生物数据科学中的代数模型选择与实验设计
Algebraic Model Selection and Experimental Design in Biological Data Science
AI总结：
提出统一实验设计与模型选择的数据驱动计算框架，利用线性变换解决生物数据科学中识别已知相互作用与可识别相互作用的问题，并应用于EGFR信号传导模型。
摘要：
实验设计与模型选择虽然是数据科学中的关键步骤，但在生物网络的研究与分析中通常被视为互不相关的过程。不考虑它们之间的相互关联性可能会引入偏差，并增加在建模过程中遗漏显著特征的风险。我们提出了一个数据驱动的计算框架，以统一离散数据集和最小多项式模型的实验设计与模型选择。我们使用一种称为线性变换的特殊仿射变换，为构成模型基础的数据集和多项式项提供支持。该框架使我们能够解决生物数据科学研究中出现的两个重要问题：寻找能够识别一组已知相互作用的数据，以及在给定一组数据的情况下寻找可识别的相互作用。我们为一个可通过网络访问的数据库奠定了理论基础。作为示例，我们将该方法应用于先前构建的表皮衍生生长因子受体（EGFR）信号传导的药效学模型。
英文摘要：
Design of experiments and model selection, though essential steps in data science, are usually viewed as unrelated processes in the study and analysis of biological networks. Not accounting for their inter-relatedness has the potential to introduce bias and increase the risk of missing salient features in the modeling process. We propose a data-driven computational framework to unify experimental design and model selection for discrete data sets and minimal polynomial models. We use a special affine transformation, called a linear shift, to provide both the data sets and the polynomial terms that form a basis for a model. This framework enables us to address two important questions that arise in biological data science research: finding the data which identify a set of known interactions and finding identifiable interactions given a set of data. We present the theoretical foundation for a web-accessible database. As an example, we apply this methodology to a previously constructed pharmacodynamic model of epidermal derived growth factor receptor (EGFR) signaling.
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