Machine learning-assisted single-cell Raman fingerprinting for in situ and nondestructive classification of prokaryotes

Accessing enormous uncultivated microorganisms (microbial dark matter) in various Earth environments requires accurate, nondestructive classification, and molecular understanding of the microorganisms in in situ and at the single-cell level. Here we demonstrate a combined approach of random forest (...

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Detalles Bibliográficos
Autores principales: Kanno, Nanako, Kato, Shingo, Ohkuma, Moriya, Matsui, Motomu, Iwasaki, Wataru, Shigeto, Shinsuke
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8397914/
https://www.ncbi.nlm.nih.gov/pubmed/34485857
http://dx.doi.org/10.1016/j.isci.2021.102975

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8397914/
https://www.ncbi.nlm.nih.gov/pubmed/34485857
http://dx.doi.org/10.1016/j.isci.2021.102975

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