Hybrid mechanistic-machine learning models in biosciences: Causality, forecasting, and lab-to-field translation
- DOI
- 10.1016/j.mbs.2026.109801
- Published
- 2026-11
- Container
- Mathematical Biosciences
- Publisher
- Elsevier BV
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1016/j.mbs.2026.109801,
title = {Hybrid mechanistic-machine learning models in biosciences: Causality, forecasting, and lab-to-field translation},
author = {Hao Wang and Amit K. Chakraborty and Esha Saha},
year = {2026},
journal = {Mathematical Biosciences},
doi = {10.1016/j.mbs.2026.109801},
url = {https://doi.org/10.1016/j.mbs.2026.109801}
}RIS
TY - JOUR TI - Hybrid mechanistic-machine learning models in biosciences: Causality, forecasting, and lab-to-field translation AU - Hao Wang AU - Amit K. Chakraborty AU - Esha Saha PY - 2026 JO - Mathematical Biosciences DO - 10.1016/j.mbs.2026.109801 UR - https://doi.org/10.1016/j.mbs.2026.109801 ER -
APA
Wang, H., Chakraborty, A. K., & Saha, E. (2026). Hybrid mechanistic-machine learning models in biosciences: Causality, forecasting, and lab-to-field translation. Mathematical Biosciences. https://doi.org/10.1016/j.mbs.2026.109801
Source records
- crossref · retrieved 2026-09-25T12:10:55.475Z