高维表面建模的贝叶斯加性自适应基张量积模型：在高通量毒性测试中的应用
Bayesian Additive Adaptive Basis Tensor Product Models for Modeling High Dimensional Surfaces: An application to high-throughput toxicity testing
摘要：
许多现代数据集都是从复杂的高维表面采样的。诸如张量积样条或高斯过程之类的方法有效/非常适合于表征二维或三维的表面，但在表示更高维的表面时可能会遇到困难。受高通量毒性测试的启发，其中观察到的剂量-响应曲线是由化学品的结构特性定义的表面的横截面，开发了一种模型来表征该表面以预测未经测试的化学品的剂量-响应。这篇手稿提出了一种新的方法，将多维表面建模为学习的基函数的总和，这些基函数形成为低维函数的张量积，这些函数本身可以通过从数据中学习的基扩展来表示。描述了该模型，提出了一种吉布斯抽样算法，并在模拟研究以及从美国环保局的ToxCast高通量毒性测试平台的数据进行了研究。
英文摘要：
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.
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