Chronic inflammatory bowel diseases: Predicting the success of biologicals
We used different sets of omics markers for machine-learning-based feature selection to assess the predictive potential of early changes in each data layer. In this step, performance could be increased by using individual disease entities and DMPs and combined DMP-DEGs were the best performing initial data sets for UC and CD, respectively. Features selected from other data moieties, e.g.
Limitations of our study include the small size of the cohorts due to the dense longitudinal sampling scheme of IBD patients, which we aimed to compensate by rigorous replication of findings in the two-cohort setting. Potential confounding effects of different sequence batches were minimized by randomization of patients across sequencing runs while keeping all longitudinal samples from a given individual in a single batch.
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