DIFFERENT GENOMIC SIGNAL PROCESSING METHODS FOR EUKARYOTIC GENE PREDICTION: A SYSTEMATIC REVIEW

Abstract

Bioinformatics field has now solidly settled itself as a control in molecular biology and incorporates an extensive variety of branches of knowledge from structural biology, genomics to gene expression studies. Bioinformatics is the application of computer technology to the management of biological information. Genomic signal processing (GSP) techniques have been connected most all around in bioinformatics and will keep on assuming an essential part in the investigation of biomedical issues. GSP refers to using the digital signal processing (DSP) methods for genomic data (e.g. DNA sequences) analysis. Recently, applications of GSP in bioinformatics have obtained great consideration such as identification of DNA protein coding regions, identification of reading frames, cancer detection and others. Cancer is one of the most dangerous diseases that the world faces and has raised the death rate in recent years, it is known medically as malignant neoplasm, so detection of it at the early stage can yield a promising approach to determine and take actions to treat with this risk. GSP is a method which can be used to detect the cancerous cells that are often caused due to genetic abnormality. This systematic review discusses some of the GSP applications in bioinformatics generally. The GSP techniques, used for cancer detection especially, are presented to collect the recent results and what has been reached at this point to be a new subject of research.