Multiomics-integrated deep language model enables in silico genome-wide detection of transcription factor binding site in unexplored biosamples.

ABSTRACT:

Motivation

Transcription factor binding sites (TFBS) are regulatory elements that have significant impact on transcription regulation and cell fate determination. Canonical motifs, biological experiments, and computational methods have made it possible to discover TFBS. However, most existing in silico TFBS prediction models are solely DNA-based, and are trained and utilized within the same biosample, which fail to infer TFBS in experimentally unexplored biosamples.

Results

Here, we propose TFBS prediction by modified TransFormer (TFTF), a multimodal deep language architecture which integrates multiomics information in epigenetic studies. In comparison to existing computational techniques, TFTF has state-of-the-art accuracy, and is also the first approach to accurately perfo

SUBMITTER: Yang Z 

PROVIDER: S-EPMC10812877 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature