A0462
Title: An efficient approach for identifying important biomarkers for biomedical diagnosis
Authors: Jing-Wen Huang - Academia Sinica (Taiwan) [presenting]
Yan-Hong Chen - Academia Sinica (Taiwan)
Frederick Kin Hing Phoa - Academia Sinica (Taiwan)
Yan-Han Lin - National Taiwan University (Taiwan)
Shau Ping Lin - National Taiwan University (Taiwan)
Abstract: The challenges associated with biomarker identification are explored for diagnosis purposes in biomedical experiments, and a novel approach is proposed, inspired by the analysis of supersaturated designs, to handle the above challenging scenario via the generalization of the Dantzig selector. To improve the efficiency of the regularization method, a transformation is introduced 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. The use of the method is illustrated in an experiment with binary response, showing superior performance on biomarker identification studies when compared to their conventional analysis. The 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.
