Machine Learning for Protein Engineering
Author:
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-16-9158-4_2
Reference28 articles.
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2. Balas, V.E., Roy, S.S., Sharma, D., Samui, P. (eds.): Handbook of Deep Learning Applications, vol. 136. Springer, New York (2019)
3. Biswas, S., Khimulya, G., Alley, E.C., Esvelt, K.M., Church, G.M.: Low-N protein engineering with data-efficient deep learning. Nat. Methods 18(4), 389–396 (2021)
4. Brannigan, J.A., Wilkinson, A.J.: Protein engineering 20 years on. Nat. Rev. Mol. Cell Biol. 3, 964–970 (2002). https://doi.org/10.1038/nrm975
5. Egloff, P., Zimmermann, I., Arnold, F.M., et al.: Engineered peptide barcodes for in-depth analyses of binding protein ensembles (2018). https://doi.org/10.1101/287813
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