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Related Experiment Video

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In vitro Cell Culture Model for Toxic Inhaled Chemical Testing

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Bayesian additive adaptive basis tensor product models for modeling high dimensional surfaces: an application to

Matthew W Wheeler¹

¹Risk Analysis Branch, National Institute for Occupational Safety and Health, Cincinnati, Ohio, U.S.A.

|August 7, 2018

Summary

This study introduces a new model for analyzing complex, high-dimensional data, particularly useful for predicting chemical toxicity dose-responses in high throughput toxicity testing.

Keywords:

Dose-response analysis EPA ToxCast Functional data analysis Machine learning Nonparametric Bayesian analysis

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Area of Science:

Computational chemistry
Toxicology
Statistical modeling

Background:

Modern datasets often originate from complex, high-dimensional surfaces, posing challenges for traditional methods like splines or Gaussian processes in dimensions beyond three.
Characterizing these surfaces is crucial for applications such as high throughput toxicity testing, where dose-response curves are influenced by chemical properties.

Purpose of the Study:

To develop a novel statistical model capable of characterizing high-dimensional surfaces.
To predict dose-response curves for untested chemicals based on their structural properties.

Main Methods:

A novel approach models multidimensional surfaces as a sum of learned basis functions.
These basis functions are formed by the tensor product of lower-dimensional functions, also learned from data.
A Gibbs sampling algorithm is proposed for model implementation.

Main Results:

The proposed model effectively characterizes complex, high-dimensional surfaces.
The approach demonstrates utility in predicting chemical dose-responses using simulated data.
Validation was performed using data from the US Environmental Protection Agency's (EPA) ToxCast platform.

Conclusions:

The developed model offers a powerful new tool for analyzing high-dimensional data in toxicology.
This method can improve the prediction of chemical dose-responses, aiding in toxicity assessment.
The approach is suitable for applications involving high throughput screening data.