## 3 Results and Discussion
### Comparison with State-of-the-Art Predictors  
To demonstrate the superiority of our predictor XGB4 mcPred for identifying 4 mC sites, we compared its performance with that of other predictors, such as iDNA4 mC [17], 4 mCPred [19], 4 mcPred-SVM [20], Meta-4 mCpred [21], and the predictor constructed using deep learning techniques 4 mCCNN [23], as they were all developed using the same benchmark datasets. Supplementary Table S1 compares the 10-fold cross-validation performance of the state-of-the-art 4 mC site predictor and the XGB4 mcPred. Compared  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Species** & **Strategies** & **ACC** & **MCC** & **SN** & **SP** & \(p\)**-Value** \\ \hline _C. elegans_ & parallel fusion & 0.851 & 0.704 & 0.865 & 0.838 & \\  & serial fusion & 0.859 & 0.718 & 0.866 & 0.853 & 0.0132 \\ _D. melanogaster_ & parallel fusion & 0.852 & 0.704 & 0.864 & 0.840 & \\  & serial fusion & 0.859 & 0.719 & 0.875 & 0.844 & 0.0207 \\ _A. thaliana_ & parallel fusion & 0.792 & 0.584 & 0.787 & 0.797 & 0.0091 \\  & serial fusion & 0.804 & 0.609 & 0.799 & 0.809 & 0.0091 \\ _E. coli_ & parallel fusion & 0.870 & 0.740 & 0.884 & 0.856 & \\  & serial fusion & 0.903 & 0.807 & 0.922 & 0.884 & 0.0002 \\ _G. subterruneus_ & parallel fusion & 0.857 & 0.715 & 0.849 & 0.866 & \\  & serial fusion & 0.864 & 0.729 & 0.846 & 0.882 & 0.0223 \\ _G. pickeringii_ & parallel fusion & 0.883 & 0.766 & 0.873 & 0.893 & \\  & serial fusion & 0.902 & 0.805 & 0.888 & 0.917 & 0.0025 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison between the parallel fusion strategy and serial fusion strategy.  
Figure 7: Performance of the two fusion strategies on the Accuracy and Matthews correlation coefficient metrics.  
to traditional machine learning methods, the results show that the ACC and MCC of the proposed predictor XGB4 mcPred on the datasets of six species exceeded the current state-of-the-art predictor to varying degrees. Especially on the _E. coli_ dataset, the ACC of the XGB4 mcPred predictor was 9.3% higher than the first predictor iDNA4 mc and 4.4% higher than the latest Meta-4 mCpred. For the _A. thaliana_ dataset and the _G. pickeringii_ datasets, the 10-fold cross-validation ACC also exceeded 80% and 90% for the first time. Similarly, the value of MCC increased by 1.8% at the lowest and 8.7% at the highest on the datasets of the six species datasets. Additionally, in comparison with the currently popular deep learning methods, the 10-fold cross-validation results of the XGB4 mcPred predictor also show varying degrees of improvement. In particular, for the _E. coli_ species, our predictor improved accuracy by 3.3% and obtained a significant improvement in MCC values of 9.6% compared to 4 mCCNN. In contrast, on _D. melanogaster_ species, our predictor performed almost as well as 4 mCCNN, outperforming it by only 0.1%. Overall, it appears that our predictor did obtain a good improvement in the 10-fold cross-validation, and Figure 8 visualizes the results for the four evaluated metrics. Additionally, compared with the previous complex ensemble model architectures, our study used a simpler feature encoding method and feature selection strategy. The proposed XGB4 mcPred predictor can more accurately identify and predict the 4 mC sites or non-4 mC sites. Therefore, we hope that our proposed model architecture can become an effective predictive tool for identifying non-4 mC sites or 4 mC sites.