# 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  
Following the identification of significant genes based on Gene Ontology (GO) categories, a supervised learning model was applied to evaluate the discriminative power of each constructed gene expression dataset. The random forest (RF) algorithm was chosen due to its robustness, resistance to overfitting, ability to handle high-dimensional data, and capacity to assess feature importance. Table 1 summarizes the RF hyperparameters that were optimized, specifying their search intervals and the final selected values. The interpretation of the obtained results is as follows. The relatively small number of estimators (50 decision trees) reflects a deliberate compromise between computational efficiency and classification performance. This setting reduces training time while preserving sufficient diversity within the ensemble to maintain predictive accuracy. The selected maximum tree depth (14), along with constraints on the minimum number of samples required for node splitting and leaf formation (e.g., \(min\_samples\_split=9\)), acts as a regularization mechanism. These constraints help to control model complexity and reduce the risk of overfitting--an essential consideration when working with high-dimensional gene expression data. A particularly low value of \(max\_features\), at 0.113, indicates that only a small fraction of available features (i.e., genes) are randomly selected at each node split. This practice enhances the diversity among trees in the ensemble and improves generalization performance by minimizing correlations between individual learners. The use of bootstrap=True enables each decision tree to be trained on a randomly resampled (bootstrapped) subset of the original data. This technique increases model variance and contributes to overall robustness by aggregating predictions across diverse training subsets. Finally, the choice of entropy as the criterion for node splitting prioritizes information gain. This makes it particularly well suited for biological classification tasks, where class distributions are often unbalanced or non-uniform. This fine-tuned random forest model was subsequently applied to each of the five datasets generated from the GO-based gene selection pipeline: three corresponding to the individual GO categories (BP, MF, and CC), one representing the intersection of significant genes across all three categories, and one corresponding to their union. Figures 6 and 7 present the classification results based on gene subsets derived from different Gene Ontology (GO) categories and their combinations. \begin{table}
\begin{tabular}{c l c c c} \hline \hline \# & **Hyperparameter** & **Search Range** & **Description** & **Optimized Value** \\ \hline
1 & Number of Trees (\(n\_estimators\)) & 10–200 & Total trees in the ensemble & 50 \\
2 & Maximum Depth (\(max\_depth\)) & 1–20 & Limits tree depth to avoid & 14 \\  & & & overfitting & 14 \\
3 & Min. Samples to Split & 2–10 & Minimum number of samples & 9 \\  & & & to split a node & \\
4 & Min. Samples per Leaf & 1–4 & Minimum number of samples & 4 \\  & & & in a terminal node & \\
5 & Max. Features (\(max\_features\)) & 0.1–1.0 & Fraction of features used for & 0.113 \\  & & & splitting & \\
6 & Bootstrap Sampling (\(bootstrap\)) & [True, False] & Enables sampling with & True \\  & & & replacement & \\
7 & Split Criterion (\(criterion\)) & \{Gini, entropy\} & Function to measure split & Entropy \\  & & & quality & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Hyperparameters optimized via Bayesian search for the random forest classifier. Figure 6: Performance metrics by class across GO-based gene subsets. Figure 7: Overall classification performance for different GO-based gene subsets. Figure 6 presents line plots of classification performance metrics (precision, recall, and F1-score) across 14 sample classes, based on gene subsets derived from different GO categories: BP, MF, CC, as well as their union and intersection.