Vine copula knockoffs for variable selection in gene expression studies

Identifying clinical and genetic markers is essential for stratifying cancer patients by survival outcomes and guiding personalized treatment strategies. However, gene expression studies often involve high‐dimensional predictors with mixed data types and complex dependence, which complicates reliable variable selection. We propose a vine copula–based knockoffs framework that enables conditional variable selection in mixed clinical and genetic data with complex dependence structures. The efficacy of the proposed method is validated through extensive simulation scenarios. Its application to an ovarian cancer dataset identifies clinical and genetic markers associated with patient survival, with results consistent with survival meta‐analyses. This approach enhances biomarker discovery by linking methodological innovation with genomic applications.