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03.Translator User Interface SOW 2021-06-10.pdf

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July 22, 2021, 5:10 p.m.
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Statement of Work Biomedical Data Translator User Interface Page 1 of 8 STATEMENT OF WORK 6/10/2021 GENERAL INFORMATION Title of Project: Translator User Interface Development Statement of Need and Purpose: As the pace of scientific research continues to advance, the problem researchers face now is not a lack of information, but pairing the right data with the right question. The vision of Translator is to help researchers more easily see connections across those data, accelerating discovery and getting more treatments to more patients more quickly. Ultimately, Translator will accomplish this by serving as a resource for computationally assisted exploration of knowledge and construction of new research hypotheses. As the Translator program enters the Development phase, the design of the interface is critical/imminent to ensure that the development of the core system supports the needs of the users. The awardees of the Biomedical Data Translator: Development funding opportunity are continuing to develop integrated tools that connect diverse types of data to augment human reasoning and inference for understanding the pathophysiology of human disease. Individual teams have subject-matter experts that utilize components or tools to validate findings, however there is no user interface for Translator. Through the Translator User Interface Contract, the program will develop an interface concurrently with the consortium developing the core system. Background Information and Objective: The Translator program completed its three-year feasibility assessment phase in January of 2020. The feasibility assessment phase that generated demonstration projects focused on dismantling barriers to integrating diverse biomedical data. Cross-team working groups formed to integrate many disparate data sources which drove the emerging knowledge graph standards. The teams focused on developing mechanisms to transform data, apply reasoning and enable standard communication messaging, resulting in the successful demonstration that questions could be posed as query graphs across disparate data sources and answers were returned as compliant with the graphs. The feasibility phase focused solely on proof-of-concept, and teams were not assigned to develop a user interface or make considerations for usability. As these efforts progressed, the program focused on augmenting human reasoning with Translator resources. This effort resulted in several tools which support chaining queries together, as well as accepting various Translator knowledge sources as inputs as defined by the reasoner standard Applications Programming Interface (API), to provide the user with a dossier of the most highly relevant information gathered from multiple sources of data and knowledge. As tools were developed, they were registered in order to allow Translator components to discover and access each other. The reasoning and architecture teams created a feedback loop which helps to identify gaps in the data as well as integrate disparate knowledge sources to demonstrate that a system can semi-autonomously respond to queries and support exploration of important classes of diverse translational research problems. Statement of Work Biomedical Data Translator User Interface Page 2 of 8 In order to convey the breadth of the capabilities that were evaluated during the feasibility assessment phase, the cross-team working groups produced case studies on specific examples called Tidbits. Each Tidbit is a vignette that tells a compelling story and demonstrates a kind of insight that could be enabled by Translator. The feasibility assessment phase demonstrated that Translator can accelerate translational research efforts in a manner that is fully transparent and open source. The awardees of the Biomedical Data Translator: Development phase will continue to fill gaps in data and knowledge used, reduce Translator’s dependence on manual analysis, and increase the breadth of data and speed with which users are able to uncover meaningful results to inform or generate hypotheses. The vendor of the User Experience Lifecycle for the Development of the Translator User Interface contract will deliver an intuitive user interface for biomedical researchers to explore knowledge and construct new research hypotheses. For the back-end Development phase, the funded teams are responsible for the implementation of three components which are described in the Biomedical Data Translator: Development funding opportunity announcement. 1. Knowledge Providers seek out, integrate, and provide high-value AI-ready data sources within a specific scope of knowledge relevant to Translator. 2. The Autonomous Relay Agents consists of agents that determine which knowledge providers to invoke in response to a query from the Autonomous Relay System. The ARS will be built by NCATS and will provide the primary interaction between the user interface and Autonomous Relay Agents. 3. Translator Standards and Reference Implementation component manages the development of the Translator standards as well as utilities that are integrated into many knowledge providers and autonomous relay agents.. A fourth component is the user interface to be developed under this contract that leverages the input and output of the Autonomous Relay System in accordance with the standards and reference implementation. The Autonomous Relay System and other components are graph-based and, in response to a query graph, will generate multiple answer graphs depicting a summary of relevant relationships discovered from searching over 100 sources of data and knowledge. The provenance of the asserted relationships will also be associated with the graphs. By drawing from so many different data sources, these computer-generated representations of knowledge go far beyond a search of indexed information. At the same time, just as with searches of indexed information, each query will result in numerous answers graphs. Our objective is to develop a user interface that facilitates biomedical researchers interactively exploring Translator resources and constructing new research hypotheses while ensuring that the emerging standards and development of the Translator component tools support user needs. User Experience Principles Statement of Work Biomedical Data Translator User Interface Page 3 of 8 The development of a user interface poses several distinct design challenges for the vendor, including but not limited to: - the presentation of multiple complex knowledge graph “answers” in response to a single query; - establishing confidence in the ability of Translator to address the user’s question by providing provenance of the knowledge returned to the user; - capture the collection and history of user decisions used to formulate, iterate, filter and merge results as they develop their query; and - gather feedback from the user. Translator requires an intuitive interface that encourages trust while enabling the user to consume complex, diverse results. The intended users of Translator – biomedical researchers – typically have a set of sites and computational tools they use that allow them to explore information in their area of research. Since these users may be super or power users of their current toolset, the interface for Translator will need to familiarize biomedical researchers with different data types from diverse sources that are the result of connections uncovered by Translator. The interface should engage users without being overwhelming or intimidating. The answers and supporting evidence that Translator will present may be novel and unfamiliar to biomedical researchers, however Translator must present these results in a way that invokes both trust and curiosity while also enabling and encouraging the user to explore (incrementally and wholly) and iterate. The interface must allow the user to formulate their intent and iteratively increase the relevance of the knowledge returned for their questions. Ultimately, we expect “answers” to be critically evaluated by our users, and want to solicit and capture feedback on both systematic and specific complications that are encountered. These obstacles will need to be communicated back to the development teams to address and iterate on. Translator knowledge providers contribute deep expertise within the different scientific domains that constitute translational research as well as technical expertise to take complex data and turn it into knowledge. The data represented by the knowledge providers ranges from basic science to clinical data and includes gene products, genomics, model organisms, tissues, chemicals, pathways, conditions and patient groups. The autonomous relay agents employ novel analytic approaches that integrate knowledge across the different knowledge providers. The vendor for the user experience will complement the existing teams by providing a foundation of deep user research experience, creative design history, dependable testing approaches, and technical development expertise that has been applied to the specific challenges of biomedical data and translational research. The Translator website will provide biomedical researchers a place to ask questions. The website will utilize the autonomous relay system to communicate with and access multiple graph “answer” sets delivered by the autonomous relay agents and knowledge providers. Result sets are available in the form of knowledge graphs, which provide a variety of answers and supporting evidence that range from known knowns to surprising and radical unknowns, though Translator need not present these answers in classic knowledge graph form. Biomedical researchers will need a place to explore the results and the processes and data sources used to generate the answers. The processes and data are complex analysis implementations and rich biomedical data objects which the user may be unfamiliar with a… Show All