XG-PseU: an eXtreme Gradient Boosting based method for identifying pseudouridine sites

As one of the most popular post-transcriptional modifications, pseudouridine (psi) participates in a series of biological processes. Therefore, the efficient detection of pseudouridine sites is very important in revealing its functions in biological processes. Although experimental techniques have been proposed for identifying psi sites at single-base resolution, they are still labor intensive and expensive. Recently, to fill the experimental method's gap, computational methods have been proposed for identifying psi sites. However, their performances are still unsatisfactory. In this paper, we proposed an eXtreme Gradient Boosting (xgboost)-based method, called XG-PseU, to identify psi sites based on the optimal features obtained using the forward feature selection together with increment feature selection method. Our results demonstrated that XG-PseU is superior or at least complementary to existing methods for identifying pseudouridine sites. Finally, a freely available online web server for XG-PseU was established at . We wish that XG-PseU will become a useful tool for computationally identifying psi sites.