GanCtrl: a generative AI approach to derive study-aligned synthetic controls for reducing concurrent control animal use.

Chandra M, Chen X, Li T, Tong W.

DOI: 10.1093/toxsci/kfag099
Published: 2026-08-01
Container: Toxicol Sci
Publisher: Not recorded
Open access: yes

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Cite this work

BibTeX

@article{allodium:10.1093/toxsci/kfag099,
  title = {GanCtrl: a generative AI approach to derive study-aligned synthetic controls for reducing concurrent control animal use.},
  author = {Chandra M and  Chen X and  Li T and  Tong W.},
  year = {2026},
  journal = {Toxicol Sci},
  doi = {10.1093/toxsci/kfag099},
  url = {https://doi.org/10.1093/toxsci/kfag099}
}

RIS

TY  - JOUR
TI  - GanCtrl: a generative AI approach to derive study-aligned synthetic controls for reducing concurrent control animal use.
AU  - Chandra M
AU  -  Chen X
AU  -  Li T
AU  -  Tong W.
PY  - 2026
JO  - Toxicol Sci
DO  - 10.1093/toxsci/kfag099
UR  - https://doi.org/10.1093/toxsci/kfag099
ER  -

APA

M, C., X, C., T, L., & W., T. (2026). GanCtrl: a generative AI approach to derive study-aligned synthetic controls for reducing concurrent control animal use.. Toxicol Sci. https://doi.org/10.1093/toxsci/kfag099

Source records

europe-pmc · retrieved 2026-09-27T00:15:05.833Z