Bayesian probit regression model for the diagnosis of pulmonary fibrosis: proof-of-principle.

ABSTRACT:

Background

The accurate diagnosis of idiopathic pulmonary fibrosis (IPF) is a major clinical challenge. We developed a model to diagnose IPF by applying Bayesian probit regression (BPR) modelling to gene expression profiles of whole lung tissue.

Methods

Whole lung tissue was obtained from patients with idiopathic pulmonary fibrosis (IPF) undergoing surgical lung biopsy or lung transplantation. Controls were obtained from normal organ donors. We performed cluster analyses to explore differences in our dataset. No significant difference was found between samples obtained from different lobes of the same patient. A significant difference was found between samples obtained at biopsy versus explant. Following preliminary analysis of the complete dataset, we selected three subsets

SUBMITTER: Meltzer EB 

PROVIDER: S-EPMC3199230 | biostudies-literature | 2011 Oct

REPOSITORIES: biostudies-literature