A Community Effort to Translate Protein Data to Knowledge: An Integrated Platform

将蛋白质数据转化为知识的社区努力：一个集成平台

• 批准号: 8935858
• 负责人: MERRY L LINDSEY
• 金额: $ 274.85万
• 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/8935858
• 关键词: Achievement; Address; Algorithmic Software; Algorithms; Architecture; Awareness; Big Data; Bioinformatics; Biological; Cardiovascular Diseases; Cardiovascular system; Clinical; Clinical Data; Cloud Computing; Communities; Computational Biology; Crowding; Data; Data Aggregation; Data Analyses; Data Set; Disease; Education and Outreach; Elderly; Environment; Exhibits; Face; Funding; Future; Gene Proteins; General Population; Generations; Genes; Goals; Half-Life; Harvest; Health; Human; Imagery; Jackson Heart Study; Knowledge; Literature; Longevity; Machine Learning; Maps; Medicine; Methods; Mining; Modeling; Modification; Molecular; Molecular Profiling; Molecular Structure; Organ; Pathway interactions; Patients; Pharmaceutical Preparations; Phenotype; Physiological; Population Research; Property; Protein Dynamics; Proteins; Recording of previous events; Research; Research Personnel; Research Proposals; Resources; Rest; Science; Scientist; Structure; Support System; System; Time; Training; Training and Education; Transcript; Translating; United States National Institutes of Health; clinical phenotype; cohort; computerized data processing; computerized tools; data management; data modeling; design; experience; improved; innovation; insight; interest; novel; operation; outreach program; protein complex; protein expression; protein metabolite; protein protein interaction; public health relevance; repository; spatiotemporal; success; text searching; tool

DESCRIPTION (provided by applicant): The inception of the BD2K Initiative is a testament to the foresight of NIH and our community. Clearly, the future of biomedicine rests on our collective ability to transform Big Data into intelligible scientific facts. In line with the BD2K objectives,our goal is to revolutionize how we address the universal challenge to discern meaning from unruly data. Capitalizing on our investigators' complementary strengths in computational biology and cardiovascular medicine, we will present a fusion of cutting-edge innovations that are grounded in a cardiovascular research focus, encompassing: (i) on-the-cloud data processing, (ii) crowd sourcing and text-mining data annotation, (iii) protein spatiotemporal dynamics, (iv) multi-omic integration, and (v) multiscale clinical data modeling. Drawing from our decade of experience in creating and refining bioinformatics tools, we propose to amalgamate established Big Data resources into a generalizable model for data annotation and collaborative research, through a new query system and cloud infrastructure for accessing multiple omics repositories, and through computational-supported crowdsourcing initiatives for mining the biomedical literature. We propose to interweave diverse data types for revealing biological networks that coalesce from molecular entities at multiple scales, through machine learning methods for structuring molecular data and defining relationships with drugs and diseases, and through novel algorithms for on-the-cloud integration and pathway visualization of multi-dimensional molecular data. Moreover, we propose to innovate advanced modeling tools to resolve protein dynamics and spatiotemporal molecular mechanisms, through mechanistic modeling of protein properties and 3D protein expression maps, and through Bayesian algorithms that correlate patient phenotypes, health histories, and multi-scale molecular profiles. The utility and customizability o our tools to the broader research population is clearly demonstrated using three archetypical workflows that enable annotations of large lists of genes, transcripts, proteins, or metabolites; powerful analysis of complex protein datasets acquired over time; and seamless aQoregation of diverse molecular, textual and literature data. These workflows will be rigorously validated using data from two significant clinical cohorts, the Jackson Heart Study and the Healthy Elderly Longevity (Wellderly). In parallel, a multifaceted strategy will be implemented to educate and train biomedical investigators, and to engage the public for promoting the overall BD2K initiative. We are convinced that a community-driven BD2K initiative will best realize its scientific potential and transform the research culture in a sustainable manner, exhibiting lasting success beyond the current funding period.

描述(申请人提供)：BD2K计划的启动证明了NIH和我们社区的远见卓识。显然，生物医学的未来取决于我们将大数据转化为可理解的科学事实的集体能力。与BD2K的目标一致，我们的目标是彻底改变我们如何应对从不守规矩的数据中识别含义的普遍挑战。利用我们研究人员在计算生物学和心血管医学方面的互补优势，我们将展示以心血管研究为重点的尖端创新的融合，包括：(I)云上数据处理，(Ii)众包和文本挖掘数据注释，(Iii)蛋白质时空动力学，(Iv)多组集成，以及(V)多尺度临床数据建模。根据我们在创建和提炼生物信息学工具方面的十年经验，我们建议通过用于访问多个组学存储库的新查询系统和云基础设施，以及通过用于挖掘生物医学文献的计算支持的众包倡议，将已建立的大数据资源合并为一个可推广的数据注释和协作研究模型。我们建议交织不同的数据类型，以揭示从分子实体在多个尺度上结合的生物网络，通过机器学习方法来组织分子数据并定义与药物和疾病的关系，以及通过新的算法来对多维分子数据进行云上集成和路径可视化。此外，我们建议创新先进的建模工具来解决蛋白质动力学和时空分子机制，通过对蛋白质属性和3D蛋白质表达图进行机械建模，以及通过将患者表型、病史和多尺度分子图谱关联的贝叶斯算法。我们的工具对更广泛的研究人群的实用性和定制化能力通过三个典型的工作流程清楚地展示出来，这些工作流程支持对大量基因、转录本、蛋白质或代谢物进行注释；对随着时间推移获得的复杂蛋白质数据集进行强大的分析；以及无缝地获取各种分子、文本和文献数据。这些工作流程将使用来自两个重要临床队列的数据进行严格验证，这两个队列是杰克逊心脏研究和健康老年长寿(Wellderly)。同时，将实施一项多方面的战略，以教育和培训生物医学研究人员，并让公众参与促进整个BD2K倡议。 我们相信，由社区推动的BD2K倡议将最好地实现其科学潜力 以可持续的方式改变研究文化，在当前的资助期之后取得持久的成功。