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Machine learning predictions of concentration-specific aggregate hazard scores of inorganic nanomaterials in embryonic zebrafish
This work has employed advanced Machine Learning techniques to create nano-QSAR tools for modeling the toxicity of metallic and metal oxide nanomaterials, both coated and uncoated and with a variety of different core compositions, and successfully correlated these properties with the associated biological responses observed in zebrafish.