A convolutional neural network-based regression model to infer the epigenetic crosstalk responsible for CG methylation patterns.

ABSTRACT:

Background

Epigenetic modifications, including CG methylation (a major form of DNA methylation) and histone modifications, interact with each other to shape their genomic distribution patterns. However, the entire picture of the epigenetic crosstalk regulating the CG methylation pattern is unknown especially in cells that are available only in a limited number, such as mammalian oocytes. Most machine learning approaches developed so far aim at finding DNA sequences responsible for the CG methylation patterns and were not tailored for studying the epigenetic crosstalk.

Results

We built a machine learning model named epiNet to predict CG methylation patterns based on other epigenetic features, such as histone modifications, but not DNA sequence. Using epiNet, we identified bio

SUBMITTER: Au Yeung WK 

PROVIDER: S-EPMC8220828 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature