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Open Access
Peer-reviewed
Research Article
Generative and interpretable machine learning for aptamer design and analysis of in vitro sequence selection
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Andrea Di Gioacchino ,
Roles Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing – original draft, Writing – review & editing
¶‡ These authors contributed equally to this work and are ordered alphabetically.
Affiliation Laboratoire de Physique de l’Ecole Normale Supérieure, PSL & CNRS UMR8063, Sorbonne Université, Université de Paris, Paris, France
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Jonah Procyk ,
Roles Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing – original draft, Writing – review & editing
¶‡ These authors contributed equally to this work and are ordered alphabetically.
Affiliation School of Molecular Sciences and Center for Molecular Design and Biomimetics, The Biodesign Institute, Arizona State University, Tempe, Arizona, United States of America
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Marco Molari,
Roles Data curation, Formal analysis, Methodology, Software, Visualization, Writing – original draft
Affiliations Laboratoire de Physique de l’Ecole Normale Supérieure, PSL & CNRS UMR8063, Sorbonne Université, Université de Paris, Paris, France, Biozentrum, University of Basel, Basel, Switzerland, Swiss Institute of Bioinformatics, Basel, Switzerland
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John S. Schreck,
Roles Formal analysis, Methodology
Affiliation National Center for Atmospheric Research, Computational and Information Systems Laboratory, Boulder, Colorado, United States of America
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Yu Zhou,
Roles Methodology
Affiliation School of Molecular Sciences and Center for Molecular Design and Biomimetics, The Biodesign Institute, Arizona State University, Tempe, Arizona, United States of America
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Yan Liu,
Roles Methodology
Affiliation School of Molecular Sciences and Center for Molecular Design and Biomimetics, The Biodesign Institute, Arizona State University, Tempe, Arizona, United States of America
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Rémi Monasson ,
Roles Conceptualization, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Writing – original draft, Writing – review & editing
* E-mail: remi.monasson@phys.ens.fr (RM); simona.cocco@phys.ens.fr (SC); psulc@asu.edu (PŠ)
Affiliation Laboratoire de Physique de l’Ecole Normale Supérieure, PSL & CNRS UMR8063, Sorbonne Université, Université de Paris, Paris, France
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Simona Cocco ,
Roles Conceptualization, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Writing – original draft, Writing – review & editing
* E-mail: remi.monasson@phys.ens.fr (RM); simona.cocco@phys.ens.fr (SC); psulc@asu.edu (PŠ)
Affiliation Laboratoire de Physique de l’Ecole Normale Supérieure, PSL & CNRS UMR8063, Sorbonne Université, Université de Paris, Paris, France
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Petr Šulc
Roles Conceptualization, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Writing – original draft, Writing – review & editing
* E-mail: remi.monasson@phys.ens.fr (RM); simona.cocco@phys.ens.fr (SC); psulc@asu.edu (PŠ)
Affiliation School of Molecular Sciences and Center for Molecular Design and Biomimetics, The Biodesign Institute, Arizona State University, Tempe, Arizona, United States of America
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Generative and interpretable machine learning for aptamer design and analysis of in vitro sequence selection
- Andrea Di Gioacchino,
- Jonah Procyk,
- Marco Molari,
- John S. Schreck,
- Yu Zhou,
- Yan Liu,
- Rémi Monasson,
- Simona Cocco,
- Petr Šulc
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- Published: September 29, 2022
- https://doi.org/10.1371/journal.pcbi.1010561