m5Cpred-XS: A New Method for Predicting RNA m5C Sites Based on XGBoost and SHAP

Abstract

As one of the most important post-transcriptional modifications of RNA, 5-cytosine-methylation (m5C) is reported to closely relate to many chemical reactions and biological functions in cells. Recently, several computational methods have been proposed for identifying m5C sites. However, the accuracy and efficiency are still not satisfactory. In this study, we proposed a new method, m5Cpred-XS, for predicting m5C sites of H. sapiens, M. musculus, and A. thaliana. First, the powerful SHAP method was used to select the optimal feature subset from seven different kinds of sequence-based features. Second, different machine learning algorithms were used to train the models. The results of five-fold cross-validation indicate that the model based on XGBoost achieved the highest prediction accuracy. Finally, our model was compared with other state-of-the-art models, which indicates that m5Cpred-XS is superior to other methods. Moreover, we deployed the model on a web server that can be accessed through http://m5cpred-xs.zhulab.org.cn/, and m5Cpred-XS is expected to be a useful tool for studying m5C sites.

i5mC-DCGA: an improved hybrid network framework based on the CBAM attention mechanism for identifying promoter 5mC sites

“…Therefore, there is an urgent need to develop computational methods to automatically detect and identify these 5mC sites, providing biologists with more efficient and convenient tools. In recent years, an increasing number of models based on machine learning constructs have been used to identify 5mC sites, such as Stacking Ensemble [ 11 ], XGBoost [ 12 ] and SVM [ 13 , 14 ]. These models have greatly facilitated the study of the 5mC site, allowing researchers to probe this methylation type in greater depth.…”