Evaluating genotoxicity of metal oxide nanoparticles: Application of advanced supervised and unsupervised machine learning techniques

Abstract

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.

Keywords: Classification; Descriptors; Genotoxicity; Metal oxide nanoparticles; Nano-QSAR; Self-organizing map.