Abstract:
The health hazards and risks of nanoparticles (NPs) and engineered nanomaterials
(ENMs) are linked to their physicochemical features. Due to their minute structure,
they can cause intracellular and genetic damage, and harm the environment by forming
toxic mixtures with other compounds. Thus, it is essential to assess them first
before they are mass-produced for public use. Traditionally, nanomaterial toxicity
involves in-vivo and in-vitro approaches, but in recent years, machine learning (ML)
algorithms have also emerged as predictive tools through in-silico means. This approach
provides a faster, cheaper, and safer way to assess the toxicological profile
of a nanomaterial. This study aims to investigate the applicability and efficiency of
using hybrid algorithms in nanomaterial toxicity classification. They are formed by
combining Genetic Algorithm (GA) with different base classifiers, namely Logistic
Regression (LR), Artificial Neural Network (ANN), and Random Forest (RF). Generally,
the hybrid algorithm-based models perform better than their base classifier
counterparts, with an increase in scores of up to 19%. Using MCC as the main
metric, results show that GA-RF with SMOTE is the best-performing model with an
MCC score of 0.34. Building upon this model, this study developed a web application
that lets the user input information about a nanomaterial and the cell-based assay
that will be exposed for a certain amount of time. It predicts the cell viability of the
assay to produce a toxicity classification for the nanomaterial.
