Predicting bacterial phenotypic traits through improved machine learning using high-quality, curated datasets
- DOI
- 10.1038/s42003-025-08313-3
- Published
- 2025-06-07
- Container
- Communications Biology
- Publisher
- Springer Science and Business Media LLC
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1038/s42003-025-08313-3,
title = {Predicting bacterial phenotypic traits through improved machine learning using high-quality, curated datasets},
author = {Julia Koblitz and Lorenz Christian Reimer and Rüdiger Pukall and Jörg Overmann},
year = {2025},
journal = {Communications Biology},
doi = {10.1038/s42003-025-08313-3},
url = {https://doi.org/10.1038/s42003-025-08313-3}
}RIS
TY - JOUR TI - Predicting bacterial phenotypic traits through improved machine learning using high-quality, curated datasets AU - Julia Koblitz AU - Lorenz Christian Reimer AU - Rüdiger Pukall AU - Jörg Overmann PY - 2025 JO - Communications Biology DO - 10.1038/s42003-025-08313-3 UR - https://doi.org/10.1038/s42003-025-08313-3 ER -
APA
Koblitz, J., Reimer, L. C., Pukall, R., & Overmann, J. (2025). Predicting bacterial phenotypic traits through improved machine learning using high-quality, curated datasets. Communications Biology. https://doi.org/10.1038/s42003-025-08313-3
Source records
- crossref · retrieved 2026-09-25T05:08:06.224Z