Preprint EpiGePT: a Pretrained Transformer model for epigenomics, 2023, Gao et al.

EpiGePT: a Pretrained Transformer model for epigenomics
Zijing Gao; Qiao Liu; Wanwen Zeng; Wing H Wong; Rui Jiang

The transformer-based models, such as GPT-31 and DALL-E2, have achieved unprecedented breakthroughs in the field of natural language processing and computer vision. The inherent similarities between natural language and biological sequences have prompted a new wave of inferring the grammatical rules underneath the biological sequences. In genomic study, it is worth noting that DNA sequences alone cannot explain all the gene activities due to epigenetic mechanism.

To investigate this problem, we propose EpiGePT, a new transformer-based language pretrained model in epigenomics, for predicting genome-wide epigenomic signals by considering the mechanistic modeling of transcriptional regulation. Specifically, EpiGePT takes the context-specific activities of transcription factors (TFs) into consideration, which could offer deeper biological insights comparing to models trained on DNA sequence only. In a series of experiments, EpiGePT demonstrates state-of-the-art performance in a diverse epigenomic signals prediction tasks as well as new prediction tasks by fine-tuning. Furthermore, EpiGePT is capable of learning the cell-type-specific long-range interactions through the self-attention mechanism and interpreting the genetic variants that associated with human diseases.

We expect that the advances of EpiGePT can shed light on understanding the complex regulatory mechanisms in gene regulation. We provide free online prediction service of EpiGePT through https://health.tsinghua.edu.cn/epigept/.

Link | PDF (Preprint: BioRxiv)

 

No idea about the validity of this. The link at the bottom of the abstract is not (currently?) functional.

 

Interesting. Gene expression--the search for how cells choose which proteins to make and how much--has perplexed scientists for so long. Hopefully this will unveil something.