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...

Full description

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

Internet

View Fulltext in Publisher

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

Internet

Similar Items