EpiGePT: a pretrained transformer-based language model for context-specific human epigenomics

EpiGePT is a transformer-based model for cross-cell-line prediction of human chromatin states by taking long DNA sequence and transcription factor profile as inputs. With EpiGePT, we can investigate the problem of how the trans-regulatory factors (e.g., TFs) regulate target gene by interacting with the cis-regulatory elements and further lead to the changes in chromatin states. Given the expression profile of hundreds of TFs from a cellular context, EpiGePT is able to predict the genome-wide chromatin states given the cellular context.

EpiGePT: a pretrained transformer-based language model for context-specific human epigenomics

EpiGePT is a transformer-based model for cross-cell-line prediction of human chromatin states by taking long DNA sequence and transcription factor profile as inputs. With EpiGePT, we can investigate the problem of how the trans-regulatory factors (e.g., TFs) regulate target gene by interacting with the cis-regulatory elements and further lead to the changes in chromatin states. Given the expression profile of hundreds of TFs from a cellular context, EpiGePT is able to predict the genome-wide chromatin states given the cellular context.