Open Access
| Issue |
BIO Web Conf.
Volume 182, 2025
The 3rd International Conference on Food Science and Bio-medicine (ICFSB 2025)
|
|
|---|---|---|
| Article Number | 02014 | |
| Number of page(s) | 4 | |
| Section | Biomedical Research and Applications | |
| DOI | https://doi.org/10.1051/bioconf/202518202014 | |
| Published online | 02 July 2025 | |
- Chen C, Zhang Y, Fu J, et al. Bidirectional learning for offline infinite-width model-based optimization[J]. Advances in Neural Information Processing Systems, 2022, 35: 29454–29467. [Google Scholar]
- Fu J, Levine S. Offline model-based optimization via normalized maximum likelihood estimation[J]. arXiv preprint arXiv:2102.07970, 2021. [Google Scholar]
- Elnaggar A, Heinzinger M, Dallago C, et al. ProtTrans: towards cracking the language of life’s code through self-supervised learning[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021, 44: 7112–7127. [Google Scholar]
- Linder J, Seelig G. Fast differentiable DNA and protein sequence optimization for molecular design[J]. arXiv preprint arXiv:2005.11275, 2020. [Google Scholar]
- Angermueller C, Dohan D, Belanger D, et al. Modelbased reinforcement learning for biological sequence design[C]//International conference on learning representations. 2019. [Google Scholar]
- Rives A, Meier J, Sercu T, et al. Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences[J]. Proceedings of the National Academy of Sciences, 2021, 118(15): e2016239118. [CrossRef] [PubMed] [Google Scholar]
- Lichtarge J, Alberti C, Kumar S. Simple and effective gradient-based tuning of sequence-to-sequence models[J]. arXiv preprint arXiv:2209.04683, 2022. [Google Scholar]
- Chen C, Zhang Y, Liu X, et al. Bidirectional learning for offline model-based biological sequence design[C]//International Conference on Machine Learning. PMLR, 2023: 5351–5366. [Google Scholar]
- Gómez-Bombarelli R, Wei J N, Duvenaud D, et al. Automatic chemical design using a data-driven continuous representation of molecules[J]. ACS central science, 2018, 4(2): 268–276. [CrossRef] [PubMed] [Google Scholar]
- Baydin A G, Cornish R, Rubio D M, et al. Online learning rate adaptation with hypergradient descent[J]. arXiv preprint arXiv:1703.04782, 2017. [Google Scholar]
- Bryant D H, Bashir A, Sinai S, et al. Deep diversification of an AAV capsid protein by machine learning[J]. Nature Biotechnology, 2021, 39(6): 691–696. [CrossRef] [PubMed] [Google Scholar]
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