# New Multi-View Feature Learning Method for Accurate Antifungal Peptide Detection
## 3 Results and Discussion
### Comparison of the Proposed Model with Existing Models  
Next, to investigate the effectiveness of our proposed model (AFP_MVLF), we compare its results against other state-of-the-art models found in the literature. The results achieved for AFP_MVLF compared to previous studies for Antif_DS1 are presented in Table 9. As demonstrated in this table, AFP_MVLF outperforms previous studies, including [9; 10; 12; 14; 19; 21; 23; 45] across all evaluation metrics. When compared to AFP-MFL, AFP-MVFL represents relative improvements of 2.1%, 2.4%, 1.3%, and 0.04 in terms of ACC, F1 score, precision, and MCC, respectively, for the Antif_DS1 dataset.  
We also compare AFP_MVLF's performance to the state-of-the-art methods found in the literature for the Antif_DS2 and Antif_DS3 datasets. The experimental results obtained from these different datasets are presented in Table 10. AFP_MVLF consistently outperforms the competitive approaches across all evaluation metrics in each dataset.  
Specifically, when tested on the Antif_DS2 dataset, the AFP-MVFL achieves improved prediction rates with an accuracy of 98.3%, precision of 99.1%, F1 score of 0.98, and MCC of 0.97. A relative increase is observed compared to the AFP-MFL model. On the Antif_DS3 dataset, the AFP-MVFL achieves an accuracy of 97.4%, precision of 98.4%, F1 score of 0.97, and MCC of 0.95, representing a relative improvement over the previous three models. These results establish that the AFP-MVFL consistently outperforms other methods in distinguishing AFPs from non-AFPs across all evaluated datasets.  
We also generated t-SNE graphs in Figures 5-7 for the Antif_DS1, Antif_DS2, and Antif_DS3 datasets to explore the importance and contribution of different features. Here we choose t-SNE to investigate feature importance since it was demonstrated as a better candidate than principal component analysis (PCA) in similar studies [46]. By plotting the t-SNE, we can visualize the data in a reduced space, which helps us to identify which features are most relevant for distinguishing between different data points [47]. By visualizing the  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Datasets** & **Model** & **ACC (\%)** & **PRE(\%)** & **F1 Score** & **MCC** \\ \hline Antif\_DS1 & Without Feature Selection & 93.5 & 93.1 & 0.92 & 0.89 \\ \hline  & With Feature Selection & 97.9 & 98.4 & 0.98 & 0.96 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparison of the AFP-MVFL model with and without feature selection of Antif_DS1.  
\begin{table}
\begin{tabular}{c l l l l} \hline \hline
**Datasets** & **Model** & **ACC (\%)** & **PRE(\%)** & **F1 score** & **MCC** \\ \hline Antif\_DS1 & MIMML [23] & 91.3 & - & - & 0.83 \\ \hline  & AntiMF [45] & 90.2 & - & - & 0.80 \\ \hline  & Antif [10] & 86.3 & - & - & 0.73 \\ \hline  & AFPDeep [19] & 90.2 & - & - & - \\ \hline  & Deep-AntiFP [12] & 89.1 & - & - & 0.78 \\ \hline  & iAFPs-En-CA [21] & 93.9 & - & - & 0.90 \\ \hline  & AFP-MFL [9] & 95.8 & 97.1 & 0.96 & 0.92 \\ \hline  & AFP-MVFL & 97.9 & 98.4 & 0.98 & 0.96 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Comparison of AFP-MVFL with other antifungal peptide predictors on the independent test dataset of Antif_DS1.  
The results above highlight the robustness and generalizability of AFP-MVFL. By integrating a co-attention mechanism to fuse semantic information, evolutionary information, and physicochemical properties, AFP-MVFL effectively generates more informative features. Consequently, AFP-MVFL exhibits superior performance compared to alternative methods, positioning it as a reliable tool for AFP prediction. The AFP-MVFL model and its source code are publicly available at [[https://github.com/MuntahaMim/AFP-MVFL.git](https://github.com/MuntahaMim/AFP-MVFL.git)]([https://github.com/MuntahaMim/AFP-MVFL.git](https://github.com/MuntahaMim/AFP-MVFL.git)) (accessed on 1 May 2024).