CrossLink: a novel method for cross-condition classification of cancer subtypes.

ABSTRACT:

Background

We considered the prediction of cancer classes (e.g. subtypes) using patient gene expression profiles that contain both systematic and condition-specific biases when compared with the training reference dataset. The conventional normalization-based approaches cannot guarantee that the gene signatures in the reference and prediction datasets always have the same distribution for all different conditions as the class-specific gene signatures change with the condition. Therefore, the trained classifier would work well under one condition but not under another.

Methods

To address the problem of current normalization approaches, we propose a novel algorithm called CrossLink (CL). CL recognizes that there is no universal, condition-independent normalization mapping of s

SUBMITTER: Ma C 

PROVIDER: S-EPMC5001207 | biostudies-literature | 2016 Aug

REPOSITORIES: biostudies-literature