A DNABERT based deep learning framework for predicting transcription factor binding sites.

ABSTRACT: Transcription factors are proteins that regulate the expression of genes by binding to specific genomic regions known as Transcription Factor Binding Sites (TFBSs), typically located in the promoter regions of those genes. Accurate prediction of these binding sites is essential for understanding the complex gene regulatory networks underlying various cellular functions. In this regard, many deep learning models have been developed for such prediction, but there is still scope of improvement. In this work, we have developed a deep learning model which uses pre-trained DNABERT, a convolutional neural network (CNN) module, a modified convolutional block attention module (MCBAM), a multi-scale convolutions with attention (MSCA) module and an output module. The pre-trained DNABERT is used for s

SUBMITTER: Dutta P 

PROVIDER: S-EPMC12921214 | biostudies-literature | 2026 Feb

REPOSITORIES: biostudies-literature