Department of Accounting and Finance, Kemmy Business School, University of Limerick, Limerick, Ireland.
Department of Mechanical Engineering, Environmental Informatics Research Group, Aristotle University of Thessaloniki, Thessaloniki, Greece.
Nanotoxicology. 2020 Jun;14(5):612-637. doi: 10.1080/17435390.2020.1729439. Epub 2020 Feb 26.
The exercise of non-testing approaches in nanoparticles (NPs) hazard assessment is necessary for the risk assessment, considering cost and time efficiency, to identify, assess, and classify potential risks. One strategy for investigating the toxicological properties of a variety of NPs is by means of computational tools that decode how nano-specific features relate to toxicity and enable its prediction. This literature review records systematically the data used in published studies that predict nano (eco)-toxicological endpoints using machine learning models. Instead of seeking mechanistic interpretations this review maps the pathways followed, involving biological features in relation to NPs exposure, their physico-chemical characteristics and the most commonly predicted outcomes. The results, derived from published research of the last decade, are summarized visually, providing prior-based data mining paradigms to be readily used by the nanotoxicology community in computational studies.
在进行纳米颗粒 (NPs) 危害评估时，考虑到成本和时间效率，有必要采用非测试方法来识别、评估和分类潜在风险。一种用于研究各种 NPs 的毒理学特性的策略是使用计算工具，这些工具可以解码纳米特定特征与毒性的关系，并能够对其进行预测。本文献综述系统地记录了已发表的研究中使用机器学习模型预测纳米（生态）毒理学终点的数据。本综述没有寻求机械解释，而是绘制了所涉及的途径，包括与 NPs 暴露相关的生物学特征、它们的物理化学特性以及最常预测的结果。这些结果源自过去十年发表的研究，以可视化方式进行总结，为纳米毒理学领域在计算研究中提供了基于先验的数据挖掘范例。
