Abstract
Asthma is a chronic inflammatory disease of the airways associated with epigenetic and genetic risk factors. Co-expressed and co-methylated gene modules-based approaches can identify gene sets that are specifically asthma-associated biological process beyond known candidate genes. However, limited studies reported predictive models associated with asthma severity and lung function based on genome-wide weighted correlation network analysis (WGCNA) and machine learning (ML) methods.
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