WBNPMD: weighted bipartite network projection for microRNA-disease association prediction
Abstract Background Recently, numerous biological experiments have indicated that microRNAs (miRNAs) play critical roles in exploring the pathogenesis of various human diseases. Since traditional experimental methods for miRNA-disease associations detection are costly and time-consuming, it becomes...
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| Format: | Article |
| Language: | English |
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BMC
2019-09-01
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| Series: | Journal of Translational Medicine |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s12967-019-2063-4 |
| _version_ | 1818400505664110592 |
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| author | Guobo Xie Zhiliang Fan Yuping Sun Cuiming Wu Lei Ma |
| author_facet | Guobo Xie Zhiliang Fan Yuping Sun Cuiming Wu Lei Ma |
| author_sort | Guobo Xie |
| collection | DOAJ |
| description | Abstract Background Recently, numerous biological experiments have indicated that microRNAs (miRNAs) play critical roles in exploring the pathogenesis of various human diseases. Since traditional experimental methods for miRNA-disease associations detection are costly and time-consuming, it becomes urgent to design efficient and robust computational techniques for identifying undiscovered interactions. Methods In this paper, we proposed a computation framework named weighted bipartite network projection for miRNA-disease association prediction (WBNPMD). In this method, transfer weights were constructed by combining the known miRNA and disease similarities, and the initial information was properly configured. Then the two-step bipartite network algorithm was implemented to infer potential miRNA-disease associations. Results The proposed WBNPMD was applied to the known miRNA-disease association data, and leave-one-out cross-validation (LOOCV) and fivefold cross-validation were implemented to evaluate the performance of WBNPMD. As a result, our method achieved the AUCs of 0.9321 and $$0.9173 \pm 0.0005$$ 0.9173±0.0005 in LOOCV and fivefold cross-validation, and outperformed other four state-of-the-art methods. We also carried out two kinds of case studies on prostate neoplasm, colorectal neoplasm, and lung neoplasm, and most of the top 50 predicted miRNAs were confirmed to have an association with the corresponding diseases based on dbDeMC, miR2Disease, and HMDD V3.0 databases. Conclusions The experimental results demonstrate that WBNPMD can accurately infer potential miRNA-disease associations. We anticipated that the proposed WBNPMD could serve as a powerful tool for potential miRNA-disease associations excavation. |
| first_indexed | 2024-12-14T07:37:39Z |
| format | Article |
| id | doaj.art-125011140000412faf32ac977c99de08 |
| institution | Directory Open Access Journal |
| issn | 1479-5876 |
| language | English |
| last_indexed | 2024-12-14T07:37:39Z |
| publishDate | 2019-09-01 |
| publisher | BMC |
| record_format | Article |
| series | Journal of Translational Medicine |
| spelling | doaj.art-125011140000412faf32ac977c99de082022-12-21T23:11:07ZengBMCJournal of Translational Medicine1479-58762019-09-0117111110.1186/s12967-019-2063-4WBNPMD: weighted bipartite network projection for microRNA-disease association predictionGuobo Xie0Zhiliang Fan1Yuping Sun2Cuiming Wu3Lei Ma4School of Computer Science, Guangdong University of TechnologySchool of Computer Science, Guangdong University of TechnologySchool of Computer Science, Guangdong University of TechnologySchool of Computer Science, Guangdong University of TechnologyInstitute of Automation, Chinese Academy of SciencesAbstract Background Recently, numerous biological experiments have indicated that microRNAs (miRNAs) play critical roles in exploring the pathogenesis of various human diseases. Since traditional experimental methods for miRNA-disease associations detection are costly and time-consuming, it becomes urgent to design efficient and robust computational techniques for identifying undiscovered interactions. Methods In this paper, we proposed a computation framework named weighted bipartite network projection for miRNA-disease association prediction (WBNPMD). In this method, transfer weights were constructed by combining the known miRNA and disease similarities, and the initial information was properly configured. Then the two-step bipartite network algorithm was implemented to infer potential miRNA-disease associations. Results The proposed WBNPMD was applied to the known miRNA-disease association data, and leave-one-out cross-validation (LOOCV) and fivefold cross-validation were implemented to evaluate the performance of WBNPMD. As a result, our method achieved the AUCs of 0.9321 and $$0.9173 \pm 0.0005$$ 0.9173±0.0005 in LOOCV and fivefold cross-validation, and outperformed other four state-of-the-art methods. We also carried out two kinds of case studies on prostate neoplasm, colorectal neoplasm, and lung neoplasm, and most of the top 50 predicted miRNAs were confirmed to have an association with the corresponding diseases based on dbDeMC, miR2Disease, and HMDD V3.0 databases. Conclusions The experimental results demonstrate that WBNPMD can accurately infer potential miRNA-disease associations. We anticipated that the proposed WBNPMD could serve as a powerful tool for potential miRNA-disease associations excavation.http://link.springer.com/article/10.1186/s12967-019-2063-4miRNA-disease associationBipartite network projectionTransfer weight assignmentInitial information configuration |
| spellingShingle | Guobo Xie Zhiliang Fan Yuping Sun Cuiming Wu Lei Ma WBNPMD: weighted bipartite network projection for microRNA-disease association prediction Journal of Translational Medicine miRNA-disease association Bipartite network projection Transfer weight assignment Initial information configuration |
| title | WBNPMD: weighted bipartite network projection for microRNA-disease association prediction |
| title_full | WBNPMD: weighted bipartite network projection for microRNA-disease association prediction |
| title_fullStr | WBNPMD: weighted bipartite network projection for microRNA-disease association prediction |
| title_full_unstemmed | WBNPMD: weighted bipartite network projection for microRNA-disease association prediction |
| title_short | WBNPMD: weighted bipartite network projection for microRNA-disease association prediction |
| title_sort | wbnpmd weighted bipartite network projection for microrna disease association prediction |
| topic | miRNA-disease association Bipartite network projection Transfer weight assignment Initial information configuration |
| url | http://link.springer.com/article/10.1186/s12967-019-2063-4 |
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