An efficient approach for identifying important biomarkers for biomedical diagnosis

Biosystems. 2024 Feb 22:105163. doi: 10.1016/j.biosystems.2024.105163. Online ahead of print.ABSTRACTIn 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.PMID:38401640 | DOI:10.1016/j.biosystems.2024.105163

Source: Biosystems - February 24, 2024 Category: Biotechnology Authors: Jing-Wen Huang Yan-Hong Chen Frederick Kin Hing Phoa Yan-Han Lin Shau-Ping Lin Source Type: research