Understanding the toxicity behavior of NPs is of great importance to ensure efficient delivery to intracellular targets without causing cytotoxicity, to measure the long-term effects of nanoparticles (NPs), and to predict risks and hazards to humans and the environment. However, the diversity and complexity of toxicity sample space, individual sampling, and missing/unreported information make it burdensome to draw general conclusions from the whole picture. To overcome the challenges of safe-by-design of NPs, here we create a machine learning approach by knowledge extraction from the literature which is based on the published toxicity data of inorganic, organic and carbon-based NPs. By integrating 15 qualitative and 10 quantitative attributes from 152 articles, we have obtained 4111 instances reflecting the toxicity of NPs with various material, cell, and experimental properties. Implementing association rule mining (ARM), we present that cytotoxicity is primarily related to the core and coating material of NPs, their synthesis route and the exposed cell type. Our model addresses the interdependence of attributes by shedding light on hidden relationships and we claim that it may contribute to the controlled and safe design of NPs in future studies.

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关联规则挖掘法分析纳米颗粒的细胞毒性

了解NPs的毒性行为对于确保有效递送至细胞内靶标而不引起细胞毒性，测量纳米颗粒（NPs）的长期作用以及预测对人类和环境的风险和危害至关重要。然而，毒性样品空间，个体采样以及信息缺失/未报告的多样性和复杂性使得从整体上得出一般性结论很麻烦。克服设计安全的挑战在本文中，我们基于文献中的知识提取创建了一种机器学习方法，该方法基于公开的基于无机，有机和碳基NPs的毒性数据。通过整合152条文章中的15个定性和10个定量属性，我们获得了4111个实例，这些实例反映了具有各种材料，细胞和实验性质的NP的毒性。实施关联规则挖掘（ARM），我们目前认为细胞毒性主要与NP的核心和包被材料，其合成途径和暴露的细胞类型有关。我们的模型通过揭示隐藏的关系来解决属性的相互依赖性，并且我们声称，它可能有助于在未来的研究中对NP进行受控和安全的设计。