## 3 Results and Discussion
### Validation of Various Methods on Independent Datasets
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & \multicolumn{4}{c}{**Independent**} & \\
**Species** & **Predictors** & **ACC** & **MCC** & **SN** & **SP** \\ \hline _C. elegans_ & 4 mCPred & 0.865 & 0.731 & 0.883 & 0.849 \\  & 4 mcPred-SVM & 0.842 & 0.684 & 0.828 & 0.856 \\  & Meta-4 mCpred & 0.870 & 0.741 & 0.843 & 0.897 \\  & 4 mCPred-IFL & 0.815 & 0.636 & 0.751 & 0.88 \\  & Deep4 mC & 0.915 & 0.832 & 0.907 & 0.925 \\  & DeepTorrent & 0.935 & 0.871 & 0.936 & 0.935 \\  & XGB4 mcPred & 0.900 & 0.800 & 0.892 & 0.908 \\ _D. melanogaster_ & 4 mCPred & 0.900 & 0.803 & 0.933 & 0.868 \\  & 4 mCPred-SVM & 0.886 & 0.771 & 0.886 & 0.885 \\  & Meta-4 mCpred & 0.906 & 0.812 & 0.913 & 0.899 \\  & 4 mcPred-IFL & 0.907 & 0.814 & 0.912 & 0.902 \\  & Deep4 mC & 0.911 & 0.822 & 0.933 & 0.890 \\  & DeepTorrent & 0.911 & 0.829 & 0.972 & 0.863 \\  & XGB4 mcPred & 0.918 & 0.836 & 0.926 & 0.910 \\ _A. thaliana_ & 4 mCPred & 0.816 & 0.632 & 0.842 & 0.789 \\  & 4 mcPred-SVM & 0.824 & 0.649 & 0.842 & 0.806 \\  & Meta-4 mCpred & 0.855 & 0.711 & 0.876 & 0.834 \\  & 4 mCPred-IFL & 0.823 & 0.650 & 0.826 & 0.824 \\  & Deep4 mC & 0.868 & 0.739 & 0.824 & 0.912 \\  & DeepTorrent & 0.886 & 0.773 & 0.902 & 0.872 \\  & XGB4 mcPred & 0.858 & 0.718 & 0.870 & 0.843 \\ _E. coli_ & 4 mCPred & 0.817 & 0.634 & 0.851 & 0.784 \\  & 4 mCPred-SVM & 0.784 & 0.569 & 0.746 & 0.821 \\  & Meta-4 mCpred & 0.825 & 0.650 & 0.806 & 0.843 \\  & 4 mcPred-IFL & 0.795 & 0.594 & 0.731 & 0.858 \\  & Deep4 mC & 0.754 & 0.517 & 0.815 & 0.713 \\  & DeepTorrent & 0.818 & 0.634 & 0.810 & 0.824 \\  & XGB4 mcPred & 0.862 & 0.726 & 0.821 & 0.903 \\ _G. subterruneus_ & 4 mCPred & 0.789 & 0.578 & 0.757 & 0.820 \\  & 4 mcPred-SVM & 0.811 & 0.624 & 0.783 & 0.840 \\  & Meta-4 mCpred & 0.850 & 0.701 & 0.817 & 0.883 \\  & 4 mCPred-IFL & 0.686 & 0.378 & 0.591 & 0.78 \\  & Deep4 mC & 0.830 & 0.664 & 0.797 & 0.871 \\  & DeepTorrent & 0.823 & 0.646 & 0.832 & 0.814 \\  & XGB4 mcPred & 0.814 & 0.629 & 0.789 & 0.840 \\ _G. pickergini_ & 4 mCPred & 0.742 & 0.503 & 0.610 & 0.875 \\  & 4 mCPred-SVM & 0.758 & 0.515 & 0.750 & 0.765 \\  & Meta-4 mCpred & 0.850 & 0.700 & 0.835 & 0.865 \\  & 4 mCPred-IFL & 0.678 & 0.358 & 0.740 & 0.615 \\  & Deep4 mC & 0.803 & 0.611 & 0.850 & 0.767 \\  & DeepTorrent & 0.860 & 0.735 & 0.950 & 0.800 \\  & XGB4 mcPred & 0.820 & 0.641 & 0.790 & 0.850 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performances of the proposed XGB4 mcPred and five state-of-the-art predictors on independent datasets from six species. Compared to the top predictors currently built through the deep learning technique Deep4 mC [24] and DeepTorrent [25] predictors, our generalization ability exceeded the Deep4 mC predictor on three species _D. melanogaster_, _E. coli_, and _G. pickeringii_, with a maximum improvement of 10.8% accuracy. Additionally, compared to the DeepTorrent predictor, our predictor slightly exceeded on the _E. coli_ and _D. melanogaster_ species, by 4.4% and 0.7%. However, on the other species, our predictors did not outperform the current top predictors Deep4 mC and DeepTorrent, lagging in the range of 1% to 4%. Therefore, we finally calculated the average accuracy values of the three predictors (XGB4 mcPred, Deep4 mC, and DeepTorrent) on six species to evaluate the differences. The results show that the average accuracy of XGB4 mcPred is 0.862, the average accuracy of Deep4 mC is 0.847, and the average accuracy of DeepTorrent is 0.872. It can be found that the average accuracy of XGB4 mcPred is slightly better than the Deep4 mC predictor by 1.5%, and the average accuracy of DeepTorrent is slightly better than the XGB4 mcPred predictor by 1%. Finally, we also compared the AUC values of XGB4 mcPred with other state-of-the-art predictors on independent datasets of six species. Figure 9 shows the results of the AUC comparison of the XGB4 mcPred predictor with the currently available top predictors. As shown in Figure 9, on the independent dataset of _D.