ArticlePloS one2012
ROCS: receiver operating characteristic surface for class-skewed high-throughput data.
Article in PloS one, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
12 citing papers in PubMed.
- Parameter Reduction and Optimisation for Point Cloud and Occupancy Mapping Algorithms.Sensors (Basel, Switzerland) · 2021Article
- Contra: Contrarian statistics for controlled variable selection.Proceedings of machine learning research · 2021Article
- Validity and Reliability Aspects of a Newly Developed Questionnaire for Auditory Localization.The journal of international advanced otology · 2019Article
- Host Taxon Predictor - A Tool for Predicting Taxon of the Host of a Newly Discovered Virus.Scientific reports · 2019Article
- Partner-specific prediction of RNA-binding residues in proteins: A critical assessment.Proteins · 2019Article
- Mitigating the adverse impact of batch effects in sample pattern detection.Bioinformatics (Oxford, England) · 2018Article
- bcROCsurface: an R package for correcting verification bias in estimation of the ROC surface and its volume for continuous diagnostic tests.BMC bioinformatics · 2017Article
- ROC Curve Analysis in the Presence of Imperfect Reference Standards.Statistics in biosciences · 2017Article
- MEG Connectivity and Power Detections with Minimum Norm Estimates Require Different Regularization Parameters.Computational intelligence and neuroscience · 2016Article
- Improving peak detection in high-resolution LC/MS metabolomics data using preexisting knowledge and machine learning approach.Bioinformatics (Oxford, England) · 2014Article
- Stability SCAD: a powerful approach to detect interactions in large-scale genomic study.BMC bioinformatics · 2014Article
- Hierarchical clustering of high-throughput expression data based on general dependences.IEEE/ACM transactions on computational biology and bioinformaticsArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
Abstract
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. most features tested belong to the true negative class. In such cases, only a small portion of the ROC curve is relevant in practical terms, rendering the ROC curve and its area under the curve (AUC) insufficient for the purpose of judging classifier performance. Here we define an ROC surface (ROCS) using true positive rate (TPR), false positive rate (FPR), and true discovery rate (TDR). The ROC surface, together with the associated quantities, volume under the surface (VUS) and FDR-controlled area under the ROC curve (FCAUC), provide a useful approach for gauging classifier performance on class-skewed high-throughput data. The implementation as an R package is available at http://userwww.service.emory.edu/~tyu8/ROCS/.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.