Robust Cancer Biomarker Identification From Matched Transcriptomic Data Via Bootstrapped Regularized Conditional Logistic Regression

Abstract Objectives: With the increasing application of high-throughput transcriptomic data in cancer research, identifying reliable cancer biomarkers in high-dimensional settings remains a major challenge. This study aims to systematically evaluate various regularized conditional logistic regression (CLR) methods under a matched case-control (MCC) design, focusing on their performance in variable selection, parameter estimation, and predictive accuracy. Special emphasis is placed on the importance of the matching design in reducing confounding effects and improving model interpretability. Methods: We utilize RNA-seq data from