Classification of DNA sequences using numerical mapping techniques and Fourier transformation

Abstract

The combinations of bases in a DNA sequence correspond to a gene in that DNA sequence, RNA copy sequences are extracted from these genes. When these copied RNA's extracted, the base sequence of gene is not read from the beginning to the end completely. The uncoded and unreadable section of the gene is called 'intron' and the coded section of the gene is called 'exon'. Where is a protein coded? How much is encoded? Where are growth and development regulated? Where are stem cells converted to other cells? The answer to all of these questions and the investigation of genetic diseases, such as cancer, are possible by DNA sequences that can be classified as the exon and intron. The aim of this study is to compare the performance of different digital mapping techniques for classification of DNA sequence as the exon or intron. For this purpose, DNA sequences of the MEFV gene in human species are transformed to numeric sequences by nine different digital mapping techniques. The Discrete Fourier Transform Method (DFT) is used to classify these transformed sequences. Four different windowing functions are used and their classification performance are compared in this method. Also, the results obtained from the Fourier-based method have been compared using the Support Vector Machine and the K-Nearest Neighbor methods. Integer mapping technique achieved the highest classification performance with 96.2% in the DFT method than other machine learning methods. Classification performance in Hamming windowing function is higher than other windowing functions.