Identification of usual interstitial pneumonia pattern using RNA-Seq and machine learning: challenges and solutions.

ABSTRACT: BACKGROUND:We developed a classifier using RNA sequencing data that identifies the usual interstitial pneumonia (UIP) pattern for the diagnosis of idiopathic pulmonary fibrosis. We addressed significant challenges, including limited sample size, biological and technical sample heterogeneity, and reagent and assay batch effects. RESULTS:We identified inter- and intra-patient heterogeneity, particularly within the non-UIP group. The models classified UIP on transbronchial biopsy samples with a receiver-operating characteristic area under the curve of ~ 0.9 in cross-validation. Using in silico mixed samples in training, we prospectively defined a decision boundary to optimize specificity at ≥85%. The penalized logistic regression model showed greater reproducibility across technical replicate

SUBMITTER: Choi Y 

PROVIDER: S-EPMC5954282 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature