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.

虽然这是一个大数据时代，但更多的数据是否能转化为更多的知识仍然是一个问题。特别是当生成数据是昂贵或耗时的，因为它通常是临床试验和生物分子实验的情况下，识别信息丰富的数据的问题变得至关重要，创建模型，可以可靠地预测未来的实验结果。关于所需数据量的结果很少，目前还没有生成明确识别预测模型的特定数据集的方法。这个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进实验设计和网络推理领域。正在开发的算法将作为实验人员确定识别感兴趣网络结构所需数据的指南。这种知识有可能大大减少因数据太多而信息太少而造成的资源浪费。研究生将在项目的每个组成部分的适当水平参与。这样的经验将为MS提供可能的主题。或博士作为发展完整理论的第一步，PI将专注于由有限值非线性多项式函数描述的模型。布尔状态多元多项式函数已成功地用于从离散数据对复杂网络进行建模;然而，很少有关于此类模型所需数据量的结果，大多数仅适用于布尔模型。PI将解决数据的最小性和特异性问题，以通过开发适当的理论，将理论结果作为算法实施，并将算法应用于重要的物理系统来唯一识别离散多项式模型。所提出的工作还将通过建立用于唯一模型识别的最小数据量来增加多项式动力系统作为复杂网络模型的实用性。