Machine learning in the identification, prediction and exploration of environmental toxicology: Challenges and perspectives
Machine learning in the identification, prediction and exploration of environmental toxicology: Challenges and perspectives
复制标题
机器学习在环境毒理学识别、预测和探索中的应用：挑战和观点
DOI：
10.1016/j.jhazmat.2022.129487
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发表时间：
2022
影响因子：
13.6
通讯作者：
Xiangang Hu
中科院分区：
文献类型：
--
作者：
Xiaotong Wu;Qixing Zhou;Li Mu（共同通讯）;Xiangang Hu
在过去的几十年里，数据驱动的机器学习(ML)区别于假设驱动的研究，最近在环境毒理学中受到了极大的关注。然而，ML在环境毒理学中的应用仍处于早期阶段，存在知识差距、数据质量方面的技术瓶颈、高维/异质/小样本数据分析和模型可解释性，以及对环境毒理学缺乏深入了解。鉴于上述问题，我们回顾了文献中的最新进展，并重点介绍了使用ML进行的最新毒理学研究(如学习和预测复杂生物系统中的毒性以及长期和大规模污染的多因素环境情景)。除了通过整合非靶向组学和不良结果途径来预测简单的生物终点外，ML的开发应该专注于揭示毒理学机制。数据驱动的ML与其他方法(如组学分析和不良结果途径框架)的集成使ML在揭示毒理学机制方面具有广泛的应用前景。毒理学和环境科学迫切需要高质量的数据库和可解释的算法。在这篇综述中解决ML的核心问题和未来的挑战可能会缩小环境毒性和计算科学之间的知识差距，并促进未来环境风险的控制。·许多未经测试的化学物质很难用于毒理学预测。·机器学习能够快速预测生物和环境风险。·可解释的机器学习揭示了毒理学机制。·迫切需要环境中的高吞吐量平台和应用程序。·未来工作应提高可解释性，共享大数据平台。
Over the past few decades, data-driven machine learning (ML) has distinguished itself from hypothesis-driven studies and has recently received much attention in environmental toxicology. However, the use of ML in environmental toxicology remains in the early stages, with knowledge gaps, technical bottlenecks in data quality, high-dimensional/heterogeneous/small-sample data analysis and model interpretability, and a lack of an in-depth understanding of environmental toxicology. Given the above problems, we review the recent progress in the literature and highlight state-of-the-art toxicological studies using ML (such as learning and predicting toxicity in complicated biosystems and multiple-factor environmental scenarios of long-term and large-scale pollution). Beyond predicting simple biological endpoints by integrating untargeted omics and adverse outcome pathways, ML development should focus on revealing toxicological mechanisms. The integration of data-driven ML with other methods ( e.g. , omics analysis and adverse outcome pathway frameworks) endows ML with widely promising application in revealing toxicological mechanisms. High-quality databases and interpretable algorithms are urgently needed for toxicology and environmental science. Addressing the core issues and future challenges for ML in this review may narrow the knowledge gap between environmental toxicity and computational science and facilitate the control of environmental risk in the future. • Many untested chemicals are challenging to use for toxicological prediction. • Machine learning enables rapid prediction of biological and environmental risks. • Interpretable machine learning reveals toxicological mechanisms. • High-throughput platforms and applications in environments are urgently needed. • Future work should improve interpretability and share big data platforms.