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

今天，大量数字数据的产生大大超过了计算方法和工具的发展，并为实现这些数据的全部变革潜力提出了严峻的挑战。例如，最近在获取多模态脑成像和全基因组阵列数据方面的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综合联合分析的瓶颈。该项目将在多视图学习、多任务学习和鲁棒分类中采用大规模数据挖掘技术的新功能，以解决系统分析大量多模态遗传、成像和其他生物标志物数据的关键挑战。具体而言，该项目将：(1)开发新的多视图学习方法，从大规模异质成像和其他生物标志物数据中检测任务相关的表型生物标志物；(2)实现新的稀疏多任务回归模型，在多个水平（如SNP、单倍型、基因和/或途径）上揭示表型生物标志物的遗传基础；(3)通过结构稀疏性设计新的鲁棒分类方法，利用综合基因型和表型数据进行结果预测。(4)将这些新方法打包成一个数据挖掘工具包并向公众发布。这个项目的智力价值不仅来自于新的数据挖掘方法的发展，而且来自于它们在成像遗传研究中的应用。这些方法旨在考虑多种数据模式之间的相关结构，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影响神经学和心理学研究，使研究人员能够有效地测试成像遗传学假说，并推进生物医学科学和技术。此外，提出的数据挖掘框架解决了大规模数据分析和集成的通用关键需求，因此，将影响大量研究领域，在这些领域中，高价值的知识和复杂的模式可能会从大量高维和异构数据集中发现。该项目将促进新型教育工具的开发，以加强德克萨斯大学阿灵顿分校和IUPUI的一些现有课程。这两所大学都是为少数民族服务的机构，而pi将让少数民族学生和服务不足的人群参与研究活动，让他们更好地接触前沿科学研究。

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.