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DNA methylation is an essential epigenetic modification for multiple biological processes. DNA methylation in mammals acts as an epigenetic mark of transcriptional repression. Aberrant levels of DNA methylation can be observed in various types of tumor cells. Thus, DNA methylation has attracted considerable attention among researchers to provide new and feasible tumor therapies. Conventional studies considered single-gene methylation or specific loci as biomarkers for tumorigenesis. However, genome-scale methylated modification has not been completely investigated. Thus, we proposed and compared two novel computational approaches based on multiple machine learning algorithms for the qualitative and quantitative analyses of methylation-associated genes and their dys-methylated patterns. This study contributes to the identification of novel effective genes and the establishment of optimal quantitative rules for aberrant methylation distinguishing tumor cells with different origin tissues.

Discriminating Origin Tissues of Tumor Cell Lines by Methylation Signatures and Dys-Methylated Rules