Predicting XXXX microRNA precursors based on an optimized feature subset generated by GA-SVM.

@entity1 @entity11216
MicroRNAs (miRNAs) are non-coding RNAs that play important roles in post-transcriptional regulation. Identification of miRNAs is crucial to understanding their biological mechanism. Recently, machine-learning approaches have been employed to predict miRNA precursors (pre-miRNAs). However, features used are divergent and consequently induce different performance. Thus, feature selection is critical for pre-miRNA prediction. We generated an optimized feature subset including 13 features using a hybrid of genetic algorithm and support vector machine (GA-SVM). Based on SVM, the classification performance of the optimized feature subset is much higher than that of the two feature sets used in microPred and miPred by five-fold cross-validation. Finally, we constructed the classifier @entity11216 -SF to predict the most recently identified @entity1 pre-miRNAs in miRBase (version 16). Compared with microPred and miPred, @entity11216 -SF achieved much higher classification performance. Accuracies were 93.97%, 86.21% and 64.66% for @entity11216 -SF, microPred and miPred, respectively. Thus, @entity11216 -SF is effective for identifying pre-miRNAs.
