Results 21 to 30 of about 304,364 (248)
IEEE Access, 2020 Identifying disease-related microRNAs (miRNAs) is crucial to understanding the etiology and pathogenesis of many diseases. However, existing computational methods are facing a few dilemmas such as lacking “negative samples” (i.e.
Junlin Xu +8 more
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Receiver Operator Characteristic Analysis of Biomarkers Evaluation in Diagnostic Research [PDF]
Journal of Clinical and Diagnostic Research, 2018 Receiver Operator Characteristic (ROC) analysis is the choice of method in evaluation of biomarkers in bioinformatics research. However, there is no single method and also no single accuracy index in evaluating diagnostic tools.
Karimollah Hajian-Tilaki
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Empirical Likelihood Inference for the Area under the ROC Curve
Biometrics, 2005 Summary For a continuous‐scale diagnostic test, the most commonly used summary index of the receiver operating characteristic curve (ROC) is the area under the curve (AUC) that measures the accuracy of the diagnostic test. In this article, we propose an empirical likelihood (EL) approach for the inference on the AUC. First we define an EL ratio for the
Qin, Gengsheng, Zhou, Xiao-Hua
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Analysis of area under the ROC curve of energy detection [PDF]
IEEE Transactions on Wireless Communications, 2010 A simple figure of merit to describe the performance of an energy detector is desirable. The area under the receiver operating characteristic (ROC) curve, denoted (AUC), is such a measure, which varies between 1/2 and 1. If the detector's performance is no better than flipping a coin, then the AUC is 1/2 , and it increases to one as the detector ...
Saman Atapattu +2 more
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Inference about time-dependent prognostic accuracy measures in the presence of competing risks
BMC Medical Research Methodology, 2020 Background Evaluating a candidate marker or developing a model for predicting risk of future conditions is one of the major goals in medicine. However, model development and assessment for a time-to-event outcome may be complicated in the presence of ...
Rajib Dey +2 more
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Reasonably conduct the multiple Logistic regression analysis combined with the ROC curve analysis
Sichuan jingshen weisheng, 2022 The purpose of this paper was to introduce how to reasonably carry out the method of the multiple Logistic regression analysis by combining the ROC curve analysis. Firstly， it introduced two groups of the basic concepts related to the ROC curve analysis，
Hu Chunyan, Hu Liangping
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Active Learning to Maximize Area Under the ROC Curve
Sixth International Conference on Data Mining (ICDM'06), 2006 In active learning, a machine learning algorithm is given an unlabeled set of examples U, and is allowed to request labels for a relatively small subset of U to use for training. The goal is then to judiciously choose which examples in U to have labeled in order to optimize some performance criterion, e.g. classification accuracy.
Culver, Matt, Kun, Deng, Scott, Stephen
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Soft Attention Based DenseNet Model for Parkinson’s Disease Classification Using SPECT Images
Frontiers in Aging Neuroscience, 2022 ObjectiveDeep learning algorithms have long been involved in the diagnosis of severe neurological disorders that interfere with patients’ everyday tasks, such as Parkinson’s disease (PD). The most effective imaging modality for detecting the condition is
Mahima Thakur +4 more
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Multi-Class Classification using Mixtures of Univariate and Multivariate ROC Curves
Journal of Biostatistics and Epidemiology, 2022 Introduction: Receiver Operating Characteristic (ROC) curve is one of the widely used supervised classification techniques to allocate/classify the individuals and also instrumental in comparing diagnostic tests.
SIVA G, Vishnu Vardhan R
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ROCS: receiver operating characteristic surface for class-skewed high-throughput data. [PDF]
PLoS ONE, 2012 The receiver operating characteristic (ROC) curve is an important tool to gauge the performance of classifiers. In certain situations of high-throughput data analysis, the data is heavily class-skewed, i.e.
Tianwei Yu
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