DeepGRN: prediction of transcription factor binding site across cell-types using attention-based deep neural networks.BACKGROUND
 In this study, we propose a novel tool (named DeepGRN) for transcription factors binding site prediction based on the combination of two components: single attention module and pairwise attention module.  The results show that DeepGRN achieves higher unified scores in 6 of 13 targets than any of the top four methods in the DREAM challenge.  We also demonstrate that the attention weights learned by the model are correlated with potential informative inputs, such as DNase-Seq coverage and motifs, which provide possible explanations for the predictive improvements in DeepGRN.


 DeepGRN can automatically and effectively predict transcription factor binding sites from DNA sequences and DNase-Seq coverage. 
DeepGRN
DeepGRN: prediction of transcription factor binding site across cell-types using attention-based deep neural networks.BACKGROUND
 In this study, we propose a novel tool (named DeepGRN) for transcription factors binding site prediction based on the combination of two components: single attention module and pairwise attention module.  The results show that DeepGRN achieves higher unified scores in 6 of 13 targets than any of the top four methods in the DREAM challenge.  We also demonstrate that the attention weights learned by the model are correlated with potential informative inputs, such as DNase-Seq coverage and motifs, which provide possible explanations for the predictive improvements in DeepGRN.


 DeepGRN can automatically and effectively predict transcription factor binding sites from DNA sequences and DNase-Seq coverage. 
DeepGRN
DeepGRN: prediction of transcription factor binding site across cell-types using attention-based deep neural networks.BACKGROUND
 In this study, we propose a novel tool (named DeepGRN) for transcription factors binding site prediction based on the combination of two components: single attention module and pairwise attention module.  The results show that DeepGRN achieves higher unified scores in 6 of 13 targets than any of the top four methods in the DREAM challenge.  We also demonstrate that the attention weights learned by the model are correlated with potential informative inputs, such as DNase-Seq coverage and motifs, which provide possible explanations for the predictive improvements in DeepGRN.


 DeepGRN can automatically and effectively predict transcription factor binding sites from DNA sequences and DNase-Seq coverage. 
DeepGRN