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

BD 2K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的科学事实的集体能力。根据BD 2K的目标，我们的目标是彻底改变我们如何应对从不规则数据中识别意义的普遍挑战。利用我们的研究人员在计算生物学和心血管医学方面的互补优势，我们将展示基于心血管研究重点的前沿创新融合，包括：（i）云数据处理，（ii）众包和文本挖掘数据注释，（iii）蛋白质时空动力学，（iv）多组学集成，以及（v）多尺度临床数据建模。从我们在创建和完善生物信息学工具方面的十年经验中，我们建议将已建立的大数据资源合并为数据注释和合作研究的可推广模型，通过新的查询系统和云基础设施访问多个组学知识库，并通过计算支持的众包计划挖掘生物医学文献。我们建议将不同的数据类型交织在一起，以揭示从多个尺度上的分子实体合并的生物网络，通过机器学习方法来构建分子数据并定义与药物和疾病的关系，并通过新的算法进行云上集成和多维分子数据的路径可视化。此外，我们建议创新先进的建模工具，以解决蛋白质动力学和时空分子机制，通过蛋白质特性和3D蛋白质表达图的机械建模，并通过贝叶斯算法，将患者表型，健康史和多尺度分子谱相关联。我们的工具对更广泛的研究人群的实用性和可定制性使用三个原型工作流程清楚地证明，这些工作流程可以注释大量的基因，转录本，蛋白质或代谢物;对随时间推移获得的复杂蛋白质数据集进行强大的分析;以及对不同分子，文本和文献数据进行无缝的aQoregation。这些工作流程将使用来自两个重要临床队列（杰克逊心脏研究和健康老年人长寿（Wellderly））的数据进行严格验证。与此同时，将实施一项多方面的战略，以教育和培训生物医学调查人员，并让公众参与促进整体BD 2K倡议。 我们相信，社区驱动的BD 2K倡议将最好地实现其科学潜力 并以可持续的方式转变研究文化，在当前资助期之后取得持久的成功。

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.