A Machine Learning-Based Genome Mining Approach Reveals Unprecedented Biarylitide Diversity.
- DOI
- 10.1021/jacsau.6c00500
- Published
- 2026 Jul 27
- Container
- JACS Au
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.1021/jacsau.6c00500,
title = {A Machine Learning-Based Genome Mining Approach Reveals Unprecedented Biarylitide Diversity.},
author = {Padva L and Gullick J and Biermann F and Coe LJ and Zhao Y and Tucker S and Zimmer L and Tailhades J and Kehraus S and Schittenhelm RB and De Voss JJ and Helfrich EJN and Cryle MJ and Crüsemann M},
year = {2026},
journal = {JACS Au},
doi = {10.1021/jacsau.6c00500},
url = {https://doi.org/10.1021/jacsau.6c00500}
}RIS
TY - JOUR TI - A Machine Learning-Based Genome Mining Approach Reveals Unprecedented Biarylitide Diversity. AU - Padva L AU - Gullick J AU - Biermann F AU - Coe LJ AU - Zhao Y AU - Tucker S AU - Zimmer L AU - Tailhades J AU - Kehraus S AU - Schittenhelm RB AU - De Voss JJ AU - Helfrich EJN AU - Cryle MJ AU - Crüsemann M PY - 2026 JO - JACS Au DO - 10.1021/jacsau.6c00500 UR - https://doi.org/10.1021/jacsau.6c00500 ER -
APA
L, P., J, G., F, B., LJ, C., Y, Z., S, T., L, Z., J, T., S, K., RB, S., JJ, D. V., EJN, H., MJ, C., & M, C. (2026). A Machine Learning-Based Genome Mining Approach Reveals Unprecedented Biarylitide Diversity.. JACS Au. https://doi.org/10.1021/jacsau.6c00500
Source records
- pubmed · retrieved 2026-09-26T18:03:07.861Z