# A Gene Ontology-Based Pipeline for Selecting Significant Gene Subsets in Biomedical Applications
## 2 Materials and Methods
### Gene Ontology-Based Pipeline for Identifying Statistically Significant Genes
The analysis uses a gene universe consisting of all genes tested in the differential expression analysis, with significance determined by adjusted \(q\)-values. The selection function identifies genes with \(q\)-values below the threshold \(p_{\text{max}}=0.05\). To ensure functional relevance and statistical robustness, only GO terms with a minimum of 10 annotated genes are considered. The enrichment of GO terms is evaluated using two complementary statistical tests. First, [PERSON]'s exact test is applied to detect the overrepresentation of significant genes within each GO term. Second, the Kolmogorov-Smirnov (KS) test is used to assess distributional shifts in gene-level scores across GO terms. GO terms are retained for downstream analysis only if they are found to be statistically significant by both tests. Gene annotations are mapped using the org.Hs.eg.db database, and all gene identifiers are standardized to the Entrez format. Extraction of Significant Genes Associated with Enriched GO Terms. Genes that are linked to significantly enriched GO terms--validated by both [PERSON]'s exact test and the Kolmogorov-Smirnov test--are selected for further analysis. The corresponding Entrez IDs of these genes are extracted and aggregated across all three GO categories (BP, MF, and CC). The resulting gene list is composed of unique identifiers that appear in at least one of the enriched GO categories. 7. _Construction of the Final Dataset._ Based on the normalized expression profiles of the selected significant genes, are distinct datasets were constructed to support downstream analysis. The first three datasets were category-specific: the BP-specific dataset contained genes identified as significant within the Biological Process (BP) category; the MF-specific dataset included genes significant in the Molecular Function (MF) category; and the CC-specific dataset comprised genes significant in the Cellular Component (CC) category. In addition, two integrative datasets were created. The intersection dataset included only those genes that were found to be significant in all three GO categories simultaneously, representing the intersection of BP, MF, and CC. The union dataset, by contrast, contained unique genes that were significant in at least one of the GO categories. Each of these datasets preserves the normalized expression values of the selected genes across all 6344 samples and serves as the foundation for the subsequent classification and evaluation procedures. 8. _Classification and Model Evaluation._ To evaluate the discriminative potential of each constructed dataset, a supervised classification model is applied. A random forest classifier was selected due to its high performance on high-dimensional data, resistance to overfitting, and ability to estimate feature importance [25; 26; 27]. Model training and evaluation are performed using a 70/30 stratified train-test split. Hyperparameter tuning was conducted exclusively on the training set using Bayesian optimization combined with 5-fold cross-validation to ensure generalizability and prevent information leakage. The independent test set was reserved for final performance evaluation using the optimized model. Bayesian optimization is a method that leverages probabilistic models (typically Gaussian processes) to iteratively select the most promising hyperparameter values based on a surrogate model of the objective function. Unlike random or grid search, Bayesian optimization strategically explores the hyperparameter space, reducing computational cost while improving convergence to optimal solutions. The objective function was defined as the average classification accuracy across validation folds during training.