## 3 Results
### Training Models Using an Extended Set of Features and Applying Feature Selection
It is interesting to note that on the Gram-positive dataset, the top 3 scoring features of the extended descriptors are isoelectric point, net charge, and disordered conformation propensity, which all belong to our initial 10 physico-chemical features. When we compare the performance metrics before and after feature selection is applied on the extended set of features, we observed that for Gram-positive and for Gram-negative datasets, the AUC performance metrics only decreased by 1% and 2%, respectively, when three selected features are used to generate the model (as compared with the 1507 features included in the extended set of features). That is to say that using only 3 features yields satisfactory performance results (96% and 94% AUC) for Gram-negative and Gram-positive datasets, respectively. Similarly, to compare the performance metrics of the models which use physico-chemical features with the models which use the extended set of features, we reduced the number of features in the original dataset to the same number of features (top 3 scoring features). For this purpose, we applied the same feature selection strategy on our original dataset which includes only physico-chemical features. We wanted to test whether a certain number of attributes will be sufficient for prediction. In Figure 9, we present the AUC values obtained using (i) 10 physico-chemical features, (ii) extended set of features (1507 features), (iii) top 3 scoring features of physico-chemical descriptors, and (iv) top 3 scoring features of extended descriptors. As illustrated in Figure 9a, for the Gram-negative dataset, the models which use the physico-chemical features yield the best AUC score (99%). For this dataset, the extended features and the top 3 scoring features (normalized hydrophobicity, normalized hydrophobic moment, net charge) of the physico-chemical features generate the same AUC values (98%). It can be observed from Figure 9b that on the Gram-positive dataset, the model which uses physico-chemical features achieves 97% AUC and hence obtains better performance metrics than the extended dataset and than the models using top 3 scoring features. On the Gram-positive dataset, the top 3 scoring features of physico-chemical descriptors are net charge, isoelectric point, and disordered conformation propensity. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Accuracy**} & \multirow{2}{*}{**Recall**} & \multirow{2}{*}{**Specificity**} & \multirow{2}{*}{**Precision**} & \multicolumn{2}{c}{**Area Under**} & \multirow{2}{*}{**F1**} & \multirow{2}{*}{**Balanced Acc.**} \\  & & & & & & & & \\ \hline AdaBoost & 0.86 \(\pm\) 0.06 & **0.91 \(\pm\) 0.10** & 0.83 \(\pm\) 0.10 & 0.74 \(\pm\) 0.12 & 0.90 \(\pm\) 0.05 & 0.80 \(\pm\) 0.07 & 0.87 \(\pm\) 0.10 \\ Decision Tree & 0.83 \(\pm\) 0.10 & 0.77 \(\pm\) 0.12 & 0.86 \(\pm\) 0.16 & 0.76 \(\pm\) 0.14 & 0.82 \(\pm\) 0.07 & 0.75 \(\pm\) 0.10 & 0.82 \(\pm\) 0.14 \\ LogitBoost & 0.87 \(\pm\) 0.05 & 0.90 \(\pm\) 0.10 & 0.86 \(\pm\) 0.08 & 0.77 \(\pm\) 0.11 & 0.91 \(\pm\) 0.04 & 0.82 \(\pm\) 0.06 & 0.88 \(\pm\) 0.09 \\ RF & **0.90 \(\pm\) 0.04** & 0.89 \(\pm\) 0.10 & **0.91 \(\pm\) 0.07** & **0.84 \(\pm\) 0.11** & **0.94 \(\pm\) 0.04** & **0.86 \(\pm\) 0.06** & **0.90 \(\pm\) 0.08** \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Comparison of the models according to performance metrics for the Gram-positive dataset after feature selection (Information gain). Figure 9: Comparison of the AUC results before and after feature selection is applied on physico-chemical features and extended set of features for (**a**) Gram-negative and (**b**) Gram-positive dataset.