Interpretable machine learning uncovers epithelial transcriptional rewiring and a role for Gelsolin in COPD.

ABSTRACT: Transcriptomic analyses have advanced the understanding of complex disease pathophysiology including chronic obstructive pulmonary disease (COPD). However, identifying relevant biologic causative factors has been limited by the integration of high dimensionality data. COPD is characterized by lung destruction and inflammation, with smoke exposure being a major risk factor. To define previously unknown biological mechanisms in COPD, we utilized unsupervised and supervised interpretable machine learning analyses of single-cell RNA-Seq data from the mouse smoke-exposure model to identify significant latent factors (context-specific coexpression modules) impacting pathophysiology. The machine learning transcriptomic signatures coupled to protein networks uncovered a reduction in network comple

SUBMITTER: Sui J 

PROVIDER: S-EPMC11601586 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature