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.

众所周知，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不同细胞的时空基因调控网络进行建模。时间和空间重新连接的基因表达网络的推论对动态疾病过程具有巨大的影响，为空间和时间上相互作用的生物过程的动态变化提供了关键的机制见解。这种互连系统的行为可以通过时空图形模型来捕获。用于推断这些模型的现有方法存在一些统计和计算缺陷，这使得它们在现实环境中不切实际。为了弥合这一知识差距，该项目旨在开发有效的计算工具来推理时空图形模型，这些模型不仅可证明是最优的，而且具有自适应性、可并行性和可在有意义的规模上实现。该提案中开发的方法将在推断肿瘤发生背后的基因网络的背景下进行研究。通过这些努力生成的数据集将配备完善的分析工具，以获取对生物过程背后的基因网络本质的机械见解。更广泛地说，所提出的机制将产生可由领域专家解释的模型，并将产生一组丰富的公开可用数据集，这些数据集可用作不同推理方法的测试平台，从而产生更广泛的人工智能（AI ）-人类协作。图模型推理的大部分进展都是基于具有宽松正则化的最大似然估计（MLE），这既不会产生理想的统计特性，也不会缩放到时空设置中遇到的维度。该项目将通过脱离正则化 MLE 范式，并采用一类具有组合性质的新型约束优化问题来解决这些挑战，这些问题可以系统地捕获时空图形模型隐藏但有用的结构。由于基于 MLE 的方法极其复杂，其实际实现无法同时保证计算效率和良好的统计性能。因此，所提出的方法将是第一个能够以统一的方式实现两全其美的系统推理框架。新型估计方法将对统计学习产生深远的影响：它将引发人们对使用易处理的离散方法及其统计特性的新兴趣，并将为发现适合大规模数据的新推理方法铺平道路。 -维度和时空设置。此外，拟议的项目将是对目前知之甚少的一类离散优化问题的第一个系统研究，从而也有助于组合和混合整数社区。鉴于其跨学科性质，该项目还将在很大程度上有助于培训未来几代数据科学研究人员。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。