From prediction to design rules: explainable machine learning analysis of cryoprotectant effects in GelMA-based cryobioprinting bioinks.
- DOI
- 10.1088/2057-1976/aeb94c
- Published
- 2026-11-04
- Container
- Biomed Phys Eng Express
- Publisher
- Not recorded
- Open access
- no
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Cite this work
BibTeX
@article{allodium:10.1088/2057-1976/aeb94c,
title = {From prediction to design rules: explainable machine learning analysis of cryoprotectant effects in GelMA-based cryobioprinting bioinks.},
author = {Arabinour M and Sultani N and Zhou X and Tayebi L.},
year = {2026},
journal = {Biomed Phys Eng Express},
doi = {10.1088/2057-1976/aeb94c},
url = {https://doi.org/10.1088/2057-1976/aeb94c}
}RIS
TY - JOUR TI - From prediction to design rules: explainable machine learning analysis of cryoprotectant effects in GelMA-based cryobioprinting bioinks. AU - Arabinour M AU - Sultani N AU - Zhou X AU - Tayebi L. PY - 2026 JO - Biomed Phys Eng Express DO - 10.1088/2057-1976/aeb94c UR - https://doi.org/10.1088/2057-1976/aeb94c ER -
APA
M, A., N, S., X, Z., & L., T. (2026). From prediction to design rules: explainable machine learning analysis of cryoprotectant effects in GelMA-based cryobioprinting bioinks.. Biomed Phys Eng Express. https://doi.org/10.1088/2057-1976/aeb94c
Source records
- europe-pmc · retrieved 2026-11-10T11:41:07.251Z