Unexpected Actors in Inflammatory Bowel Disease Revealed by Machine Learning from Whole-Blood Transcriptomic Data.

ABSTRACT: Although big data from transcriptomic analyses have helped transform our understanding of inflammatory bowel disease (IBD), they remain underexploited. We hypothesized that the application of machine learning using lasso regression to transcriptomic data from IBD patients and controls can help identify previously overlooked genes. Transcriptomic data provided by Ostrowski et al. (ENA PRJEB28822) were subjected to a two-stage process of feature selection to discriminate between IBD and controls. First, a principal component analysis was used for dimensionality reduction. Second, the least absolute shrinkage and selection operator (lasso) regression was employed to identify genes potentially involved in the pathobiology of IBD. The study included data from 294 participants: 100 with ulcerati

SUBMITTER: Nowak JK 

PROVIDER: S-EPMC9498489 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature