We address the problem of predicting the position of a miRNA duplex on a 
microRNA hairpin via the development and application of a novel SVM-based 
methodology. Our method combines a unique problem representation and an unbiased 
optimization protocol to learn from mirBase19.0 an accurate predictive model, 
termed MiRduplexSVM. This is the first model that provides precise information 
about all four ends of the miRNA duplex. We show that (a) our method outperforms 
four state-of-the-art tools, namely MaturePred, MiRPara, MatureBayes, MiRdup as 
well as a Simple Geometric Locator when applied on the same training datasets 
employed for each tool and evaluated on a common blind test set. (b) In all 
comparisons, MiRduplexSVM shows superior performance, achieving up to a 60% 
increase in prediction accuracy for mammalian hairpins and can generalize very 
well on plant hairpins, without any special optimization. (c) The tool has a 
number of important applications such as the ability to accurately predict the 
miRNA or the miRNA*, given the opposite strand of a duplex. Its performance on 
this task is superior to the 2nts overhang rule commonly used in computational 
studies and similar to that of a comparative genomic approach, without the need 
for prior knowledge or the complexity of performing multiple alignments. 
Finally, it is able to evaluate novel, potential miRNAs found either 
computationally or experimentally. In relation with recent confidence evaluation 
methods used in miRBase, MiRduplexSVM was successful in identifying high 
confidence potential miRNAs.