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; 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：自主继电器代理。 我们将开发一个名为Evidara的ARA 评估Knowle查询的回报 使用新认识论的来源（KS）： “推理”是基于对经验的检查 原始数据（测量）中可用的证据 而不是演绎推理 （图►）。 Evidara将协助自主 继电器系统（ARS）识别返回的路径 知识图（kg）可能 与现实的证据冲突，并将查询转移到适当的专业KS或数据库中。 （2）解决的问题：如果正确处理，来自大型队列的EHR和多摩斯原始数据 [ 提供了一个新的机会，可以系统地提取有关关系的经验知识 （“蛋白质P水平与疾病D风险相关”），而不是依靠特定的流行病学 分析。利用原始数据以代替演绎推理的问题 是从不同类型的知识来源中提取要评估的KGS 路径的相关性取决于查询上下文q。 从英国Biobank等项目中消化了新兴的多摩斯数据。 （3）实施计划：将进行研究以评估一个新的认知领域： 提供“推理”中心的经验证据。我们已经组装了一组功能工具 克服鸡蛋蛋的开始，开始一个项目的问题，并开始开发开发和 EVIDARA的测试：（i）最大的生物医学知识网络之一（kn）已集成 25种KS的KS分为一个（NEO4J）的一个（NEO4J）200万个节点的网络数据库，并将服务 作为研究的测试基础，我们才能使用知识提供者生产的公斤。 （ii）使用来自EHR和多摩学研究的原始数据来评估返回的kgs的算法。 例如，我们通过随机步行算法计算整个KN中所有节点的权重 在原始数据中观察到的给定条件Q的作用有偏见。 （iii）EHR以外的原始数据： 来自ISB的研究的多媒体概况，其> 10K变量大大超过了覆盖范围 EHR提供的KNS中可观察的节点。示例查询：“维生素K刺激干细胞信号，， 这可能会促进癌症。什么是分子途径？机制返回为公斤 Evidara将修剪并根据原始数据中的相关证据进行检查：是否存在 拿起VIT的证据。 K或其拮抗剂降低了癌症的风险？重要的是，自Evidara以来 在多种类型的KS网络上学习，它将向AR提供有关哪种类型的信息 KS/知识提供商将在下一个（迭代查询中）提高知识图。 （4）专业知识和资源：MPI，Drs。 S. Baranzini（UCSF）和S. Huang（ISB）是研究人员 有了悠久的医疗大数据的历史，因此提供了技术专长和 批判性中小企业的观点。 SB的团队创建并保持了讲话。独特的独立 辐条网络将允许NCATS员工测试其他ARA。 SH带来了数十年的经验 在疾病机制和医学认识论的研究中。他的团队将提供多摩ic 数据集和数据分析专业知识。在NCATS翻译计划中的先前工作，他是 有毒，可以最大程度地提高团队科学效率，并有助于将其愿景转化为有形的结果。 （5）潜在的挑战。 （i）证据支持的质量取决于原始数据的质量。质量 控制超出了Evidara的范围，但可以由专注的知识提供者提供 在新的多摩斯数据集（例如案卷）上。 （ii）从知识对其他KS进行测试 提供者）可能会因互操作性问题（例如不兼容标识符）而减慢。这样的 问题将在第一年初在标准和参考组的帮助下解决。