Development of machine learning-based multi-task quantitative structure-activity relationship models for predicting toxicities in six human organ systems.
- DOI
- 10.1016/j.comtox.2025.100399
- Published
- 2026 Mar
- Container
- Computational toxicology (Amsterdam, Netherlands)
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.1016/j.comtox.2025.100399,
title = {Development of machine learning-based multi-task quantitative structure-activity relationship models for predicting toxicities in six human organ systems.},
author = {Wu PY and Chou WC and Kamineni VN and Chen CY and Hsieh JH and Vulpe CD and Lin Z},
year = {2026},
journal = {Computational toxicology (Amsterdam, Netherlands)},
doi = {10.1016/j.comtox.2025.100399},
url = {https://doi.org/10.1016/j.comtox.2025.100399}
}RIS
TY - JOUR TI - Development of machine learning-based multi-task quantitative structure-activity relationship models for predicting toxicities in six human organ systems. AU - Wu PY AU - Chou WC AU - Kamineni VN AU - Chen CY AU - Hsieh JH AU - Vulpe CD AU - Lin Z PY - 2026 JO - Computational toxicology (Amsterdam, Netherlands) DO - 10.1016/j.comtox.2025.100399 UR - https://doi.org/10.1016/j.comtox.2025.100399 ER -
APA
PY, W., WC, C., VN, K., CY, C., JH, H., CD, V., & Z, L. (2026). Development of machine learning-based multi-task quantitative structure-activity relationship models for predicting toxicities in six human organ systems.. Computational toxicology (Amsterdam, Netherlands). https://doi.org/10.1016/j.comtox.2025.100399
Source records
- pubmed · retrieved 2026-09-25T08:39:29.579Z