Network-based biomarkers enhance classical approaches to prognostic gene expression signatures.

ABSTRACT:

Background

Classical approaches to predicting patient clinical outcome via gene expression information are primarily based on differential expression of unrelated genes (single-gene approaches) or genes related by, for example, biologic pathway or function (gene-sets). Recently, network-based approaches utilising interaction information between genes have emerged. An open problem is whether such approaches add value to the more traditional methods of signature modelling. We explored this question via comparison of the most widely employed single-gene, gene-set, and network-based methods, using gene expression microarray data from two different cancers: melanoma and ovarian. We considered two kinds of network approaches. The first of these identifies informative genes using gene exp

SUBMITTER: Barter RL 

PROVIDER: S-EPMC4290694 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature