iMDA-BN: Identification of miRNA-disease associations based on the biological network and graph embedding algorithm
Benefiting from advances in high-throughput experimental techniques, important regulatory roles of miRNAs, lncRNAs, and proteins, as well as biological property information, are gradually being complemented. As the key data support to promote biomedical research, domain knowledge such as intermolecu...
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| Format: | Article |
| Language: | English |
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Elsevier
2020-01-01
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| Series: | Computational and Structural Biotechnology Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2001037020303767 |
| _version_ | 1818452317393911808 |
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| author | Kai Zheng Zhu-Hong You Lei Wang Zhen-Hao Guo |
| author_facet | Kai Zheng Zhu-Hong You Lei Wang Zhen-Hao Guo |
| author_sort | Kai Zheng |
| collection | DOAJ |
| description | Benefiting from advances in high-throughput experimental techniques, important regulatory roles of miRNAs, lncRNAs, and proteins, as well as biological property information, are gradually being complemented. As the key data support to promote biomedical research, domain knowledge such as intermolecular relationships that are increasingly revealed by molecular genome-wide analysis is often used to guide the discovery of potential associations. However, the method of performing network representation learning from the perspective of the global biological network is scarce. These methods cover a very limited type of molecular associations and are therefore not suitable for more comprehensive analysis of molecular network representation information. In this study, we propose a computational model based on the Biological network for predicting potential associations between miRNAs and diseases called iMDA-BN. The iMDA-BN has three significant advantages: I) It uses a new method to describe disease and miRNA characteristics which analyzes node representation information for disease and miRNA from the perspective of biological networks. II) It can predict unproven associations even if miRNAs and diseases do not appear in the biological network. III) Accurate description of miRNA characteristics from biological properties based on high-throughput sequence information. The iMDA-BN predictor achieves an AUC of 0.9145 and an accuracy of 84.49% on the miRNA-disease association baseline dataset, and it can also achieve an AUC of 0.8765 and an accuracy of 80.96% when predicting unknown diseases and miRNAs in the biological network. Compared to existing miRNA-disease association prediction methods, iMDA-BN has higher accuracy and the advantage of predicting unknown associations. In addition, 45, 49, and 49 of the top 50 miRNA-disease associations with the highest predicted scores were confirmed in the case studies, respectively. |
| first_indexed | 2024-12-14T21:21:10Z |
| format | Article |
| id | doaj.art-d91a15d097d5436d87232643e65badd0 |
| institution | Directory Open Access Journal |
| issn | 2001-0370 |
| language | English |
| last_indexed | 2024-12-14T21:21:10Z |
| publishDate | 2020-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Computational and Structural Biotechnology Journal |
| spelling | doaj.art-d91a15d097d5436d87232643e65badd02022-12-21T22:46:56ZengElsevierComputational and Structural Biotechnology Journal2001-03702020-01-011823912400iMDA-BN: Identification of miRNA-disease associations based on the biological network and graph embedding algorithmKai Zheng0Zhu-Hong You1Lei Wang2Zhen-Hao Guo3School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, ChinaXinjiang Technical Institutes of Physics and Chemistry, Chinese Academy of Science, Urumqi 830011, China; Corresponding authors at: Xinjiang Technical Institutes of Physics and Chemistry, Chinese Academy of Science, Urumqi 830011, China and College of Information Science and Engineering, Zaozhuang University, Zaozhuang 277100, China (L. Wang).Xinjiang Technical Institutes of Physics and Chemistry, Chinese Academy of Science, Urumqi 830011, China; College of Information Science and Engineering, Zaozhuang University, Zaozhuang 277100, China; Corresponding authors at: Xinjiang Technical Institutes of Physics and Chemistry, Chinese Academy of Science, Urumqi 830011, China and College of Information Science and Engineering, Zaozhuang University, Zaozhuang 277100, China (L. Wang).Xinjiang Technical Institutes of Physics and Chemistry, Chinese Academy of Science, Urumqi 830011, ChinaBenefiting from advances in high-throughput experimental techniques, important regulatory roles of miRNAs, lncRNAs, and proteins, as well as biological property information, are gradually being complemented. As the key data support to promote biomedical research, domain knowledge such as intermolecular relationships that are increasingly revealed by molecular genome-wide analysis is often used to guide the discovery of potential associations. However, the method of performing network representation learning from the perspective of the global biological network is scarce. These methods cover a very limited type of molecular associations and are therefore not suitable for more comprehensive analysis of molecular network representation information. In this study, we propose a computational model based on the Biological network for predicting potential associations between miRNAs and diseases called iMDA-BN. The iMDA-BN has three significant advantages: I) It uses a new method to describe disease and miRNA characteristics which analyzes node representation information for disease and miRNA from the perspective of biological networks. II) It can predict unproven associations even if miRNAs and diseases do not appear in the biological network. III) Accurate description of miRNA characteristics from biological properties based on high-throughput sequence information. The iMDA-BN predictor achieves an AUC of 0.9145 and an accuracy of 84.49% on the miRNA-disease association baseline dataset, and it can also achieve an AUC of 0.8765 and an accuracy of 80.96% when predicting unknown diseases and miRNAs in the biological network. Compared to existing miRNA-disease association prediction methods, iMDA-BN has higher accuracy and the advantage of predicting unknown associations. In addition, 45, 49, and 49 of the top 50 miRNA-disease associations with the highest predicted scores were confirmed in the case studies, respectively.http://www.sciencedirect.com/science/article/pii/S2001037020303767miRNADiseaseHeterogenous informationThe biological networkGraph embedding algorithm |
| spellingShingle | Kai Zheng Zhu-Hong You Lei Wang Zhen-Hao Guo iMDA-BN: Identification of miRNA-disease associations based on the biological network and graph embedding algorithm Computational and Structural Biotechnology Journal miRNA Disease Heterogenous information The biological network Graph embedding algorithm |
| title | iMDA-BN: Identification of miRNA-disease associations based on the biological network and graph embedding algorithm |
| title_full | iMDA-BN: Identification of miRNA-disease associations based on the biological network and graph embedding algorithm |
| title_fullStr | iMDA-BN: Identification of miRNA-disease associations based on the biological network and graph embedding algorithm |
| title_full_unstemmed | iMDA-BN: Identification of miRNA-disease associations based on the biological network and graph embedding algorithm |
| title_short | iMDA-BN: Identification of miRNA-disease associations based on the biological network and graph embedding algorithm |
| title_sort | imda bn identification of mirna disease associations based on the biological network and graph embedding algorithm |
| topic | miRNA Disease Heterogenous information The biological network Graph embedding algorithm |
| url | http://www.sciencedirect.com/science/article/pii/S2001037020303767 |
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