D-GPM: A Deep Learning Method for Gene Promoter Methylation Inference.

ABSTRACT: Whole-genome bisulfite sequencing generates a comprehensive profiling of the gene methylation levels, but is limited by a high cost. Recent studies have partitioned the genes into landmark genes and target genes and suggested that the landmark gene expression levels capture adequate information to reconstruct the target gene expression levels. This inspired us to propose that the methylation level of the promoters in landmark genes might be adequate to reconstruct the promoter methylation level of target genes, which would eventually reduce the cost of promoter methylation profiling. Here, we propose a deep learning model called Deep-Gene Promoter Methylation (D-GPM) to predict the whole-genome promoter methylation level based on the promoter methylation profile of the landmark genes from

SUBMITTER: Pan X 

PROVIDER: S-EPMC6826513 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature