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

This study highlights the value of integrating a matched case-control design with regularized CLR methods for the analysis of high-dimensional transcriptomic data. The proposed analytical framework offers improved accuracy, robustness, and biological relevance, providing a practical and scalable approach for cancer genomics research. It also supports the advancement of precision medicine and translational applications.