectively, to employ the local structure and reduce the sparsity effect. Secondly, we define a computing layer to which a heterogeneous matrix composing of the updated association matrix and integrated miRNA similarity and integrated disease similarity is fed, and output predicted scores for miRNA-disease associations by linear projection method. Thirdly, we design multiple computing layers where the heterogeneous matrix input for the current layer is constructed based on the predicted miRNA-disease association scores from the last layer. Finally, we capture possible missing miRNA-disease associations by integrating prediction scores from each single view. We obtain AUC values 0.9847, 0.9883, 0.9899 for one dataset under 2-fold, 5-fold and 10-fold cross-validations, respectively, and their corresponding AUPR values are 0.7777, 0.7806 and 0.7518, which outperforms seven state-of-the-art methods. At last, we predict the potential miRNAs associated with three diseases, most of which are verified with some evidence. |
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MLPMDA: Multi-layer linear projection for predicting miRNA-disease association |
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