부산대학교 도서관

The age of -omics has set a precedent for precision medicine, yet one of the most prevalent challenges is multi-omics data integration, with no platform yet that can efficiently integrate clinical and multi-omic data. This thesis uses several integrative data strategies to deconvolute complex multisystemic disease states, utilising clinical, cytokine profiling, metagenomic and metabonomic data to gain greater depth and understanding. High-throughput metabolic profiling methods have been employed for in-depth systemic analysis of SARS-CoV-2 infection, Primary Biliary Cholangitis and paediatric burn injury. Correlation analysis and derived clustering methods were utilised for data integration, following multivariate analyses and reveal disease state specific signatures. Similarly, different biofluids (plasma, urine, faeces) contributed specific complementary information. Several systemic biomarkers were identified for each disease, each with hallmarks of organ dysfunction. This work highlights the value of data integration in heavily confounded data and disease.