Machine learning implicates the IL-18 signaling axis in severe asthma.

ABSTRACT: Asthma is a common disease with profoundly variable natural history and patient morbidity. Heterogeneity has long been appreciated, and much work has focused on identifying subgroups of patients with similar pathobiological underpinnings. Previous studies of the Severe Asthma Research Program (SARP) cohort linked gene expression changes to specific clinical and physiologic characteristics. While invaluable for hypothesis generation, these data include extensive candidate gene lists that complicate target identification and validation. In this analysis, we performed unsupervised clustering of the SARP cohort using bronchial epithelial cell gene expression data, identifying a transcriptional signature for participants suffering exacerbation-prone asthma with impaired lung function. Clinicall

SUBMITTER: Camiolo MJ 

PROVIDER: S-EPMC8663569 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature