Collaborative Research: CDS&E: Scalable Inference for Spatio-Temporal Markov Random Fields

合作研究：CDS

• 批准号: 2152777
• 负责人: Andres Gomez
• 金额: $ 15万
• 依托单位: University of Southern California
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-01 至 2025-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2152777&HistoricalAwards=false
• 关键词: Collaborative; Research; CDS& Scalable; Inference

Modern systems are known to be massive-scale, with a hierarchy of complex, dynamic, and unknown topologies. For example, in genomics, the interactions among genes can be modeled via spatio-temporal gene regulatory networks across different cells. The inference of temporal and spatially-rewired gene expression networks carries enormous implications for dynamic disease processes, offering key mechanistic insights into the dynamic variations of interacting biological processes in space and time. The behavior of such interconnected systems can be captured via spatio-temporal graphical models. The existing methods for inferring these models suffer from several statistical and computational drawbacks which render them impractical in realistic settings. With the goal of bridging this knowledge gap, this project aims at developing efficient computational tools for the inference of spatio-temporal graphical models that are not only provably optimal, but also adaptive, parallelizable, and implementable in meaningful scales. The methods developed in this proposal will be studied in the context of inferring gene networks underlying oncogenesis. The datasets generated through these efforts will be accompanied with well-developed analytics tools to derive mechanistic insights into the nature of gene-networks underlying biological processes. More broadly, the proposed machinery will give rise to models that are interpretable by domain experts, and will lead to a rich set of publicly-available datasets that can be used as test-bed for different inference methods, resulting in broader artificial intelligence (AI)-human collaborations.Much of the progress in the inference of graphical models is based on the maximum likelihood estimation (MLE) with relaxed regularization, which neither result in ideal statistical properties nor scale to dimensions encountered in spatio-temporal settings. This project will address these challenges by departing from the regularized MLE paradigm, and resorting to a new class of constrained optimization problems with combinatorial nature that can systematically capture the hidden-but-useful structure of the spatio-temporal graphical models. Due to the prohibitively complex nature of the MLE-based methods, their practical implementations cannot simultaneously guarantee computational efficiency and favorable statistical performance. Therefore, the proposed approach will be the first systematic inference framework that can achieve the best of both worlds in a unified fashion. The new class of estimation methods will have a profound impact in statistical learning: it will lead to a renewed interest in the use of tractable discrete approaches and their statistical properties, and will pave the way towards the discovery of new inference methods suitable for the large-dimensional and spatio-temporal settings. In addition, the proposed project will be the first systematic study of a class of discrete optimization problems that are currently poorly understood, thus contributing to the combinatorial and mixed-integer communities as well. Given its interdisciplinary nature, the project will also largely contribute to training of future generations of researchers in data science.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

已知现代系统是大规模的，具有复杂，动态和未知拓扑的层次结构。例如，在基因组学中，基因之间的相互作用可以通过跨不同细胞的时空基因调节网络进行建模。时间和空间培养的基因表达网络的推论对动态疾病过程具有巨大的影响，从而为时空中相互作用的生物学过程的动态变化提供了关键的机械见解。可以通过时空图形模型捕获此类互连系统的行为。推断这些模型的现有方法遭受了几种统计和计算缺陷，这些统计和计算缺陷使它们在现实的设置中不切实际。为了弥合这个知识差距，该项目旨在开发有效的计算工具，以推断时空图形模型，这些模型不仅是最佳的，而且在有意义的尺度上也是适应性，可行的，可平行的和可实现的。该提案中开发的方法将在推断肿瘤发生的基因网络的背景下进行研究。通过这些努力产生的数据集将伴随着发达的分析工具，以获取对生物学过程基因网络性质的机械见解。更广泛地说，所提出的机械将产生由领域专家可以解释的模型，并将导致一系列可公开可用的数据集，这些数据集可用作不同推理方法的测试床，从而使更广泛的人工智能（AI） - 人类合作均取得了众多模型的最大程度的估计（MILEI）的概述（MILEI）的范围，这是MiveSrized的最大程度的发展。在时空设置中遇到的统计属性或规模。该项目将通过脱离正规MLE范式来解决这些挑战，并诉诸于组合性质的新一类约束优化问题，这些问题可以系统地捕获时空图形模型的隐藏且可用的结构。由于基于MLE的方法的过于复杂的性质，其实际实现无法同时保证计算效率和有利的统计绩效。因此，拟议的方法将是第一个可以以统一的方式实现两全其美的系统推理框架。新的估计方法将在统计学习中产生深远的影响：它将对使用可拖动的离散方法及其统计特性产生重新兴趣，并将为发现适合大维和时空环境的新推理方法铺平道路。此外，拟议的项目将是对当前知之甚少的一类离散优化问题的首次系统研究，从而有助于组合和混合人社区。鉴于其跨学科性质，该项目还将在很大程度上有助于对子孙后代的数据科学研究人员的培训。该奖项反映了NSF的法定任务，并使用基金会的智力优点和更广泛的影响评估审查标准，认为值得通过评估来获得支持。