## 3 Results
### Training Models Using an Extended Set of Features and Applying Feature Selection  
There are a high number of features (1507) in the extended feature set. To remove redundant features and select informative ones, we repeated our experiments with different feature selection methods including Information Gain (IG) [107], Maximum Relevance-Minimum Redundancy (MRMR) [108], Conditional Mutual Information Maximization (CMIM) [109], SelectKBest (SKB) [110], XGBoost (XGB) [111], Fast Correlation-Based Filter (FCBF) [112]. We have focused on the top 3 scoring features in both Gram-negative and Gram-positive datasets. The performance metrics obtained after feature selection are presented in Tables 9 and 10 for Gram-negative and Gram-positive datasets, respectively. For the Gram-negative dataset, the generated LogitBoost model with the three selected features by XGBoost resulted in the best performance metric (96% AUC) among all other tested classifiers and all other tested feature selection methods. The top 3 selected features on the Gram-negative dataset are GearyAuto_Steric14 from Geary Autocorrelation set, PAAC42 from pseudo amino acid composition, and PolarityT13 from composition, transition, and distribution of physico-chemical properties. On the Gram-negative dataset, the performance of the physico-chemical feature set (99% AUC with RF with 10 features) was still higher than the performance of the extended feature set (98% AUC with LogitBoost with 1507 features), and also higher than the performance of the extended feature set after feature selection (96% AUC with LogitBoost with 3 features). \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Model** & **Accuracy** & **Recall** & **Specificity** & **Precision** &
\begin{tabular}{c} **Area Under** \\ **Curve** \\ \end{tabular} & **F1** & **Balanced Acc.** \\ \hline AdaBoost & 0.96 \(\pm\) 0.03 & 0.98 \(\pm\) 0.04 & 0.95 \(\pm\) 0.05 & 0.94 \(\pm\) 0.06 & 0.98 \(\pm\) 0.02 & 0.96 \(\pm\) 0.03 & 0.96 \(\pm\) 0.05 \\ Decision Tree & 0.90 \(\pm\) 0.06 & 0.90 \(\pm\) 0.09 & 0.90 \(\pm\) 0.08 & 0.88 \(\pm\) 0.09 & 0.90 \(\pm\) 0.06 & 0.88 \(\pm\) 0.07 & 0.90 \(\pm\) 0.08 \\ LogitBoost & **0.97 \(\pm\) 0.03** & **0.98 \(\pm\) 0.03** & **0.95 \(\pm\) 0.06** & **0.95 \(\pm\) 0.06** & **0.98 \(\pm\) 0.01** & **0.96 \(\pm\) 0.03** & **0.97 \(\pm\) 0.04** \\ RF & 0.95 \(\pm\) 0.04 & 0.98 \(\pm\) 0.04 & 0.94 \(\pm\) 0.06 & 0.93 \(\pm\) 0.07 & 0.98 \(\pm\) 0.02 & 0.95 \(\pm\) 0.04 & 0.96 \(\pm\) 0.05 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of the models according to performance metrics for the Gram-negative dataset with 1507 features. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Model** & **Accuracy** & **Recall** & **Specificity** & **Precision** & \begin{tabular}{c} **Area Under** \\ **Curve** \\ \end{tabular}  & **F1** & **Balanced Acc. ** \\ \hline AdaBoost & 0.89 \(\pm\) 0.05 & 0.88 \(\pm\) 0.10 & 0.90 \(\pm\) 0.08 & 0.82 \(\pm\) 0.11 & 0.93 \(\pm\) 0.04 & 0.84 \(\pm\) 0.07 & 0.89 \(\pm\) 0.09 \\ Decision Tree & 0.82 \(\pm\) 0.10 & 0.74 \(\pm\) 0.14 & 0.86 \(\pm\) 0.16 & 0.75 \(\pm\) 0.13 & 0.80 \(\pm\) 0.07 & 0.73 \(\pm\) 0.10 & 0.80 \(\pm\) 0.15 \\ LogitBoost & 0.90 \(\pm\) 0.05 & 0.89 \(\pm\) 0.09 & 0.91 \(\pm\) 0.07 & 0.84 \(\pm\) 0.11 & 0.94 \(\pm\) 0.03 & 0.85 \(\pm\) 0.06 & 0.90 \(\pm\) 0.08 \\ RF & **0.92 \(\pm\) 0.04** & **0.91 \(\pm\) 0.09** & **0.92 \(\pm\) 0.06** & **0.86 \(\pm\)For the Gram-positive dataset, the generated RF model with the three selected features by Information Gain resulted in the best performance metric (94% AUC) among all other tested classifiers and all other tested feature selection methods. On the Gram-positive dataset, the performance of the physico-chemical feature set (97% AUC with RF with 10 features) was still higher than the performance of the extended feature set (95% AUC with RF with 1507 features), and also higher than the performance of the extended feature set after feature selection (94% AUC with RF with 3 features).