A personalized committee classification approach to improving prediction of breast cancer metastasis.

ABSTRACT:

Motivation

Metastasis prediction is a well-known problem in breast cancer research. As breast cancer is a complex and heterogeneous disease with many molecular subtypes, predictive models trained for one cohort often perform poorly on other cohorts, and a combined model may be suboptimal for individual patients. Furthermore, attempting to develop subtype-specific models is hindered by the ambiguity and stereotypical definitions of subtypes.

Results

Here, we propose a personalized approach by relaxing the definition of breast cancer subtypes. We assume that each patient belongs to a distinct subtype, defined implicitly by a set of patients with similar molecular characteristics, and construct a different predictive model for each patient, using as training data, only the pati

SUBMITTER: Jahid MJ 

PROVIDER: S-EPMC4071199 | biostudies-literature | 2014 Jul

REPOSITORIES: biostudies-literature