ArticlePatterns (New York, N.Y.)2024
The receiver operating characteristic curve accurately assesses imbalanced datasets.
Article in Patterns (New York, N.Y.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 82 papers, 2 of them syntheses that pooled it.
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.
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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
82 citing papers in PubMed, 2 syntheses or guidelines pooled it.
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- Predicting the Current Potential Suitable Habitat ofEcology and evolution · 2026Article
- A Multitask Deep Learning Model for Pediatric Echocardiography Analysis.Circulation · 2026Article
- Malaria Prevalence Near African Mangroves: Negative Association With Mangrove Extent, but Positive Association With Mangrove Greenness.Global change biology · 2026Article
- Cortico-basal oscillations index naturalistic movements during deep brain stimulation.Brain : a journal of neurology · 2026Article
- Genetic Ancestry and Colorectal Cancer in the All of Us Dataset.JAMA network open · 2026Article
- Mitigating Clinical Confounding in AI Models: A Comparative Analysis of Associative and Causal-Fused AI for ICU Readmissions.Healthcare (Basel, Switzerland) · 2026Article
- Benchmarking AlphaFold and related deep learning approaches for modeling antibody and TCR antigen recognition.bioRxiv : the preprint server for biology · 2026Article
- AI-Integrated Multi-Target Validation ofFoods (Basel, Switzerland) · 2026Article
- Decoding Smell from Receptor Structure.Research square · 2026Article
- Enhancing healthcare explainability through multiobjective counterfactual explanations.Scientific reports · 2026Article
- Non-Targeted Hyperspectral Imaging Screening of Adulterants and Congeneric Species inFoods (Basel, Switzerland) · 2026Article
- Radiomics in Medical Imaging: Methods, Applications, and Challenges.Journal of imaging · 2026Review
- An integrated framework for evaluating sustainable outcomes of vocational college graduates under ethnic heterogeneity and unequal college level resource allocation.Scientific reports · 2026Article
- Evaluating model robustness in landslide susceptibility mapping using a unified data-consistent framework in northern Thailand.Scientific reports · 2026Article
- An Integrative Computational Pipeline for CK2 Inhibitor Discovery in Triple-Negative Breast Cancer Using Virtual Screening, Molecular Dynamics, Machine Learning, and Density Functional Theory.Pharmaceuticals (Basel, Switzerland) · 2026Article
22 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Many problems in biology require looking for a "needle in a haystack," corresponding to a binary classification where there are a few positives within a much larger set of negatives, which is referred to as a class imbalance. The receiver operating characteristic (ROC) curve and the associated area under the curve (AUC) have been reported as ill-suited to evaluate prediction performance on imbalanced problems where there is more interest in performance on the positive minority class, while the precision-recall (PR) curve is preferable. We show via simulation and a real case study that this is a misinterpretation of the difference between the ROC and PR spaces, showing that the ROC curve is robust to class imbalance, while the PR curve is highly sensitive to class imbalance. Furthermore, we show that class imbalance cannot be easily disentangled from classifier performance measured via PR-AUC.
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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.