## 4 Conclusions  
In this study, the proposed XGB4 mcPred predictor is a novel predictor for the prediction of 4 mC sites. It is a single model. This predictor encodes different types of DNA sequence information based on One-Hot encoding and illustrates the effectiveness of this coding approach by analyzing nucleotide composition preferences. The feature is optimized in two steps using the XGBoost classifier to improve the sequence information representation. By analyzing the spatial distribution of non-4 mC sites and 4 mC sites, we found that the non-4 mC site sequences and 4 mC site sequences were steadily separated, thus improving the predictive performance. Then, KNN, SVM, RF, and XGBoost classifiers were used to train and adjust the hyperparameters of the determined optimal feature subset, and the model of XGBoost training was finally determined as the optimal classifier. Two feature fusion strategies, the parallel fusion strategy and serial fusion strategy, were compared and analyzed, and finally, the serial fusion strategy was determined. Finally, using the optimal predictor XGB4 mcPred in 10-fold cross-validation and independent test evaluation shows that, compared with the state-of-the-art classifiers determined by multiple feature extraction methods, multiple classifiers, or different ensemble methods, the proposed XGB4 mcPred predictor gets different degrees of improvement. It is anticipated that XGB4 mcPred will become an essential computational tool for identifying 4 mC sites in _G.pickeringi_, _G.subterraneus_, _E.coli_, _C.elegans_, _A.thaliana_, and _D.melanogaster_. XGB4 mcPred  
Figure 10: The heatmap of the predictors for six species in cross-species testing. will hopefully become a useful tool for 4 mC site identification in the future. The user-friendly webserver that the XGB4 mcPred predictor was developed with 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 September 2021). Additionally, we made our source code publicly available and stored it under our GitHub [[https://github.com/lyn-007/XGB4](https://github.com/lyn-007/XGB4) mcPred]([https://github.com/lyn-007/XGB4](https://github.com/lyn-007/XGB4) mcPred) (accessed on 19 September 2021). The code includes the processing of the dataset, the extraction of features, the training of the model, and the prediction of the final model. In this way, we hope that readers will be able to make predictions not only on the website predictor we provide, but also to replicate our experiments and make predictions on their own computers. The following are available online at [[https://www.mdpi.com/article/10.3390/a14100283/s1](https://www.mdpi.com/article/10.3390/a14100283/s1)]([https://www.mdpi.com/article/10.3390/a14100283/s1](https://www.mdpi.com/article/10.3390/a14100283/s1)), Figure S1: Sequence logos between 4 mC sites and non-4 mC sites of _Caenorhabditis elegans_, _Arabidopsis thaliana_, and _Geobacter pickeringii_, Figure S2: t-SNE visualization of the _C. elegans_, _A. thaliana_, and _G. pickeringii_ datasets in a two-dimensional feature space, Table S1: Performances of the proposed XGB4 mcPred predictor and four state-of-the-art predictors on six benchmark datasets from different species. Formal analysis, X.L. and R.W. ; Methodology, X.W. ; Project administration, X.W. ; Validation, L.-J.H. and Z.-Y.D. ; Writing--original draft, X.L. ; Writing--review and editing, X.W., X.L. and K.-Q.F. All authors have read and agreed to the published version of the manuscript. This work is supported in part by the National Natural Science Foundation of China (No. 61402422), the Key Science and Technology Development Program of Henan Province (No. 202102210144), the Training Program of Young Backbone Teachers in Colleges and Universities of Henan Province (No. 2019 GJS132), and the Doctorate Research Funding of Zhengzhou University of Light Industry (No. 2013 BSJJ082). Not applicable.