# A Gene Ontology-Based Pipeline for Selecting Significant Gene Subsets in Biomedical Applications
## 1 Introduction and Literature Survey  
In modern bioinformatics and precision medicine, gene expression analysis plays a central role in developing advanced tools for early disease detection. The association between gene activity and pathological states provides a robust framework for uncovering novel diagnostic opportunities. This is particularly critical given the rising prevalence of multifactorial diseases such as cancer, Parkinson's disease, and Alzheimer's disease,which continue to challenge global healthcare systems. Enhancing methods for analyzing gene expression data is thus both timely and vital for improving the precision with which complex diseases are diagnosed. Gene expression profiling enables comprehensive examination of the transcriptional landscape within cells, thereby elucidating the molecular mechanisms that drive disease progression. These high-throughput datasets capture dynamic gene activity under specific pathological conditions, laying the groundwork for identifying biomarkers and regulatory pathways. In the context of personalized medicine, such insights are invaluable for tailoring diagnostics and treatments to individual patients. Methods such as Gene Ontology (GO) analysis, clustering, and biclustering provide powerful means to extract biologically relevant and co-expressed gene subsets, thereby enhancing data interpretability and classification accuracy. This approach supports not only early diagnosis but also stratification of patients based on molecular profiles--a cornerstone of personalized therapy. Consequently, gene expression analysis bridges the gap between genomic research and clinical practice, contributing to both improved diagnostics and precision therapeutics. Ontology, in a broader philosophical sense, pertains to the categorization and nature of being [1,2]. In bioinformatics, ontology refers to the formal representation of domain-specific knowledge using structured concepts and defined relationships. This framework supports data integration, interoperability, and cross-study comparisons. Specifically, Gene Ontology enables modeling of complex biological systems and facilitates both data interpretation and hypothesis generation. GO provides a controlled and hierarchical vocabulary for annotating genes and their products, thereby allowing systematic interpretation of genomic data. It is structured into three primary domains: _Biological Process_ (BP), which encompasses sequences of molecular events; _Molecular Function_ (MF), which encompasses basic activities such as binding or catalysis; and _Cellular Component_ (CC), which encompasses locations within cellular structures where gene products are active [3,4]. GO enrichment analysis (GOEA) is widely employed to identify statistically overrepresented GO terms among differentially expressed genes (DEGs) derived from technologies such as DNA microarrays or RNA sequencing (RNA-seq) [5,6]. This method reveals potentially disrupted biological pathways and provides insights into disease mechanisms, supporting biomarker discovery for diagnostics and therapy. GO's ability to integrate functional, spatial, and process-based gene annotations makes it essential for genome-wide studies, enabling cross-species comparisons and the identification of evolutionarily conserved functions. Therefore, constructing biologically meaningful gene subsets via GOEA is both a methodological requirement and a key component of precision diagnostics. The application of GO-based functional annotation has become a cornerstone for interpreting DEGs across various diseases. For instance, in [7], GOEA was used to investigate the biological impact of DEGs in respiratory diseases, demonstrating that microplastic exposure disrupts key molecular pathways. Similarly, ref. [8] explored colorectal cancer (CRC) in patients with type 2 diabetes mellitus (T2 DM), identifying enrichment in O-linked glycosylation processes and revealing mechanistic links and therapeutic targets. [PERSON] et al.