# A Gene Ontology-Based Pipeline for Selecting Significant Gene Subsets in Biomedical Applications
## 1 Introduction and Literature Survey
[9] examined CHAF1B in lung adenocarcinoma (LUAD), finding associations with immune modulation, glycolysis, and cell proliferation. Another study [10] on atomic postpartum hemorrhage (PPH) showed immune-inflammatory enrichment involving T cells and macrophages; biomarkers such as CD163 and FGL2 were proposed for early diagnosis. Extending GO analysis beyond transcriptomics, [PERSON] et al. [11] developed IDSLGOA, a tool for GO-based metabolomic enrichment. This enabled deeper insight into biological processes at the metabolite level, as demonstrated by identifying 82 enriched GO terms related to metabolism in the aging brain cortex. Similarly, ref. [12] used integrative genomics--including GWAS and plasma proteomics--combined with Mendelian randomization to identify proteins such as GSTM3 and FAM171B as candidate biomarkers for small cell lung cancer (SCLC). [PERSON] et al. [13] applied a similar approach to identify TIMP4 as a potential therapeutic target in acne vulgaris. Advances in GO analysis tools have further enhanced accessibility and reproducibility. GOnet [14] provides interactive visualization of gene-term relationships, while FunSet [15] clusters semantically similar terms and enables reproducible analysis via version-controlled ontologies. GOTermViewer [16] supports dynamic interpretation of GO enrichment across multiple experiments, such as drug-response studies or time-series analyses. Despite these advances, key challenges remain. GO term enrichment results often vary across datasets and disease types, leading to inconsistencies. The choice of statistical methods (e.g., [PERSON]'s exact test or the Kolmogorov-Smirnov test) and DEG selection criteria further influence reproducibility. Moreover, integration with other omics layers--such as proteomics and metabolomics--is still limited, restricting holistic interpretation. Gene-gene interactions, pathway crosstalk, reliance on public datasets such as TCGA, and species-specific annotation discrepancies introduce additional complexity. To address these issues, future approaches must incorporate multi-omics integration, machine learning for prioritization, and more robust statistical frameworks, ultimately improving the translational potential of GO-based diagnostics. This study proposes a novel pipeline that integrates GO-based feature selection with ensemble learning to classify samples based on gene expression data. The methodology includes four main stages. First, gene expression data are preprocessed through standardization and normalization, with non-informative features removed to improve the quality of downstream analysis. Second, functionally significant gene subsets are extracted using Gene Ontology Enrichment Analysis (GOEA) across the three primary domains: Biological Processes (BP), Molecular Functions (MF), and Cellular Components (CC), to ensure biological relevance. Third, an ensemble classifier, specifically the random forest algorithm, is applied to assess the predictive capacity of the selected gene subsets. Finally, the performance of the classification models is evaluated using standard metrics, including accuracy, precision, recall, and F1-score. The core contribution of this research is the development of an integrated, hybrid pipeline that enhances the robustness and accuracy of biomarker discovery. This includes preprocessing, DEG identification, multi-domain GO enrichment, and statistical validation using [PERSON]'s exact test and the Kolmogorov-Smirnov test. A distinctive innovation lies in the hybrid strategy for GO term integration, where gene sets enriched in BP, MF, and CC are intersected and combined to optimize biological relevance and classification performance. Furthermore, model performance is improved via hyperparameter optimization using Bayesian methods within the random forest framework. Together, these contributions establish a comprehensive and biologically grounded methodology for gene-expression-based disease classification and personalized diagnostics.