Biomedical Data Translator Development of Autonomous Relay Agent: ARAX

该项目将通过一个多地点继续在翻译联盟内开展协作工作 俄勒冈州立大学(派·斯蒂芬·拉姆齐)和两个合作伙伴的团队(“X光团队”) 宾夕法尼亚州立大学(Pi Koslicki)和系统生物学研究所 (PiEric Deutsch；Co-I Jared Roach)团队X光对翻译的可行性具有很高的生产力 评估阶段，团队为Translator带来了重要的专业知识(请参阅参考资料)。 组件类型：我们建议创建、验证和集成一个自主的中继代理 (Ara)名为Arax。ARAX将成为新的翻译器架构中的一个中间件组件 这将大大超出原型推理工具(RTX)的能力， 我们是在可行性评估阶段创建的。根据输入请求，Arax的Main 输出将是具有清楚解释的排名基础的排序子图。Arax将利用代码 和来自RTX的算法，并将具有明确的应用重点领域，如下所述。 Arax试图解决的主要问题：生物医学知识中的联系 图具有高度可变的(I)置信度(由于歧义谓词和/或DUE 到高度可变的知识类型的可靠性程度)和(Ii)与 用户的查询。这种边缘意义的可变性会导致不正确和难以解释 这些结果共同构成了为以下方面创建广泛有用的工具的重大问题 基于计算机的生物医学推理。我们建议通过明确的方式来解决这个问题 考虑到这两种类型的边缘可变性的推理算法--跨越广泛 一系列生物医学查询类型-Arax将提供给Translator。除了这些宽泛之外 功能，如项目计划中所述，我们将在ARAX中整合先进的算法 答复与疾病治疗有关的询问，包括(1)已知的药物重新定位 疾病，利用关于疾病发病机制的知识[1]；和(2)治疗 根据症状和推测的因果遗传变异对罕见疾病的建议。 项目实施计划：在我们的项目计划中，我们描述了五年的时间表 在Translator中创建、验证和集成Arax，从三个月的冲刺开始 从而在2020年3月中旬制造出Arax的原型。该计划的主要组成部分包括：(1) 利用BioThings Explorer软件框架使Arax能够动态绘制地图 化合物、蛋白质、途径、变异、表型和疾病之间的关系 翻译员登记册中的知识源应用程序编程接口；(2)利用 COHD和相关翻译器资源，以获取生物医学语义距离信息； (3)利用RTX生物医学知识的应用编程接口端点 图，KG2；以及(4)实现利用出处的概率推理算法 信息和动态确定的边缘相关性分数，以改进推理。我们会 系统地使用机器学习来使排名分数与产出质量的衡量标准保持一致。 我们团队的协作优势包括：(I)开发技术标准 翻译软件代理之间的通信(利用Pi Deutsch丰富的历史 经验)；(2)制定知识图谱标准(利用PI、Ramsey和PI Koslicki的专业知识)；以及(Iii)派生与变革性 翻译的潜力(利用Co-I Roach和Pi Ramsey的专业知识)。在开发中 在这一阶段，我们的团队将继续与其他团队以及NIH的利益相关者在 自适应、高带宽和团队边界不可知的方式，如项目计划中所述。 建立拟议系统的主要挑战是：(1)需要能够将 工具之间的分析步骤和(2)需要合作开发符合以下条件的标准 使翻译组件能够交互；我们在项目计划中对其进行了详细说明。

This project would continue collaborative work within the Translator consortium by a multi-site team (“Team X-ray”) at Oregon State University (PI Stephen Ramsey) and at two partner institutions, Pennsylvania State University (PI Koslicki) and the Institute for Systems Biology (PI Eric Deutsch; Co-I Jared Roach). Team X-ray was highly productive in Translator's feasibility assessment phase and the team brings critical expertise to Translator (see Resources). Component type: We propose to create, validate, and integrate an autonomous relay agent (ARA) called ARAX . ARAX will be a middleware component in the new Translator architecture that will extend significantly beyond the capabilities of the prototype reasoning tool (RTX) that we created in the feasibility assessment phase. Depending on the input request, ARAX's main output will be ranked subgraphs with clearly explained ranking basis. ARAX will leverage code and algorithms from RTX and will have an explicit application focus area, as described below. Main problems that ARAX is trying to address: Connections within a biomedical knowledge graph have highly variable degrees of (i) confidence (due to ambiguous predicates and/or due to highly variable degrees of reliability of knowledge types) and (ii) potential relevance to the user's query. Such edge-significance variability leads to both incorrect and difficult-to-interpret results which together pose a significant problem for creating broadly useful tools for computer-based biomedical reasoning. We propose to address this problem by explicitly accounting for these two types of edge variability in the reasoning algorithms–spanning a broad range of biomedical query types–that ARAX will provide to Translator. In addition to these broad capabilities, as described in the Project Plan, we will incorporate advanced algorithms in ARAX for responding to queries relating to disease therapy, including (1) drug repositioning for known disease, leveraging knowledge about the disease’s pathogenesis [1] ; and (2) therapeutic recommendations for rare diseases based on symptoms and the putative causal genetic variant. Plan for implementation of the project: In our Project Plan we describe a five-year timeline for creating, validating, and integrating ARAX within Translator, beginning with a three-month sprint leading to a prototype of ARAX by mid-March 2020. Key components of the plan include: (1) leveraging the BioThings Explorer software framework to enable ARAX to dynamically map between compounds, proteins, pathways, variants, phenotypes, and diseases based on knowledge source application programming interfaces in the Translator registry; (2) leveraging COHD and related Translator resources to obtain biomedical semantic distance information; (3) leveraging an application programming interface endpoint for the RTX biomedical knowledge graph, KG2; and (4) implementing probabilistic reasoning algorithms leveraging provenance information and dynamically determined edge relevance scores to improve reasoning. We will systematically use machine-learning to align ranking scores with measures of output quality. Collaboration strengths of our team include (i) developing technical standards for communications between Translator software agents (leveraging PI Deutsch’s extensive past experience); (ii) developing knowledge graph standards (leveraging PI Ramsey’s and PI Koslicki’s expertise); and (iii) deriving use-case vignettes that speak to the transformative potential of Translator (leveraging Co-I Roach’s and PI Ramsey’s expertise). In the development phase, our team would continue to collaborate with other teams and with NIH stakeholders in an adaptive, high-bandwidth, and team-boundary-agnostic fashion, as detailed in the Project Plan. Key challenges to building the proposed system are (1) the need to be able to "chain" together analytical steps between tools and (2) the need for cooperative development of standards that enable Translator components to interact; we address them in detail in the Project Plan.