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:
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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
Source:
Biometrics. 75(1):193-201
Pubmed ID:
30081432
Pubmed Central ID:
PMC6363906
Document Type:
Journal Article
Funding:
CC999999/Intramural CDC HHS/United States
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:

Supporting Files

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