Large-scale prediction of microRNA-disease associations by combinatorial prioritization algorithm
文献类型: 外文期刊
作者: Yu, Hua 2 ; Chen, Xiaojun 1 ; Lu, Lu 3 ;
作者机构: 1.Ningxia Acad Agr & Forestry Sci, Agr Biotechnol Ctr, Key Lab Agr Biotechnol Ningxia, 590 Huanghe East Rd, Yinchuan 750002, Ningxia, Peoples R China
2.Chinese Acad Sci, Inst Genet & Dev Biol, State Key Lab Plant Genom, 1 West Beichen Rd, Beijing 100101, Peoples R China
3.Beijing Acad Sci & Technol, Beijing Comp Ctr, Bldg 3 BeiKe Ind Pk,Fengxian Rd 7, Beijing 100094, Peoples R China
期刊名称:SCIENTIFIC REPORTS ( 影响因子:4.379; 五年影响因子:5.133 )
ISSN: 2045-2322
年卷期: 2017 年 7 卷
页码:
收录情况: SCI
摘要: Identification of the associations between microRNA molecules and human diseases from large-scale heterogeneous biological data is an important step for understanding the pathogenesis of diseases in microRNA level. However, experimental verification of microRNA-disease associations is expensive and time-consuming. To overcome the drawbacks of conventional experimental methods, we presented a combinatorial prioritization algorithm to predict the microRNA-disease associations. Importantly, our method can be used to predict microRNAs (diseases) associated with the diseases (microRNAs) without the known associated microRNAs (diseases). The predictive performance of our proposed approach was evaluated and verified by the internal cross-validations and external independent validations based on standard association datasets. The results demonstrate that our proposed method achieves the impressive performance for predicting the microRNA-disease association with the Area Under receiver operation characteristic Curve (AUC), 86.93%, which is indeed outperform the previous prediction methods. Particularly, we observed that the ensemble-based method by integrating the predictions of multiple algorithms can give more reliable and robust prediction than the single algorithm, with the AUC score improved to 92.26%. We applied our combinatorial prioritization algorithm to lung neoplasms and breast neoplasms, and revealed their top 30 microRNA candidates, which are in consistent with the published literatures and databases.
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