Handling DNA malfunctions by unsupervised machine learning model

The cell cycle is a rich field for research, especially, the DNA damage. DNA damage, which happenednaturallyor as a result of environmental influences causes change in the chemical structure of DNA. The extent of DNA damage has a significant impact on the fate of the cell in later stages. In this paper, we introduced an Unsupervised Machine learning Model for DNA Damage Diagnosis and Analysis. Mainly, we employed K-means clustering unsupervised machine learning algorithms. Unsupervised algorithms com monly draw conclusions from datasets by solely utilizing input vectors, disregarding any known or labeled outcomes. ThemodelprovideddeepinsightaboutDNAdamageandexposestheproteinlevelsforproteinswhenworktogetherin sub-networkmodeltodealwithDNAdamageoccurrence,theunsupervisedartificialmodelexplainedthesub-network biological modelactivitiesin regardto thechangingintheir concentrations inseveralclusters,they have beengrouped in such as (0- no damage, 1- low, 2- medium, 3- high, and 4- excess) DNA damage clusters. The results provided a rational and persuasive explanation for numerous important phenomena, including the oscillation of the protein p53, in a clear and understandable manner. Which is encouraging since it demonstrates that the K-meansclustering approachcanbeeasilyapplied to manysimilar biologicalsystems, whichaidsinbetter un derstanding the key dynamics of these systems.