Derivation of molecular signatures for breast cancer recurrence prediction using a two-way validation approach.

ABSTRACT: Previous studies have demonstrated the potential value of gene expression signatures in assessing the risk of post-surgical breast cancer recurrence, however, many of these predictive models have been derived using simple computational algorithms and validated internally or using one-way validation on a single dataset. We have recently developed a new feature selection algorithm that overcomes some limitations inherent to high-dimensional data analysis. In this study, we applied this algorithm to two publicly available gene expression datasets obtained from over 400 patients with breast cancer to investigate whether we could derive more accurate prognostic signatures and reveal common predictive factors across independent datasets. We compared the performance of three advanced computationa

SUBMITTER: Sun Y 

PROVIDER: S-EPMC2844120 | biostudies-literature | 2010 Feb

REPOSITORIES: biostudies-literature