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SSCMDA: spy and super cluster strategy for MiRNA-disease association prediction
文献类型：期刊论文
| 作者 | Zhao, Qi1,2; Xie, Di1; Liu, Hongsheng2,3; Wang, Fan4,5; Yan, Gui-Ying6 ; Chen, Xing7
|
| 刊名 | ONCOTARGET
 |
| 出版日期 | 2018-01-05 |
| 卷号 | 9期号:2页码:1826-1842 |
| ISSN号 | 1949-2553 |
| 关键词 | microRNA disease association prediction spy strategy super cluster strategy |
| DOI | 10.18632/oncotarget.22812 |
| 英文摘要 | In the biological field, the identification of the associations between microRNAs (miRNAs) and diseases has been paid increasing attention as an extremely meaningful study for the clinical medicine. However, it is expensive and time-consuming to confirm miRNA-disease associations by experimental methods. Therefore, in recent years, several effective computational models for predicting the potential miRNA-disease associations have been developed. In this paper, we proposed the Spy and Super Cluster strategy for MiRNA-Disease Association prediction (SSCMDA) based on known miRNA-disease associations, integrated disease similarity and integrated miRNA similarity. For problems of mixed unknown miRNA-disease pairs containing both potential associations and real negative associations, which will lead to inaccurate prediction, spy strategy is adopted by SSCMDA to identify reliable negative samples from the unknown miRNA-disease pairs. Moreover, the super-cluster strategy could gather as many positive samples as possible to improve the accuracy of the prediction by overcoming the shortage of lacking sufficient positive training samples. As a result, the AUCs of global leave-one-out cross validation (LOOCV), local LOOCV and 5-fold cross validation were 0.9007, 0.8747 and 0.8806+/-0.0025, respectively. According to the AUC results, SSCMDA has shown a significant improvement compared with some previous models. We further carried out case studies based on various version of HMDD database to test the prediction performance robustness of SSCMDA. We also implemented case study to examine whether SSCMDA was effective for new diseases without any known associated miRNAs. As a result, a large proportion of the predicted miRNAs have been verified by experimental reports. |
| 资助项目 | National Natural Science Foundation of China[61772531] ; National Natural Science Foundation of China[11631014] ; National Natural Science Foundation of China[31570160] ; National Natural Science Foundation of China[11371355] ; Education Department of Liaoning Province[LT2015011] ; Doctor Startup Foundation from Liaoning Province[20170520217] ; Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD) ; annual general university graduate research and innovation program of Jiangsu Province, China[KYLX16_0526] |
| WOS研究方向 | Oncology ; Cell Biology |
| 语种 | 英语 |
| 出版者 | IMPACT JOURNALS LLC |
| WOS记录号 | WOS:000419623200027 |
| 源URL | [http://ir.amss.ac.cn/handle/2S8OKBNM/29633]  |
| 专题 | 应用数学研究所 |
| 通讯作者 | Zhao, Qi; Chen, Xing |
| 作者单位 | 1.Liaoning Univ, Sch Math, Shenyang, Liaoning, Peoples R China 2.Res Ctr Comp Simulating & Informat Proc Biomacrom, Shenyang, Liaoning, Peoples R China 3.Liaoning Univ, Sch Life Sci, Shenyang, Liaoning, Peoples R China 4.China Univ Min & Technol, Sch Mechatron Engn, Xuzhou, Peoples R China 5.China Univ Min & Technol, Jiangsu Key Lab Mine Mech & Elect Equipment, Xuzhou, Peoples R China 6.Chinese Acad Sci, Acad Math & Syst Sci, Beijing, Peoples R China 7.China Univ Min & Technol, Sch Informat & Control Engn, Xuzhou, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhao, Qi,Xie, Di,Liu, Hongsheng,et al. SSCMDA: spy and super cluster strategy for MiRNA-disease association prediction[J]. ONCOTARGET,2018,9(2):1826-1842. |
| APA | Zhao, Qi,Xie, Di,Liu, Hongsheng,Wang, Fan,Yan, Gui-Ying,&Chen, Xing.(2018).SSCMDA: spy and super cluster strategy for MiRNA-disease association prediction.ONCOTARGET,9(2),1826-1842. |
| MLA | Zhao, Qi,et al."SSCMDA: spy and super cluster strategy for MiRNA-disease association prediction".ONCOTARGET 9.2(2018):1826-1842. |
入库方式： OAI收割
来源：数学与系统科学研究院
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