WBNPMD: weighted bipartite network projection for microRNA-disease association prediction
文献类型：期刊论文
| 作者 | Xie, Guobo1; Fan, Zhiliang1; Sun, Yuping1; Wu, Cuiming1; Ma, Lei2
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| 刊名 | JOURNAL OF TRANSLATIONAL MEDICINE
 |
| 出版日期 | 2019-09-23 |
| 卷号 | 17期号:1页码:11 |
| 关键词 | miRNA-disease association Bipartite network projection Transfer weight assignment Initial information configuration |
| DOI | 10.1186/s12967-019-2063-4 |
| 通讯作者 | Sun, Yuping(syp@gdut.edu.cn) |
| 英文摘要 | 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 +/- 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. |
| WOS关键词 | CANCER ; MIRNA ; EXPRESSION ; SIMILARITY ; GROWTH |
| 资助项目 | National Natural Science Foundation of China[618002072] ; Natural Science Foundation of Guangdong Province[2018A030313389] ; Science and Technology Plan Project of Guangdong Province[2019B010139002] ; Science and Technology Plan Project of Guangdong Province[2017A040405050] ; Science and Technology Plan Project of Guangdong Province[2016B030306004] ; Science and Technology Plan Project of Guangdong Province[2015B010129014] ; Science and Technology Program of Guangzhou[201902020006] |
| WOS研究方向 | Research & Experimental Medicine |
| 语种 | 英语 |
| WOS记录号 | WOS:000490958300007 |
| 出版者 | BMC |
| 资助机构 | National Natural Science Foundation of China ; Natural Science Foundation of Guangdong Province ; Science and Technology Plan Project of Guangdong Province ; Science and Technology Program of Guangzhou |
| 源URL | [http://ir.ia.ac.cn/handle/173211/26678]  |
| 专题 | 类脑芯片与系统研究 |
| 通讯作者 | Sun, Yuping |
| 作者单位 | 1.Guangdong Univ Technol, Sch Comp Sci, Guangzhou, Guangdong, Peoples R China 2.Chinese Acad Sci, Inst Automat, Beijing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Xie, Guobo,Fan, Zhiliang,Sun, Yuping,et al. WBNPMD: weighted bipartite network projection for microRNA-disease association prediction[J]. JOURNAL OF TRANSLATIONAL MEDICINE,2019,17(1):11. |
| APA | Xie, Guobo,Fan, Zhiliang,Sun, Yuping,Wu, Cuiming,&Ma, Lei.(2019).WBNPMD: weighted bipartite network projection for microRNA-disease association prediction.JOURNAL OF TRANSLATIONAL MEDICINE,17(1),11. |
| MLA | Xie, Guobo,et al."WBNPMD: weighted bipartite network projection for microRNA-disease association prediction".JOURNAL OF TRANSLATIONAL MEDICINE 17.1(2019):11. |
入库方式： OAI收割
来源：自动化研究所
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