EVIDARA: Automated Evidential Support from Raw Data for relay agents in Biomedical KG Queries

EVIDARA：生物医学 KG 查询中中继代理的原始数据自动证据支持

• 批准号: 10706762
• 负责人: SERGIO E BARANZINI
• 金额: $ 53.29万
• 依托单位: INSTITUTE FOR SYSTEMS BIOLOGY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-01-24 至 2023-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10706762
• 关键词: Address; Algorithms; Big Data; Chickens; Conflict (Psychology); Data; Data Analytics; Data Set; Databases; Development; Disease; Epidemiology; Epistemology; Knowledge; Learning; Malignant Neoplasms; Measurement; Medical; Molecular; Multiomic Data; Names; National Center for Advancing Translational Sciences; Pathway interactions; Process; Proteins; Provider; Quality Control; Recording of previous events; Research; Research Personnel; Resources; Role; Science; Signal Transduction; Source; System; Technical Expertise; Testing; Vision; Vitamin K; Walking; Weight; Work; algorithmic bias; antagonist; biobank; cancer risk; cohort; disorder risk; egg; experience; improved; interoperability; knowledge graph; medical specialties; multiple omics; programs; stem cells; tool

1) Component: Autonomous Relay Agent. We will develop an ARA named EVIDARA to evaluate returns from queries in knowledge sources (KS) using a new epistemology: The “reasoning” is based on checking against empirical evidence available in raw data (measurements) instead of deductive reasoning (FIG.►). EVIDARA will assist the Autonomous Relay System (ARS) to identify paths in returned knowledge graphs (KG) that may conflict with real-word evidence and to relay queries to appropriate specialty KS or database. (2) Problem addressed: EHR and multi-omics raw data from large cohorts, if properly preprocessed [e.g., by Knowledge Providers, such as the DOCKET, see application by Dr. Glusman], offers a new opportunity for ad hoc systematic extraction of empirical knowledge on relationships (“Protein P level correlates with risk for disease D”) instead of relying on specific epidemiological analyses. The problem in harnessing raw data for empirical support in lieu of deductive reasoning is that the KGs to be evaluated are extracted from knowledge sources of distinct types and that the relevance of paths depends on the query context Q. Also the ARA algorithm should be scalable to digest the emerging multi-omics data from projects like All-of-Us, the UK Biobank. (3) Plan for implementation: Research will be conducted to evaluate a new epistemic realm: make empirical evidence central to “reasoning”. We have assembled a set of functioning tools to overcome the chicken-egg problem of getting a project started and jumpstart development and testing of EVIDARA: (i) SPOKE, one of the largest biomedical knowledge network (KN) has integrated 25 diverse of KS into a single (neo4j) network database of 2 million nodes and will serve as testing ground for research well before we can use KGs produced by the Knowledge Providers. (ii) Algorithms that use raw data from EHR and multi-omics studies to evaluate the returned KGs. For instance, we compute weights of all nodes in the entire KN through a random-walk algorithm biased by their role for a given condition Q observed in the raw data. (iii) Raw data beyond EHR: multi-omics profiles from a study at ISB with >10k variables which vastly exceeds coverage of observable nodes in KNs offered by EHRs. Example query: “Vitamin K stimulates stem-cell signaling, thus could promote cancer. What is the molecular pathway? Mechanisms returned as KG will be pruned by EVIDARA and checked against correlative evidence in the raw data: Is there evidence that taking Vit. K or its antagonist reduces cancer risk?”. Importantly, since EVIDARA learns on a network of many types of KS, it will provide information to the ARS about which type of KS/Knowledge Provider to invoke next (in iterative queries) to improve the knowledge graph. (4) Expertise & resources: The MPIs, Drs. S. Baranzini (UCSF) and S. Huang (ISB) are researchers with long history of working with medical big data, thus offering technical expertise and the critical SME perspective. SB’s team has created and maintains SPOKE. The uniquely self-contained SPOKE network will allow NCATS staff to test other ARAs. SH brings decades of experience in research of disease mechanisms and medical epistemology. His team will provide multi-omics datasets and data analytics expertise. With his prior work in the NCATS Translator program, he is well poised to maximize team science efficiency and help convert its vision into tangible results. (5) Potential challenges. (i) Quality of evidential support depends on quality of raw data. A quality control is beyond the scope of EVIDARA but could be provided by Knowledge Providers focusing on new multi-omics data sets (e.g. DOCKET). (ii) Testing EVIDARA on other KS from Knowledge Providers) may be slowed down by interoperability issues (e.g. incompatible identifiers). Such issues will be addressed early in Year 1 with help of the Standard and Reference group.

