Key Laboratory of Karst Georesources and Environment, Ministry of Education, (Guizhou University), Guiyang, Guizhou, 550025, China.
Key Laboratory of Karst Georesources and Environment, Ministry of Education, (Guizhou University), Guiyang, Guizhou, 550025, China; College of Resources and Environmental Engineering, Guizhou University, Guiyang, Guizhou, 550025, China.
Environ Pollut. 2024 Oct 1;358:124473. doi: 10.1016/j.envpol.2024.124473. Epub 2024 Jun 28.
Machine learning (ML) as a novel model-based approach has been used in studying aquatic toxicology in the environmental field. Zebrafish, as an ideal model organism in aquatic toxicology research, has been widely used to study the toxic effects of various pollutants. However, toxicity testing on organisms may cause significant harm, consume considerable time and resources, and raise ethical concerns. Therefore, ML is used in related research to reduce animal experiments and assist researchers in conducting toxicological research. Although ML techniques have matured in various fields, research on ML-based aquatic toxicology is still in its infancy due to the lack of comprehensive large-scale toxicity databases for environmental pollutants and model organisms. Therefore, to better understand the recent research progress of ML in studying the development, behavior, nerve, and genotoxicity of zebrafish, this review mainly focuses on using ML modeling to assess and predict the toxic effects of zebrafish exposure to different toxic chemicals. Meanwhile, the opportunities and challenges faced by ML in the field of toxicology were analyzed. Finally, suggestions and perspectives were proposed for the toxicity studies of ML on zebrafish in future applications.
机器学习（ML）作为一种新的基于模型的方法，已被用于环境领域的水生毒理学研究。斑马鱼作为水生毒理学研究的理想模式生物，已被广泛用于研究各种污染物的毒性作用。然而，对生物体进行毒性测试可能会造成重大伤害，消耗大量时间和资源，并引发伦理问题。因此，ML 被用于相关研究中，以减少动物实验并协助研究人员进行毒理学研究。尽管 ML 技术在各个领域已经成熟，但由于缺乏针对环境污染物和模式生物的全面大规模毒性数据库，基于 ML 的水生毒理学研究仍处于起步阶段。因此，为了更好地了解 ML 在研究斑马鱼发育、行为、神经和遗传毒性方面的最新研究进展，本综述主要侧重于使用 ML 建模来评估和预测斑马鱼暴露于不同有毒化学物质的毒性效应。同时，分析了 ML 在毒理学领域面临的机遇和挑战。最后，针对 ML 在未来应用中对斑马鱼的毒性研究提出了建议和展望。
