Predicting base editing outcomes with an attention-based deep learning algorithm trained on high-throughput target library screens.

Marquart, Kim F; Allam, Ahmed; Janjuha, Sharan; Sintsova, Anna; Villiger, Lukas; Frey, Nina; Krauthammer, Michael; Schwank, Gerald

Marquart, Kim F; Allam, Ahmed; Janjuha, Sharan; Sintsova, Anna; Villiger, Lukas; Frey, Nina; Krauthammer, Michael; Schwank, Gerald.

Afiliación

Marquart KF; Institute of Molecular Health Sciences, ETH Zurich, Zurich, Switzerland.
Allam A; Department of Pharmacology and Toxicology, University of Zurich, Zurich, Switzerland.
Janjuha S; Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland.
Sintsova A; Department of Pharmacology and Toxicology, University of Zurich, Zurich, Switzerland.
Villiger L; Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland.
Frey N; Institute of Microbiology, ETH Zurich, Zurich, Switzerland.
Krauthammer M; Department of Pharmacology and Toxicology, University of Zurich, Zurich, Switzerland.
Schwank G; McGovern Institute for Brain Research at MIT, Massachusetts Institute of Technology, Cambridge, MA, USA.

Nat Commun ; 12(1): 5114, 2021 08 25.

Article en En | MEDLINE | ID: mdl-34433819

Asunto(s)

Aprendizaje Profundo; Biblioteca de Genes; Algoritmos; Emparejamiento Base; Edición Génica; Genoma; Secuenciación de Nucleótidos de Alto Rendimiento; Humanos

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Texto completo: 1 Base de datos: MEDLINE Asunto principal: Biblioteca de Genes / Aprendizaje Profundo Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Nat Commun Asunto de la revista: BIOLOGIA / CIENCIA Año: 2021 Tipo del documento: Article

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