scholarly journals Combination of Dichotomizers for Maximizing the Partial Area under the ROC Curve

2010 ◽  
pp. 660-669 ◽  
Author(s):  
Maria Teresa Ricamato ◽  
Francesco Tortorella
Keyword(s):  
Roc Curve ◽  
Partial Area

scholarly journals On use of partial area under the ROC curve for evaluation of diagnostic performance

2013 ◽  
Vol 32 (20) ◽  
pp. 3449-3458 ◽  
Author(s):  
Hua Ma ◽  
Andriy I. Bandos ◽  
Howard E. Rockette ◽  
David Gur
Keyword(s):  
Roc Curve ◽  
Diagnostic Performance ◽  
Partial Area

Non-parametric interval estimation for the partial area under the ROC curve

2011 ◽  
Vol 39 (1) ◽  
pp. 17-33 ◽  
Author(s):  
Gengsheng Qin ◽  
Xiaoping Jin ◽  
Xiao-Hua Zhou
Keyword(s):  
Roc Curve ◽  
Interval Estimation ◽  
Partial Area ◽  
Non Parametric

On the use of partial area under the ROC curve for comparison of two diagnostic tests

2014 ◽  
Vol 57 (2) ◽  
pp. 304-320 ◽  
Author(s):  
Hua Ma ◽  
Andriy I. Bandos ◽  
David Gur
Keyword(s):  
Roc Curve ◽  
Diagnostic Tests ◽  
Partial Area

Deviation from Perfect Performance Measures the Diagnostic Utility of Eyewitness Lineups but Partial Area Under the ROC Curve Does Not

2019 ◽  
Vol 8 (1) ◽  
pp. 50-59 ◽  
Author(s):  
Andrew M. Smith ◽  
James Michael Lampinen ◽  
Gary L. Wells ◽  
Laura Smalarz ◽  
Simona Mackovichova
Keyword(s):  
Performance Measures ◽  
Roc Curve ◽  
Diagnostic Utility ◽  
Perfect Performance ◽  
Partial Area

Multi-objective evolutionary algorithm for optimizing the partial area under the ROC curve

2019 ◽  
Vol 170 ◽  
pp. 61-69 ◽  
Author(s):  
Fan Cheng ◽  
Guanglong Fu ◽  
Xingyi Zhang ◽  
Jianfeng Qiu
Keyword(s):  
Evolutionary Algorithm ◽  
Roc Curve ◽  
Multi Objective ◽  
Partial Area

scholarly journals Combining multiple biomarkers linearly to maximize the partial area under the ROC curve

2017 ◽  
Vol 37 (4) ◽  
pp. 627-642 ◽  
Author(s):  
Qingxiang Yan ◽  
Leonidas E. Bantis ◽  
Janet L. Stanford ◽  
Ziding Feng
Keyword(s):  
Roc Curve ◽  
Partial Area ◽  
Multiple Biomarkers

scholarly journals Functional covariate-adjusted partial area under the specificity-ROC curve with an application to metabolic syndrome diagnosis

2016 ◽  
Vol 10 (3) ◽  
pp. 1472-1495 ◽  
Author(s):  
Vanda Inácio de Carvalho ◽  
Miguel de Carvalho ◽  
Todd A. Alonzo ◽  
Wenceslao González-Manteiga
Keyword(s):  
Metabolic Syndrome ◽  
Roc Curve ◽  
Partial Area ◽  
Syndrome Diagnosis

scholarly journals Evaluating the Performances of Biomarkers over a Restricted Domain of High Sensitivity

Mathematics ◽  
2021 ◽  
Vol 9 (21) ◽  
pp. 2826
Author(s):  
Manuel Franco ◽  
Juana-María Vivo
Keyword(s):  
Roc Curve ◽  
Performance Metrics ◽  
High Sensitivity ◽  
Roc Curves ◽  
Upper And Lower Bounds ◽  
Area Index ◽  
Colon Cancers ◽  
Partial Area ◽  
Variance Estimates ◽  
Inappropriate Expression

The burgeoning advances in high-throughput technologies have posed a great challenge to the identification of novel biomarkers for diagnosing, by contemporary models and methods, through bioinformatics-driven analysis. Diagnostic performance metrics such as the partial area under the ROC (pAUC) indexes exhibit limitations to analysing genomic data. Among other issues, the inability to differentiate between biomarkers whose ROC curves cross each other with the same pAUC value, the inappropriate expression of non-concave ROC curves, and the lack of a convenient interpretation, restrict their use in practice. Here, we have proposed the fitted partial area index (FpAUC), which is computable through an algorithm valid for any ROC curve shape, as an alternative performance summary for the evaluation of highly sensitive biomarkers. The proposed approach is based on fitter upper and lower bounds of the pAUC in a high-sensitivity region. Through variance estimates, simulations, and case studies for diagnosing leukaemia, and ovarian and colon cancers, we have proven the usefulness of the proposed metric in terms of restoring the interpretation and improving diagnostic accuracy. It is robust and feasible even when the ROC curve shows hooks, and solves performance ties between competitive biomarkers.

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