Extended Data Fig. 1. Infographics for noise injection methods and multi-omic data integration with StablSRM.

A. Noise injection methods. Left panel depicting the original dataset with n samples and p features with strong correlation between features f! and f" as well as medium correlation between f# and f$. Middle panel showing MX knockoffs as noise injection method where generated artificial features preserve the original features’ correlation structure. Right panel showing random permutations as alternative noise generation method, which does not preserve the correlation structure. B. Multi-omic data integration with StablSRM. Early fusion approaches of multi-omic data integration combine all features of all omics to a concatenated dataset to derive a multivariate model. Late fusion approaches build predictive models on each omic layer individually, then concatenate the model predictions together and build a predictive model. StablSRM’s method builds models in a bootstrapping fashion on each omic individually to select the informative features, then concatenates all selected (informative) features and builds a final predictive model on all selected features.