Abstract\nBackground: With the increase in the amount of DNA methylation and gene expression data, the epigenetic\nmechanisms of cancers can be extensively investigate. Available methods integrate the DNA methylation and gene\nexpression data into a network by specifying the anti-correlation between them. However, the correlation between\nmethylation and expression is usually unknown and difficult to determine.\nResults: To address this issue, we present a novel multiple network framework for epigenetic modules, namely,\nEpigenetic Module based on Differential Networks (EMDN) algorithm, by simultaneously analyzing DNA methylation\nand gene expression data. The EMDN algorithm prevents the specification of the correlation between methylation\nand expression. The accuracy of EMDN algorithm is more efficient than that of modern approaches. On the basis of\nThe Cancer Genome Atlas (TCGA) breast cancer data, we observe that the EMDN algorithm can recognize positively\nand negatively correlated modules and these modules are significantly more enriched in the known pathways than\nthose obtained by other algorithms. These modules can serve as bio-markers to predict breast cancer subtypes by\nusing methylation profiles, where positively and negatively correlated modules are of equal importance in the\nclassification of cancer subtypes. Epigenetic modules also estimate the survival time of patients, and this factor is\ncritical for cancer therapy.\nConclusions: The proposed model and algorithm provide an effective method for the integrative analysis of DNA\nmethylation and gene expression. The algorithm is freely available as an R-package at https://github.com/\nwilliam0701/EMDN.
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