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Sorting multiple classes in multi-dimensional ROC analysis: parametric and nonparametric approaches.

Publication ,  Journal Article
Li, J; Chow, Y; Wong, WK; Wong, TY
Published in: Biomarkers
February 2014

In large-scale data analysis, such as in a microarray study to identify the most differentially expressed genes, diagnostic tests are frequently used to classify and predict subjects into their different categories. Frequently, these categories do not have an intrinsic natural order even though the quantitative test results have a relative order. As identifying the correct order for a proper definition of accuracy measures is important for a high-dimensional receiver operating characteristic (ROC) analysis, we propose rigorous and automated approaches to sort out the multiple categories using simple summary statistics such as means and relative effects. We discuss the hypervolume under the ROC manifold (HUM), its dependence on the order of the test results and the minimum acceptable HUM values in a general multi-category classification problem. Using a leukemia data set and a liver cancer data set, we show how our approaches provide accurate screening results when we have a large number of tests.

Duke Scholars

Published In

Biomarkers

DOI

EISSN

1366-5804

Publication Date

February 2014

Volume

19

Issue

1

Start / End Page

1 / 8

Location

England

Related Subject Headings

  • Transcriptome
  • Toxicology
  • Statistics, Nonparametric
  • ROC Curve
  • Models, Statistical
  • Liver Neoplasms
  • Leukemia
  • Humans
  • Gene Expression Profiling
  • Data Interpretation, Statistical
 

Citation

APA
Chicago
ICMJE
MLA
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Li, J., Chow, Y., Wong, W. K., & Wong, T. Y. (2014). Sorting multiple classes in multi-dimensional ROC analysis: parametric and nonparametric approaches. Biomarkers, 19(1), 1–8. https://doi.org/10.3109/1354750X.2013.868516
Li, Jialiang, Yanyu Chow, Weng Kee Wong, and Tien Yin Wong. “Sorting multiple classes in multi-dimensional ROC analysis: parametric and nonparametric approaches.Biomarkers 19, no. 1 (February 2014): 1–8. https://doi.org/10.3109/1354750X.2013.868516.
Li, Jialiang, et al. “Sorting multiple classes in multi-dimensional ROC analysis: parametric and nonparametric approaches.Biomarkers, vol. 19, no. 1, Feb. 2014, pp. 1–8. Pubmed, doi:10.3109/1354750X.2013.868516.

Published In

Biomarkers

DOI

EISSN

1366-5804

Publication Date

February 2014

Volume

19

Issue

1

Start / End Page

1 / 8

Location

England

Related Subject Headings

  • Transcriptome
  • Toxicology
  • Statistics, Nonparametric
  • ROC Curve
  • Models, Statistical
  • Liver Neoplasms
  • Leukemia
  • Humans
  • Gene Expression Profiling
  • Data Interpretation, Statistical