DRUMBEAT temporally resolved interpretable machine learning model for characterizing state transitions in protein dynamics
- DOI
- 10.1038/s42003-026-09995-z
- Published
- 2026-04-15
- 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/s42003-026-09995-z,
title = {DRUMBEAT temporally resolved interpretable machine learning model for characterizing state transitions in protein dynamics},
author = {Babgen Manookian and Elizaveta Mukhaleva and Grigoriy Gogoshin and Supriyo Bhattacharya and Nagarajan Vaidehi and Andrei S. Rodin and Sergio Branciamore},
year = {2026},
journal = {Communications Biology},
doi = {10.1038/s42003-026-09995-z},
url = {https://doi.org/10.1038/s42003-026-09995-z}
}RIS
TY - JOUR TI - DRUMBEAT temporally resolved interpretable machine learning model for characterizing state transitions in protein dynamics AU - Babgen Manookian AU - Elizaveta Mukhaleva AU - Grigoriy Gogoshin AU - Supriyo Bhattacharya AU - Nagarajan Vaidehi AU - Andrei S. Rodin AU - Sergio Branciamore PY - 2026 JO - Communications Biology DO - 10.1038/s42003-026-09995-z UR - https://doi.org/10.1038/s42003-026-09995-z ER -
APA
Manookian, B., Mukhaleva, E., Gogoshin, G., Bhattacharya, S., Vaidehi, N., Rodin, A. S., & Branciamore, S. (2026). DRUMBEAT temporally resolved interpretable machine learning model for characterizing state transitions in protein dynamics. Communications Biology. https://doi.org/10.1038/s42003-026-09995-z
Source records
- crossref · retrieved 2026-09-26T06:02:49.738Z