Iteratively refining breast cancer intrinsic subtypes in the METABRIC dataset.

ABSTRACT:

Background

Multi-gene lists and single sample predictor models have been currently used to reduce the multidimensional complexity of breast cancers, and to identify intrinsic subtypes. The perceived inability of some models to deal with the challenges of processing high-dimensional data, however, limits the accurate characterisation of these subtypes. Towards the development of robust strategies, we designed an iterative approach to consistently discriminate intrinsic subtypes and improve class prediction in the METABRIC dataset.

Findings

In this study, we employed the CM1 score to identify the most discriminative probes for each group, and an ensemble learning technique to assess the ability of these probes on assigning subtype labels using 24 different classifiers. Our ana

SUBMITTER: Milioli HH 

PROVIDER: S-EPMC4712506 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature