Prediction of Lysine Pupylation Sites with Machine Learning Methods

Post translational modification is a crucial type of protein post-translational modification, which is involved in many important cellular processes and serious diseases. In practice, identification of protein pupylated sites through traditional experiment methods is time-consuming and laborious. Computational methods are not suitable to identify a large number of acetylated sites quickly. Therefore, machine learning methods are still very valuable to accelerate lysine acetylated site finding. Post translational modification of protein is one of the most important biological processions in the field of proteomics and bioinformatics. In this work, the random forest algorithm is employed as the classification model and the PseAAC has been employed as the classification features. Considering the different feature types of PseAAC playing different role in the classification model, the random forest voting method has been proposed in this framework. The results demonstrate that such method will work well in such classification issue.