Volume 6 Supplement 3

Selected articles from the IEEE International Conference on Bioinformatics and Biomedicine 2012: Medical Genomics

Open Access

ErratumTo: A comparison of statistical methods for the detection of hepatocellular carcinoma based on serum biomarkers and clinical variables

  • Mengjun Wang1,
  • Anand Mehta1,
  • Timothy M Block1,
  • Jorge Marrero2,
  • Adrian M Di Bisceglie3 and
  • Karthik Devarajan4
BMC Medical Genomics20136(Suppl 3):S11

DOI: 10.1186/1755-8794-6-S3-S11

Published: 20 December 2013

The original article was published in BMC Medical Genomics 2013 6:S9

Correction

Our original article was published in BMC Medical Genomics in the supplement containing selected articles from the IEEE International Conference on Bioinformatics and Biomedicine 2012 (IEEE BIBM 2012) [1]. After publication, it was noticed that the ROC curves in Figures 1, 2, 3, 4 displayed Sensitivity vs. Specificity rather than Sensitivity vs. 1-Specificity, as labeled. These figures have been reproduced here in the correct format, displaying Sensitivity vs. 1-Specificity, and should replace the corresponding figures in the original article. However, AUC values remain unaffected by this change.
https://static-content.springer.com/image/art%3A10.1186%2F1755-8794-6-S3-S11/MediaObjects/12920_2013_Article_440_Fig1_HTML.jpg
Figure 1

ROC curves based on multivariable stepwise penalized logistic regression models ( stepPLR ) using the stratified male-only subset. The age-adjusted final model for λ = 0.1 showed the best performance in terms of AUC. A clear distinction is seen in the ROC curves for age-adjusted models compared to age-unadjusted models. Age-adjusted models demonstrated superior performance overall across all choices of λ. See Table 1 for detailed results and the text for discussion of these results.

https://static-content.springer.com/image/art%3A10.1186%2F1755-8794-6-S3-S11/MediaObjects/12920_2013_Article_440_Fig2_HTML.jpg
Figure 2

ROC curves based on multivariable stepwise penalized logistic regression models ( stepPLR ) adjusting for gender effect. Models that are also adjusted for age effect outperformed those that did not control for age, across all choices of the parameter λ. The age-adjusted final model for λ = 0.1 showed the best performance in terms of AUC. See Table 1 for detailed results and the text for discussion of these results.

https://static-content.springer.com/image/art%3A10.1186%2F1755-8794-6-S3-S11/MediaObjects/12920_2013_Article_440_Fig3_HTML.jpg
Figure 3

ROC curves based on multivariable model-based CART analyses ( mob ) using the stratified male-only subset. Age-adjusted models demonstrated superior performance in terms of AUC. A clear distinction is seen in the ROC curves for age-adjusted models (solid lines) compared to age-unadjusted models (dotted lines). See Table 1 for detailed results and the text for discussion of these results.

https://static-content.springer.com/image/art%3A10.1186%2F1755-8794-6-S3-S11/MediaObjects/12920_2013_Article_440_Fig4_HTML.jpg
Figure 4

ROC curves based on multivariable model-based CART analyses ( mob ) incorporating gender and/or age. Age-adjusted models demonstrated superior performance in terms of AUC when gender effect is accounted for in each model. A clear distinction is seen in the ROC curves for age-adjusted models (solid lines) compared to age-unadjusted models (dotted lines). Table 1 lists the performance measures for these models. A detailed discussion of the results is provided in the text.

Notes

Declarations

Acknowledgements

This article has been published as part of BMC Medical Genomics Volume 6 Supplement 3, 2013: Selected articles from the IEEE International Conference on Bioinformatics and Biomedicine 2012: Medical Genomics. The full contents of the supplement are available online at http://www.biomedcentral.com/bmcmedgenomics/supplements/6/S3.

Authors’ Affiliations

(1)
Drexel University College of Medicine
(2)
Division of Gastroenterology, University of Michigan
(3)
Saint Louis University School of Medicine
(4)
Department of Biostatistics and Bioinformatics, Fox Chase Cancer Center

References

  1. Wang M, Mehta A, Block TM, Marrero J, Di Bisceglie AM, Devarajan K: A comparison of statistical methods for the detection of hepatocellular carcinoma based on serum biomarkers and clinical variables. BMC Medical Genomics. 2013, 6 (Suppl 3): S9-10.1186/1755-8794-6-S3-S9.PubMed CentralView ArticlePubMedGoogle Scholar

Copyright

© Wang et al.; licensee BioMed Central Ltd. 2013

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

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