From literature to predictive modeling: Insights and machine learning applications from in vitro comet assays related to the genotoxicity of titanium dioxide nanomaterials

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values of 0.906 and 0.788 for the P-chem and Experimental dataset, respectively. Exposure concentration, cold lysis conditions, and electrophoresis parameters emerged as key predictors of DNA damage, alongside contributions from NM properties. These findings highlight the intricate interplay between NM properties and experimental conditions in genotoxicity assessments. By providing a FAIR dataset, this study facilitates future research, allowing for the integration of additional variables and quality criteria to enhance the modeling approach. This work reinforces the value of nano-informatics in nanosafety and serves as a footing for advancing data-driven hazard assessment methodologies, positioning ML-enabled read-across strategies as a valuable tool for regulatory nanosafety framework.