## 4 Discussion
### Comparative Performance Evaluation of the EC-Nomenclature-Based G-S-M Model  
We have comparatively evaluated the EC-nomenclature-based G-S-M with traditional feature selection methods, including XGB, 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). In our earlier studies using the Grouping-Scoring-Modeling approach, we have shown that RF classifiers outperform other classifiers [25]. Therefore, in this study, we utilize the RF classifier within the EC-nomenclature-based G-S-M. Additionally, to show the performance of different classifiers on the G-S-M model, we experimented with XGBoost and DT. The TFS methods with the RF classifier yielded the following AUC values: 0.763 (XGB), 0.762 (SKB), 0.725 (IG), 0.495 (FCBF), 0.519 (MRMR), and 0.584 (CMIM), using 90 features. As one can notice from Figure 6, the other classifiers have either similar performance results or lower performance results compared with the RF classifier.  
On the other hand, our experimental findings imply that the EC-nomenclature-based G-S-M model using RF has the capability to compete with other tested FS methods (as shown in Figure 6). Furthermore, as plotted in Figure 17, the superiority of the EC-nomenclature-based G-S-M approach becomes apparent (in terms of accuracy, specificity, and AUC performance metrics) when it is compared with the performance metrics of different feature selection methods on the CRC-associated metagenomic dataset. The experimental results underscore that, while the performance metrics of both the traditional feature selection methods and the EC-nomenclature-based G-S-M model are not very high, due to the complexity of the problem, the EC-nomenclature-based G-S-M model consistently outperforms the traditional feature selection methods, as illustrated in Figures 6 and 17. In addition to its performance, the EC-nomenclature-based G-S-M model also has the capability to select informative groups, which can be associated with the disease under investigation, which was CRC in this study.  
The correlation among the top 100 features (enzymes) that are selected by different feature selection methods and the EC-nomenclature-based G-S-M approach is illustrated in Figure 18. It is shown that XGB and SKB have the highest Jaccard similarity (0.14).  
Figure 19 presents the commonalities among the top 100 features (enzymes) that are selected by different feature selection algorithms and the EC-nomenclature-based G-S-M approach when tested on the CRC-associated metagenomic dataset, including the relative abundance values of the enzymes. Malate dehydrogenase (EC: 1.1.1.37) has been detected as an important enzyme by the XGB, SKB, FCBF, and CMIM methods. Similarly, citrate  
Figure 17: Performance metrics of different feature selection methods (XGB, SKB, IG, MRMR, FCBF, and CMIM) coupled with the RF classifier, RCE with the SVM classifier, and the EC-nomenclature-based G-S-M approach with RF when tested on the CRC-associated metagenomic dataset, including relative abundance values of the enzymes.  
Figure 18: Correlations among the top 100 features that are selected by different feature selection algorithms and the EC-nomenclature-based G-S-M approach when tested on the CRC-associated metagenomic dataset, including relative abundance values of the enzymes.