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    政大機構典藏 > 商學院 > 統計學系 > 期刊論文 >  Item 140.119/75541
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/75541

    Title: Biomarker selection for medical diagnosis using the partial area under the ROC curve
    Authors: Hsu, M.-J.;Chang, Yuan-Chin Ivan;Hsueh, Huey-Miin
    Contributors: 統計系
    Keywords: biological marker;algorithm;area under the curve;article;blood;breast disease;computer simulation;coronary artery disease;diagnosis;Duchenne muscular dystrophy;genetics;heterozygote detection;impedance;normal distribution;pathology;receiver operating characteristic;sensitivity and specificity;Algorithms;Area Under Curve;Biological Markers;Breast Diseases;Computer Simulation;Coronary Artery Disease;Diagnosis;Electric Impedance;Heterozygote Detection;Muscular Dystrophy;Duchenne;Normal Distribution;ROC Curve;Sensitivity and Specificity
    Date: 2014-01
    Issue Date: 2015-06-03 11:16:46 (UTC+8)
    Abstract: Background: A biomarker is usually used as a diagnostic or assessment tool in medical research. Finding an ideal biomarker is not easy and combining multiple biomarkers provides a promising alternative. Moreover, some biomarkers based on the optimal linear combination do not have enough discriminatory power. As a result, the aim of this study was to find the significant biomarkers based on the optimal linear combination maximizing the pAUC for assessment of the biomarkers. Methods. Under the binormality assumption we obtain the optimal linear combination of biomarkers maximizing the partial area under the receiver operating characteristic curve (pAUC). Related statistical tests are developed for assessment of a biomarker set and of an individual biomarker. Stepwise biomarker selections are introduced to identify those biomarkers of statistical significance. Results: The results of simulation study and three real examples, Duchenne Muscular Dystrophy disease, heart disease, and breast tissue example are used to show that our methods are most suitable biomarker selection for the data sets of a moderate number of biomarkers. Conclusions: Our proposed biomarker selection approaches can be used to find the significant biomarkers based on hypothesis testing. © 2014 Hsu et al.; licensee BioMed Central Ltd.
    Relation: BMC Research Notes, 7(1), 論文編號 25
    Data Type: article
    DOI 連結: http://dx.doi.org/10.1186/1756-0500-7-25
    DOI: 10.1186/1756-0500-7-25
    Appears in Collections:[統計學系] 期刊論文

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