# A Gene Ontology-Based Pipeline for Selecting Significant Gene Subsets in Biomedical Applications
## 3 Results and Discussion
### Application of the Random Forest Classifier to the Generated Gene Expression Datasets
Each subplot corresponds to a specific class and illustrates how predictive performance varies depending on the selected subset. Figure 7 complements this by summarizing the overall classification accuracy and weighted F1-score across all categories. The CC category consistently yields the highest performance, both by class and overall, with metrics often exceeding 0.99 for multiple cancer types. This highlights the value of genes related to spatial localization and structural organization in distinguishing tumor profiles; while the union subset integrates additional genes from BP and MF, it offers only marginal gains over CC, suggesting that the most predictive features are already encompassed within the CC subset. A closer look at class-specific behavior shows that model performance varies not only by GO category but also by cancer type. For example, the CESC class exhibits considerable differences in recall depending on the subset--particularly MF and the intersection of the sets--indicating that critical genes for this class may reside outside the intersection of GO domains. This underscores the importance of considering each GO category independently. BP and MF subsets demonstrate slightly lower, yet still reliable, classification scores. The intersection subset--limited to genes common across all GO categories--yields the weakest results, likely due to its reduced size and the exclusion of functionally unique genes. The overall consistency of performance across subsets, especially in weighted metrics, reflects the robustness of the RF classifier. Its resilience to noise and suitability for high-dimensional data make it an effective choice for gene expression analysis. This comparison also supports the use of biologically informed dimensionality reduction. GO-based gene selection not only improves interpretability but also facilitates downstream analyses, such as clustering and module detection. In this context, the CC subset presents an effective balance between simplicity and predictive power. Although this study focuses on classification accuracy, future research will explore interpretability through feature importance methods such as Gini-based ranking and SHAP (Shapley Additive Explanations). This will enhance understanding of which genes and GO terms are most influential in model predictions, thereby increasing biological transparency and clinical relevance. To provide a comprehensive benchmark, we also evaluated classification using the full set of 25,672 normalized genes, without GO filtering. The performance was highly comparable to that of the union subset, with differences in weighted F1-scores generally below 1%. This confirms that GO-based subsets retain most of the predictive capacity while substantially reducing the feature space. However, GO-based selection has limitations. The filtering and thresholding processes may exclude relevant genes, and the evolving nature of GO annotations introduces variability over time. Therefore, while beneficial for efficiency and clarity, this approach must be applied with caution and awareness of these trade-offs. In the broader context of personalized medicine, the ability to define compact and functionally coherent gene subsets supports the development of accurate, interpretable, and scalable diagnostic models. These findings form a strong foundation for downstream biomarker discovery and classification tasks in precision oncology.