A network approach to compute hypervolume under receiver operating characteristic manifold for multi-class biomarkers
From MaRDI portal
Publication:6629968
DOI10.1002/SIM.9646zbMATH Open1548.62319MaRDI QIDQ6629968
Pei Fen Kuan, Jianan Chen, Qunqiang Feng, Pan Liu, Fei Zou, Jialiang Li
Publication date: 30 October 2024
Published in: Statistics in Medicine (Search for Journal in Brave)
mild cognitive impairmentnetwork graphdiagnostic medicinepost-traumatic stress disorderhypervolume under ROC manifoldexposome
Cites Work
- Title not available (Why is that?)
- Title not available (Why is that?)
- ROC analysis with multiple classes and multiple tests: methodology and its application in microarray studies
- Statistical analysis of network data. Methods and models
- On the scalability of ordered multi-class ROC analysis
- Large computation of the maximum rank correlation estimator
- Multiple-event forced-choice tasks in the theory of signal detectability
- Hypervolume under ROC manifold for discrete biomarkers with ties
- Comparing the Areas Under Two Correlated ROC Curves: Parametric and Non‐Parametric Approaches
- A simple generalisation of the area under the ROC curve for multiple class classification problems
- Evaluating classification accuracy for modern learning approaches
- Computing the polytomous discrimination index
This page was built for publication: A network approach to compute hypervolume under receiver operating characteristic manifold for multi-class biomarkers
Report a bug (only for logged in users!)Click here to report a bug for this page (MaRDI item Q6629968)