One-step extrapolation of the prediction performance of a gene signature derived from a small study.

ABSTRACT:

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

SUBMITTER: Wang LY 

PROVIDER: S-EPMC4401865 | biostudies-literature | 2015 Apr

REPOSITORIES: biostudies-literature