A novel pre-miRNA classification approach for the prediction of microRNA genes

MicroRNAs (miRNAs) are small non coding RNAs that play a significant role in gene regulation. Prediction of microRNA genes is a challenging bioinformatics problem. In our approach we applied an novel classification method, which combines the efficiency and robustness of Support Vector Machines with Genetic Algorithms for feature selection and parameters optimization. We tested our method with commonly used data and feature sets and we achieve higher performance in terms of sensitivity (99,10%), specificity (97,95%) and accuracy (98,68%) than the existing classifiers. Finally, we managed to extract a minimum subset consisted of 7 features that can standalone yield very high classification performance.