Identification of biomarkers associated with pediatric asthma using machine learning algorithms: A review.

ABSTRACT: Pediatric asthma is a complex disease with a multifactorial etiology. The identification of biomarkers associated with pediatric asthma can provide insights into the pathogenesis of the disease and aid in the development of novel diagnostic and therapeutic strategies. This study aimed to identify potential biomarkers for pediatric asthma using Weighted Gene Co-expression Network Analysis (WGCNA) and machine learning algorithms. We obtained gene expression data from publicly available databases and performed WGCNA to identify gene co-expression modules associated with pediatric asthma. We then used machine learning algorithms, including random forest, lasso regression algorithm, and support vector machine-recursive feature elimination, to classify asthma cases and controls based on the iden

SUBMITTER: Lin K 

PROVIDER: S-EPMC10681392 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature