## 3 Results and Discussion
### Analysis of Three Different Feature Models Based on Stacking  
During the exploratory phase of model development work, we used paired feature combinations to generate fused features and estimated models using three ML methods [49]. The results showed that fused feature encoding outperformed non-fused feature encoding in certain models. The single features of AAindex and UniRep had dimensions of 566 and 1900, respectively, while the dimension of the fused feature reached 2466. The risks of model overfitting and redundancy in information became more prominent as the dimensionality of features increased. To address this issue, we employed the stacking algorithm to build models using the three feature input methods separately. Figure 3 presents a comparison of the performance of models built based on stacking using different dimensions of single and fused features.  
Figure 2: Performance metrics using different features and a range of algorithms. **(A)** The results of 5-fold cross-validation; **(B)** the results of independent testing. Different colors represent different features used by each algorithm, with red indicating AAindex single feature, blue indicating the UniRep single feature, and green indicating AAindex + UniRep fused feature. (**A1**,**B1**) ACC, accuracy; (**A2**,**B2**) MCC, Matthews correlation coefficient; (**A3**,**B3**) AUC, the area under the receiver operating characteristic curve; (**A4**,**B4**) P, precision; (**A5**,**B5**) Sp, specificity; (**A6**,**B6**) Sn, recall.  
From the comparison presented in Figure 3, it can be observed that, for models using the fused feature, AAindex + UniRep (green), stacking models with feature selection exhibited better performance (Figure 3(A\({}_{1}\))), 21.5% improvement in ACC in five-fold cross-validation (Figure 3(A\({}_{2}\))), 44.6% improvement in MCC in five-fold cross-validation; however, for models using single features, there was no significant effect, and the performance may even have been worse (Figure 3(A\({}_{6}\),B\({}_{6}\))). Notably, in contrast to the higher performance of the fused feature models mentioned in the previous section, stacking models using UniRep single feature exhibited the best overall performance (Figure 3(A\({}_{1}\)-A\({}_{6}\),B\({}_{1}\)-B\({}_{6}\))). In the end, we chose the single UniRep feature to construct a stacking-based IL-10-induced peptide recognition model. Although the stacking model with a feature-selected 300D UniRep single feature showed better AUC (1.1% higher), P (1.0% higher), and Sp (1.7% higher) values in independent testing (Figure 3(B\({}_{3}\)-B\({}_{5}\))), we still considered the stacking model based on the original dimension of UniRep single feature as the one that provided the best overall performance.  
Figure 3: Performance metrics of single or fused feature models using the stacking algorithm. (**A**) The results of 5-fold cross-validation; (**B**) the results of independent testing. Different colors represent different features used by each algorithm, with red indicating AAindex single feature, blue indicating the UniRep single feature, and green indicating AAindex + UniRep fused feature. (**A\({}_{1}\),B\({}_{1}\)**) ACC, accuracy; (**A\({}_{2}\),B\({}_{2}\)**) MCC, Matthews correlation coefficient; (**A\({}_{3}\),B\({}_{3}\)**) AUC, the area under the receiver operating characteristic curve; (**A\({}_{4}\),B\({}_{4}\)**) P, precision; (**A\({}_{5}\),B\({}_{5}\)**) Sp, specificity; (**A\({}_{6}\),B\({}_{6}\)**) Sn, recall.