## 3 Results
### Comparative Performance Evaluation of G-S-M with Traditional Feature Selection Methods  
We have comparatively evaluated the performance of EC-nomenclature-based G-S-M models with the performances of traditional feature selection methods, including XGB,  
Figure 5: Performance metrics of the EC-nomenclature-based G-S-M method when applied to population-specific CRC-associated metagenomic dataset, including relative abundance values of the enzymes. (**A**) AUC values and (**B**) # of enzymes (features) selected for population-specific datasets. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Enzyme Group** & **Enzyme Group Name** & \(\mathbf{p}\)**-Value** &
\begin{tabular}{c} **\# of** \\ Enzymes** \\ \end{tabular} & **Enzymes (EC)** \\ \hline
3.2.1 & Glycosadases & \(4.13\times 10^{-17}\) & 74 &
\begin{tabular}{c} 3.2.1.1, 3.2.1.10, \\ 3.2.1.101, \\ 2.8.3 \\ \end{tabular} \\
2.8.3 & CoA-transferase & \(3.86\times 10^{-14}\) & 12 &
\begin{tabular}{c} 2.8.3,10, 2.8.3,12, \\ 2.8.3,15... \\ 4.2.1.101, \\ 4.2.1.103, \\ 3.1.1 \\ \end{tabular} \\
4.3.1 & \begin{tabular}{c} \
\begin{tabular}{c} 4.3.1 \(\times 10^{-9}\) \\ \end{tabular} \\ \end{tabular} & 28 &
\begin{tabular}{c} 3.1.1, 3.1.1, 11, \\ 3.1.1.1, \\ 3.1.1.1, \\ 3.1.1.1, \\ 3.1.1.1, \\ 3.1.1.3, \\ 4.3.1.1, 4.3.1.16, \\ 4.3.1.18... \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Identified enzyme groups and their associated enzymes when the robust rank aggregation method in G-S-M is applied to the CRC-associated metagenomic dataset, including relative abundance values of the enzymes. SKB, IG, CMIM, MRMR, and FCBF, coupled with different classifiers, such as Adaboost, DT, LogitBoost, RF, SVM_opt, Stack_Logitboost_Kmeans, Stack_SVM_Kmeans, and XGBoost (as shown in Figure 6). As shown in Table 2, there are 90 enzymes on average within the top two scoring groups selected by the EC-nomenclature-based G-S-M model when applied to the CRC-associated metagenomic dataset. To conduct a comparative analysis, we have focused on the performance metrics that were obtained using the 90 features selected by different methods. In our previous studies, we have shown that, for different GSM architectures, RF classifiers outperform other classifiers [25]. Also, in the literature, it is shown that RF classifiers are widely used for human microbiome studies, including selecting relevant features, categorizing patients, identifying disease-associated biomarkers, and revealing host-microbial patterns [53]. Therefore, we have incorporated RF as the main classifier in the EC-nomenclature-based G-S-M approach. Still, to investigate the performance of the EC-nomenclature-based G-S-M using different classifiers, we have run G-S-M using RF, XGBoost, and DT classifiers for 10 iterations. Figure 6 summarizes the AUC metrics obtained using the EC-nomenclature-based G-S-M when coupled with RF, XGBoost, and DT classifiers and the AUC metrics of TFS methods coupled with different classifiers and tested on the CRC-associated metagenomic dataset. Different classifiers generated different performance metrics for different TFS methods, however, Figure 6 consistently shows the dominance of the RF and XGBoost classifiers. The EC-nomenclature-based G-S-M with RF generated a 0.769 AUC value, and the TFS methods with the RF classifier have AUC values as of 0.763 (XGB), 0.762 (SKB), 0.725 (IG), 0.495 (FCBF), 0.519 (MRMR), and 0.584 (CMIM), using 90 features. As can be seen from Figure 6, when compared with the RF classifier, the other classifiers reported either similar or lower AUC values. Our results demonstrate that the proposed method outperforms the conventional approaches, delivering more reliable insights into the molecular and biological associations between the human gut microbiome and CRC.