A robust data-driven genomic signature for idiopathic pulmonary fibrosis with applications for translational model selection.

ABSTRACT: Idiopathic pulmonary fibrosis (IPF) is a chronic and progressive lung disease affecting ~5 million people globally. We have constructed an accurate model of IPF disease status using elastic net regularized regression on clinical gene expression data. Leveraging whole transcriptome microarray data from 230 IPF and 89 control samples from Yang et al. (2013), sourced from the Lung Tissue Research Consortium (LTRC) and National Jewish Health (NJH) cohorts, we identify an IPF gene expression signature. We performed optimal feature selection to reduce the number of transcripts required by our model to a parsimonious set of 15. This signature enables our model to accurately separate IPF patients from controls. Our model outperforms existing published models when tested with multiple independent c

SUBMITTER: Ammar R 

PROVIDER: S-EPMC6472794 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature