MetaKTSP: a meta-analytic top scoring pair method for robust cross-study validation of omics prediction analysis.

ABSTRACT:

Motivation

Supervised machine learning is widely applied to transcriptomic data to predict disease diagnosis, prognosis or survival. Robust and interpretable classifiers with high accuracy are usually favored for their clinical and translational potential. The top scoring pair (TSP) algorithm is an example that applies a simple rank-based algorithm to identify rank-altered gene pairs for classifier construction. Although many classification methods perform well in cross-validation of single expression profile, the performance usually greatly reduces in cross-study validation (i.e. the prediction model is established in the training study and applied to an independent test study) for all machine learning methods, including TSP. The failure of cross-study validation has largely dimin

SUBMITTER: Kim S 

PROVIDER: S-EPMC6280887 | biostudies-literature | 2016 Jul

REPOSITORIES: biostudies-literature