Machine learning interprets genome sequences to map possible microbial symbionts

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DOI: 10.1038/s41587-026-03212-2
Published: 2026-09-02
Container: Nature Biotechnology
Publisher: Springer Science and Business Media LLC
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.1038/s41587-026-03212-2,
  title = {Machine learning interprets genome sequences to map possible microbial symbionts},
  year = {2026},
  journal = {Nature Biotechnology},
  doi = {10.1038/s41587-026-03212-2},
  url = {https://doi.org/10.1038/s41587-026-03212-2}
}

RIS

TY  - JOUR
TI  - Machine learning interprets genome sequences to map possible microbial symbionts
PY  - 2026
JO  - Nature Biotechnology
DO  - 10.1038/s41587-026-03212-2
UR  - https://doi.org/10.1038/s41587-026-03212-2
ER  -

APA

Machine learning interprets genome sequences to map possible microbial symbionts. (2026). Nature Biotechnology. https://doi.org/10.1038/s41587-026-03212-2

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crossref · retrieved 2026-11-08T05:06:57.194Z