# Bayesian Additive Adaptive Basis Tensor Product Models for Modeling High Dimensional Surfaces: An application to high-throughput toxicity testing

## TL;DR

This paper introduces a Bayesian tensor product model that efficiently captures complex high-dimensional surfaces, demonstrated on high-throughput toxicity data to predict chemical dose-responses.

## Contribution

It develops a novel Bayesian additive model using tensor product basis functions for high-dimensional surface modeling, suitable for high-throughput toxicity testing.

## Key findings

- Effective modeling of high-dimensional toxicity surfaces
- Accurate prediction of untested chemical dose-responses
- Demonstrated superior performance in simulation and real data

## Abstract

Many modern data sets are sampled with error from complex high-dimensional surfaces. Methods such as tensor product splines or Gaussian processes are effective/well suited for characterizing a surface in two or three dimensions but 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, a Gibbs sampling algorithm proposed, and is investigated in a simulation study as well as data taken from the US EPA's ToxCast high throughput toxicity testing platform.