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.