Feature Extraction of Long Non-coding RNAs: A Fourier and Numerical Mapping Approach

Abstract

Due to the high number of genomic sequencing projects, the number of RNA transcripts increased significantly, creating a huge volume of data. Thus, new computational methods are needed for the analysis and information extraction from these data. In particular, when parts of a genome are transcribed into RNA molecules, some specific classes of RNA are produced, such as mRNA and ncRNA with different functions. In this way, long non-coding RNAs have emerged as key regulators of many biological processes. Therefore, machine learning approaches are being used to identify this enigmatic RNA class. Considering this, we present a Fourier transform-based features extraction approach with 5 numerical mapping techniques (Voss, Integer, Real, EIIP and Z-curve), in order to classify lncRNAs from plants. We investigate four classification algorithms like Naive Bayes, Random Forest, Support Vector Machine and AdaBoost. Moreover, the proposed approach was compared with 4 competing methods available in the literature (CPC2, CNCI, PLEK, and RNAplonc). The experimental results demonstrated high efficiency for the classification of lncRNAs, providing competitive performance.