Hierarchical Rank Aggregation with Applications to Nanotoxicology |
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Authors: | Trina Patel Donatello Telesca Robert Rallo Saji George Tian Xia André E Nel |
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Institution: | 1. Department of Biostatistics, UCLA Fielding School of Public Health, 650 Charles E. Young Drive South, Los Angeles, CA, 90095, USA 2. UC Center for Environmental Implications of Nanotechnology (UC CEIN), 6522 CNSI Bldg, 570 Westwood Plaza, Los Angeles, CA, 90095-7227, USA 3. Dep. d’Enginyeria Informàtica i Matemàtiques, Universitat Rovira I Virgili, Av. Pa?sos Catalans, 26, 43007, Tarragona, Catalunya, Spain 4. UCLA School of Medicine, Los Angeles, CA, 90095-1736, USA
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Abstract: | The development of high throughput screening (HTS) assays in the field of nanotoxicology provide new opportunities for the hazard assessment and ranking of engineered nanomaterials (ENMs). It is often necessary to rank lists of materials based on multiple risk assessment parameters, often aggregated across several measures of toxicity and possibly spanning an array of experimental platforms. Bayesian models coupled with the optimization of loss functions have been shown to provide an effective framework for conducting inference on ranks. In this article we present various loss-function-based ranking approaches for comparing ENM within experiments and toxicity parameters. Additionally, we propose a framework for the aggregation of ranks across different sources of evidence while allowing for differential weighting of this evidence based on its reliability and importance in risk ranking. We apply these methods to high throughput toxicity data on two human cell-lines, exposed to eight different nanomaterials, and measured in relation to four cytotoxicity outcomes. This article has supplementary material online. |
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