Interdisciplinary Center for Nanotoxicity, Jackson State University, Jackson, MS, USA; Department of Computer Science, Dartmouth College, Hanover, 03755, NH, USA.
Department of Chemoinformatics, National Institute of Chemistry, Ljubljana, 1000, Slovenia.
Ecotoxicol Environ Saf. 2019 Dec 15;185:109733. doi: 10.1016/j.ecoenv.2019.109733. Epub 2019 Sep 30.
Presence of missing data points in datasets is among main challenges in handling the toxicological data for nanomaterials. As the processing of missing data is an important part of data analysis, we have introduced a read-across approach that uses a combination of supervised and unsupervised machine learning techniques to fill the missing values. A series of classification models (supervised learning) was developed to predict class label, and self-organizing map approach (unsupervised learning) was used to estimate relative distances between nanoparticles and refine results obtained during supervised learning. In this study, genotoxicity of 49 silicon and metal oxide nanoparticles in Ames and Comet tests. Collected literature data did not demonstrate significant variations related to the change of size including selected bulk materials. Genotoxicity-related features of nanomaterials were represented by ionic characteristics. General tendencies found in the current study were convincingly linked to known theories of genotoxic action at nano-level. Mechanisms of primary and secondary genotoxic effects were discussed in the context of developed models.
数据集存在缺失数据点是处理纳米材料毒理学数据的主要挑战之一。由于缺失数据的处理是数据分析的重要组成部分，我们引入了一种基于文本的方法，该方法结合了监督和无监督机器学习技术来填补缺失值。开发了一系列分类模型（监督学习）来预测类别标签，并且使用自组织映射方法（无监督学习）来估计纳米颗粒之间的相对距离，并在监督学习期间细化结果。在这项研究中，49 种硅和金属氧化物纳米颗粒在 Ames 和彗星试验中的遗传毒性。收集的文献数据没有显示出与尺寸变化相关的显著变化，包括选定的块状材料。纳米材料的遗传毒性相关特征由离子特性表示。在当前研究中发现的一般趋势与纳米级遗传作用的已知理论令人信服地联系在一起。在开发的模型背景下讨论了初级和次级遗传效应的机制。
