Prediction of tRNA Based on LS-SVM Algorithm with Principal Component Analysis

Abstract

Recently, non-coding RNA prediction is the one of the most important researches in bioinformatics. In this paper, on the basis of principal component analysis, we present a tRNA prediction strategy by using least squares support vector machine (LS-SVM). Appearance frequencies of single nucleotide, 2 – nucleotides and (G-C) %, (A-T) % were chosen as characteristics inputs. Results from tests showed that the prediction accuracy was 90.51% on prokaryotic tRNA dataset. Experimental results indicate that the method is effective for prokaryotic ncRNA prediction.