A categorical network approach for discovering differentially expressed regulations in cancer.

ABSTRACT:

Background

The problem of efficient utilization of genome-wide expression profiles for identification and prediction of complex disease conditions is both important and challenging. Polygenic pathologies such as most types of cancer involve disregulation of many interacting genes which has prompted search for suitable statistical models for their representation. By accounting for changes in gene regulations between comparable conditions, graphical statistical models are expected to improve prediction precision.

Methods

In comparison problems with two or more experimental conditions, we represent the classes by categorical Bayesian networks that share one and the same graph structure but have class-specific probability parameters. The graph structure is learned by a score-bas

SUBMITTER: Balov N 

PROVIDER: S-EPMC3980263 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature