III: Small: Collaborative Research: A Large-Scale Data Mining Framework for Genome-Wide Mapping of Multi-Modal Phenotypic Biomarkers and Outcome Prediction

今天的海量数字数据的产生大大超过了计算方法和工具的发展，对实现这些数据的全部变革潜力提出了严峻的挑战。例如，在获取多模式脑成像和全基因组阵列数据方面的最新进展为研究遗传变异对大脑结构和功能的影响提供了令人兴奋的新机会。然而，由于这些数据前所未有的规模和复杂性，主要的计算挑战是对这些数据进行全面联合分析的瓶颈。该项目将利用大规模数据挖掘技术在多视角学习、多任务学习和稳健分类方面的新能力，以解决在系统分析海量多模式遗传、成像和其他生物标志物数据方面的关键挑战。具体地说，本项目将：(1)开发新的多视角学习方法，以从大规模异质成像和其他生物标记数据中检测与任务相关的表型生物标记物；(2)实施新的稀疏多任务回归模型，以揭示多个水平(如SNP、单倍型、基因和/或途径)的表型生物标记物的遗传基础；(3)设计新的基于结构稀疏性的稳健分类方法，用于综合基因和表型数据的结果预测；以及(4)将这些新方法封装到数据挖掘工具包中并向公众发布。该项目的智力优势不仅来自于新数据挖掘方法的发展，还来自于它们在基因成像研究中的应用。这些方法被设计来考虑多个数据模式之间的相互关联的结构，并提供系统的策略来揭示结构成像遗传关联。所提出的方法和工具有望对神经学和心理学研究产生影响，使研究人员能够有效地检验成像遗传学假说，促进生物医学科学和技术的发展。此外，拟议的数据挖掘框架解决了大规模数据分析和集成的一般关键需求，因此将影响大量研究领域，在这些领域中，可能会从海量高维和异质数据集中发现高价值知识和复杂模式。该项目将促进开发新的教育工具，以加强德克萨斯大学阿灵顿分校和国际理工学院的几门现有课程。这两所大学都是为少数群体服务的机构，私人投资机构将让少数群体学生和服务不足的人群参与研究活动，让他们更好地接触尖端科学研究。

Today's massive generation of digital data is greatly outpacing the development of computational methods and tools and presents critical challenges for achieving the full transformative potential of these data. For example, recent advances in acquiring multi-modal brain imaging and genome-wide array data provide exciting new opportunities to study the influence of genetic variation on brain structure and function. Major computational challenges are, however, bottlenecks for comprehensive joint analysis of these data due to their unprecedented scale and complexity. This project will employ the new capabilities of large-scale data mining techniques in multi-view learning, multi-task learning, and robust classification to address critical challenges in systematically analyzing massive multi-modal genetic, imaging, and other biomarker data. Specifically, this project will: (1) develop new multi-view learning methods to detect task-relevant phenotypic biomarkers from large scale heterogeneous imaging and other biomarker data, (2) implement new sparse multi-task regression models to reveal the genetic basis of phenotypic biomarkers at multiple levels (e.g., SNP, haplotype, gene and/or pathway), (3) design novel robust classification methods via structural sparsity for outcome prediction using integrated genotypic and phenotypic data, and (4) package these new methods into a data mining toolkit and release it to the public. The intellectual merits of this project derive not only from the development of novel data mining methods, but also from their application to imaging genetic studies. These methods are designed to take into account interrelated structures among multiple data modalities and offer systematic strategies to reveal structural imaging genetic associations. The proposed methods and tools are expected to impact neurological and psychological research and enable investigators to effectively test imaging genetics hypothesis and advance biomedical science and technology. In addition, the proposed data mining framework addresses generic critical needs of large-scale data analysis and integration and, therefore, will impact a large number of research areas where high-value knowledge and complex patterns can potentially be discovered from massive high-dimensional and heterogeneous data sets. This project will facilitate the development of novel educational tools to enhance several current courses at UT Arlington and IUPUI. Both universities are minority-serving institutions, and the PIs will engage the minority students and under-served populations in research activities to give them a better exposure to cutting-edge scientific research.