A survey for the methods of detection and classification of genetic mutations

Abstract

Research into pathogenic mutations is vital and useful for understanding illness progression, prognosis, and gene-disease connections. Furthermore, pathogenic mutations might have negative implications, such as the development of illnesses or medical problems. In this article, we critically review published studies that are concerned with the detection of genetic mutations and their types using genomic data. Using the reporting items for systematic reviews and meta-analysis criteria, a complete search was conducted on IEEE, Scopus, the Web of Science, Google Scholar, and Elsevier. In our review, we included 73 papers out of a total of more than 150 that were initially discovered. The most common data types used to detect and predict models are deoxyribose nucleic acid (DNA) and protein sequencing data. The examined models have a good level of accuracy. We devised a methodology for developing a detective and predictive model that takes into account all stages of mutation identification and classification.