A0180
Title: An efficient approach for identifying important biomarkers for biomedical diagnosis
Authors: Frederick Kin Hing Phoa - Academia Sinica (Taiwan) [presenting]
Jing-Wen Huang - Academia Sinica (Taiwan)
Yan-Hong Chen - Academia Sinica (Taiwan)
Yan-Han Lin - National Taiwan University (Taiwan)
Shau Ping Lin - National Taiwan University (Taiwan)
Abstract: The challenges associated with biomarker identification for diagnosis purpose in biomedical experiments are explored. A novel approach is proposed 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 in an experiment with binary response, showing superior performance in 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.
