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Related Concept Videos

Receiver Operating Characteristic Plot01:15

Receiver Operating Characteristic Plot

A ROC (Receiver Operating Characteristic) plot is a graphical tool used to assess the performance of a binary classification model by illustrating the trade-off between sensitivity (true positive rate) and specificity (false positive rate). By plotting sensitivity against 1 - specificity across various threshold settings, the ROC curve shows how well the model distinguishes between classes, with a curve closer to the top-left corner indicating a more accurate model. The area under the ROC curve...
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Related Experiment Video

Updated: Jun 22, 2026

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

Model selection based on FDR-thresholding optimizing the area under the ROC-curve.

Alexandra C Graf1, Peter Bauer

  • 1Medical University of Vienna. alexandra.graf@meduniwien.ac.at

Statistical Applications in Genetics and Molecular Biology
|July 4, 2009
PubMed
Summary

This study introduces a cross-validation method for selecting variables in high-dimensional data to predict clinical outcomes. The approach optimizes prediction accuracy while controlling the false discovery rate (FDR), crucial for reliable results.

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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

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Last Updated: Jun 22, 2026

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

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Published on: August 30, 2013

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Area of Science:

  • Biostatistics
  • Machine Learning
  • Genomics

Background:

  • High-dimensional data analysis presents challenges in variable selection for accurate clinical outcome prediction.
  • Controlling the false discovery rate (FDR) is essential for reliable statistical inference.
  • Existing methods struggle to balance prediction accuracy with controlled structure estimation.

Purpose of the Study:

  • To develop and evaluate a novel cross-validation procedure for optimal variable selection in high-dimensional datasets.
  • To combine accurate prediction of clinical outcomes with controlled estimation of feature importance.
  • To identify the optimal threshold for controlling the false discovery rate (FDR) to maximize predictive performance.

Main Methods:

  • Variable selection using multiple tests that control the false discovery rate (FDR).
  • Prediction model development using a linear score based on selected variables.
  • Assessment of prediction quality via Receiver Operating Characteristic (ROC) curves and Area Under the Curve (AUC).
  • Implementation of a new cross-validation procedure utilizing the maximum rank correlation estimator to determine the optimal selection threshold.

Main Results:

  • The optimal FDR threshold for maximizing AUC varies significantly and is not known in advance.
  • The proposed cross-validation procedure effectively selects appropriate criteria and provides accurate FDR estimates.
  • The method is computationally feasible for moderate to small sample sizes.
  • Low cross-validated AUC and high cross-validated FDR suggest insufficient prognostic variables or small sample sizes.

Conclusions:

  • The developed cross-validation method offers a robust approach for variable selection in high-dimensional data for clinical prediction.
  • This technique successfully integrates prediction goals with controlled structure estimation.
  • The findings highlight the importance of sample size and variable prognostic power in achieving reliable predictions.