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Generative and interpretable machine learning for aptamer design and analysis of in vitro sequence selection.

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Bibliographic Details
Title: Generative and interpretable machine learning for aptamer design and analysis of in vitro sequence selection.
Authors: Di Gioacchino, Andrea1 (AUTHOR), Procyk, Jonah2 (AUTHOR), Molari, Marco1,3,4 (AUTHOR), Schreck, John S.5 (AUTHOR), Zhou, Yu2 (AUTHOR), Liu, Yan2 (AUTHOR), Monasson, Rémi1 (AUTHOR) remi.monasson@phys.ens.fr, Cocco, Simona1 (AUTHOR) remi.monasson@phys.ens.fr, Šulc, Petr2 (AUTHOR) remi.monasson@phys.ens.fr
Source: PLoS Computational Biology. 9/29/2022, Vol. 18 Issue 9, p1-31. 31p. 2 Black and White Photographs, 2 Diagrams, 1 Chart, 5 Graphs.
Database: Academic Search Ultimate
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Description
ISSN:1553734X
DOI:10.1371/journal.pcbi.1010561