Determination of minimum training sample size for microarray-based cancer outcome prediction-an empirical assessment.

ABSTRACT: The promise of microarray technology in providing prediction classifiers for cancer outcome estimation has been confirmed by a number of demonstrable successes. However, the reliability of prediction results relies heavily on the accuracy of statistical parameters involved in classifiers. It cannot be reliably estimated with only a small number of training samples. Therefore, it is of vital importance to determine the minimum number of training samples and to ensure the clinical value of microarrays in cancer outcome prediction. We evaluated the impact of training sample size on model performance extensively based on 3 large-scale cancer microarray datasets provided by the second phase of MicroArray Quality Control project (MAQC-II). An SSNR-based (scale of signal-to-noise ratio) protocol

SUBMITTER: Shao L 

PROVIDER: S-EPMC3702597 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature