Making External Validation Valid for Molecular Classifier Development.

ABSTRACT:

Purpose

Accurate assessment of a molecular classifier that guides patient care is of paramount importance in precision oncology. Recent years have seen an increasing use of external validation for such assessment. However, little is known about how it is affected by ubiquitous unwanted variations in test data because of disparate experimental handling and by the use of data normalization for alleviating such variations.

Methods

In this paper, we studied these issues using two microarray data sets for the same set of tumor samples and additional data simulated by resampling under various levels of signal-to-noise ratio and different designs for array-to-sample allocation.

Results

We showed that (1) unwanted variations can lead to biased classifier assessment and (2) da

SUBMITTER: Wu Y 

PROVIDER: S-EPMC8345919 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature