Biomedical Data Translator Technical Feasibility Assessment and Architecture Design

我们的愿景：我们提出DeepLink，一个多功能的数据转换器，集成多尺度， 异构和多源生物医学和临床数据。DeepLink的主要目标是 为了实现临床和分子科学之间有意义的双向转换， 不同尺度的模型和知识之间的互操作性差距。译者 通过基础和转化研究的分子见解增强临床科学（例如， 遗传变异、蛋白质相互作用、通路功能和细胞组织），并使 通过将生物学发现与其病理生理学联系起来， 后果（如疾病、体征和症状、药理学作用、生理学作用） 系统）。用于描述模型的语言和语义的根本差异 并且临床和分子领域之间的知识导致互操作性差距。 DeepLink将系统地、全面地缩小这一差距。我们将开始与最新的 语义知识图中的技术，以支持动态 数据联合和知识统一。我们将设计一个多尺度模型系统 集成是基于本体，并将联合收割机模型执行与事先策划的 生物医学知识。我们的设计策略将是迭代和参与性的，并以 10个重大里程碑。在DeepLink的功能的一系列演示中，我们将解决一个 转化科学面临的主要挑战：生物医学研究的可重复性 这些发现是基于不断发展的分子数据集。分析结果的重现， 成果的复制是科学进步的核心。许多具有里程碑意义的研究都使用了 这些数据会随着时间的推移不断更新、整理和删除。我们的系列 示范项目的目的是原型所需的技术， 强大的翻译器，以及我们将使用的技术，以缩小互操作性的差距， 具体用例。示范项目本身将是一个重要的和新颖的 对科学的贡献。 DeepLink将能够回答目前神秘的问题。示例包括： - 来自临床医生：Y疾病的所有治疗方法的相对有效性是什么 考虑到病人的遗传/代谢/蛋白质组特征中的功能变体有哪些 X型细胞与不同的治疗结果有关吗？什么代谢物 Y型细胞的扰动与疾病X的不同亚型有关？ - 来自基础科学研究人员：在所有模型中对疾病Y的了解 生物体（即使不是设计为Y型）？哪些临床表型 是由蛋白X功能的改变引起的吗哪些生物学途径受到 致病性变异的疾病Y？哪些患者数据可用于评估分子衍生的 临床假设？ 挑战和我们的方法：DeepLink将缩小目前 阻碍了分子发现导致临床创新。DeepLink将在 技术驱动，解决与大型、异构、 语义模糊，不断变化，部分重叠，上下文 使用（1）可扩展、分布式和版本化的图形存储;（2）语义 本体和关联数据等技术;（3）网络分析质量控制方法; (4)以机器学习为核心的数据融合方法;（5）上下文感知的文本挖掘，实体 识别和关系提取;（6）多尺度知识发现， 分子数据;（7）向临床医生和基础科学家提供可操作的知识 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.