kinGEMs: A robust and scalable framework for resource-constraint models through stochastic tuning of deep learning-predicted kinetic parameters
- DOI
- 10.1371/journal.pcbi.1014881
- Published
- 2026-11-06
- Container
- PLOS Computational Biology
- Publisher
- Public Library of Science (PLoS)
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1371/journal.pcbi.1014881,
title = {kinGEMs: A robust and scalable framework for resource-constraint models through stochastic tuning of deep learning-predicted kinetic parameters},
author = {Rana A. Barghout and Lya Chiñas Serrano and Benjamin Sanchez-Lengeling and Radhakrishnan Mahadevan},
year = {2026},
journal = {PLOS Computational Biology},
doi = {10.1371/journal.pcbi.1014881},
url = {https://doi.org/10.1371/journal.pcbi.1014881}
}RIS
TY - JOUR TI - kinGEMs: A robust and scalable framework for resource-constraint models through stochastic tuning of deep learning-predicted kinetic parameters AU - Rana A. Barghout AU - Lya Chiñas Serrano AU - Benjamin Sanchez-Lengeling AU - Radhakrishnan Mahadevan PY - 2026 JO - PLOS Computational Biology DO - 10.1371/journal.pcbi.1014881 UR - https://doi.org/10.1371/journal.pcbi.1014881 ER -
APA
Barghout, R. A., Serrano, L. C., Sanchez-Lengeling, B., & Mahadevan, R. (2026). kinGEMs: A robust and scalable framework for resource-constraint models through stochastic tuning of deep learning-predicted kinetic parameters. PLOS Computational Biology. https://doi.org/10.1371/journal.pcbi.1014881
Source records
- crossref · retrieved 2026-11-08T08:11:52.713Z