BIGDATA: Collaborative Research: IA: Big Imaging-Omics Data Mining Framework for Precision Medicine

这项建议的研究目标是解决基于成像组学的精确医学上的创新BigData应用程序中的计算挑战。高通量成像(如组织病理学图像)和多组学(如DNA序列、RNA表达、甲基化等)的最新进展技术为使用定量方法探索组织学、分子事件和临床结果之间的关系创造了新的机会。然而，这些成像数据前所未有的规模和复杂性已经提出了关键的计算瓶颈，需要新的概念和使能工具。该项目构建了一个新的计算框架，将新的大数据挖掘算法与云和高性能计算策略相集成，以揭示组织病理图像、多组学和表型结果之间的复杂关系。该项目具有创新性，不仅对促进新的大数据挖掘技术的开发至关重要，而且对解决成像组学和许多其他生物医学应用中新出现的科学问题也是至关重要的。开发的方法和工具预计将影响其他癌症基因组学研究，并使从事癌症医学工作的研究人员能够有效地检验他们的科学假设。该项目有助于开发新的教育工具，以加强几门现有课程。德克萨斯大学阿灵顿分校是一所为少数族裔服务的机构，拥有大量西班牙裔和黑人美国人。该项目让少数族裔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。为了解决大成像组学数据挖掘中的关键和挑战问题，该项目探索了以下研究任务。首先，开发了大规模非凸稀疏学习模型，用于从大的组织病理学图像中识别与结果相关的表型特征。其次，利用生物学领域知识指导稀疏学习模型揭示复杂性状的分子基础。第三，数据集成模型的设计是为了集成来自多源的成像组学数据，并发现异质生物标志物。第四，探索了贝叶斯学习模型来预测纵向癌症的结果。第五，云计算和高性能计算战略被开发来支持大型成像组学数据挖掘，例如针对异构硬件(如GPU和NUMA多核处理器)上的各种数据挖掘工作负载进行优化，以充分释放数据中心硬件的潜力。将大数据挖掘算法与云和高性能计算集成到成像组学中是一项创新，这为精确医学的系统生物学带来了巨大的希望。

The research objective of this proposal is to address the computational challenges in an innovative BIGDATA application on imaging-omics based precision medicine. Recent advances in high-throughput imaging (such as histopathology image) and multi-omics (such as DNA sequence, RNA expression, methylation, etc.) technologies created new opportunities for exploring relationships between histology, molecular events, and clinical outcomes using quantitative methods. However, the unprecedented scale and complexity of these imaging-omic data have presented critical computational bottlenecks requiring new concepts and enabling tools. This project builds a new computational framework to integrate novel big data mining algorithms with cloud and high-performance computing strategies for revealing complex relationships between histopathology images, multi-omics, and phenotypic outcomes. This project is innovative and crucial not only to facilitating the development of new big data mining techniques, but also to addressing emerging scientific questions in imaging-omics and many other biomedical applications. The developed methods and tools are expected to impact other cancer genomics research and enable investigators working on cancer medicine to effectively test their scientific hypothesis. This project facilitates the development of novel educational tools to enhance several current courses. University of Texas at Arlington is a minority-serving institution and has large population of Hispanic and Black Americans. This project engages the minority students and under-served populations in research activities to give them a better exposure to cutting-edge science research.To solve the key and challenge problems in big imaging-omics data mining, this project explores the following research tasks. First, the large-scale non-convex sparse learning models are developed for identifying outcome-relevant phenotypic traits from big histopathology images. Second, the biological domain knowledge is utilized to guide the sparse learning models to uncover the molecular bases of complex traits. Third, the data integration models are designed to integrate imaging-omics data from multiple sources and discover the heterogeneous biomarkers. Fourth, the Baysian learning model is explored to predict longitudinal cancer outcomes. Fifth, the cloud computing and high-performance computing strategies are developed to support the big imaging-omics data mining, such as optimizations for various data mining workloads on heterogeneous hardware (e.g. GPU and NUMA multicore processors) to fully unlock the potential of data center hardware. It is innovative to integrate big data mining algorithms with cloud and high-performance computing to imaging-omics that hold great promise for a systems biology of the precision medicine.