  In this paper, we explore the challenges associated with biomarker
identification for diagnosis purpose in biomedical experiments, and propose a
novel approach to handle the above challenging scenario via the generalization
of the Dantzig selector. To improve the efficiency of the regularization
method, we introduce a transformation from an inherent nonlinear programming
due to its nonlinear link function into a linear programming framework. We
illustrate the use of of our method on an experiment with binary response,
showing superior performance on biomarker identification studies when compared
to their conventional analysis. Our proposed method does not merely serve as a
variable/biomarker selection tool, its ranking of variable importance provides
valuable reference information for practitioners to reach informed decisions
regarding the prioritization of factors for further investigations.
