Bayesian Network Resource for Meta‐Analysis: Cellular Toxicity of Quantum Dots

A web‐based resource for meta‐analysis of nanomaterials toxicity is developed whereby the utility of Bayesian networks (BNs) is illustrated for exploring the cellular toxicity of Cd‐containing quantum dots (QDs). BN models are developed based on a dataset compiled from 517 publications comprising 3028 cell viability data samples and 837 IC50 values. BN QD toxicity (BN‐QDTox) models are developed using both continuous (i.e., numerical) and categorical attributes. Using these models, the most relevant attributes identified for correlating IC50 are: QD diameter, exposure time, surface ligand, shell, assay type, surface modification, and surface charge, with the addition of QD concentration for the cell viability analysis. Data exploration via BN models further enables identification of possible association rules for QDs cellular toxicity. The BN models as web‐based applications can be used for rapid intelligent query of the available body of evidence for a given nanomaterial and can be readily updated as the body of knowledge expands. A Bayesian networks (BNs) approach is developed for meta‐analysis of nanomaterials' toxicity. The BN based models are demonstrated for exploring the cellular toxicity of quantum dots (QDs) for i) causal analysis of cellular toxicity, ii) identification of the relevant attributes, iii) intelligent query of the body of evidence, iv) quantification of data uncertainties, and v) identification of toxicity association rules.