Fei Cheng, Beate I Escher, Huizhen Li, Maria König, Yujun Tong, Jiehui Huang, Liwei He, Xinyan Wu, Xiaohan Lou, Dali Wang, Fan Wu, Yuanyuan Pei, Zhiqiang Yu, Bryan W Brooks, Eddy Y Zeng, Jing You
Identifying causative toxicants in mixtures is critical, but this task is challenging when mixtures contain multiple chemical classes. Effect-based methods are used to complement chemical analyses to identify toxicants, yet conventional bioassays typically rely on an apical and/or single endpoint, providing limited diagnostic potential to guide chemical prioritization. We proposed an event-driven taxonomy framework for mixture risk assessment that relied on high-throughput screening bioassays and toxicant identification integrated by deep learning...
May 2, 2024: Environmental Science & Technology