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CCmiR: A computational approach for competitive and cooperative microRNA binding prediction.
Bioinformatics 2017 September 26
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: CCmiR is freely available at https://hulab.ucf.edu/research/projects/miRNA/CCmiR.
Contact: [email protected], [email protected].
Supplementary information: Supplementary data are available at Bioinformatics online.
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: CCmiR is freely available at https://hulab.ucf.edu/research/projects/miRNA/CCmiR.
Contact: [email protected], [email protected].
Supplementary information: Supplementary data are available at Bioinformatics online.
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