Biomedical Data Translator Technical Feasibility Assessment and Architecture Design

我们的愿景：我们提出DeepLink，一个集成了多尺度、 异质、多源的生物医学和临床数据。DeepLink的主要目标是 通过关闭以实现临床和分子科学之间有意义的双向转换 不同规模的模型和知识之间的互操作性差距。翻译者将 利用基础研究和转化性研究的分子见解加强临床科学(例如 遗传变异、蛋白质相互作用、途径功能和细胞组织)，并使 将生物学发现与其病理生理学联系起来的分子科学 后果(例如疾病、体征和症状、药理作用、生理 系统)。用于描述模型的语言和语义的根本区别 而临床和分子领域之间的知识导致了互操作性差距。 DeepLink将系统、全面地缩小这一差距。我们将从最新的开始 语义知识图中的技术，以支持动态的可扩展体系结构 数据联合和知识协调。我们将设计一个多尺度模型的系统 集成是基于本体的，并将模型执行与先前的、经过策划的 生物医学知识。我们的设计战略将是迭代和参与式的，并以 十大里程碑。在DeepLink功能的一系列演示中，我们将介绍一个 翻译科学面临的主要挑战之一：生物医学研究的可重复性 这些发现是基于不断进化的分子数据集。分析和分析的重现性 结果的复制是科学进步的核心。许多里程碑式的研究都使用了 随着时间的推移，不断更新、整理和削减的数据。我们的系列 演示项目旨在为可扩展和 强大的转换器以及我们将用来弥合 具体的使用案例。示范项目本身将是一项意义重大和新颖的 对科学的贡献。 DeepLink将能够回答目前令人费解的问题。示例包括： -来自临床医生：Y病的所有治疗方法的比较效果是什么 考虑到病人的遗传/代谢/蛋白质组学特征？中的功能变体有哪些 X细胞类型与不同的治疗结果有关？什么代谢物 细胞类型Y的扰动与疾病X的不同亚型有关？ -来自基础科学研究人员：所有模型中关于Y疾病的已知情况 有机体(即使那些不是为模型Y而设计的生物)？所有的临床表型是什么 是蛋白X功能改变的结果吗？哪些生物途径受到一种 Y病的致病变种？有哪些患者数据可以用来评估分子衍生的 临床假说？ 挑战和我们的方法：DeepLink将缩小目前的互操作性差距 禁止分子发现导致临床创新。DeepLink将成为 技术驱动，解决与大型、异类、 语义不明确、不断变化、部分重叠和上下文相关 通过使用(1)可伸缩、分布式和版本化的图形存储来依赖数据；(2)语义 本体、关联数据等技术；(3)网络分析质量控制方法； (4)基于机器学习的数据融合方法；(5)上下文感知文本挖掘，实体 识别和关系抽取；(6)基于患者和关系的多尺度知识发现 分子数据；以及(7)向临床医生和基础科学家介绍可操作的知识 通过用户友好的界面。

Our Vision: We propose DeepLink, a versatile data translator that integrate multi-scale, heterogeneous, and multi-source biomedical and clinical data. The primary goal of DeepLink is to enable meaningful bidirectional translation between clinical and molecular science by closing the interoperability gap between models and knowledge at different scales. The translator will enhance clinical science with molecular insights from basic and translational research (e.g. genetic variants, protein interactions, pathway functions, and cellular organization), and enable the molecular sciences by connecting biological discoveries with their pathophysiological consequences (e.g. diseases, signs and symptoms, pharmacological effects, physiological systems). Fundamental differences in the language and semantics used to describe the models and knowledge between the clinical and molecular domains results in an interoperability gap. DeepLink will systematically and comprehensively close this gap. We will begin with the latest technology in semantic knowledge graphs to support an extensible architecture for dynamic data federation and knowledge harmonization. We will design a system for multi-scale model integration that is ontology-based and will combine model execution with prior, curated biomedical knowledge. Our design strategy will be iterative and participatory and anchored by 10 major milestones. In a series of demonstrations of DeepLink’s functions, we will address one of the major challenges facing translational science: reproducibility of biomedical research findings that are based on evolving molecular datasets. Reproducibility of analyses and replication of results are central to scientific advancement. Many landmark studies have used data that are constantly being updated, curated, and pared down over time. Our series of demonstrations projects are designed to prototype the technology required for a scalable and robust translator as well as the techniques we will use to close the interoperability gap for a specific use case. The demonstration project will, itself, will be a significant and novel contribution to science. DeepLink will be able to answer questions that are currently enigmatic. Examples include: - From clinicians: What is the comparative effectiveness of all the treatments for disease Y given a patient's genetic/metabolic/proteomic profile? What are the functional variants in cell type X that are associated with differential treatment outcomes? What metabolite perturbations in cell type Y are associated with different subtypes of disease X? - From basic science researchers: What is known about disease Y across all model organisms (even those not designed to model Y)? What are all the clinical phenotypes that result from a change in function in protein X? Which biological pathways are affected by a pathogenic variant of disease Y? What patient data are available to evaluate a molecularlyderived clinical hypothesis? Challenges and Our Approaches: DeepLink will close the interoperability gap that currently prohibits molecular discoveries from leading to clinical innovations. DeepLink will be technologically driven, addressing the challenges associated with large, heterogeneous, semantically ambiguous, continuously changing, partially overlapping, and contextually dependent data by using (1) scalable, distributed, and versioned graph stores; (2) semantic technologies such as ontologies and Linked Data; (3) network analysis quality control methods; (4) machine-learning focused data fusion methods; (5) context-aware text mining, entity recognition and relation extraction; (6) multi-scale knowledge discovery using patient and molecular data; and (7) presentation of actionable knowledge to clinicians and basic scientists via user-friendly interfaces.