Serum proteins predict incident inflammatory bowel disease 14 years before diagnosis: a machine-learning and Cox modelling in the UK Biobank

Using an analysis pipeline that combines machine learning-based feature selection with classical statistical modelling, we aimed to predict incident inflammatory bowel disease (IBD) and to identify candidate proteins associated with disease development. We used UK Biobank data in an observational cohort study of 52,331 participants with proteomics data and without a history of IBD. The cohort was split into an 80/20 train/test dataset, and three separate extreme gradient boosting models were trained on the training dataset. Using Shapley values, we ranked the predictive importance of 2,912 pro

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