## 3 Results and Discussion
### Comparative Analysis on Two Feature Fusion Strategies  
In this section, to further evaluate the optimal genomic sequence information determined above, we comparatively analyze the effects of two fusion strategies, serial fusion and parallel fusion. The serial fusion strategy means that the information of the five genomic sequences represented by the One-Hot encoding is directly serially merged, and later the features are screened using the two-step feature selection method described above as the optimal genomic sequence information representation for the 4 mC sites. The parallel fusion strategy means that the five genomic sequences' information, represented by One-Hot encoding, is respectively screened for each sequence using the two-step feature selection method described above, and then a subset of these five genomic sequences' information is combined and represented as the optimal genomic sequence information for the 4 mC sites. Figure 7 shows the comparative results of both ACC and MCC metrics for these two fusion strategies on the six species. Specifically, on the _C. elegans_, _D. melanogaster_, and _G. subteruneus_ datasets, the results of the parallel and serial fusion strategies were not very different, and overall, the serial fusion strategy slightly outperformed the refrigerator fusion strategy, with MCC values improving by 1.4%, 1.5%, and 1.4%, respectively. Whereas on the _E. coli_ dataset, the serial fusion strategy was substantially better than the parallel fusion strategy with a 6.7% higher value on MCC metric. Additionally, more detailed information can be obtained in Table 3. The results showed that using serial fusion strategy achieved an average 0.7-3.3% improvement in ACC as compared to the parallel fusion strategy, and the MCC improved by an average of 1.4-6.7%. Additionally, Figure 7 intuitively shows that the two metrics of the serial fusion strategy outperform the two metrics of the parallel fusion strategy on all six species datasets. We speculate that, since the parallel fusion strategy is a separate feature screening of these several genomic sequences' information, there may still be some duplicate and redundant sequence information in the screened subsets of these genomic sequences, which affects the recognition performance of the final predictor. While using the serial fusion strategy is a direct serial merging of them, the feature screening process is a search in the full space, the search is relatively more thorough, and the final selected genomic sequence information representation will be relatively more precise; therefore, the identification performance of the final predictor will be more accurate. Figure 6: Performance comparison of XGBoost and other three machine learning algorithms on six benchmark datasets from multiple species. At the same time, we also conducted a significance test of the final prediction results of parallel fusion and serial fusion. The test criterion was that the prediction accuracy was calculated by 10% of the DNA sequences randomly selected from independent datasets and the operation was repeated 50 times for each species. The significance test was performed from the prediction accuracy for both models. Therefore, for the variance test, the test hypothesis is that there is no significant difference between the prediction accuracy of the serial fusion model and the parallel fusion model. Through calculation, we concluded that the \(p\)-value of each species is less than 2.5% in the six species. Among them, the \(D\). _melanogaster_ species and \(G\). _subterruneus_ species, which had slightly improved accuracy values, had \(p\)-values that also reached 0.0207 and 0.0223, respectively. Therefore, it can be explained that the original hypothesis is not valid for the six species. The details are shown in Table 3. Therefore, as shown in the table, it can be explained that in C. _elegans_ species, \(D\). _melanogaster_ species, and \(G\).