Results 271 to 280 of about 3,583,799 (330)

Interleukin‐39 is a Prognostic Biomarker and Therapy Target for Sepsis

Advanced Science, EarlyView.
Sepsis triggers a significant increase in IL‐39 production, with circulating levels markedly elevated and positively correlated with disease severity and poor clinical prognosis in sepsis patients. Mechanistically, macrophage‐derived IL‐39 activates the GP130 signaling pathway via a ligand‐receptor interaction, thereby fueling the pro‐inflammatory ...
Feng‐zhi Zhang, Hai‐jun Liu, Yan Yue, Jun‐jie Gao, Jin Zhang, Wen‐wu Zhang, Yun‐long Zhang, Wei‐hua Lu, Sheng‐wei Jin, Pu‐hong Zhang   +9 more
wiley   +1 more source

Anion‐Cation Synergistic Passivation for High‐Performance FA‐Cs Halide Perovskite Photodetectors

Advanced Electronic Materials, EarlyView.
An anion‐cation synergistic passivation strategy is developed to improve perovskite photodetectors. Surface‐localized DMePDA2+ cations and bulk‐diffused Cl− anions simultaneously reduce defects, enhancing charge transport and suppressing recombination.
Hyeon‐Jong Shin, Kwang‐Ro Yun, Tae Joon Park, Tae‐Yeon Seong   +3 more
wiley   +1 more source

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ROC SURFACE: A GENERALIZATION OF ROC CURVE ANALYSIS

Journal of Biopharmaceutical Statistics, 2000
Receiver operating characteristic (ROC) curve analysis is widely used in biomedical research to assess the performance of diagnostic tests. Much of the work has been directed at developing accurate indices to describe ROC curves and appropriate statistics to test differences between them.
Harry Yang
exaly   +3 more sources

Analyzing a Portion of the ROC Curve

Medical Decision Making, 1989
The area under the ROC curve is a common index summarizing the information contained in the curve. When comparing two ROC curves, though, problems arise when interest does not lie in the entire range of false-positive rates (and hence the entire area). Numerical integration is suggested for evaluating the area under a portion of the ROC curve. Variance
exaly   +3 more sources

Time‐dependent ROC curve estimation for interval‐censored data

Biometrical journal. Biometrische Zeitschrift, 2022
The receiver‐operating characteristic (ROC) curve is the most popular graphical method for evaluating the classification accuracy of a diagnostic marker.
K. Beyene, A. El Ghouch
semanticscholar   +1 more source

Some mathematical properties of the ROC curve and their applications

Ricerche Di Matematica, 2015
Maria Longobardi, Camilla Cali
exaly   +2 more sources

On the statistical analysis of ROC curves

Statistics in Medicine, 1989
AbstractWe introduce a new accuracy index for receiver operating characteristic (ROC) curves, namely the partial area under the binormal ROC graph over any specified region of interest. We propose a simple but general procedure, based on a conventional analysis of variance, for comparing accuracy indices derived from two or more different modalities ...
M L, Thompson, W, Zucchini
openaire   +2 more sources

ROC curves and the binormal assumption

The Journal of Neuropsychiatry and Clinical Neurosciences, 1991
Previous articles in this series have described how receiver operating characteristic (ROC) graphs provide comprehensive graphic representations of the diagnostic performance of non-binary tests and have explained how one constructs "trapezoidal" ROC graphs in which discrete cutoff points are plotted and connected with line segments.
E, Somoza, D, Mossman
openaire   +2 more sources

Managing bias in ROC curves

Journal of Computer-Aided Molecular Design, 2008
Two modifications to the standard use of receiver operating characteristic (ROC) curves for evaluating virtual screening methods are proposed. The first is to replace the linear plots usually used with semi-logarithmic ones (pROC plots), including when doing "area under the curve" (AUC) calculations.
Robert D. Clark, Daniel J. Webster-Clark
openaire   +2 more sources