Journal
JOURNAL OF COMPUTATIONAL BIOLOGY
Volume 10, Issue 2, Pages 119-142Publisher
MARY ANN LIEBERT, INC
DOI: 10.1089/106652703321825928
Keywords
gene expression profiling; molecular pattern recognition; DNA microarrays; microarray analysis; sample size estimation
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A statistical methodology for estimating dataset size requirements for classifying microarray data using learning curves is introduced. The goal is to use existing classification results to estimate dataset size requirements for future classification experiments and to evaluate the gain in accuracy and significance of classifiers built with additional data. The method is based on fitting inverse power-law models to construct empirical learning curves. It also includes a permutation test procedure to assess the statistical significance of classification performance for a given dataset size. This procedure is applied to several molecular classification problems representing a broad spectrum of levels of complexity.
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