| @article{dealmeida2022deepstarr, |
| title = {DeepSTARR predicts enhancer activity from DNA sequence and enables the de novo design of synthetic enhancers}, |
| author = {de Almeida, Bernardo P. and Reiter, Franziska and Pagani, Michaela and Stark, Alexander}, |
| journal = {Nature Genetics}, |
| volume = {54}, |
| pages = {613--624}, |
| year = {2022}, |
| doi = {10.1038/s41588-022-01048-5}, |
| url = {https://www.nature.com/articles/s41588-022-01048-5} |
| } |
| |
| @article{zrimec2022expressiongan, |
| title = {Controlling gene expression with deep generative design of regulatory DNA}, |
| journal = {Nature Communications}, |
| year = {2022}, |
| doi = {10.1038/s41467-022-32818-8}, |
| url = {https://www.nature.com/articles/s41467-022-32818-8} |
| } |
| |
| @article{taskiran2024celltypedirected, |
| title = {Cell-type-directed design of synthetic enhancers}, |
| journal = {Nature}, |
| year = {2024}, |
| doi = {10.1038/s41586-023-06936-2}, |
| url = {https://www.nature.com/articles/s41586-023-06936-2} |
| } |
| |
| @article{gosai2024coda, |
| title = {Machine-guided design of cell-type-targeting cis-regulatory elements}, |
| journal = {Nature}, |
| year = {2024}, |
| doi = {10.1038/s41586-024-08070-z}, |
| url = {https://www.nature.com/articles/s41586-024-08070-z} |
| } |
| |
| @article{dasilva2026dnadiffusion, |
| title = {Designing synthetic regulatory elements using the generative AI framework DNA-Diffusion}, |
| journal = {Nature Genetics}, |
| volume = {58}, |
| pages = {180--194}, |
| year = {2026}, |
| doi = {10.1038/s41588-025-02441-6}, |
| url = {https://www.nature.com/articles/s41588-025-02441-6} |
| } |
| |
| @article{sarkar2024d3, |
| title = {Designing DNA With Tunable Regulatory Activity Using Score-Entropy Discrete Diffusion}, |
| author = {Sarkar, Anirban and Kang, Yijie and Somia, Nirali and Mantilla Puccetti, Pablo and Zhou, Jessica and Nagai, Masayuki and Tang, Ziqi and Zhao, Chris and Koo, Peter K.}, |
| journal = {bioRxiv}, |
| year = {2024}, |
| doi = {10.1101/2024.05.23.595630}, |
| url = {https://repository.cshl.edu/id/eprint/41570} |
| } |
| |
| @article{wang2024drakes, |
| title = {Fine-Tuning Discrete Diffusion Models via Reward Optimization with Applications to DNA and Protein Design}, |
| author = {Wang, Chenyu and Uehara, Masatoshi and He, Yichun and Wang, Amy and Biancalani, Tommaso and Lal, Avantika and Jaakkola, Tommi and Levine, Sergey and Wang, Hanchen and Regev, Aviv}, |
| journal = {arXiv preprint arXiv:2410.13643}, |
| year = {2024}, |
| doi = {10.48550/arXiv.2410.13643}, |
| url = {https://arxiv.org/abs/2410.13643} |
| } |
| |
| @article{su2025atgcgen, |
| title = {Language Models for Controllable DNA Sequence Design}, |
| author = {Su, Xingyu and Li, Xiner and Lin, Yuchao and Xie, Ziqian and Zhi, Degui and Ji, Shuiwang}, |
| journal = {arXiv preprint arXiv:2507.19523}, |
| year = {2025}, |
| doi = {10.48550/arXiv.2507.19523}, |
| url = {https://arxiv.org/abs/2507.19523} |
| } |
| |
| @article{yang2025rldna, |
| title = {Regulatory DNA sequence Design with Reinforcement Learning}, |
| author = {Yang, Zhao and Su, Bing and Cao, Chuan and Wen, Ji-Rong}, |
| journal = {arXiv preprint arXiv:2503.07981}, |
| year = {2025}, |
| doi = {10.48550/arXiv.2503.07981}, |
| url = {https://arxiv.org/abs/2503.07981} |
| } |
| |
| |
