Predicting Reversible Post-Translational Modifications by Machine Learning
A Brief Review of Machine Learning Techniques for Protein Phosphorylation Sites Prediction
Reversible post-translational modifications (ptms) have vital roles in extending the functional diversity of proteins and effect meaningfully the regulation of protein functions in prokaryotic and eukaryotic organisms.Many types of proteins are containing phosphorylation and significant roles in many biological processes.Therefore, it is necessary to predict the phosphorylation of targetresidues in an uncharacterized amino acid sequence.Most experimental techniques for predicting phosphorylation are time-consuming, costly, and error-prone.By the way, computational methods have replaced these techniques.In this study, at first, all datasets of ptms that include phosphorylation sites (p-sites) were comprehensively reviewed.Then, we showed that there are basically two main approaches for phosphorylation prediction by machine learning : end-to-end and conventional.Wegave an overview for both of them.
