Combining machine learning and nanopore construction creates an artificial intelligence nanopore for coronavirus detection
Rapid, accurate and specific point-of-care diagnostics can help manage and contain fast-spreading infections. Here, the authors present a nanopore-based system that uses artificial intelligence to discriminate between four coronaviruses in saliva, with little need for sample pre-processing.
Guardado en:
Autores principales: | Masateru Taniguchi, Shohei Minami, Chikako Ono, Rina Hamajima, Ayumi Morimura, Shigeto Hamaguchi, Yukihiro Akeda, Yuta Kanai, Takeshi Kobayashi, Wataru Kamitani, Yutaka Terada, Koichiro Suzuki, Nobuaki Hatori, Yoshiaki Yamagishi, Nobuei Washizu, Hiroyasu Takei, Osamu Sakamoto, Norihiko Naono, Kenji Tatematsu, Takashi Washio, Yoshiharu Matsuura, Kazunori Tomono |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Nature Portfolio
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/7cfcb758d04548a9925462a3e647155b |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Field effect control of translocation dynamics in surround-gate nanopores
por: Makusu Tsutsui, et al.
Publicado: (2021) -
Charging dynamics of an individual nanopore
por: Ran Tivony, et al.
Publicado: (2018) -
Ion selectivity of graphene nanopores
por: Ryan C. Rollings, et al.
Publicado: (2016) -
Sequencing DNA with nanopores: Troubles and biases.
por: Clara Delahaye, et al.
Publicado: (2021) -
Protein identification by nanopore peptide profiling
por: Florian Leonardus Rudolfus Lucas, et al.
Publicado: (2021)