1) 组件：自主中继代理。 我们将开发一个名为 EVIDARA 的 ARA 评估知识查询的回报 使用新认识论的来源（KS）： “推理”是基于对经验的检验 原始数据中可用的证据（测量） 而不是演绎推理 （图►）。 EVIDARA 将协助自治 中继系统 (ARS) 用于识别返回的路径 知识图谱（KG） 与真实证据相冲突，并将查询转发到适当的专业知识库或数据库。 (2) 解决的问题：如果经过适当的预处理，来自大型队列的 EHR 和多组学原始数据 [例如，通过知识提供者，例如 DOCKET，请参阅 Glusman 博士的申请]， 为临时系统地提取关系经验知识提供了新的机会 （“蛋白 P 水平与疾病 D 的风险相关”）而不是依赖于特定的流行病学 分析。利用原始数据作为实证支持代替演绎推理的问题 是要评估的知识图谱是从不同类型的知识源中提取的，并且 路径的相关性取决于查询上下文 Q。此外，ARA 算法应该是可扩展的 消化来自 All-of-Us、英国生物银行等项目的新兴多组学数据。 (3) 实施计划：将进行研究以评估新的认知领域： 使经验证据成为“推理”的核心。我们组装了一套功能工具来 克服启动项目和快速启动开发的先有鸡还是先有蛋的问题 EVIDARA 测试：(i) SPOKE，最大的生物医学知识网络 (KN) 之一已集成 将 25 个不同的 KS 放入包含 200 万个节点的单个 (neo4j) 网络数据库中并提供服务 在我们可以使用知识提供者生成的知识图谱之前，将其作为研究的试验场。 (ii) 使用 EHR 和多组学研究的原始数据来评估返回的 KG 的算法。 例如，我们通过随机游走算法计算整个KN中所有节点的权重 因其在原始数据中观察到的给定条件 Q 的作用而产生偏差。 (iii) EHR 之外的原始数据： 来自 ISB 一项研究的多组学概况，其中包含超过 10k 个变量，远远超出了 EHR 提供的 KN 中的可观察节点。查询示例：“维生素 K 刺激干细胞信号传导， 从而可能促进癌症。分子途径是什么？机制以 KG 形式返回 将被 EVIDARA 修剪并对照原始数据中的相关证据进行检查：是否存在 服用维生素的证据。 K 或其拮抗剂可降低癌症风险？”。重要的是，自从 EVIDARA 在多种类型 KS 的网络上学习，它将向 ARS 提供有关哪种类型的信息 KS/知识提供者调用下一步（在迭代查询中）来改进知识图。 (4) 专业知识和资源：MPI、博士。 S. Baranzini (UCSF) 和 S. Huang (ISB) 是研究人员 拥有使用医疗大数据的悠久历史，从而提供技术专业知识和 批判性的中小企业观点。 SB 团队创建并维护了 SPOKE。独特的独立性 SPOKE 网络将允许 NCATS 工作人员测试其他 ARA。 SH带来数十年的经验 主要从事疾病机制和医学认识论研究。他的团队将提供多组学 数据集和数据分析专业知识。凭借之前在 NCATS 翻译项目中的工作，他 做好充分准备，最大限度地提高团队科学效率，并帮助将其愿景转化为切实的成果。 (5)潜在的挑战。 (i) 证据支持的质量取决于原始数据的质量。一个品质 控制权超出了 EVIDARA 的范围，但可以由知识提供者提供，重点关注 新的多组学数据集（例如 DOCKET）。 (ii) 在 Knowledge 的其他 KS 上测试 EVIDARA 提供商）可能会因互操作性问题（例如不兼容的标识符）而减慢速度。这样的 问题将在第一年初期在标准和参考小组的帮助下得到解决。