Plant-DTI: Extending the landscape of TF protein and DNA interaction in plants by a machine learning-based approach.

ABSTRACT: As a sessile organism, plants hold elaborate transcriptional regulatory systems that allow them to adapt to variable surrounding environments. Current understanding of plant regulatory mechanisms is greatly constrained by limited knowledge of transcription factor (TF)-DNA interactions. To mitigate this problem, a Plant-DTI predictor (Plant DBD-TFBS Interaction) was developed here as the first machine-learning model that covered the largest experimental datasets of 30 plant TF families, including 7 plant-specific DNA binding domain (DBD) types, and their transcription factor binding sites (TFBSs). Plant-DTI introduced a novel TFBS feature construction, called TFBS base-preference, which enhanced the specificity of TFBS to DBD types. The proposed model showed better predictive performance wi

SUBMITTER: Ruengsrichaiya B 

PROVIDER: S-EPMC9445498 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature