Support Vector Machine for Classification of Plants and Animal miRNA

Abstract

MicroRNAs (miRNAs) constitute a large family of non coding RNAs that function to regulate gene expression. Wet lab experiments usually used to classify the miRNA of plants and animals are highly expensive, labor intensive and time consuming. Thus there arises a need for computational approach for classification of plant and animal miRNA. These computational approaches are fast and economical as compared to wet lab techniques. The new SVM learning algorithm called Weka LibSVM has been used for classification of plant and animal miRNA. The model has been tested on available data and it gives results with 95% accuracy.