Incorporating higher-order representative features improves prediction in network-based cancer prognosis analysis.

ABSTRACT:

Background

In cancer prognosis studies with gene expression measurements, an important goal is to construct gene signatures with predictive power. In this study, we describe the coordination among genes using the weighted coexpression network, where nodes represent genes and nodes are connected if the corresponding genes have similar expression patterns across samples. There are subsets of nodes, called modules, that are tightly connected to each other. In several published studies, it has been suggested that the first principal components of individual modules, also referred to as "eigengenes", may sufficiently represent the corresponding modules.

Results

In this article, we refer to principal components and their functions as representative features". We investigate higher

SUBMITTER: Ma S 

PROVIDER: S-EPMC3037289 | biostudies-literature | 2011 Jan

REPOSITORIES: biostudies-literature