CCmiR：一种用于竞争性和合作性微小RNA结合预测的计算方法。
CCmiR: a computational approach for competitive and cooperative microRNA binding prediction.
作者信息
Ding Jun, Li Xiaoman, Hu Haiyan
机构信息
Department of Computer Science, University of Central Florida, Orlando, FL, USA.
Burnett School of Biomedical Science, University of Central Florida, Orlando, FL, USA.
出版信息
Bioinformatics. 2018 Jan 15;34(2):198-206. doi: 10.1093/bioinformatics/btx606.
MOTIVATION
The identification of microRNA (miRNA) target sites is important. In the past decade, dozens of computational methods have been developed to predict miRNA target sites. Despite their existence, rarely does a method consider the well-known competition and cooperation among miRNAs when attempts to discover target sites. To fill this gap, we developed a new approach called CCmiR, which takes the cooperation and competition of multiple miRNAs into account in a statistical model to predict their target sites.
RESULTS
Tested on four different datasets, CCmiR predicted miRNA target sites with a high recall and a reasonable precision, and identified known and new cooperative and competitive miRNAs supported by literature. Compared with three state-of-the-art computational methods, CCmiR had a higher recall and a higher precision.
AVAILABILITY AND IMPLEMENTATION
CCmiR is freely available at http://hulab.ucf.edu/research/projects/miRNA/CCmiR.
SUPPLEMENTARY INFORMATION
Supplementary data are available at Bioinformatics online.
动机
识别微小RNA（miRNA）的靶位点很重要。在过去十年中，已经开发了数十种计算方法来预测miRNA靶位点。尽管有这些方法，但在尝试发现靶位点时，很少有方法考虑到miRNA之间众所周知的竞争与合作关系。为了填补这一空白，我们开发了一种名为CCmiR的新方法，该方法在统计模型中考虑了多个miRNA的合作与竞争，以预测它们的靶位点。
结果
在四个不同的数据集上进行测试时，CCmiR预测miRNA靶位点具有较高的召回率和合理的精度，并识别出文献支持的已知和新的合作与竞争miRNA。与三种最先进的计算方法相比，CCmiR具有更高的召回率和精度。
可用性与实现
CCmiR可在http://hulab.ucf.edu/research/projects/miRNA/CCmiR免费获取。
补充信息
补充数据可在《生物信息学》在线获取。
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