Integration of breast cancer gene signatures based on graph centrality.

ABSTRACT:

Background

Various gene-expression signatures for breast cancer are available for the prediction of clinical outcome. However due to small overlap between different signatures, it is challenging to integrate existing disjoint signatures to provide a unified insight on the association between gene expression and clinical outcome.

Results

In this paper, we propose a method to integrate different breast cancer gene signatures by using graph centrality in a context-constrained protein interaction network (PIN). The context-constrained PIN for breast cancer is built by integrating complete PIN and various gene signatures reported in literatures. Then, we use graph centralities to quantify the importance of genes to breast cancer. Finally, we get reliable gene signatures that are

SUBMITTER: Wang J 

PROVIDER: S-EPMC3287565 | biostudies-literature | 2011

REPOSITORIES: biostudies-literature