High-throughput microbial culturomics using automation and machine learning.

Huang Y, Sheth RU, Zhao S, Cohen LA, Dabaghi K, Moody T, Sun Y, Ricaurte D, Richardson M, Velez-Cortes F, Blazejewski T, Kaufman A, Ronda C, Wang HH

Open source

DOI: 10.1038/s41587-023-01674-2
Published: 2023 Oct
Container: Nature biotechnology
Publisher: Not recorded
Open access: yes

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Cite this work

BibTeX

@article{allodium:10.1038/s41587-023-01674-2,
  title = {High-throughput microbial culturomics using automation and machine learning.},
  author = {Huang Y and Sheth RU and Zhao S and Cohen LA and Dabaghi K and Moody T and Sun Y and Ricaurte D and Richardson M and Velez-Cortes F and Blazejewski T and Kaufman A and Ronda C and Wang HH},
  year = {2023},
  journal = {Nature biotechnology},
  doi = {10.1038/s41587-023-01674-2},
  url = {https://doi.org/10.1038/s41587-023-01674-2}
}

RIS

TY  - JOUR
TI  - High-throughput microbial culturomics using automation and machine learning.
AU  - Huang Y
AU  - Sheth RU
AU  - Zhao S
AU  - Cohen LA
AU  - Dabaghi K
AU  - Moody T
AU  - Sun Y
AU  - Ricaurte D
AU  - Richardson M
AU  - Velez-Cortes F
AU  - Blazejewski T
AU  - Kaufman A
AU  - Ronda C
AU  - Wang HH
PY  - 2023
JO  - Nature biotechnology
DO  - 10.1038/s41587-023-01674-2
UR  - https://doi.org/10.1038/s41587-023-01674-2
ER  -

APA

Y, H., RU, S., S, Z., LA, C., K, D., T, M., Y, S., D, R., M, R., F, V., T, B., A, K., C, R., & HH, W. (2023). High-throughput microbial culturomics using automation and machine learning.. Nature biotechnology. https://doi.org/10.1038/s41587-023-01674-2

Source records

pubmed · retrieved 2026-09-25T20:11:42.940Z