"Generative and interpretable machine learning for aptamer design and ..."

Andrea Di Gioacchino et al. (2022)

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DOI: 10.1371/JOURNAL.PCBI.1010561

access: open

type: Journal Article

metadata version: 2023-05-17

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Andrea Di Gioacchino, Jonah Procyk, Marco Molari, John S. Schreck, Yu Zhou, Yan Liu, Rémi Monasson, Simona Cocco, Petr Sulc:
Generative and interpretable machine learning for aptamer design and analysis of in vitro sequence selection. PLoS Comput. Biol. 18(9): 1010561 (2022)

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