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    Drought is a critical risk factor that impacts rice growth and yields.
Previous studies have focused on the regulatory roles of individual
transcription factors in response to drought stress. However, there is
limited understanding of multi-factor stresses gene regulatory networks
and their mechanisms of action. In this study, we utilized data from the
jaspar database to compile a comprehensive dataset of transcription
factors and their binding sites in rice, Arabidopsis, and barley
genomes. We employed the pytorch framework for machine learning to
develop a 9-layer convolutional deep neural network TFBind; its accuracy
was 90% through literature review and dual-luciferase assay validation.
Subsequently, we obtained rice RNA-seq and ATAC-seq data related to
abiotic stress from the public database. Utilizing integrative analysis
of WGCNA and ATAC-seq, we effectively identified transcription factors
associated with open chromatin regions in response to drought.
Interestingly, only 81% of the transcription factors directly bound to
the opened genes by testing with TFBind model. By this approach we
identified 15 drought-responsive transcription factors corresponding to
open chromatin regions of targets, which enriched in the terms related
to protein transport, protein allocation, nitrogen compound transport.
This approach provides a valuable tool for predicting TF-TAG modules
during biological processes.