Prognostic transcriptional association networks: a new supervised approach based on regression trees.

ABSTRACT:

Motivation

The application of information encoded in molecular networks for prognostic purposes is a crucial objective of systems biomedicine. This approach has not been widely investigated in the cardiovascular research area. Within this area, the prediction of clinical outcomes after suffering a heart attack would represent a significant step forward. We developed a new quantitative prediction-based method for this prognostic problem based on the discovery of clinically relevant transcriptional association networks. This method integrates regression trees and clinical class-specific networks, and can be applied to other clinical domains.

Results

Before analyzing our cardiovascular disease dataset, we tested the usefulness of our approach on a benchmark dataset with control

SUBMITTER: Nepomuceno-Chamorro I 

PROVIDER: S-EPMC3018815 | biostudies-literature | 2011 Jan

REPOSITORIES: biostudies-literature