Bayesian Additive Adaptive Basis Tensor Product Models for Modeling High Dimensional Surfaces: An Application to High-throughput Toxicity Testing.

Details

• Alternative Title:
  Biometrics
• Personal Author:
  Wheeler, Matthew W.
• Description:
  Many modern datasets are sampled with error from complex high-dimensional surfaces. Methods such as tensor product splines or Gaussian processes are effective and well suited for characterizing a surface in two or three dimensions, but they may suffer from difficulties when representing higher dimensional surfaces. Motivated by high throughput toxicity testing where observed dose-response curves are cross sections of a surface defined by a chemical's structural properties, a model is developed to characterize this surface to predict untested chemicals' dose-responses. This manuscript proposes a novel approach that models the multidimensional surface as a sum of learned basis functions formed as the tensor product of lower dimensional functions, which are themselves representable by a basis expansion learned from the data. The model is described and a Gibbs sampling algorithm is proposed. The approach is investigated in a simulation study and through data taken from the US EPA's ToxCast high throughput toxicity testing platform.
• Subjects:
  Animals Article Bayes Theorem Computer Simulation Dose-response Analysis Dose-Response Relationship, Drug Environmental Pollutants EPA ToxCast Functional Data Analysis High-Throughput Screening Assays Humans Machine Learning Nonparametric Bayesian Analysis Normal Distribution Quantitative Structure-Activity Relationship Toxicity Tests

  More +
• Source:
  Biometrics. 75(1):193-201
• Pubmed ID:
  30081432
• Pubmed Central ID:
  PMC6363906
• Document Type:
  Journal Article
• Funding:
  CC999999/Intramural CDC HHS/United States
• Volume:
  75
• Issue:
  1
• Collection(s):
  CDC Public Access
• Main Document Checksum:
  urn:sha256:e22e7b027568f9c43c20a2b40f2b98b003a6f63b6eb1991c8b3d59df7ca248d5
• Download URL:
  https://stacks.cdc.gov/view/cdc/75828/cdc_75828_DS1.pdf
• File Type:
  [PDF - 770.83 KB ]