CMIC: predicting DNA methylation inheritance of CpG islands with embedding vectors of variable-length k-mers.

ABSTRACT:

Background

Epigenetic modifications established in mammalian gametes are largely reprogrammed during early development, however, are partly inherited by the embryo to support its development. In this study, we examine CpG island (CGI) sequences to predict whether a mouse blastocyst CGI inherits oocyte-derived DNA methylation from the maternal genome. Recurrent neural networks (RNNs), including that based on gated recurrent units (GRUs), have recently been employed for variable-length inputs in classification and regression analyses. One advantage of this strategy is the ability of RNNs to automatically learn latent features embedded in inputs by learning their model parameters. However, the available CGI dataset applied for the prediction of oocyte-derived DNA methylation inheritan

SUBMITTER: Maruyama O 

PROVIDER: S-EPMC9469632 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature