| dc.contributor | 統計系 | |
| dc.creator (作者) | Hsu, M.-J.;Chang, Yuan-Chin Ivan;Hsueh, Huey-Miin | |
| dc.creator (作者) | 張源俊;薛慧敏 | zh_TW |
| dc.date (日期) | 2014-01 | |
| dc.date.accessioned | 3-Jun-2015 11:16:46 (UTC+8) | - |
| dc.date.available | 3-Jun-2015 11:16:46 (UTC+8) | - |
| dc.date.issued (上傳時間) | 3-Jun-2015 11:16:46 (UTC+8) | - |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/75541 | - |
| dc.description.abstract (摘要) | 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. | |
| dc.format.extent | 609716 bytes | - |
| dc.format.mimetype | application/pdf | - |
| dc.relation (關聯) | BMC Research Notes, 7(1), 論文編號 25 | |
| dc.subject (關鍵詞) | 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 | |
| dc.title (題名) | Biomarker selection for medical diagnosis using the partial area under the ROC curve | |
| dc.type (資料類型) | article | en |
| dc.identifier.doi (DOI) | 10.1186/1756-0500-7-25 | |
| dc.doi.uri (DOI) | http://dx.doi.org/10.1186/1756-0500-7-25 | |
