Predicting gene expression from histone modifications with self-attention based neural networks and transfer learning.

ABSTRACT: It is well known that histone modifications play an important part in various chromatin-dependent processes such as DNA replication, repair, and transcription. Using computational models to predict gene expression based on histone modifications has been intensively studied. However, the accuracy of the proposed models still has room for improvement, especially in cross-cell lines gene expression prediction. In the work, we proposed a new model TransferChrome to predict gene expression from histone modifications based on deep learning. The model uses a densely connected convolutional network to capture the features of histone modifications data and uses self-attention layers to aggregate global features of the data. For cross-cell lines gene expression prediction, TransferChrome adopts tran

SUBMITTER: Chen Y 

PROVIDER: S-EPMC9797047 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature