## 1 Introduction
A total of 56 probabilistic features were generated based on seven different feature representations and four different machine learning algorithms, and then probabilistic features were used as the input of the support vector machine to finally establish a Meta-4 mCpred predictor [21]. After that, [PERSON] proposed a predictor 4 mcPred-IFL [22] that used an iterative feature representation method based on composition information, physicochemical information, and location-specific information, which aimed to identify 4 mC-modified sites with higher accuracy. Recently, several 4 mC site prediction methods based on deep learning techniques have been developed with decent enhancements. [PERSON] et al. constructed a 4 mCCNN [23] predictor using end-to-end convolutional neural networks. [PERSON] et al. applied convolutional neural networks with four representative features to construct the Deep4 mC [24] predictor. [PERSON] et al. used a multi-layer convolutional and pooling layer to extract features, followed by the Attention mechanism, LSTM, and fully connected layers for training; finally, they constructed the DeepTorrent [25] predictor, which achieved the top results. Although the final results of these recent papers have well improved on the benchmark datasets, the complexity of the models has also greatly improved, which causes the predictor's robustness to decrease along with other characteristics, and in some independent tests, the identifying effect on the set is always unsatisfactory. Therefore, it is essential to develop a simple, effective, and robust predictor. This study proposed a novel predictor named XGB4 mcPred based on a One-Hot encoding genomic sequence information. The framework of the proposed XGB4 mcPred predictor is shown in Figure 1. To extract more information features, this study encodeddifferent types of DNA sequence information, including adjacent information and spaced information of different spaced sizes. Afterward, the extracted genomic sequence information optimized through a two-step feature optimization strategy to obtain a DNA sequence information representation of the 4 mC sites. The first step was to use the XGBoost classifier to pre-train the One-Hot encoded feature vectors to obtain the feature importance values of the number of feature dimensions. After that, in the second step, all the calculated feature importance values were deduplicated and sorted from small to large, and then non-repetitive feature importance values were used as the threshold for feature selection to perform feature filtering in order to determine the optimal DNA sequence information for each predictor. The filtering evaluation method used the prediction accuracy of the default XGBoost classifier. After that, the four machine learning algorithms of KNN, SVM, RF, and XGBoost were used to supervise training and hyperparameter adjustment on the determined optimal DNA sequence information, and finally, a novel predictor with better performance was constructed based on the XGBoost learning algorithm. After that, two different feature fusion strategies, parallel fusion and serial fusion, were compared. Through comparative analysis, it was concluded that using a serial fusion strategy can make genome sequence information representation more effective. Therefore, it was finally determined that using the feature fusion strategy of serial fusion, the predictor obtained by training the selected optimal feature subset on the XGBoost machine learning algorithm had the best accuracy. A user-friendly webserver where the XGB4 mcPred predictor was developed can be accessed for free at [[http://www.xwanglab.com/XGB4](http://www.xwanglab.com/XGB4) mcPred/]([http://www.xwanglab.com/XGB4](http://www.xwanglab.com/XGB4) mcPred/) (accessed on 19 July 2021).