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We make use of ideas from the theory of complex networks to implement a machine learning classification of human DNA methylation data, that carry signatures of cancer development. The data were obtained from patients with various kinds of cancers and represented as parenclictic networks, wherein nodes correspond to genes, and edges are weighted according to pairwise variation from control group subjects. We demonstrate that for the 10 types of cancer under study, it is possible to obtain a highdoi:10.1371/journal.pone.0169661 pmid:28107365 pmcid:PMC5249089 fatcat:dby5ybsqobflrmvkyzsoxelh6a