Prediction of miRNA Targets

摘要: Abstract Computational methods for miRNA target prediction are currently undergoing extensive review and evaluation. There is still a great need improvement of these tools bioinformatics approaches looking towards high-throughput experiments in order to validate predictions. The combination large-scale techniques with computational will not only provide greater credence predictions but also lead the better understanding specific biological questions. Current utilize probabilistic learning algorithms, machine even empirical biologically defined rules build models based on experimentally verified targets. Large-scale protein downregulation assays next-generation sequencing (NGS) now being used methodologies compare performance existing tools. Tools that exhibit correlation between or RNA considered state art. Moreover, efficiency targets concurrently provides additional validity further highlights competitive advantage their efficacy extracting significant results. In this paper, we discuss detailed comparison features utilized by each tool. an overview current state-of-the-art prediction.