Collaborative research: Data selection for unique model identification

合作研究：独特模型识别的数据选择

• 批准号: 1419038
• 负责人: Elena Dimitrova
• 金额: $ 10万
• 依托单位: Clemson University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1419038&HistoricalAwards=false
• 关键词: Collaborative; research; Data; selection; unique

While this is the age of big data, there is still a question of whether more data translates to more knowledge. Particularly when generating data is expensive or time consuming, as it is often the case with clinical trials and biomolecular experiments, the problem of identifying information-rich data becomes crucial for creating models that can reliably predict the outcome of future experiments. Few results have been published on the amount of necessary data, and currently there are no methods for generating specific data sets which would unambiguously identify a predictive model. This research project addresses fundamental mathematical and computational questions in data selection. The theoretical results will advance the fields of design of experiments and network inference through the determination of criteria for selecting data sets to uniquely identify models. The algorithms under development will serve as a guide for experimentalists in determining the data that are needed to identify the structure of a network of interest. Such knowledge has the potential to drastically reduce wasted resources that arise from too much data with too little information. Graduate students will participate at the appropriate level in each component of the project. Such an experience will provide possible topics for M.S. or Ph.D. dissertations and will very likely inspire career-long involvement of the participants in the STEM disciplines.As a first step towards developing a complete theory, the PIs will focus on models described by finite-valued nonlinear polynomial functions. Finite-state multivariate polynomial functions have successfully been used to model complex networks from discretized data; however, few results have been published on the amount of data necessary for such models, with the majority applying to Boolean models only. The PIs will address the issue of the minimality and specificity of data to uniquely identify discrete polynomial models by developing the appropriate theory, implementing the theoretical results as algorithms, and applying the algorithms to important physical systems. The proposed work will also increase the utility of polynomial dynamical systems as models of complex networks by establishing the minimal amount of the data for unique model identification.

尽管这是大数据的时代，但仍然存在一个问题，即是否有更多数据转化为更多知识。特别是在生成数据昂贵或耗时的情况下，临床试验和生物分子实验通常是这种情况，识别信息丰富数据的问题对于创建可以可靠地预测未来实验结果的模型至关重要。关于必要数据数量的结果很少，目前尚无生成特定数据集的方法，这些数据集可以明确识别预测模型。该研究项目解决了数据选择中的基本数学和计算问题。 理论结果将通过确定选择数据集以唯一识别模型的标准来推动实验和网络推断的设计领域。开发的算法将作为实验者确定确定目标网络所需的数据的指南。这种知识有可能大幅度减少浪费的资源，这些资源是由太多的数据产生的，信息太少。研究生将在项目的每个组成部分的适当水平上参加。这样的经验将为M.S.提供可能的主题。或博士学位论文并很可能会激发参与者参与STEM学科的职业生涯。作为发展完整理论的第一步，PI将重点关注有限价值的非线性多项式功能所描述的模型。有限状态多元多项式函数已成功地用于从离散数据中对复杂网络进行建模。但是，关于此类模型所需的数据量的结果很少，大多数仅适用于布尔模型。 PI将通过开发适当的理论，将理论结果作为算法实现并将算法应用于重要的物理系统，以唯一识别数据的最小和特异性问题，以唯一识别离散的多项式模型。提出的工作还将通过确定唯一模型标识的数据量最小的数据来增加多项式动力学系统作为复杂网络的模型的实用性。