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AbstractDNA methylation (DNAm) alterations have been heavily implicated in carcinogenesis and the pathophysiology of diseases through upstream regulation of gene expression. DNAm deep-learning approaches are able to capture features associated with aging, cell type, and disease progression, but lack incorporation of prior biological knowledge. Here, we present modular, user-friendly deep-learning methodology and software, MethylCapsNet and MethylSPWNet, that group CpGs into biologicallydoi:10.1038/s41540-021-00193-7 pmid:34417465 pmcid:PMC8379254 fatcat:gehkq2j53vbo3njmuj2s5mib2q