Caveats and pitfalls of ROC analysis in clinical microarray research (and how to avoid them)
Open accessAbstract
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.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
There are no grounds to label AUROC as misleading or overoptimistic.
“There are no grounds to label AUROC as misleading or overoptimistic. 36,49”
From Evaluation of performance measures in predictive artificial intelligence models to support medical decisions: overview and guidance (2025) · cites this paper as [36]
The definition of discrimination implies that discrimination measures should rely only on the ranks of the estimated probabilities in the dataset.
“The definition of discrimination implies that discrimination measures should rely only on the ranks of the estimated probabilities in the dataset. 36”
From Evaluation of performance measures in predictive artificial intelligence models to support medical decisions: overview and guidance (2025) · cites this paper as [36]
Naive comparison of models validated using different external datasets, reflecting different populations from different settings, can lead to wrong conclusions.
“Naive comparison of models validated using different external datasets, reflecting different populations from different settings, can lead to wrong conclusions.”
From Evaluation of performance measures in predictive artificial intelligence models to support medical decisions: overview and guidance (2025) · cites this paper as [36]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
6 retracted
Genomic signatures to guide the use of chemotherapeutics
Potti, A., Dressman, H. K., Bild, A., Riedel, R. F., Chan, G., Sayer, R. A., Cragun, J. M., Cottrill, H. M., Kelley, M. J., Petersen, R. P., et al. (2006) · Nature Medicine · doi:10.1038/nm1491 · Cited by 595 · see citing works
RetractedDressman, H. K., Berchuck, A., Chan, G., Zhai, J., Bild, A. H., Sayer, R. A., Cragun, J. M., Clarke, J., Whitaker, R. S., Li, L.-H., et al. (2007) · Journal of Clinical Oncology · doi:10.1200/jco.2006.06.3743 · Cited by 281 · see citing works
RetractedBonnefoi, H. R., Potti, A., Delorenzi, M., Mauriac, L., Campone, M., Tubiana-Hulin, M., Petit, T., Rouanet, P., Jassem, J., Blot, E., et al. (2007) · The Lancet Oncology · doi:10.1016/s1470-2045(07)70345-5 · Cited by 212 · see citing works
RetractedHsu, D. S., Balakumaran, B. S., Acharya, C. R., Vlahovic, V., Walters, K. S., Garman, K. S., Anders, C. K., Riedel, R. F., Lancaster, J. M., Harpole, D. H., et al. (2007) · Journal of Clinical Oncology · doi:10.1200/jco.2007.11.0593 · Cited by 106 · see citing works
RetractedAn Integrated Approach to the Prediction of Chemotherapeutic Response in Patients with Breast Cancer
Salter, K. H., Acharya, C. R., Walters, K. S., Redman, R. C., Anguiano, A., Garman, K. S., Anders, C. K., Mukherjee, S., Dressman, H. K., Barry, W. T., et al. (2008) · PLoS ONE · doi:10.1371/journal.pone.0001908 · Cited by 90 · see citing works
RetractedGarman, K. S., Acharya, C. R., Edelman, E. J., Grade, M., Gaedcke, J., Sud, S., Barry, W. T., Diehl, A. M., Provenzale, D., Ginsburg, G. S., et al. (2008) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0806674105 · Cited by 86 · see citing works
RetractedThe meaning and use of the area under a receiver operating characteristic (ROC) curve.
Hanley, J. A., & McNeil, B. J. (1982) · Radiology · doi:10.1148/radiology.143.1.7063747 · Cited by 21,856 · see citing works
A Simple Sequentially Rejective Multiple Test Procedure
(1979) · Scandinavian Journal of Statistics · doi:10.2307/4615733 · Cited by 21,526 · see citing works
The WEKA data mining software: an update
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H. (2009) · ACM SIGKDD Explorations Newsletter · doi:10.1145/1656274.1656278 · Cited by 17,750 · see citing works
Sørlie, T., Perou, C., Tibshirani, R., Aas, T., Geisler, S., Johnsen, H., Hastie, T., Eisen, M., van de Rijn, M., Jeffrey, S., et al. (2001) · Proceedings of the National Academy of Sciences of the United States of America · doi:10.1073/pnas.191367098 · Cited by 11,695 · see citing works
Gene expression profiling predicts clinical outcome of breast cancer
van 't Veer, L. J., Dai, H., van de Vijver, M. J., He, Y. D., Hart, A. A. M., Mao, M., Peterse, H. L., van der Kooy, K., Marton, M. J., Witteveen, A. T., et al. (2002) · Nature · doi:10.1038/415530a · Cited by 9,536 · see citing works
Encyclopedia of Machine Learning
Sammut, C., & Webb, G. I. (2010) · Springer eBooks · doi:10.1007/978-0-387-30164-8 · Cited by 3,499 · see citing works
AUC: A misleading measure of the performance of predictive distribution models
Lobo, J. M., Jiménez‐Valverde, A., & Real, R. (2008) · Global Ecology and Biogeography · doi:10.1111/j.1466-8238.2007.00358.x · Cited by 3,499 · see citing works
Better Bootstrap Confidence Intervals
Efron, B. (1987) · Journal of the American Statistical Association · doi:10.1080/01621459.1987.10478410 · Cited by 3,462 · see citing works
ROCR: visualizing classifier performance in R
Sing, T., Sander, O., Beerenwinkel, N., & Lengauer, T. (2005) · Computer applications in the biosciences · doi:10.1093/bioinformatics/bti623 · Cited by 3,172 · see citing works
Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer
Wang, Y., Klijn, J. G. M., Zhang, Y., Sieuwerts, A. M., Look, M. P., Yang, F., Talantov, D., Timmermans, M. A. M., Gelder, M. E. M., Yu, J. X., et al. (2005) · The Lancet · doi:10.1016/s0140-6736(05)17947-1 · Cited by 2,848 · see citing works
Sotiriou, C. P., Wirapati, P., Loi, S., Harris, A. L., Fox, S., Smeds, J., Nordgren, H., Farmer, P. J., Praz, V., Haibe‐Kains, B., et al. (2006) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/djj052 · Cited by 2,060 · see citing works
Confidence intervals rather than P values: estimation rather than hypothesis testing.
