DATA SCIENCE RESEARCH

数据科学研究

• 批准号: 8910929
• 负责人: MERRY L LINDSEY
• 金额: $ 152.12万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-29 至 2018-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8910929
• 关键词: Address; Algorithms; Architecture; Area; Awareness; Big Data; Biological; Cardiovascular Diseases; Cardiovascular system; Clinical Data; Cloud Computing; Communities; Community Participation; Complex; Data; Data Aggregation; Data Set; Discipline; Disease; Elderly; Face; Gene Proteins; Genomics; Health; Individual; Jackson Heart Study; Knowledge; Literature; Longevity; Medicine; Methods; Modeling; Molecular; Molecular Profiling; Organelles; Pathway interactions; Pharmaceutical Preparations; Phenotype; Physiological; Protein Dynamics; Proteins; Proteomics; Recording of previous events; Research; Research Infrastructure; Research Personnel; Resources; Science; Solid; Structure; System; Translating; Validation; analytical tool; base; cloud based; cohort; computerized tools; design; federated computing; innovation; novel; outreach; protein metabolite; spatiotemporal; text searching; tool

A critical challenge in Big Data science is the overall lack of data ahalysis platforms available for transforming Big Data into biological knowledge. To address this challenge, we propose a set of interconnected computational tools capable of organizing and analyzing heterogeneous data to support combined inquiries and to de-convolute complex relationships embedded within large-scale data. We demonstrate its utility with a cardiovascular-centric platform that is easily generalizable to similar efforts in other disciplines. Our Center has designed a federated data architecture of existing resources substantiated by a solid and growing user base, and innovations to elevate functionality. Novel crowdsourcing and text-mining methods will extract the wealth of untapped knowledge embedded in biomedical literature, and novel in-depth proteomics analytical tools will unprecedentedly elucidate dynamic protein features. A key strength of our platform will be the rigorous validation using clinical data from Jackson Heart Study and the Healthy Elderly Active Longevity (HEAL; Wellderly) cohorts. Our proposal includes nine scientific aims that address three main focus areas: (i) we will build a new model platform that amalgamates community-supported Big Data resources, enabling data annotations and collaborative analyses; (ii) we will integrate molecular data with drug and disease information, both structured and unstructured, for knowledge aggregation, and (iii) we will create on-the-cloud analytical and modeling tools to power in-depth protein discoveries. Specifically, we will create a novel distributed query system and cloud-based infrastructure that is capable of providing unified access to multi-omics datasets; we will develop computational and crowdsourcing methods to systematically define relationships between genes, proteins, diseases, and drugs from the literature, emphasizing cardiovascular medicine; we will rally community participation and promote awareness of collaborative research through outreach and educational games; we will create a platform to analyze and visualize multi-scale pathway models of genes, proteins, and metabolites; we will develop tools and algorithms to mechanistically model spatiotemporal protein networks in organelles and to. predict higher physiological phenotypes; and we will correlate individual phenotypes, health histories, and multi-scale molecular profiles to examine cardiovascular disease mechanisms. These tools will be implemented, delivered, and executed on the cloud infrastructure to minimize the computational power required of users.

大数据科学面临的一个关键挑战是总体上缺乏可用于 将大数据转化为生物学知识。为了应对这一挑战，我们提出了一套 能够组织和分析异构数据的互连计算工具， 组合查询和去卷积嵌入在大规模数据中的复杂关系。我们 证明其与心血管中心平台的效用，该平台易于推广到类似的工作， 其他学科。我们的中心设计了现有资源的联合数据架构 通过稳固和不断增长的用户基础以及提升功能的创新来实现。小说 众包和文本挖掘方法将提取嵌入其中的未开发知识财富， 生物医学文献和新颖的深入蛋白质组学分析工具将前所未有地阐明 动态蛋白质特征我们平台的一个关键优势将是使用临床数据进行严格验证 来自杰克逊心脏研究和健康老年人积极长寿（HEAL; Wellderly）队列。我们的建议 包括九个科学目标，涉及三个主要重点领域：（i）我们将建立一个新的模型平台， 整合社区支持的大数据资源，实现数据注释和协作 分析;（ii）我们将整合分子数据与药物和疾病信息，结构化和 非结构化，用于知识聚合，以及（iii）我们将创建云分析和建模工具 为深入的蛋白质发现提供动力具体来说，我们将创建一个新的分布式查询系统， 基于云的基础设施，能够提供对多组学数据集的统一访问;我们将 开发计算和众包方法，系统地定义基因之间的关系， 蛋白质，疾病和药物的文献，强调心血管医学;我们将团结起来， 社区参与，并通过推广和教育提高对合作研究的认识 我们将创建一个平台来分析和可视化基因，蛋白质， 和代谢物;我们将开发工具和算法来机械地模拟时空蛋白质 细胞器中的网络和。预测更高的生理表型;我们将把个体 表型、健康史和多尺度分子谱来检查心血管疾病 机制等这些工具将在云基础架构上实施、交付和执行， 最小化用户所需的计算能力。