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

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

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 t...

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Bibliographic Details
Main Authors: Birchler, J.A (Author), Chen, C. (Author), Cheng, J. (Author), Hou, J. (Author), Shi, X. (Author), Yang, H. (Author)
Format: Article
Language:English
Published: BioMed Central Ltd 2021
Subjects:
Attention mechanism
Attention mechanisms
binding site
Binding site predictions
Binding sites
Binding Sites
biology
chromatin
Chromatin
Computational biology
Computational Biology
Deep learning
Deep neural networks
DNA
DNA binding site prediction
DNA sequences
Forecasting
Gene encoding
genetics
Long-range dependencies
Massively parallel sequencing
metabolism
Neural networks
Neural Networks, Computer
Parallel sequencing
protein binding
Protein Binding
Transcription
transcription factor
Transcription factor
Transcription factor binding sites
Transcription factors
Transcription Factors
Visualization technique
Online Access:View Fulltext in Publisher

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