Results 91 to 100 of about 302,402 (283)

Development and Validation of Machine Learning‐Based Marker for Early Detection and Prognosis Stratification of Nonalcoholic Fatty Liver Disease

Advanced Science, EarlyView.
This study constructs a stacked multimodal machine learning model for nonalcoholic fattly liver disease (NAFLD) by integrating genetic and clinical features, and synthesize an in‐silico quantitative marker (ISNLD) that enables personalized risk stratification for intrahepatic and extrahepatic outcomes of high‐risk individuals of NAFLD.
Lushan Xiao, Lin Zeng, Jiaren Wang, Chang Hong, Ziyong Zhang, Chengkai Wu, Hao Cui, Yan Li, Ruining Li, Shengxing Liang, Qijie Deng, Wenyuan Li, Xuejing Zou, Pengcheng Ma, Li Liu   +14 more
wiley   +1 more source

ROC curve analyses of neuropsychological tests in Alzheimer's disease [PDF]

, 2000
Raymond L. Ownby
openalex   +2 more sources

Diagnostic accuracy of urine dipstick for proteinuria in older outpatients

Kidney Research and Clinical Practice, 2014
Background: The urine dipstick is widely used as an initial screening tool for the evaluation of proteinuria; however, its diagnostic accuracy has not yet been sufficiently evaluated.
Dongmin Lim, Dong-Young Lee, Soung Ha Cho, One Zoong Kim, Sang Woo Cho, Su Kyoung An, Hwe Won Kim, Kyoung Hyoub Moon, Myung Hee Lee, Beom Kim   +9 more
doaj   +1 more source

ROC Curves from an Ear Model [PDF]

, 1965
Lloyd A. Jeffress, Audley D. Gaston
openalex   +1 more source

Genetic Deconvolution of Embryonic and Maternal Cell‐Free DNA in Spent Culture Medium of Human Preimplantation Embryo Through Deep Learning

Advanced Science, EarlyView.
DECENT is a deep learning method that enhances noninvasive preimplantation genetic testing by accurately reconstructing embryonic copy number variations (CNVs) from cell‐free DNA in spent embryo culture media. By mitigating maternal contamination, DECENT improves diagnostic accuracy, even with high contamination levels, offering a reliable, noninvasive
Zhenyi Zhang, Jie Qiao, Yidong Chen, Peijie Zhou   +3 more
wiley   +1 more source

ROC Curves Determined under Several Experimental Conditions [PDF]

, 1969
Barry Leshowitz
openalex   +1 more source

Model Assessment with ROC Curves [PDF]

, 2009
Classification models and in particular binary classification models are ubiquitous in many branches of science and business. Consider, for example, classification models in bioinformatics that classify catalytic protein structures as being in an active or inactive conformation.
openaire   +1 more source

Revealing Cell‐Free Mitochondrial DNA Breakage Patterns as Novel Biomarkers for Sepsis

Advanced Science, EarlyView.
Uncovering distinct plasma cfmtDNA breakage patterns opens new frontiers for early sepsis detection and prognosis. Unique cleavage hotspots in RNR2 and COX2 regions outperform traditional biomarkers, while mechanistic insights suggest bacterial endonuclease involvement.
Haitang Liao, Ruiyan Ma, Shuai Hao, Xuxin Tan, Xue Zeng, Rui Song, Bing Chen, Zhezhe Cao, Wei Shen, Zhenchun Luo, Jianfeng Huang, He Huang, Liangming Liu, Chenyang Duan   +13 more
wiley   +1 more source

Detection of thrombus by using phase-image MR scans: ROC curve analysis [PDF]

, 1989
NJ Tavares, W Auffermann, JJ Brown, TJ Gilbert, Christian P. Sommerhoff, C B Higgins   +5 more
openalex   +1 more source

ODC and ROC Curves, Comparison Curves and Stochastic Dominance

International Statistical Review
SummaryWe discuss two novel approaches to inter‐distributional comparisons in the classical two‐sample problem. Our starting point is properly standardised and combined, very popular in several areas of statistics and data analysis, ordinal dominance and receiver characteristic curves, denoted by ODC and ROC, respectively.
Teresa Ledwina, Adam Zagdański
openaire   +2 more sources