Permutation-calibrated stability discovery under p >> n : A leak-controlled Machine Learning framework identifies candidate proteomics panels in antiseizure medications-related side effects

Abstract We investigated whether the plasma proteome distinguishes people with epilepsy who report central nervous system (CNS) side effects from antiseizure medications (ASMs) from those who do not. In 161 patients profiled using proximity extension assay-based proteomics Neurology and Inflammation panels (∼1,447 proteins), we applied an ensemble leak-controlled machine-learning (ML) workflow based on LASSO (linear) and random forest (RF) (non-linear) with repeated nested cross-validation and stability selection. We engineered nested machine-learning workflows that embed permutation-based Monte Carlo p-value estimation directly within model training, enabling statistically calibrated feature discovery under high-dimensional, noisy proteomic data. Discovery phases were explicitly optimized for association and feature robustness, not prediction. The RF yielded a 61-protein candidate panel, for which an “exploratory nested RF” model achieved strong internal discrimination of CNS side effects (AUROC ∼ 0.92, 95% CI ∼ 0.86–0.96). The LASSO yielded a three-protein candidate panel all of which overlapped with those of the RF (SMOC2, TANK and IMPG1). Because per-protein testing across all 1447 proteins produced false discovery rates (FDRs) close to 1, we performed post-hoc, data-driven routed per-protein inference restricted to the 61-protein panel, identifying 13 proteins with FDR