From modeling dose-response relationships to improved performance of decision-tree classifiers for predictive toxicology of nanomaterials

Abstract

The development and application of predictive models towards toxicity of engineered nanomaterials is still far from being satisfactory. One promising contribution to confront this challenge is to effectively augment the performance of machine learning classifiers by progressing the approach towards balancing experimental toxicity data. We propose an improved balancing methodology by fitting the in-vitro toxicological dose-response datasets of engineered nanomaterials to three, four, and five, free parameter dose-response models. The four-free parameter model displays the best fit (in terms of adjusted R²) for most of the examined data. The fitted curve yields, in each case, a continuous sequence of data points, which extends the restricted experimental data and generates additional fitted data points for the minority class, leading to the formation of balanced data for predicting the nanoparticle’s toxicology by decision tree classifiers. The ability to best predict the experimental toxicity data, by applying the decision tree model, was tested by forming three versions of the same experimental data: the imbalanced raw experimental data, the balanced data by applying the common Synthetic Minority Oversampling Technique, and by using the approach of Balanced Fitted Dose-Response method, introduced in the present study. We demonstrate that our approach provides improved performance of decision trees in predicting nanoparticles’ toxicity, a method that pertains also to chemical toxicity, central in health and environmental research.