Causal modeling using network ensemble simulations of genetic and gene expression data predicts genes involved in rheumatoid arthritis.

ABSTRACT: Tumor necrosis factor α (TNF-α) is a key regulator of inflammation and rheumatoid arthritis (RA). TNF-α blocker therapies can be very effective for a substantial number of patients, but fail to work in one third of patients who show no or minimal response. It is therefore necessary to discover new molecular intervention points involved in TNF-α blocker treatment of rheumatoid arthritis patients. We describe a data analysis strategy for predicting gene expression measures that are critical for rheumatoid arthritis using a combination of comprehensive genotyping, whole blood gene expression profiles and the component clinical measures of the arthritis Disease Activity Score 28 (DAS28) score. Two separate network ensembles, each comprised of 1024 networks, were built from molecular measures f

SUBMITTER: Xing H 

PROVIDER: S-EPMC3053315 | biostudies-literature | 2011 Mar

REPOSITORIES: biostudies-literature