Gardner, M. J., & Altman, D. G. (1986) · BMJ · doi:10.1136/bmj.292.6522.746 · Cited by 2,014 · see citing works
What's In A Name? Malay Seals As Onomastic Sources
Gallop, A. T. (2018) · Malay Literature · doi:10.37052/ml.31(1)no1 · Cited by 1,495 · see citing works
Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer
Wang, Y., Klijn, J. G. M., ZHANG, Y., et al. (2005) · The Lancet · doi:10.1016/s0140-6736(05)70933-8 · Cited by 1,233 · see citing works
Measuring classifier performance: a coherent alternative to the area under the ROC curve
Hand, D. J. (2009) · Machine Learning · doi:10.1007/s10994-009-5119-5 · Cited by 1,037 · see citing works
Prediction of cancer outcome with microarrays: a multiple random validation strategy
Michiels, S., Koscielny, S., & Hill, C. (2005) · The Lancet · doi:10.1016/s0140-6736(05)17866-0 · Cited by 1,029 · see citing works
The Prognostic Role of a Gene Signature from Tumorigenic Breast-Cancer Cells
Liu, R., Wang, X., Chen, G., Dalerba, P. D., Gurney, A. L., Hoey, T. C., Sherlock, G. J., Lewicki, J. A., Shedden, K., & Clarke, M. F. (2007) · New England Journal of Medicine · doi:10.1056/nejmoa063994 · Cited by 984 · see citing works
Chang, H. Y., Nuyten, D. S. A., Sneddon, J. B., Hastie, T., Tibshirani, R., Sørlie, T., Dai, H., He, Y. D., van't Veer, L. J., Bartelink, H., et al. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0409462102 · Cited by 981 · see citing works
Chang, J. C., Wooten, E. C., Tsimelzon, A. I., Hilsenbeck, S. G., Gutierrez, M. C., Elledge, R. M., Mohsin, S. K., Osborne, C. K., Chamness, G. C., Allred, D. C., et al. (2003) · The Lancet · doi:10.1016/s0140-6736(03)14023-8 · Cited by 875 · see citing works
Classifier Technology and the Illusion of Progress
Hand, D. J. (2006) · Statistical Science · doi:10.1214/088342306000000060 · Cited by 723 · see citing works
Hess, K. R., Anderson, K., Symmans, W. F., Valero, V., Ibrahim, N. K., Mejia, J. A., Booser, D. J., Theriault, R. L., Buzdar, A. U., Dempsey, P. J., et al. (2006) · Journal of Clinical Oncology · doi:10.1200/jco.2006.05.6861 · Cited by 720 · see citing works
Better Bootstrap Confidence Intervals
Efron, B. (1987) · Journal of the American Statistical Association · doi:10.2307/2289144 · Cited by 718 · see citing works
Analyzing a Portion of the ROC Curve
McClish, D. K. (1989) · Medical Decision Making · doi:10.1177/0272989x8900900307 · Cited by 709 · see citing works
Chi, J., Wang, Z., Nuyten, D. S. A., Rodriguez, E. H., Schaner, M. E., Salim, A., Wang, Y., Kristensen, G. B., Helland, Å., Børresen‐Dale, A., et al. (2006) · PLoS Medicine · doi:10.1371/journal.pmed.0030047 · Cited by 622 · see citing works
Ayers, M., Symmans, W. F., Stec, J., Damokosh, A. I., Clark, E., Hess, K., Lecocke, M., Metivier, J., Booser, D., Ibrahim, N., et al. (2004) · Journal of Clinical Oncology · doi:10.1200/jco.2004.05.166 · Cited by 566 · see citing works
AUC: a statistically consistent and more discriminating measure than accuracy
Ling, C. X., Huang, J., & Zhang, H. (2003) · Cited by 501 · see citing works
Bias as a threat to the validity of cancer molecular-marker research
Ransohoff, D. F. (2005) · Nature reviews. Cancer · doi:10.1038/nrc1550 · Cited by 480 · see citing works
Roadmap for Developing and Validating Therapeutically Relevant Genomic Classifiers
Simon, R. M. (2005) · Journal of Clinical Oncology · doi:10.1200/jco.2005.02.8712 · Cited by 382 · see citing works
Low P-Values or Narrow Confidence Intervals: Which Are More Durable?
Poole, C. (2001) · Epidemiology · doi:10.1097/00001648-200105000-00005 · Cited by 363 · see citing works
ROC Curves in Clinical Chemistry: Uses, Misuses, and Possible Solutions
Obuchowski, N. A., Lieber, M. L., & Wians, F. H. (2004) · Clinical Chemistry · doi:10.1373/clinchem.2004.031823 · Cited by 358 · see citing works
Molecular Classification of Tamoxifen-Resistant Breast Carcinomas by Gene Expression Profiling
Jansen, M. P. H. M., Foekens, J. A., van Staveren, I. L., Dirkzwager-Kiel, M. M., Ruigrok-Ritstier, K., Look, M. P., Gelder, M. E. M., Sieuwerts, A. M., Portengen, H., Dorssers, L. C. J., et al. (2005) · Journal of Clinical Oncology · doi:10.1200/jco.2005.05.145 · Cited by 340 · see citing works
Cost curves: An improved method for visualizing classifier performance
Drummond, C., & Holte, R. C. (2006) · Machine Learning · doi:10.1007/s10994-006-8199-5 · Cited by 328 · see citing works
Small-sample precision of ROC-related estimates
Hanczar, B., Hua, J., Sima, C., Weinstein, J., Bittner, M., & Dougherty, E. R. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq037 · Cited by 323 · see citing works
Baggerly, K., & Coombes, K. R. (2009) · The Annals of Applied Statistics · doi:10.1214/09-aoas291 · Cited by 311 · see citing works
Prediction of Docetaxel Response in Human Breast Cancer by Gene Expression Profiling
Iwao‐Koizumi, K., Matoba, R., Ueno, N., Kim, S. J., Ando, A., Miyoshi, Y., Maeda, E., Noguchi, S., & Kato, K. (2005) · Journal of Clinical Oncology · doi:10.1200/jco.2005.09.078 · Cited by 309 · see citing works
Søreide, K. (2008) · Journal of Clinical Pathology · doi:10.1136/jcp.2008.061010 · Cited by 301 · see citing works
Rothman, K. J. (1978) · New England Journal of Medicine · doi:10.1056/nejm197812142992410 · Cited by 281 · see citing works
Bueno-de-Mesquita, J. M., van Harten, W. H., Retèl, V. P., Veer, L. V. ‘., van Dam, F. S. A. M., Karsenberg, K., Douma, K. F. L., van Tinteren, H., Peterse, J. L., Wesseling, J., et al. (2007) · The Lancet Oncology · doi:10.1016/s1470-2045(07)70346-7 · Cited by 274 · see citing works
StAR: a simple tool for the statistical comparison of ROC curves
Vergara, I. A., Norambuena, T., Ferrada, E., Slater, A. W., & Melo, F. (2008) · BMC Bioinformatics · doi:10.1186/1471-2105-9-265 · Cited by 196 · see citing works
The Area under the ROC Curve and Its Competitors
Hilden, J. (1991) · Medical Decision Making · doi:10.1177/0272989x9101100204 · Cited by 183 · see citing works
Comparing classifiers when the misallocation costs are uncertain
Adams, N. M., & Hand, D. J. (1999) · Pattern Recognition · doi:10.1016/s0031-3203(98)00154-x · Cited by 171 · see citing works
Confidence Intervals for the Area Under the ROC Curve
Cortes, C., & Mohri, M. (2004) · neural information processing systems · Cited by 158 · see citing works
A CROC stronger than ROC: measuring, visualizing and optimizing early retrieval
Swamidass, S. J., Azencott, C., Daily, K., & Baldi, P. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq140 · Cited by 115 · see citing works
Clark, R. D., & Webster-Clark, D. J. (2008) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-008-9181-z · Cited by 110 · see citing works
Søreide, K., Kørner, H., & Søreide, J. A. (2011) · Annals of Surgery · doi:10.1097/sla.0b013e318204a892 · Cited by 109 · see citing works
Rothman, K. J. (1998) · Epidemiology · doi:10.1097/00001648-199805000-00019 · Cited by 102 · see citing works
Genomics, Prior Probability, and Statistical Tests of Multiple HypothesesPreprint
Manly, K. F., Nettleton, D. S., & Hwang, J. T. G. (2004) · Genome Research · doi:10.1101/gr.2156804 · Cited by 86 · see citing works
Run Batch Effects Potentially Compromise the Usefulness of Genomic Signatures for Ovarian Cancer
Baggerly, K., Coombes, K. R., & Neeley, E. S. (2008) · Journal of Clinical Oncology · doi:10.1200/jco.2007.15.1951 · Cited by 72 · see citing works
A critical analysis of variants of the AUC
Vanderlooy, S., & Hüllermeier, E. (2008) · Machine Learning · doi:10.1007/s10994-008-5070-x · Cited by 59 · see citing works
Simon, R. M. (2003) · ACM SIGKDD Explorations Newsletter · doi:10.1145/980972.980978 · Cited by 54 · see citing works
Statistical Issues in Translational Cancer Research
George, S. L. (2008) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-07-4537 · Cited by 43 · see citing works
Gene expression variation to predict 10-year survival in lymph-node-negative breast cancer
Karlsson, E. A., Delle, U., Danielsson, A., Olsson, B. E., Abel, F., Karlsson, P., & Helou, K. (2008) · BMC Cancer · doi:10.1186/1471-2407-8-254 · Cited by 39 · see citing works
Microarrays and breast cancer clinical studies: forgetting what we have not yet learnt
Ahmed, A. A., & Brenton, J. D. (2005) · Breast Cancer Research · doi:10.1186/bcr1017 · Cited by 29 · see citing works
ON THE DISTRIBUTION OF THE UNPAIRED t-STATISTIC WITH PAIRED DATA
Proschan, M. A. (1996) · Statistics in Medicine · doi:10.1002/(sici)1097-0258(19960530)15:10 · Cited by 23 · see citing works
What Properties Should an Overall Measure of Test Performance Possess?
Hilden, J. (2005) · Clinical Chemistry · doi:10.1373/clinchem.2004.041376 · Cited by 12 · see citing works
Improving Text Classifier Performance based on AUC
Wong, A., Lee, J., & Yeung, D. S. (2006) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2006.705 · Cited by 4 · see citing works
Flagged first, then by citations
Research works printed but not linked: 3
- Hilden J, Glasziou P. Regret graphs, diagnostic uncertainty and Youden's index. Stat Med 1996;15:969-86.
- 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.
- 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).
Cited by
Sorted by citations
Saito, T., & Rehmsmeier, M. (2015) · PLOS ONE · doi:10.1371/journal.pone.0118432 · Cited by 5,309 · see citing works
Translational biomarker discovery in clinical metabolomics: an introductory tutorial
Xia, J., Broadhurst, D., Wilson, M., & Wishart, D. S. (2012) · Metabolomics · doi:10.1007/s11306-012-0482-9 · Cited by 958 · see citing works
Chicco, D., & Jurman, G. (2023) · BioData Mining · doi:10.1186/s13040-023-00322-4 · Cited by 667 · see citing works
Huang, L., Shea, A. L., Qian, H., Masurkar, A., Deng, H., & Liu, D. (2019) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2019.103291 · Cited by 437 · see citing works
Bayes’ Theorem and Naive Bayes Classifier
Berrar, D. (2018) · Elsevier eBooks · doi:10.1016/b978-0-12-809633-8.20473-1 · Cited by 442 · see citing works
Bennett, W. W., & Canfield, D. E. (2020) · Earth-Science Reviews · doi:10.1016/j.earscirev.2020.103175 · Cited by 374 · see citing works
The receiver operating characteristic (ROC) curve
Yang, S., & Berdine, G. G. (2017) · The Southwest Respiratory and Critical Care Chronicles · doi:10.12746/swrccc.v5i19.391 · Cited by 250 · see citing works
Cerniauskas, I., Winterer, J., de Jong, J. W., Lukacsovich, D., Yang, H., Khan, F., Peck, J. R., Obayashi, S. K., Lilascharoen, V., Lim, B. K., et al. (2019) · Neuron · doi:10.1016/j.neuron.2019.09.005 · Cited by 187 · see citing works
Somatic Mutations and Neoepitope Homology in Melanomas Treated with CTLA-4 Blockade
Nathanson, T., Ahuja, A., Rubinsteyn, A., Aksoy, B. A., Hellmann, M. D., Miao, D., Allen, E. M. V., Merghoub, T., Wolchok, J. D., Snyder, A., et al. (2016) · Cancer Immunology Research · doi:10.1158/2326-6066.cir-16-0019 · Cited by 208 · see citing works
Marchese Robinson, R. L., Palczewska, A., Palczewski, J., & Kidley, N. (2017) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6b00753 · Cited by 167 · see citing works
Computational models for predicting drug responses in cancer research
Azuaje, F. J. (2016) · Briefings in Bioinformatics · doi:10.1093/bib/bbw065 · Cited by 162 · see citing works
Performance Evaluation in Machine Learning: The Good, the Bad, the Ugly, and the Way Forward
Flach, P. A. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33019808 · Cited by 159 · see citing works
Koromina, M., Pandi, M.-T., & Patrinos, G. P. (2019) · OMICS A Journal of Integrative Biology · doi:10.1089/omi.2019.0151 · Cited by 140 · see citing works
Munro, S. A., Lund, S. P., Pine, P. S., Binder, H., Clevert, D.-A., Conesa, A., Dopazo, J., Fasold, M., Hochreiter, S., Hong, H., et al. (2014) · Nature Communications · doi:10.1038/ncomms6125 · Cited by 126 · see citing works
Area under the ROC Curve has the most consistent evaluation for binary classification
Li, J. (2024) · PLoS ONE · doi:10.1371/journal.pone.0316019 · Cited by 117 · see citing works
Romagnolo, C., Leon, A. E., Fabricio, A. S. C., Taborelli, M., Polesel, J., Pup, L. D., Steffan, A., Cervo, S., Ravaggi, A., Zanotti, L., et al. (2016) · Gynecologic Oncology · doi:10.1016/j.ygyno.2016.01.016 · Cited by 113 · see citing works
Van Calster, B., Collins, G. S., Vickers, A. J., Wynants, L., Kerr, K. F., Barreñada, L., Varoquaux, G., Singh, K., Moons, K. G., Hernandez-Boussard, T., et al. (2025) · The Lancet Digital Health · doi:10.1016/j.landig.2025.100916 · Cited by 112 · see citing works
Kim, W. H., Lee, J. U., Jeon, M. J., Park, K. H., & Sim, S. J. (2022) · Biosensors and Bioelectronics · doi:10.1016/j.bios.2022.114116 · Cited by 96 · see citing works
Performance Measures for Binary Classification
Berrar, D. (2018) · Elsevier eBooks · doi:10.1016/b978-0-12-809633-8.20351-8 · Cited by 96 · see citing works
Model Evaluation Guidelines for Geomagnetic Index Predictions
Liemohn, M. W., McCollough, J. P., Jordanova, V. K., Ngwira, C. M., Morley, S. K., Cid, C., Tobiska, W. K., Wintoft, P., Ganushkina, N., Welling, D. T., et al. (2018) · Space Weather · doi:10.1029/2018sw002067 · Cited by 86 · see citing works
Schrodi, S. J., Mukherjee, S., Shan, Y., Tromp, G. C., Sninsky, J. J., Callear, A. P., Carter, T. C., Ye, Z., Haines, J. L., Brilliant, M. H., et al. (2014) · Frontiers in Genetics · doi:10.3389/fgene.2014.00162 · Cited by 71 · see citing works
Christopher, M. A., Nakahara, K., Bowd, C., Proudfoot, J. A., Belghith, A., Goldbaum, M. H., Rezapour, J., Weinreb, R. N., Fazio, M. A., Girkin, C. A., et al. (2020) · Translational Vision Science & Technology · doi:10.1167/tvst.9.2.27 · Cited by 65 · see citing works
Movahedi, F., Padman, R., & Antaki, J. F. (2021) · Journal of Thoracic and Cardiovascular Surgery · doi:10.1016/j.jtcvs.2021.07.041 · Cited by 63 · see citing works
Canbek, G., Temizel, T. T., & Sağıroğlu, Ş. (2022) · SN Computer Science · doi:10.1007/s42979-022-01409-1 · Cited by 60 · see citing works
Navigating the metric maze: a taxonomy of evaluation metrics for anomaly detection in time series
Sørbø, S., & Ruocco, M. (2024) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-023-00988-8 · Cited by 58 · see citing works
“The use of AUCROC has been criticized for its integration over all thresholds, which can result in a large portion of the score coming from thresholds that may not be relevant for a specific use case [91, 92, 93].”
Deep Learning-Based Damage Mapping With InSAR Coherence Time Series
Stephenson, O. L., Köhne, T., Zhan, E., Cahill, B. E., Yun, S., Ross, Z. E., & Simons, M. (2021) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2021.3084209 · Cited by 54 · see citing works
Khalid, U., Newbury, L. J., Simpson, K., Jenkins, R. L., Bowen, T., Bates, L., Sheerin, N., Chavez, R., & Fraser, D. J. (2019) · Scientific Reports · doi:10.1038/s41598-019-38642-3 · Cited by 51 · see citing works
Optimizing area under the ROC curve using semi-supervised learning
Wang, S., Li, D., Petrick, N., Sahiner, B., Linguraru, M. G., & Summers, R. M. (2014) · Pattern Recognition · doi:10.1016/j.patcog.2014.07.025 · Cited by 48 · see citing works
ROC Curves for the Statistical Analysis of Microarray Data
Cao, R., & López‐de‐Ullibarri, I. (2019) · Methods in molecular biology · doi:10.1007/978-1-4939-9442-7_11 · Cited by 48 · see citing works
Chandrashekar, P. B., Alatkar, S., Wang, J., Hoffman, G. E., He, C., Jin, T., Khullar, S., Bendl, J., Fullard, J. F., Roussos, P., et al. (2023) · Genome Medicine · doi:10.1186/s13073-023-01248-6 · Cited by 37 · see citing works
Dinnendahl, R., Tschimmel, D., Löw, V., Cornely, M. E., & Hucho, T. B. (2024) · PAIN Reports · doi:10.1097/pr9.0000000000001155 · Cited by 38 · see citing works
Ferté, C., Trister, A. D., Huang, E. S., Bot, B. M., Guinney, J., Commo, F., Sieberts, S. K., André, F., Besse, B., Soria, J., et al. (2013) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-12-3937 · Cited by 36 · see citing works
Sighting acute myocardial infarction through platelet gene expression
Gobbi, G., Carubbi, C., Tagliazucchi, G. M., Masselli, E., Mirandola, P., Pigazzani, F., Crocamo, A., Notarangelo, M. F., Suma, S., Paraboschi, E. M., et al. (2019) · Scientific Reports · doi:10.1038/s41598-019-56047-0 · Cited by 36 · see citing works
Unraveling networks of co-regulated genes on the sole basis of genome sequences
Brohée, S., Janky, R., Abdel‐Sater, F., Vanderstocken, G., André, B., & van Helden, J. (2011) · Nucleic Acids Research · doi:10.1093/nar/gkr264 · Cited by 36 · see citing works
Lussu, M., Camboni, T., Piras, C., Serra, C., Carratore, F. D., Griffin, J. L., Atzori, L., & Manzin, A. (2017) · BMC Microbiology · doi:10.1186/s12866-017-1108-1 · Cited by 33 · see citing works
ROC curve equivalence using the Kolmogorov–Smirnov test
Bradley, A. P. (2013) · Pattern Recognition Letters · doi:10.1016/j.patrec.2012.12.021 · Cited by 31 · see citing works
Stylianakis, A., Schinas, G., Thomaidis, P. C., Papaparaskevas, J., Ziogas, D. C., Gamaletsou, M. N., Daikos, G. L., Pneumaticos, S. G., & Sipsas, N. V. (2018) · Diagnostic Microbiology and Infectious Disease · doi:10.1016/j.diagmicrobio.2018.04.008 · Cited by 31 · see citing works
Grenier, B., Hackl, M., Skalicky, S., Thamhesl, M., Moll, W.-D., Berrios, R., Schatzmayr, G., & Nagl, V. (2019) · Scientific Reports · doi:10.1038/s41598-019-45784-x · Cited by 29 · see citing works
Drug2ways: Reasoning over causal paths in biological networks for drug discovery
Rivas-Barragan, D., Mubeen, S., Guim, F., Hofmann‐Apitius, M., & Domingo‐Fernándéz, D. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1008464 · Cited by 28 · see citing works
Noviandy, T. R., Idroes, G. M., Hardi, I., Afjal, M., & Ray, S. (2024) · Infolitika Journal of Data Science · doi:10.60084/ijds.v2i1.199 · Cited by 27 · see citing works
Chacón, L., Barrantes, K., Santamaría‐Ulloa, C., Barquero, M. S., Reyes, L., Taylor, L., Valiente, C., Symonds, E. M., & Achí, R. (2020) · Applied and Environmental Microbiology · doi:10.1128/aem.00616-20 · Cited by 26 · see citing works
Shen, L., Jin, Y., Pan, A., Wang, K., Ye, R., Lin, Y., Anwar, S., Xia, W., Zhou, M., & Guo, X. (2024) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2024.108561 · Cited by 25 · see citing works
Im, H., Hwang, S.-H., Kim, B., & Choi, S.-H. (2021) · Proceedings of the National Academy of Sciences of the United States of America · doi:10.1073/pnas.2018877118 · Cited by 16 · see citing works
Turewicz, M., Kohl, M., Ahrens, M., Mayer, G., Uszkoreit, J., Naboulsi, W., Bracht, T., Megger, D. A., Sitek, B., Marcus, K., et al. (2017) · Journal of Biotechnology · doi:10.1016/j.jbiotec.2017.06.005 · Cited by 24 · see citing works
Russell, L. E., Zhou, Y., Lauschke, V. M., & Kim, R. B. (2020) · Molecular Pharmaceutics · doi:10.1021/acs.molpharmaceut.9b01200 · Cited by 24 · see citing works
Khatami, S. G., Mubeen, S., Bharadhwaj, V. S., Kodamullil, A. T., Hofmann‐Apitius, M., & Domingo‐Fernándéz, D. (2021) · npj Systems Biology and Applications · doi:10.1038/s41540-021-00199-1 · Cited by 24 · see citing works
ROCS: Receiver Operating Characteristic Surface for Class-Skewed High-Throughput Data
Yu, T. (2012) · PLoS ONE · doi:10.1371/journal.pone.0040598 · Cited by 23 · see citing works
IoT-based expert system for fault detection in Japanese Plum leaf-turgor pressure WSN
Barriga, A., Barriga, J. A., Espino, M. J. M., & Clemente, P. J. (2023) · Internet of Things · doi:10.1016/j.iot.2023.100829 · Cited by 22 · see citing works
Bayes’ Theorem and Naive Bayes Classifier
Berrar, D. (2024) · Elsevier eBooks · doi:10.1016/b978-0-323-95502-7.00118-4 · Cited by 22 · see citing works
Zhao, Y., Coulson, E. J., Su, X., Zhang, J., Sha, B., Xu, H., Deng, Y., Chen, Y., Cao, J. J., Wang, Y., et al. (2021) · iScience · doi:10.1016/j.isci.2021.102043 · Cited by 20 · see citing works
Chronic stress causes striatal disinhibition mediated by SOM-interneurons in male mice
Rodrigues, D., Jacinto, L., Falcão, M., Castro, A. C., Cruz, A., Santa, C., Manadas, B. J., Marques, F., Sousa, N., & Monteiro, P. (2022) · Nature Communications · doi:10.1038/s41467-022-35028-4 · Cited by 19 · see citing works
Evaluating Probabilistic Forecasts with Bayesian Signal Detection Models
Steyvers, M., Wallsten, T. S., Merkle, E. C., & Turner, B. M. (2013) · Risk Analysis · doi:10.1111/risa.12127 · Cited by 19 · see citing works
Calderon-Ramirez, S., Murillo-Hernandez, D., Rojas-Salazar, K., Elizondo, D. A., Yang, S., Moemeni, A., & Molina‐Cabello, M. A. (2022) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-021-02497-6 · Cited by 19 · see citing works
Pediatric contact allergy: A comparative study with adults
Boonchai, W., Chaiyabutr, C., Charoenpipatsin, N., & Sukakul, T. (2020) · Contact Dermatitis · doi:10.1111/cod.13672 · Cited by 18 · see citing works
Demirel, M. E., & Türel, C. A. (2023) · Cureus · doi:10.7759/cureus.43258 · Cited by 18 · see citing works
Braatveit, K. J., Torsheim, T., & Hove, O. (2018) · Nordic Journal of Psychiatry · doi:10.1080/08039488.2018.1468923 · Cited by 18 · see citing works
On the validity of time-dependent AUC estimators
Schmid, M. C., Kestler, H. A., & Potapov, S. (2013) · Briefings in Bioinformatics · doi:10.1093/bib/bbt059 · Cited by 18 · see citing works
Improving Performance Prediction on Education Data with Noise and Class Imbalance
Radwan, A. M., & Cataltepe, Z. (2017) · Intelligent Automation & Soft Computing · doi:10.1080/10798587.2017.1337673 · Cited by 16 · see citing works
Pathway-Dependent Effectiveness of Network Algorithms for Gene Prioritization
Shim, J. E., Hwang, S., & Lee, I. (2015) · PLoS ONE · doi:10.1371/journal.pone.0130589 · Cited by 17 · see citing works
Domingo‐Fernándéz, D., Gadiya, Y., Patel, A., Mubeen, S., Rivas-Barragan, D., Diana, C. W., Misra, B. B., Healey, D., Rokicki, J., & Colluru, V. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1009909 · Cited by 15 · see citing works
Brankovic, A., Hassanzadeh, H., Good, N. M., Mann, K. D., Khanna, S., Abdel‐Hafez, A., & Cook, D. (2022) · Scientific Reports · doi:10.1038/s41598-022-15877-1 · Cited by 15 · see citing works
Early identification of bovine pregnancy status and embryonic mortality
Bishop, J. V., Güzeloğlu, A., Scheller, T., Docheff, J. J., Gonzalez-Berrios, C. L., Campen, H. V., Nett, T. M., Zezeski, A. L., Geary, T. W., Thatcher, W. W., et al. (2025) · Biology of Reproduction · doi:10.1093/biolre/ioaf066 · Cited by 14 · see citing works
Al-Nema, M. Y., Gaurav, A., Lee, V. S., Gunasekaran, B., Lee, M. T., Okechukwu, P. N., & Nimmanpipug, P. (2022) · RSC Advances · doi:10.1039/d1ra07649c · Cited by 14 · see citing works
Al-Nema, M. Y., Gaurav, A., & Akowuah, G. A. (2018) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2018.09.001 · Cited by 12 · see citing works
Rainfall Predictive Approach for La Trinidad, Benguet using Machine Learning Classification
Macabiog, R. E. N., & Cruz, J. C. D. (2019) · IEEE International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM) · doi:10.1109/hnicem48295.2019.9072761 · Cited by 14 · see citing works
Pressure Injury Prediction in Intensive Care Units Using Artificial Intelligence: A Scoping Review
Alves, J., Azevedo, R., Marques, A., Encarnação, R., & Alves, P. J. P. (2025) · Nursing Reports · doi:10.3390/nursrep15040126 · Cited by 12 · see citing works
Enhancing prediction on imbalance data by thresholding technique with noise filtering
Radwan, A. M. (2017) · doi:10.1109/icitech.2017.8080033 · Cited by 13 · see citing works
Seok, J., Jeong, S. T., Yoon, S. Y., Lee, J. Y., Kim, S., Cho, H., & Kang, W. S. (2023) · Scientific Reports · doi:10.1038/s41598-023-47700-w · Cited by 12 · see citing works
Micarelli, A., Viziano, A., Micarelli, B., Fulvio, G. D., & Alessandrini, M. (2021) · Journal of Vestibular Research · doi:10.3233/ves-190729 · Cited by 12 · see citing works
Chen, X., Zhang, K., Li, Y., Wang, C., Lou, H., & Yu, S. Y. (2025) · Molecular Psychiatry · doi:10.1038/s41380-025-03314-4 · Cited by 14 · see citing works
Machine learning model with output correction: Towards reliable bradycardia detection in neonates
Rahman, J. S., Brankovic, A., & Khanna, S. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108658 · Cited by 11 · see citing works
McGarry, B. L., Damion, R. A., Chew, I., Knight, M. J., Harston, G., Carone, D., Jezzard, P., Sitaram, A., Muir, K. W., Clatworthy, P. L., et al. (2020) · Journal of Central Nervous System Disease · doi:10.1177/1179573520943314 · Cited by 11 · see citing works
Chávez-Badiola, A., Mendizabal‐Ruiz, G., Farias, A. F.-S., Garcia-Sánchez, R., & Drakeley, A. (2019) · Human Reproduction · doi:10.1093/humrep/dez263 · Cited by 10 · see citing works
Whitney, H. M., Drukker, K., & Giger, M. L. (2022) · Journal of medical imaging · doi:10.1117/1.jmi.9.3.035502 · Cited by 9 · see citing works
A Comparative Analysis of Novel Biomarkers in Sepsis and Cardiovascular Disease
Jirak, P., Haertel, F., Mirna, M., Rezar, R., Lichtenauer, M., Paar, V., Motloch, L. J., Topf, A., Yılmaz, A., Hoppe, U. C., et al. (2022) · Applied Sciences · doi:10.3390/app12031419 · Cited by 9 · see citing works
Half-AUC for the evaluation of sensitive or specific classifiers
Bradley, A. P. (2013) · Pattern Recognition Letters · doi:10.1016/j.patrec.2013.11.015 · Cited by 9 · see citing works
An Empirical Evaluation of Ranking Measures With Respect to Robustness to Noise
Berrar, D. (2014) · Journal of Artificial Intelligence Research · doi:10.1613/jair.4136 · Cited by 9 · see citing works
Robinson, R. M., Sarimveis, H., Doganis, P., Jia, X., Kotzabasaki, M. I., Gousiadou, C., Harper, S. L., & Wilkins, T. (2021) · Beilstein Journal of Nanotechnology · doi:10.3762/bjnano.12.97 · Cited by 7 · see citing works
What (not) to expect when classifying rare events
Blagus, R., & Goeman, J. J. (2016) · Briefings in Bioinformatics · doi:10.1093/bib/bbw107 · Cited by 8 · see citing works
Zhang, W., Zhang, Q., Xie, Z., Che, L., Xia, T., Cai, X., & Liu, S. (2022) · Frontiers in Genetics · doi:10.3389/fgene.2022.839957 · Cited by 8 · see citing works
Fontes, C. F., Bidu, N. S., Freitas, F. R., Maranhão, R. C., Monteiro, A. D. S. S., Couto, R. D., & Netto, E. M. (2023) · Journal of Medical Microbiology · doi:10.1099/jmm.0.001726 · Cited by 8 · see citing works
Nascimento, E. J. M., Norwood, B., Parker, A., Braun, R. P., Kpamegan, E. P., & Dean, H. J. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms222112004 · Cited by 7 · see citing works
Brankovic, A., Rolls, D. A., Boyle, J., Niven, P., & Khanna, S. (2022) · Scientific Reports · doi:10.1038/s41598-022-20907-z · Cited by 7 · see citing works
Partner‐specific prediction of RNA‐binding residues in proteins: A critical assessment
Jung, Y. G., EL‐Manzalawy, Y., Dobbs, D. L., & Honavar, V. (2018) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.25639 · Cited by 7 · see citing works
Informational analysis: a Shannon theoretic approach to measure the performance of a diagnostic test
Girometti, R., & Fabris, F. (2015) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-015-1294-7 · Cited by 7 · see citing works
Söndergaard, S. (2023) · Trends in Anaesthesia and Critical Care · doi:10.1016/j.tacc.2023.101292 · Cited by 6 · see citing works
Uhlig, M., Hein, M., Habigt, M., Tolba, R. H., Braunschweig, T. A., Helmedag, M. J., Klinge, U., Koch, A. H., Trautwein, C., & Mechelinck, M. (2021) · PLoS ONE · doi:10.1371/journal.pone.0256790 · Cited by 6 · see citing works
Supervised Machine Learning Thyroid Carcinoma Diagnosis Using Wide-Field SHG Microscopy
Padrez, Y., Hristu, R., Timoshchenko, I. A., Eftimie, L. G., Rutkauskas, D., & Golubewa, L. (2025) · IEEE Access · doi:10.1109/access.2025.3583435 · Cited by 5 · see citing works
EEG microstate analysis in children with prolonged disorders of consciousness
Zhang, Y., Hui, Z., Su, Y., Qi, W., Zhang, G., Zhou, L. F., Zhang, J., Shi, K., Yang, Y., Yang, L., et al. (2025) · Scientific Reports · doi:10.1038/s41598-025-11038-2 · Cited by 5 · see citing works
Ahmad, D., Serna-Gallegos, D. R., Jackson, A., Kaczorowski, D. J., Bonatti, J. O., West, D. M., Yoon, P., Chu, D., Squire, J., Thoma, F. W., et al. (2024) · JTCVS Open · doi:10.1016/j.xjon.2024.12.007 · Cited by 5 · see citing works
Mohammadi, A., Chiang, S. H., Li, F., Wei, F., Lau, C. S., Aziz, M. A., Ibarrondo, F. J., Fulcher, J. A., Yang, O. O., Chia, D., et al. (2024) · Scientific Reports · doi:10.1038/s41598-024-81019-4 · Cited by 5 · see citing works
Kondrakhin, Y. V., Valeev, T., Sharipov, R., Yevshin, I., Kolpakov, F., & Kel, A. E. (2016) · EuPA Open Proteomics · doi:10.1016/j.euprot.2016.09.001 · Cited by 4 · see citing works
Valle, C., Shrestha, S., Godeke, G.-J., Hoogerwerf, M. N., Reimerink, J. H. J., Eggink, D., & Reusken, C. B. E. M. (2024) · Viruses · doi:10.3390/v16020286 · Cited by 4 · see citing works
Anomaly Detection in Spacecraft Telemetry: Forecasting vs. Classification
Lakey, D. T., & Schlippe, T. (2024) · IEEE Space Computing Conference (SCC) · doi:10.1109/scc61854.2024.00009 · Cited by 4 · see citing works
Rate-Oriented Point-Wise Confidence Bounds for ROC Curves
Millard, L. A. C., Kull, M., & Flach, P. A. (2014) · Lecture notes in computer science · doi:10.1007/978-3-662-44851-9_26 · Cited by 4 · see citing works
Lee, J., Hong, N., Kim, B. M., Kim, D. J., Yun, M., Jeong, J. J., & Rhee, Y. (2020) · Journal of Bone and Mineral Metabolism · doi:10.1007/s00774-020-01085-2 · Cited by 4 · see citing works
Ahmad, D., Sá, M. P., Brown, J. A., Yousef, S., Wang, Y., Thoma, F. W., Chu, D., Kaczorowski, D. J., West, D. M., Bonatti, J. O., et al. (2024) · Journal of Thoracic and Cardiovascular Surgery · doi:10.1016/j.jtcvs.2024.05.004 · Cited by 4 · see citing works
Awe, O. O., Salako, J., Rodrigues, P. C., Dukhi, N., & Dias, R. (2024) · STEAM-H: Science, Technology, Engineering, Agriculture, Mathematics & Health · doi:10.1007/978-3-031-72215-8_11 · Cited by 3 · see citing works
Vecchione, M. B., Angerami, M. T., Suárez, G., Turk, G., Laufer, N., Ben, G., Ameri, D., González, D., Parodi, L. M., Giavedoni, L. D., et al. (2021) · Tuberculosis · doi:10.1016/j.tube.2020.102045 · Cited by 3 · see citing works
More accurate simulation for insurance data based on a modified SVM polynomial method1
Nurhidayat, I., Pimpunchat, B., & Klomsungcharoen, W. (2023) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-222879 · Cited by 3 · see citing works
- Authors
- Berrar, D., & Flach, P.
- Affiliations
- Tokyo Institute of Technology (JP)
- University of Bristol (GB)
- ORCID
- Venue
- Briefings in Bioinformatics
- Publisher
- Oxford University Press (OUP)
- 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
- bronze
- doi.org
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review