## 5 Result and Discussion
### The Influence of Sliding Window and Oversampling  
For the DIS2209 data set, we use a ten-fold cross-validation method to randomly divide the protein sequence into 10 subsets of roughly the same size, and use GBDT and LightGBM to train and predict data with different window sizes. The specific results are shown in Table 5.  
By comparing various indicators, as the window length increases, the MCC value and F1 value gradually increase. When the window size is greater than 35, their values tend to be stable, so we choose a window length of 35 to process our features. The above-mentioned trend of change is shown in Figures 5 and 6. Similarly, we use GBDT and LightGBM to perform three oversampling schemes on the DIS2209 data set. When the SMOTE sampling scheme is used, the prediction effect is the best. The specific results are shown in Table 6. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Sens** & **Spec** & **F1** & **MCC** \\ \hline Permutation entropy included & **0.766** & **0.789** & **0.565** & **0.463** \\ Permutation entropy not included & 0.744 & 0.760 & 0.528 & 0.413 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The influence of permutation entropy on GBDT-PE. Figure 4: The importance of five features in the algorithm. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Sens** & **Spec** & **F1** & **MCC** \\ \hline Permutation entropy included & **0.766** & **0.789** & **0.565** & **0.463** \\ Permutation entropy not included & 0.744 & 0.760 & 0.528 & 0.413 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The influence of permutation entropy on LightGBM-PE. Figure 5: Different window size performance in GBDT. Figure 6: Different window size performance in LightGBM. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Length**} & \multicolumn{2}{c}{**GBDT**} & \multicolumn{2}{c}{**LightGBM**} \\ \cline{2-5}  & **F1** & **MCC** & **F1** & **MCC** \\ \hline
11 & 0.4658 & 0.3842 & 0.5438 & 0.4281 \\
15 & 0.4749 & 0.3958 & 0.5575 & 0.4323 \\
19 & 0.4995 & 0.4132 & 0.5733 & 0.4431 \\
23 & 0.5089 & 0.4246 & 0.5901 & 0.4561 \\
27 & 0.5265 & 0.4448 & 0.6039 & 0.4671 \\
31 & 0.5425 & 0.4584 & 0.6147 & 0.4786 \\
35 & 0.5456 & **0.4624** & 0.6095 & **0.4868** \\
39 & 0.5521 & 0.4610 & 0.6057 & 0.4767 \\
43 & 0.5323 & 0.4489 & 0.5624 & 0.4648 \\
47 & 0.5354 & 0.4421 & 0.5443 & 0.4608 \\
51 & 0.5134 & 0.4312 & 05538 & 0.4615 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance comparison of different window sizes. Our pre-processing scheme improves the accuracy and stability of the prediction results of each learning method. Finally, we compare the MCC values before and after pre-processing in Table 7, taking sliding window size 35 and SMOTE oversampling as examples. We selected the Remark and Permutation Entropy features of all residues in the DIS2209 dataset and compared their performance before and after pretreatment at a window length of 31, as shown in Figure 7.  
In order to test the predictions of the model, we take the IAPP protein associated with type-2 diabetes from DisProt as an example and acquire the prediction result as Figure 8. At the same time, the standard prediction results are shown as Figure 9. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**GBDT**} & \multicolumn{2}{c}{**LightGBM**} \\ \cline{2-5}
**Scheme** & **F1** & **MCC** & **F1** & **MCC** \\ \hline Not Sampled & 0.4343 & 0.4145 & 0.5622 & 0.4598 \\ Random Sampled & 0.5604 & 0.4554 & 0.6091 & 0.4729 \\ SMOTE Sampled & 0.5652 & **0.4652** & 0.6205 & **0.4816** \\ Synthetic Sampled & 0.5648 & 0.4632 & 0.6224 & 0.4799 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance comparison of different oversampling schemes. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **GBDT** & **LightGBM** \\ \hline Before pretreatment & 0.4143 & 0.4598 \\ After pretreatment & **0.4705** & **0.5258** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The influence of the preprocessing scheme.