SVM Model for Prediction and Classification of Drosophila Based on Nucleotide Composition

Abstract

MicroRNAs (miRNA) are single-stranded RNA molecules of about 21–23 nucleotides in length. MicroRNAs(miRNAs) constitute a large family of non coding RNAs that function to regulate gene expression. Till today wet lab experiments have been used to classify the miRNA of plants and animals. The wet lab techniques are highly expensive, labour intensive and time consuming. Thus there arises a need for computational approach for classification of plants and animal miRNA. These computational approaches are fast and economical as compared to wet lab techniques. In this paper an attempt has been made for the classification of Drosophila and its subclasses.The overall prediction accuracy of SVM modules based on mono nucleotide composition was 83.12% respectively. The accuracy of all the modules was evaluated using a 10-fold cross-validation technique.