Machine learning guided aptamer refinement and discovery

Current aptamer discovery approaches are unable to probe the complete space of possible sequences. Here, the authors use machine learning to facilitate the development of DNA aptamers with improved binding affinities, and truncate them without significantly compromising binding affinity.

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Detalles Bibliográficos
Autores principales:	Ali Bashir, Qin Yang, Jinpeng Wang, Stephan Hoyer, Wenchuan Chou, Cory McLean, Geoff Davis, Qiang Gong, Zan Armstrong, Junghoon Jang, Hui Kang, Annalisa Pawlosky, Alexander Scott, George E. Dahl, Marc Berndl, Michelle Dimon, B. Scott Ferguson
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/9508da0079cf405d9f6f644c1119f62f
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spelling	oai:doaj.org-article:9508da0079cf405d9f6f644c1119f62f2021-12-02T17:33:41ZMachine learning guided aptamer refinement and discovery10.1038/s41467-021-22555-92041-1723https://doaj.org/article/9508da0079cf405d9f6f644c1119f62f2021-04-01T00:00:00Zhttps://doi.org/10.1038/s41467-021-22555-9https://doaj.org/toc/2041-1723Current aptamer discovery approaches are unable to probe the complete space of possible sequences. Here, the authors use machine learning to facilitate the development of DNA aptamers with improved binding affinities, and truncate them without significantly compromising binding affinity.Ali BashirQin YangJinpeng WangStephan HoyerWenchuan ChouCory McLeanGeoff DavisQiang GongZan ArmstrongJunghoon JangHui KangAnnalisa PawloskyAlexander ScottGeorge E. DahlMarc BerndlMichelle DimonB. Scott FergusonNature PortfolioarticleScienceQENNature Communications, Vol 12, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Ali Bashir Qin Yang Jinpeng Wang Stephan Hoyer Wenchuan Chou Cory McLean Geoff Davis Qiang Gong Zan Armstrong Junghoon Jang Hui Kang Annalisa Pawlosky Alexander Scott George E. Dahl Marc Berndl Michelle Dimon B. Scott Ferguson Machine learning guided aptamer refinement and discovery
description	Current aptamer discovery approaches are unable to probe the complete space of possible sequences. Here, the authors use machine learning to facilitate the development of DNA aptamers with improved binding affinities, and truncate them without significantly compromising binding affinity.
format	article
author	Ali Bashir Qin Yang Jinpeng Wang Stephan Hoyer Wenchuan Chou Cory McLean Geoff Davis Qiang Gong Zan Armstrong Junghoon Jang Hui Kang Annalisa Pawlosky Alexander Scott George E. Dahl Marc Berndl Michelle Dimon B. Scott Ferguson
author_facet	Ali Bashir Qin Yang Jinpeng Wang Stephan Hoyer Wenchuan Chou Cory McLean Geoff Davis Qiang Gong Zan Armstrong Junghoon Jang Hui Kang Annalisa Pawlosky Alexander Scott George E. Dahl Marc Berndl Michelle Dimon B. Scott Ferguson
author_sort	Ali Bashir
title	Machine learning guided aptamer refinement and discovery
title_short	Machine learning guided aptamer refinement and discovery
title_full	Machine learning guided aptamer refinement and discovery
title_fullStr	Machine learning guided aptamer refinement and discovery
title_full_unstemmed	Machine learning guided aptamer refinement and discovery
title_sort	machine learning guided aptamer refinement and discovery
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/9508da0079cf405d9f6f644c1119f62f
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Machine learning guided aptamer refinement and discovery

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