Abstract:
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Genotoxicity of chemicals is commonly tested using the Ames Assay. Although it is considered the gold standard, the Ames assay is unsuitable for high-throughput testing since it is time consuming and requires a large of amount of compound. Also, from a public health perspective, it would be advantageous to predict the results of an Ames assay prior to chemical development. Thus, to address these problems, we developed a Bayesian nonparametric model that can be used to predict features of a dose-response. Our model assumes that chemical-specific dose-response functions come from two different populations based on whether or not the function exhibits a particular feature (e.g., genotoxicity). Feature probabilities are modeled using a logistic regression dependent on chemical-specific predictors such as chemical properties or highthroughput assay results. The individual dose-response functions within each population are subject to an umbrella-ordering constraint and clustered using a Dirichlet Process, which provides further insight on relationships between chemicals. The model is used to predict the genotoxicity of 492 chemicals using genotoxic alert data.
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