# ABPCaps: A Novel Capsule Network-Based Method for the Prediction of Antibacterial Peptides
## 3 Results and Discussion  
### Comparisons of Baseline Models  
A detailed analysis of the results was performed and compared with baseline models and existing identification tools. Selecting appropriate baselines is critical to evaluating the effectiveness of proposed models. In this study, machine learning-based models with sequence features were carefully selected as baselines. This was done to ensure a comprehensive comparison. These baselines include widely used features such as amino acid composition (AAC), dipeptide composition (DPC), composition of k-spaced amino acid pairs (CKSAAGP), pseudo amino acid composition (PAAC), and eight different physicochemical features (PHYC). The baseline models were developed using different machine learning algorithms, k-nearest neighbor (KNN) and RF. In addition, to capture more complex sequence patterns, CNN-based and RNN-based models, such as LSTM, GRU, bidirectional LSTM, and bidirectional GRU (biGRU), were also applied as baseline classifiers. The architecture of these baseline models is shown in Supplementary Figure S2, providing a clear overview of their design and complexity. The performance of the models was evaluated using an independent test set and the results are presented in Table 2. In terms of accuracy, F1-score, AUROC and recall, the proposed model outperformed all the baseline models. Specifically, the proposed model achieved an impressive AUROC of 0.9289, an F1-score of 0.9134, a recall of 90.95%, and an accuracy of 93.33%. In comparison to other baseline models, these results demonstrate the superior predictive power of the proposed capsule network for the identification of ABPs. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Accuracy** & **Recall** & **Precision** & **F1-Score** & **AUROC** \\ \hline LSTM & 91.35\% & 87.69\% & 89.72\% & 0.8869 & 0.9067 \\ GRU & 90.48\% & 87.60\% & 87.75\% & 0.8768 & 0.8995 \\ CNN & 90.09\% & 86.43\% & 87.76\% & 0.8709 & 0.8941 \\ biLSTM & 91.19\% & 88.11\% & 89.00\% & 0.8855 & 0.9062 \\ biGRU & 90.48\% & 87.10\% & 88.14\% & 0.8762 & 0.8985 \\ KNN & 88.43\% & 90.12\% & 81.83\% & 0.8577 & 0.8874 \\ RF & 92.19\% & 87.27\% & **92.13\%** & 0.8963 & 0.9128 \\
**ABPCaps** & **93.33\%** & **90.95\%** & 91.72\% & **0.9134** & **0.9289** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance comparison between ABPCaps and baselines on the independent test set This table provides a comprehensive evaluation of the proposed model and the baselines, including their accuracy, F1-score, and AUROC on an independent test set. The best performance in each category is highlighted in bold. To further visualize the effectiveness of the proposed model, ROC curves were plotted to compare the performance of the proposed model with the baseline models, and the curves are shown in Figure 2. The ROC curve of the proposed model was at the outermost point of all the curves, wrapping around the ROC curves of the baseline models. This suggests that the proposed model has excellent discriminative power to distinguish between ABPs and non-ABPs. In addition, the AUROC value of the proposed model for the independent test set was higher than that of the baseline models. This further supports the superior performance of the proposed model. As shown in Table 3, the proposed model was also compared with existing identification tools, including iAMPpred [49], ClassAMP [9], and AMPfun [50]. The results showed that the proposed capsule network-based method outperformed all existing tools and provided the most accurate prediction of ABPs. These results highlight the potential of capsule networks for sequence analysis tasks. The excellent performance of ABPCaps can be attributed to the design of the network, which can be broadly attributed to the following factors. Firstly, the initial modeling of peptide sequences is performed by the CNN and LSTM network, with the former being used to extract local features and the latter capturing the long-range dependencies of the sequence.