@inproceedings{
lal2023reglm,
title={reg{LM}: Designing realistic regulatory {DNA} with autoregressive language models},
author={Avantika Lal and Tommaso Biancalani and G{\"o}kcen Eraslan},
booktitle={NeurIPS 2023 Generative AI and Biology (GenBio) Workshop},
year={2023},
url={https://openreview.net/forum?id=F6LNvZNBsp}
}
Designing cis-regulatory DNA elements (CREs) with desired properties is a challenging task with many therapeutic applications. Here, we used autoregressive language models trained on yeast and human putative CREs, in conjunction with supervised sequence-to-function models, to design regulatory DNA with desired patterns of activity. Our framework, regLM, compares favorably to existing CRE design approaches at generating realistic and diverse regulatory DNA, while also providing insights into the cis-regulatory code.
regLM: Designing realistic regulatory DNA with autoregressive language models
Avantika Lal Tommaso Biancalani Gökcen Eraslan