FAJAR ARSHAD2025-12-082025-12-082020https://escholar.umt.edu.pk/handle/123456789/15375N4-methylcytosine 4mC is an essential epigenetic modification that occurs enzymatically by DNA methyltransferase. 4mC sites exist in prokaryotes and play a vital role in regulating gene expression, DNA replication, and cell cycle. The efficient and accurate prediction of 4mC sites has a significant role in the insight of 4mC biological properties and functions. Therefore, we have proposed a sequence-based predictor, namely 4mC-RF, for identifying 4mC sites in prokaryotes by integrating statistical moments along with position and composition dependent features. Relative and absolute position based features are computed to extract the optimal features. A popular machine learning classifier Random Forest was used to training the model. Validation results were obtained under rigorous processes of Self-consistency, 10-fold crossvalidation, Independent testing, and Jackknife testing giving 95.01%, 95.02%, 97.02%, and 95.36% accuracies. Our proposed model depicts the highest prediction accuracies as compared with the literature results. Thus, the developed 4mC-RF model was constructed into a web server. A significant and more accurate predictor of 4mC Methylcytosine sites helps experimental scientists gather results moderately.enThesis