Cited by 136Gene expression and cancer classificationImbalanced Data Classification TechniquesBreast Cancer Treatment Studies

Caveats and pitfalls of ROC analysis in clinical microarray research (and how to avoid them)

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Berrar, D., Flach, P.·2012·Briefings in Bioinformatics

Abstract

The receiver operating characteristic (ROC) has emerged as the gold standard for assessing and comparing the performance of classifiers in a wide range of disciplines including the life sciences. ROC curves are frequently summarized in a single scalar, the area under the curve (AUC). This article discusses the caveats and pitfalls of ROC analysis in clinical microarray research, particularly in relation to (i) the interpretation of AUC (especially a value close to 0.5); (ii) model comparisons based on AUC; (iii) the differences between ranking and classification; (iv) effects due to multiple hypotheses testing; (v) the importance of confidence intervals for AUC; and (vi) the choice of the appropriate performance metric. With a discussion of illustrative examples and concrete real-world studies, this article highlights critical misconceptions that can profoundly impact the conclusions about the observed performance.

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  1. Hilden J, Glasziou P. Regret graphs, diagnostic uncertainty and Youden's index. Stat Med 1996;15:969-86.
  2. Holland B. On the application of three modified Bonferroni procedures to pairwise multiple comparisons in balanced repeated measures designs. Comp Stat Quarterly 1991;6: 219-31.
  3. Flach P. The many faces of ROC analysis in machine learn- ing. Tutorial Notes of the 21st International Conference on Machine Learning, 2004 (ICML04) 2004. http://www.cs.bris.ac.uk/ $flach/ICML04tutorial/index.html (31 January 2011, date last accessed).

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Authors
Berrar, D., & Flach, P.
Affiliations
  • Tokyo Institute of Technology (JP)
  • University of Bristol (GB)
Venue
Briefings in Bioinformatics
Published
2011-03-21
Volume
13
Issue
1
Pages
83-97
ISSN
  • 1467-5463 (ISSN-L)
  • 1477-4054
OpenAlex
W2150196286
Cited by
136
References
61
Citations by year
2026: 13 · 2025: 11 · 2024: 18 · 2023: 11 · 2022: 13 · 2021: 11 · 2020: 10 · 2019: 10 · 2018: 8 · 2017: 7 · 2016: 6 · 2015: 4 · 2014: 5 · 2013: 5 · 2012: 3
FWCI
1.82
Type
journal article
Language
en
Open access
Locations
  • Briefings in Bioinformatics (publishedVersion)
  • PubMed (publishedVersion)
  • Open Research Online (The Open University) (acceptedVersion)
  • Bristol Research (University of Bristol) (submittedVersion)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
  • Physical Sciences › Computer Science › Artificial Intelligence › Imbalanced Data Classification Techniques
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Breast Cancer Treatment Studies
Keywords
receiver operating characteristic, area under the curve, classifier evaluation, model comparison, multiple comparisons, confidence intervals, performance indicators, diagnostic biomarkers
MeSH terms
Research Design, ROC Curve, Reproducibility of Results, Area Under Curve, Biomedical Research, Microarray Analysis

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