Analysis and Classification of Plant MicroRNAs Using Decision Tree Based Approach

Abstract

MicroRNA (miRNA) analysis have progressed tremendously in recent past, but further indepth computational study is required to know the complete potential of these RNAs. Due to its short length (~20 nucleotides), it is difficult to use the conventional lab techniques for microRNA prediction and analysis. This has led to this work in the domain of computational biology. These are the non coding small RNAs which are responsible for the gene regulation at the post translational level by binding to the mRNAs and thereby stopping the translation activities. Therefore,the effect of microRNAs on the various proteins is important. In this paper we have studied 1010 microRNA and precursor microRNA sequences from monocots. Our study in this paper is on the microRNA classification using decision trees and determining dominating attributes. We have used WEKA, a data mining tool which helps us to study the large data and classify it. The decision trees based classification was best suited for the miRNA study and the dominating attributes derived are biologically significant. © Springer International Publishing Switzerland 2014.