Supervised and unsupervised pattern recognition analysis of immunological parameters in clinical setting

High-dimensional immunological profiling provides critical insights into disease mechanisms, yet its translation into robust clinical evidence remains constrained by persistent analytical bottlenecks in data quality, population identification, and downstream statistical-computational analysis. This thesis presents a comprehensive, end-to-end computational framework designed to address these challenges systematically across four interconnected aims from raw data preprocessing to clinical modelling. A unifying principle is the adoption of data-driven, non-parametric approaches across all stages. First, an automated quality control pipeline was developed using deep learning architectures such as dense autoencoders and temporal convolutional networks to detect and remove acquisition artefacts in flow cytometry data. This approach reduced event loss from approximately 30% under conventional rule-based filtering to under 10%, preserving genuine biological variance whilst ensuring signal integrity. Second, an extended context-aware supervised deep learning framework was developed to replace subjective manual gating, achieving expert-level precision in identifying major leukocyte lineages and robustness in the detection of rare immune subpopulations (prevalence <1%). This method showed near-perfect performance (AUROC >0.99) whilst maintaining low false-positive rates under severe class imbalance. Third, the framework addressed structural challenges arising from incomplete, spatially resolved immune datasets through the integration of non-parametric multiple imputation with Adaptive Index Modelling. This strategy was implemented on tissue microarray data from soft-tissue sarcomas, yielding a stable Sarcoma Immune Score that stratified disease-free survival and recovered prognostic signals obscured by conventional complete-case analysis. Fourth, improved Functional Data Analysis was applied to patients with Familial Adenomatous Polyposis undergoing dietary intervention, enabling the identification of distinct immunometabolic phenotypes through unsupervised trajectory-based meta-clustering. These kinetic signatures closely reflected clinical response and provided discriminative insights inaccessible through static baseline comparisons. Collectively, these findings demonstrate that methodological fragmentation introduces bias and reduces reproducibility, undermining clinical translation towards a standardised pipeline for transforming raw immunological data into reproducible, operator-independent clinical evidence.