This project aims to investigate molecular patterns in a large-scale cohort by integrating proteomic and metabolomic data to improve biological stratification and disease understanding. We will analyze circulating protein and metabolite levels from the UK Biobank to uncover co-association modules-sets of biomolecules that covary across individuals-and assess their biological and clinical relevance.
We will also perform unsupervised subtyping of participants using metabolomic and proteomic profiles separately, then compare the resulting subtype structures to evaluate their concordance and divergence. These molecular subtypes will be evaluated for their relevance to disease traits and biological variation, with a focus on cancer-related outcomes.
We will apply data-driven analytical techniques to identify molecular co-association structures and to stratify individuals based on their proteomic and metabolomic profiles. Mendelian randomization or genetic association analyses may be used to support biological interpretation, where suitable.
This study does not develop new computational tools but applies established methods to integrate and interpret existing UK Biobank data. When applicable, we may use external datasets for replication and validation. Findings from this study are expected to inform future biomarker discovery and improve understanding of how metabolomic and proteomic variation relates to disease risk.
