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（由申请人提供）：BD2K倡议的成立证明了NIH和我们社区的远见。显然，生物医学的未来取决于我们将大数据转化为可理解科学事实的集体能力。与BD2K目标一致，我们的目标是革新我们如何应对不守规矩的含义的普遍挑战。 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临床数据建模。从我们创建和完善生物信息学工具的十年经验中，我们建议通过新的查询系统和云基础架构访问多个OMICS存储库，并通过计算支持的群众群众培养化的生物学来，将大数据资源合并为数据注释和协作研究的可概括模型，以进行数据注释和云基础架构。我们建议通过机器学习方法，通过机器学习方法结构分子数据并定义与药物和疾病的关系，以及通过新颖的算法以及在云中的整合和多维分子数据的途径可视化的新型算法，通过机器学习方法来揭示各种数据类型，以揭示从多个尺度分子实体共聚的生物网络。此外，我们建议通过蛋白质特性和3D蛋白质表达图的机械建模以及通过将患者表型，健康病史，多尺度分子概率与贝叶斯算法相关联，提议通过蛋白质特性和3D蛋白质表达图的机械建模来解决蛋白质动力学和时空分子机制，以解决蛋白质动力学和时空分子机制，以解决蛋白质动力学和时空分子机制，以解决蛋白质动力学和时空分子机制，从而创新高级建模工具，以解决先进的建模工具。使用三种原型工作流程清楚地证明了我们对更广泛研究人群的工具的实用性和可定制性，这些工具可以通过大量基因，成绩单，蛋白质或代谢物的注释来注释；随着时间的推移获得的复杂蛋白质数据集的强大分析；以及各种分子，文本和文献数据的无缝aqoregation。这些工作流将使用来自两个重要的临床队列，杰克逊心脏研究和健康的老年人寿命（Wellderly）的数据进行严格验证。同时，将实施一项多方面的战略来教育和培训生物医学研究人员，并吸引公众促进整体BD2K倡议。 我们坚信，由社区驱动的BD2K倡议将最能实现其科学潜力 并以可持续的方式改变研究文化，在当前的融资期内取得了持久的成功。