# ABPCaps: A Novel Capsule Network-Based Method for the Prediction of Antibacterial Peptides
## 4 Conclusions  
In this study, we have proposed a novel classification scheme, ABPCaps, to distinguish ABPs from non-ABPs. Harnessing the ability to automatically extract essential features simultaneously from both positive and negative samples, the capsule network-based model (ABPCaps) achieved state-of-the-art performance on the elaborate dataset for identifying antibacterial peptides with an AUROC of 0.9289, an F1-score of 0.9134, a recall of 90.95% and an accuracy of 93.33%. Furthermore, ABPCaps captured the characteristic features of ABP sequences better and outperformed the other five hand-crafted features for all four classifiers. In particular, the t-SNE plots showed that ABPCaps can easily distinguish ABPs from non-ABPs, while the models built with traditional hand-crafted features failed to do so. Our results demonstrate that the capsule network-based model can indeed overcome some limitations encountered in the current sequence analysis approaches, in terms of predicting ABPs. To sum up, ABPCaps can identify ABPs with a high precision and simultaneously with a high accuracy. It is an important step forward in predicting ABPs, and hence contributes to the development of new therapies based on ABPs. In future research, we \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Target Bacteria** & \begin{tabular}{c} **Number of** \\ **Positive Samples** \\ \end{tabular} & \begin{tabular}{c} **Number of** \\ **Negative Samples** \\ \end{tabular} & \
\begin{tabular}{c} **Number of Samples** \\ **in Train Set** \\ \end{tabular} &
\begin{tabular}{c} **Number of Samples** \\ **in Test Set** \\ \end{tabular} \\ \hline _E. coli_ & 4166 & 4166 & 6661 & 1671 \\ _S. aureus_ & 3713 & 3713 & 5941 & 1485 \\ _P. aeruginosa_ & 2252 & 2252 & 3603 & 901 \\ _C. albicans_ & 1601 & 1601 & 2562 & 640 \\ _B. subtilis_ & 1325 & 1325 & 2120 & 530 \\ _S. epidermidis_ & 840 & 840 & 1344 & 336 \\ _M. luteus_ & 701 & 701 & 1122 & 280 \\ _K. pneumoniae_ & 694 & 694 & 1110 & 278 \\ _S. typhimurium_ & 677 & 677 & 1083 & 271 \\ _E. faecalis_ & 613 & 613 & 981 & 245 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The number of peptide sequences in ten subsets. This table shows the number of peptide sequences used in each of the ten subsets used for evaluation. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Precision** & **Recall** & **Accuracy** & **F1-Score** & **AUROC** \\ \hline CNN & 93.32\% & 92.78\% & 93.08\% & 0.9304 & 0.9309 \\ biGRU & 91.38\% & 91.84\% & 91.59\% & 0.9157 & 0.9164 \\ biLSTM & 93.51\% & 92.33\% & 92.99\% & 0.9291 & 0.9299 \\ GRU & 91.81\% & 91.75\% & 91.77\% & 0.9173 & 0.9182 \\ LSTM & 93.18\% & 92.67\% & 92.95\% & 0.9291 & 0.9298 \\ KNN & 83.89\% & 79.50\% & 79.50\% & 0.7876 & 0.7956 \\ RF & 87.50\% & 87.31\% & 87.31\% & 0.8731 & 0.8738 \\
**ABPCaps** & **96.31\%** & **93.57\%** & **95.00\%** & **0.9491** & **0.9502** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Evaluation metrics averaged over ten subsets. This table shows the average evaluation metrics for the proposed model and the baselines across the ten subsets. The best performance in each category is highlighted in bold. will fully exploit the characteristics of capsule networks to enhance the interpretability of ABPCaps and investigate the intrinsic features of ABPs and non-ABPs. The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app13126965/s1](https://www.mdpi.com/article/10.3390/app13126965/s1)]([https://www.mdpi.com/article/10.3390/app13126965/s1](https://www.mdpi.com/article/10.3390/app13126965/s1)), Algorithm S1: Dynamic Routing; Figure S1: The computation process between primary capsule layer and class capsule layer in ABPCaps; Figure S2: The architectures of the baseline models used for comparison in this study; Figure S3: Receiver operating characteristic (ROC) curves of the proposed model and baselines on ten subsets; Table S1: Performance comparison of ABPCaps and baseline models on subsets with the functions against different bacteria.