Biomedical Data Translator Technical Feasibility Assessment and Architecture Design

我们的领导力横跨翻译领域-临床(Chant、Robinson、Koeller、Hamosh)， 生物学(Haendel，Hoatlin，Doheny)和计算(Mungall，Su，Liu，McWeeney，Overby)， 具有各种数据源、类型和模型以及数据集成方面的专业知识 战略、标准和算法。我们投资于开放科学、可重复性和 领导开发开放软件、数据标准和众包管理平台。 我们的愿景是通过以下方式展示罕见疾病和常见疾病之间的联系 基因、途径和病理生理学。我们将包括疾病与表型的关联， 富含时间信息并分解成生物单位的。创新融合 机构和功能的组合将允许为每个稀有项目创建候选机构图 疾病。我们将使用图匹配和概率技术来支持基础研究 假设检验以及临床询问(诊断、预后和治疗选择)。 最后，我们的团队坚定地致力于实现所有公共生物医学数据的集体使用 通过使其可互操作，并在所有环境下对所有用户开放访问。 语义学对整合很重要。该图突出显示了现有数据资源的格局， 每一个都包含具有特定相关含义(A)的数据的一部分。仅聚合 通常会导致失去意义(B)。语义和概率整合方法规定 以获取更高级的查询回答功能(C)。我们有克服困难的第一手经验 在大型集成项目中发现的挑战，但更重要的是，我们 非常熟悉本提案旨在集成的数据源和类型。例如，击倒 斑马鱼中的TP53被用来减少细胞凋亡；天真地使用这些数据可能会 对靶基因的表型效应。其他问题是知道何时以及如何整合 实体之间的关联不等价的数据，例如当一个源注释时 一种疾病与一种基因有关，另一种与一种变种有关。 我们现有的基础设施已成功集成和利用多式联运数据 罕见疾病诊断。在这里，我们使用新的数据类型和新的方法来扩展这些系统 在不同疾病和不同背景下普遍存在。转变后的知识图谱将拥有 管理和链接现象学世界观的智能、适应性脚手架 具有基础科学的机械性重点的临床要素。生物学上的联系 实体和事件将直接表示，或通过链接表示，从而能够使用 强大的查询和推理算法。图表中的元素将按以下任一项进行分层 经典的、严格的分类法(疾病分类、组织和细胞类型)或通过动态分组 基于分子病理生理学的共同机制。可以比较外部数据 使用不同的标准。例如，两个患者(一个罕见病，一个常见病)可能 在经典病因学上是相距遥远的，但在路径空间上是相邻的--暗示着一种治疗方法。这个 GRAPH将从包含多种数据类型的开放数据源中播种，并由 从文献和临床数据中获得的知识。Transmed也将很容易地连接到其他 使用质量标识符策略的数据存储，即预测等价概率的方法 来自相关联的元数据，以及基于相似成员匹配图的算法。 总结。熟悉数据，结合我们的技术经验，并连接到 现实世界中的用例使我们很好地定位于在我们的愿景中既相关又成功。

Our leadership spans the translational spectrum - clinical (Chute, Robinson, Koeller, Hamosh), biological (Haendel, Hoatlin, Doheny), and computational (Mungall, Su, Liu, McWeeney, Overby), with expertise in a wide variety of data sources, types, and models as well as data integration strategies, standards, and algorithms. We are invested in open science, reproducibility, and lead efforts in developing open software, data standards, and crowdsourcing curation platforms. Our vision is to demonstrate connectivity between rare disease and common diseases via genes, pathways, and pathophysiology. We will include disease-phenotype associations, enriched with temporal information and decomposed into biological units. Innovative integration of mechanism and function will allow creation of candidate mechanistic graphs for each rare disease. We will use graph matching and probabilistic techniques to support basic research hypothesis testing as well as clinical inquiry (diagnosis, prognosis, and treatment selection). Finally, our team is deeply committed to enabling the collective use of all public biomedical data by making it interoperable and openly accessible for all users, in all contexts. Semantics matter to integration. The figure highlights the landscape of existing data resources, each contain a portion of data with specific, relevant meaning (A). Aggregation alone often results in loss of meaning (B). Semantic and probabilistic integration approaches provision for more advanced query answering capabilities (C). We have first hand experience overcoming challenges found within large-scale integration projects in general, but more important, we are very familiar with data sources and types this proposal aims to integrate. For example, knockdown of TP53 in zebrafish is used to reduce apoptosis; naive use of the data might attribute phenotypic effects to targeted genes. Other issues are in knowing when and how to integrate data where the associations between entities are not equivalent, such as when one source annotates a disease to a gene and another to a variant. Our existing infrastructure has successfully integrated and leveraged multimodal data for rare disease diagnosis. Here we extend these systems with new data types and new methodologies that generalize across diseases and contexts. The TransMed Knowledge Graph will have an intelligent, adaptive scaffolding for managing and linking the phenomenological worldview of clinical elements with the mechanistic emphases of basic science. Connections between biological entities and events will be represented either directly, or through chaining, enabling the use of powerful algorithms for query and inference. Elements in the graph will be stratified by either classical, rigid taxonomies (disease nosologies, tissue and cell type) or through dynamic groups based on shared mechanisms of molecular pathophysiology. External data can be compared using different criteria. For instance, two patients (one rare disease, one common disease) may be distant in classical nosology, but neighbors in pathway space - suggesting a treatment. The graph will be seeded from open data sources containing diverse data types, supplemented by knowledge from the literature and clinical data. TransMed will also be readily connected to other data stores using a quality identifier strategy, methods that predict probability of equivalency from associated metadata, and algorithms that match graphs based on similar members. Summary. Familiarity with the data, combined with our technical experience, and connection to real-world use cases positions us well to be both relevant and successful in our vision.