Imputing missing RNA-sequencing data from DNA methylation by using a transfer learning-based neural network.

ABSTRACT:

Background

Gene expression plays a key intermediate role in linking molecular features at the DNA level and phenotype. However, owing to various limitations in experiments, the RNA-seq data are missing in many samples while there exist high-quality of DNA methylation data. Because DNA methylation is an important epigenetic modification to regulate gene expression, it can be used to predict RNA-seq data. For this purpose, many methods have been developed. A common limitation of these methods is that they mainly focus on a single cancer dataset and do not fully utilize information from large pan-cancer datasets.

Results

Here, we have developed a novel method to impute missing gene expression data from DNA methylation data through a transfer learning-based neural network, namel

SUBMITTER: Zhou X 

PROVIDER: S-EPMC7350980 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature