Back to search

Article

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

2026-02-26

Abstract excerpt

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 r...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work
d00e2795-6919-5d2b-ab87-492ea7040f96
DOI
10.64898/2026.02.24.707860
Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest
Interactive article relationship graphSelect a related publication card to move it into the centre and load its closest explainable connections. Solid lines are source-backed structured connections. Dashed lines are semantic discovery signals and are not scientific evidence.
Permutation-calibrated stability discovery under p >> n : A leak-controlled Machine Learning framework identifies candidate proteomics panels in antiseizure medications-related side effectsDOI 10.64898/2026.02.24.707860
Select a neighboring publication to make it the new centre.