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.

翻译：本文探讨了生物医学实验中用于诊断目的的生物标志物识别所面临的挑战，并通过推广Dantzig选择器提出了一种应对上述难题的新方法。为提高正则化方法的效率，我们引入了一种变换，将原本因非线性链接函数而导致的非线性规划问题转化为线性规划框架。我们通过一个二值响应实验展示了该方法的应用效果，结果表明其在生物标志物识别研究中相较于传统分析方法具有更优越的性能。所提出的方法不仅作为变量/生物标志物选择工具，其对变量重要性的排序还为实践者提供了有价值的参考信息，有助于他们就后续研究因素的优先次序做出明智决策。