This project will investigate the role of circulating host and microbiome-associated metabolites in hepatobiliary cancer (HBC) development using cutting-edge multi-omics analyses. You will apply advanced Machine Learning approaches (e.g., XGBoost, Random Forest, Deep Forest, SVMs) and Mendelian Randomisation to large-scale existing metabolomics and GWAS data from major international cohorts (including EPIC, UK Biobank, and others) to identify predictive metabolic signatures for early detection,…
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