DeepGRN: prediction of transcription factor binding site across cell-types using attention-based deep neural networks.

ABSTRACT:

Background

Due to the complexity of the biological systems, the prediction of the potential DNA binding sites for transcription factors remains a difficult problem in computational biology. Genomic DNA sequences and experimental results from parallel sequencing provide available information about the affinity and accessibility of genome and are commonly used features in binding sites prediction. The attention mechanism in deep learning has shown its capability to learn long-range dependencies from sequential data, such as sentences and voices. Until now, no study has applied this approach in binding site inference from massively parallel sequencing data. The successful applications of attention mechanism in similar input contexts motivate us to build and test new methods that can a

SUBMITTER: Chen C 

PROVIDER: S-EPMC7852092 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature