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Updated: Apr 14, 2026
Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
Published on: October 11, 2018
One-step extrapolation of the prediction performance of a gene signature derived from a small study
1Research Center for Genes, Environment and Human Health, and Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan Department of Medical Research, Tzu Chi General Hospital, Hualien, Taiwan.
Objective:
Microarray-related studies often involve a very large number of genes and small sample size. Cross-validating or bootstrapping is therefore imperative to obtain a fair assessment of the prediction/classification performance of a gene signature. A deficiency of these methods is the reduced training sample size because of the partition process in cross-validation and sampling with replacement in bootstrapping. To address this problem, we aim to obtain a prediction performance estimate that strikes a good balance between bias and variance and has a small root mean squared error.
Methods:
We propose to make a one-step extrapolation from the fitted learning curve to estimate the prediction/classification performance of the model trained by all the samples.
Results:
Simulation studies show that the method strikes a good balance between bias and variance and has a small root mean squared error. Three microarray data sets are used for demonstration.
Conclusions:
Our method is advocated to estimate the prediction performance of a gene signature derived from a small study.
