# A Gene Ontology-Based Pipeline for Selecting Significant Gene Subsets in Biomedical Applications
## 3 Results and Discussion
### Application of GO Analysis to Identify a Subset of Significant Genes  
Figure 4 illustrates the distribution of \(p\)-values obtained from [PERSON]'s exact test (X-axis) and the Kolmogorov-Smirnov test (Y-axis) for GO term enrichment across the three Gene Ontology categories.  
Each point in the plots represents a single GO term. The color indicates the number of associated genes: red points correspond to GO terms with a gene count above a predefined threshold, while blue points represent terms with fewer associated genes. The size of each point is proportional to the number of genes linked to that GO term, allowing visual emphasis on gene-rich functional groups. The horizontal and vertical axes both reflect statistical significance: smaller values indicate stronger enrichment. Consequently, GO terms located near the origin \((0,0)\) are considered highly significant by both tests and represent promising candidates for downstream analysis.  
Analysis of the distributions reveals several important patterns. A substantial number of GO terms lie along the axes, exhibiting low \(p\)-values in one test but high values in the other. This indicates differing sensitivities of the two tests to distinct data characteristics. In all three GO categories, some GO terms have \(p\)-values greater than 0.5 in one test, suggesting a lack of enrichment by that measure, while simultaneously showing \(p\)-values close to 0 in the other test, highlighting significant distributional shifts.  
This discrepancy underscores the complementarity of the two methods. [PERSON]'s exact test is sensitive to the presence or absence of gene sets in categories, whereas the Kolmogorov-Smirnov test captures differences in ranked distributions of expression values. Applying both tests in parallel ensures robust detection of functionally enriched GO terms. Their intersection forms a reliable basis for extracting gene subsets consistently enriched from different statistical perspectives, reducing false positives and increasing biological relevance.  
The results also allow comparisons across GO categories. The BP category (Figure 4a) shows a broader distribution of points and higher density near the origin, indicating a richer set of biologically meaningful terms. In contrast, the MF and CC categories (panels b and c) contain fewer high-confidence terms, though notable clusters of significant terms still emerge.  
From a biological standpoint, GO terms clustered near the origin highlight disease-relevant processes. In the BP category, these terms are predominantly related to the immune response, cell proliferation, apoptosis, and inflammatory signaling. There are well-established mechanisms involved in cancer and complex diseases. The MF category includes kinase activity, receptor binding, and transcription factor activity, reflecting regulation of signaling and gene expression. The CC category shows enrichment in the plasma  
Figure 4: Distribution of \(p\)-values from [PERSON]’s exact test (X-axis) and the Kolmogorov–Smirnov test (Y-axis) for enriched GO terms in various GO categories: (**a**) Biological Process, (**b**) Molecular Function, and (**c**) Cellular Component.  
membrane, extracellular exosomes, and nucleosomes, structures associated with cellular communication and remodeling during tumor progression and metastasis.  
Thus, the consistent clustering of significant GO terms across the two tests reinforces the robustness of the method and provides a biologically meaningful gene subset for downstream classification and potential biomarker discovery.  
Overall, these results confirm the necessity of using multiple enrichment tests and integrating their outcomes to reliably identify functionally and biologically interpretable subsets of significant genes.