Hierarchical Rank Aggregation with Applications to Nanotoxicology

  • Trina Patel
  • Donatello Telesca
  • Robert Rallo
  • Saji George
  • Tian Xia
  • André E. Nel
Article

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.

Key Words

Bayesian hierarchical models Hazard ranking Loss functions Nanotoxicology 

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Copyright information

© International Biometric Society 2013

Authors and Affiliations

  • Trina Patel
    • 1
    • 2
  • Donatello Telesca
    • 1
    • 2
  • Robert Rallo
    • 2
    • 3
  • Saji George
    • 2
    • 4
  • Tian Xia
    • 2
    • 4
  • André E. Nel
    • 2
    • 4
  1. 1.Department of BiostatisticsUCLA Fielding School of Public HealthLos AngelesUSA
  2. 2.UC Center for Environmental Implications of Nanotechnology (UC CEIN)Los AngelesUSA
  3. 3.Dep. d’Enginyeria Informàtica i MatemàtiquesUniversitat Rovira I VirgiliTarragonaSpain
  4. 4.UCLA School of MedicineLos AngelesUSA

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