RegNetB: predicting relevant regulator-gene relationships in localized prostate tumor samples.

ABSTRACT:

Background

A central question in cancer biology is what changes cause a healthy cell to form a tumor. Gene expression data could provide insight into this question, but it is difficult to distinguish between a gene that causes a change in gene expression from a gene that is affected by this change. Furthermore, the proteins that regulate gene expression are often themselves not regulated at the transcriptional level. Here we propose a Bayesian modeling framework we term RegNetB that uses mechanistic information about the gene regulatory network to distinguish between factors that cause a change in expression and genes that are affected by the change. We test this framework using human gene expression data describing localized prostate cancer progression.

Results

The top regu

SUBMITTER: Alvarez A 

PROVIDER: S-EPMC3128037 | biostudies-literature | 2011 Jun

REPOSITORIES: biostudies-literature