AI Article Synopsis
- - The paper examines the difficulties in identifying biomarkers for diagnosis in biomedical experiments and introduces a new method based on an enhanced version of the Dantzig selector to tackle these issues.
- - By transforming a nonlinear programming problem into a linear programming framework, the proposed method improves the efficiency of traditional regularization techniques under certain assumptions about logistic probabilities.
- - The authors demonstrate their approach using a binary response experiment, showing that it outperforms conventional methods in biomarker identification and offers a valuable ranking of variable importance for better decision-making in further research.
Article Abstract
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 under a reasonable assumption on the logistic probability range. We illustrate the use 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.
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| http://dx.doi.org/10.1016/j.biosystems.2024.105163 | DOI Listing |
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