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

Daniel Berrar, Peter Flach

研究成果: Article

47 引用 (Scopus)

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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.

元の言語English
記事番号bbr008
ページ(範囲)83-97
ページ数15
ジャーナルBriefings in Bioinformatics
13
発行部数1
DOI
出版物ステータスPublished - 2012 1 1

ASJC Scopus subject areas

  • Information Systems
  • Molecular Biology

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