The Effect of Numerical Mapping Techniques on Performance in Genomic Research

Abstract

In genomic signal processing applications, digitization of these signals is needed to process and analyze DNA signals. In the digitization process, the mapping technique to be chosen greatly affects the performance of the system for the genomic domain to be studied. The purpose of this review is to analyze how numerical mapping techniques used in digitizing DNA sequences affect performance in genomic studies. For this purpose, all digital coding techniques presented in the literature in the studies conducted in the last 10 years have been examined, and the numerical representations of these techniques are given in a sample DNA sequence. In addition, the frequency of use of these coding techniques in four popular genomic areas such as exon region identification, exon-intron classification, phylogenetic analysis, gene detection, and the min-max range of the performances obtained by using these techniques in that area are also given. This study is thought to be a guide for researchers who want to work in the field of bioinformatics.

Human exons and introns classification using pre-trained Resnet-50 and GoogleNet models and 13-layers CNN model

“…Deep learning is applied in a variety of fields, including bioinformatics, 13 genomics, 14 robotics, 15 language processing, 16 image recognition, 17 voice recognition, 18 and technology for diagnostic support in the medical field. 19 .…”