MiRNA features for automated classification

We present a system for miRNA classification that implements a wide variety of miRNA features found in literature: structural, thermodynamical, information-theoretical, statistical, and comparative. A total of 1485 features are computed and various tests are performed. The classifier of choice used is Random Forests, which is also employed along with various feature selection strategies to determine the most salient features and increase automate classification performance.