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.
Guardado en:
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: | |
Acceso en línea: | https://doaj.org/article/9508da0079cf405d9f6f644c1119f62f |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
- Aptamers
-
Single virus genomics: a new tool for virus discovery.
por: Lisa Zeigler Allen, et al.
Publicado: (2011) -
Distance Learning for Food Security and Rural Development: A Perspective from the United Nations Food and Agriculture Organization
por: Scott McLean, et al.
Publicado: (2002) -
Distribution of oil refining resources in Russia in the context of the capacity development of refiners and regions
por: Vladimir P. Klepikov, et al.
Publicado: (2021) -
Aptamers as Versatile Ligands for Biomedical and Pharmaceutical Applications
por: Guan B, et al.
Publicado: (2020)