# A Gene Ontology-Based Pipeline for Selecting Significant Gene Subsets in Biomedical Applications
## 4 Conclusions  
This study presents a comprehensive pipeline that integrates Gene Ontology (GO) enrichment analysis with ensemble-based machine learning to identify statistically and biologically significant gene subsets from high-dimensional gene expression data. The proposed methodology was evaluated across the three principal GO categories, namely,Biological Process (BP), Molecular Function (MF), and Cellular Component (CC), as well as their set-theoretic combinations (union and intersection). The experimental results demonstrate that gene subsets derived from the CC category consistently yield the highest classification performance, both by class and overall. This indicates that genes associated with spatial localization and structural cellular roles are particularly informative for distinguishing cancer subtypes. The union of all GO categories performs similarly, suggesting that most discriminative genes in BP and MF are already present in the CC subset. By contrast, subsets based solely on BP, MF, or their intersection show reduced classification accuracy, likely due to loss of complementary or category-specific markers. To validate the effectiveness of GO-based selection, we additionally evaluated the classification performance of the full set of 25,672 normalized genes without any prior filtering. The resulting scores were comparable to those obtained from the union subset, with less than a 1% difference in weighted F1-score. This supports the utility of GO-based feature selection as a form of biologically informed dimensionality reduction that retains most of the predictive power of the data while improving interpretability and computational efficiency. The random forest classifier demonstrated remarkable robustness across all gene subsets, with minimal variation in performance metrics. Its ensemble structure, ability to handle high-dimensional and noisy data, and resilience to redundant features make it especially suitable for transcriptomic classification tasks. A critical strength of the pipeline lies in its dual validation strategy for GO enrichment. By jointly applying [PERSON]'s exact test and the Kolmogorov-Smirnov test, the method ensures that selected GO terms exhibit both categorical overrepresentation and distributional divergence. This two-layered filtering enhances the statistical rigor of the resulting gene sets and reduces the likelihood of false positives. Beyond classification accuracy, the pipeline offers an interpretable framework for biologically grounded gene selection; while this study primarily focused on predictive performance, future work will investigate model interpretability through feature importance measures such as Gini impurity reduction and SHAP (Shapley Additive Explanations). These analyses will identify key genes and GO terms driving classification outcomes and enhance the biological transparency of the results. In the broader context of precision medicine, the ability to extract compact and functionally coherent gene subsets supports the development of efficient, accurate, and interpretable diagnostic models. These findings form a solid foundation for downstream applications such as biomarker discovery, co-expression network analysis, and multi-omics integration. Future research will extend this methodology to deep learning models and hybrid ensemble approaches for improved generalization and biomarker stability across disease cohorts. The individual contributions of the authors are the following: conceptualization, methodology, formal analysis, resources, simulation, writing--review and editing: S.B., O.Y. and I.L. ; software, validation, statistical analysis, writing--original draft preparation: O.Y. ; visualization, writing--original draft preparation: I.L. and N.S. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.