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BackgroundEfforts are underway to semantically integrate large biomedical knowledge graphs using common upper-level ontologies to federate graph-oriented application programming interfaces (APIs) to the data. However, federation poses several challenges, including query routing to appropriate knowledge sources, generation and evaluation of answer subsets, semantic merger of those answer subsets, and visualization and exploration of results. ObjectiveWe aimed to develop an interactive environment for query, visualization, and deep exploration of federated knowledge graphs. MethodsWe developed a biomedical query language and web application interphase—termed as Translator Query Language (TranQL)—to query semantically federated knowledge graphs and explore query results. TranQL uses the Biolink data model as an upper-level biomedical ontology and an API standard that has been adopted by the Biomedical Data Translator Consortium to specify a protocol for expressing a query as a graph of Biolink data elements compiled from statements in the TranQL query language. Queries are mapped to federated knowledge sources, and answers are merged into a knowledge graph, with mappings between the knowledge graph and specific elements of the query. The TranQL interactive web application includes a user interface to support user exploration of the federated knowledge graph. ResultsWe developed 2 real-world use cases to validate TranQL and address biomedical questions of relevance to translational science. The use cases posed questions that traversed 2 federated Translator API endpoints: Integrated Clinical and Environmental Exposures Service (ICEES) and Reasoning Over Biomedical Objects linked in Knowledge Oriented Pathways (ROBOKOP). ICEES provides open access to observational clinical and environmental data, and ROBOKOP provides access to linked biomedical entities, such as “gene,” “chemical substance,” and “disease,” that are derived largely from curated public data sources. We successfully posed queries to TranQL that traversed these endpoints and retrieved answers that we visualized and evaluated. ConclusionsTranQL can be used to ask questions of relevance to translational science, rapidly obtain answers that require assertions from a federation of knowledge sources, and provide valuable insights for translational research and clinical practice.

BackgroundIn a multisite clinical research collaboration, institutions may or may not use the same common data model (CDM) to store clinical data. To overcome this challenge, we proposed to use Health Level 7’s Fast Healthcare Interoperability Resources (FHIR) as a meta-CDM—a single standard to represent clinical data. ObjectiveIn this study, we aimed to create an open-source application termed the Clinical Asset Mapping Program for FHIR (CAMP FHIR) to efficiently transform clinical data to FHIR for supporting source-agnostic CDM-to-FHIR mapping. MethodsMapping with CAMP FHIR involves (1) mapping each source variable to its corresponding FHIR element and (2) mapping each item in the source data’s value sets to the corresponding FHIR value set item for variables with strict value sets. To date, CAMP FHIR has been used to transform 108 variables from the Informatics for Integrating Biology & the Bedside (i2b2) and Patient-Centered Outcomes Research Network data models to fields across 7 FHIR resources. It is designed to allow input from any source data model and will support additional FHIR resources in the future. ResultsWe have used CAMP FHIR to transform data on approximately 23,000 patients with asthma from our institution’s i2b2 database. Data quality and integrity were validated against the origin point of the data, our enterprise clinical data warehouse. ConclusionsWe believe that CAMP FHIR can serve as an alternative to implementing new CDMs on a project-by-project basis. Moreover, the use of FHIR as a CDM could support rare data sharing opportunities, such as collaborations between academic medical centers and community hospitals. We anticipate adoption and use of CAMP FHIR to foster sharing of clinical data across institutions for downstream applications in translational research.

Clinical, biomedical, and translational science has reached an inflection point in the breadth and diversity of available data and the potential impact of such data to improve human health and well-being. However, the data are often siloed, disorganized, and not broadly accessible due to discipline-specific differences in terminology and representation. To address these challenges, the Biomedical Data Translator Consortium has developed and tested a pilot knowledge graph-based "Translator" system capable of integrating existing biomedical data sets and "translating" those data into insights intended to augment human reasoning and accelerate translational science. Having demonstrated feasibility of the Translator system, the Translator program has since moved into development, and the Translator Consortium has made significant progress in the research, design, and implementation of an operational system. Herein, we describe the current system's architecture, performance, and quality of results. We apply Translator to several real-world use cases developed in collaboration with subject-matter experts. Finally, we discuss the scientific and technical features of Translator and compare those features to other state-of-the-art, biomedical graph-based question-answering systems.

Within clinical, biomedical, and translational science, an increasing number of projects are adopting graphs for knowledge representation. Graph-based data models elucidate the interconnectedness among core biomedical concepts, enable data structures to be easily updated, and support intuitive queries, visualizations, and inference algorithms. However, knowledge discovery across these "knowledge graphs" (KGs) has remained difficult. Data set heterogeneity and complexity; the proliferation of ad hoc data formats; poor compliance with guidelines on findability, accessibility, interoperability, and reusability; and, in particular, the lack of a universally accepted, open-access model for standardization across biomedical KGs has left the task of reconciling data sources to downstream consumers. Biolink Model is an open-source data model that can be used to formalize the relationships between data structures in translational science. It incorporates object-oriented classification and graph-oriented features. The core of the model is a set of hierarchical, interconnected classes (or categories) and relationships between them (or predicates) representing biomedical entities such as gene, disease, chemical, anatomic structure, and phenotype. The model provides class and edge attributes and associations that guide how entities should relate to one another. Here, we highlight the need for a standardized data model for KGs, describe Biolink Model, and compare it with other models. We demonstrate the utility of Biolink Model in various initiatives, including the Biomedical Data Translator Consortium and the Monarch Initiative, and show how it has supported easier integration and interoperability of biomedical KGs, bringing together knowledge from multiple sources and helping to realize the goals of translational science.

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Objectives: NKG2D is an activating receptor expressed by natural killer (NK) and CD8+ T cells and activation intensity varies by NKG2D expression level or nature of its ligand. An NKG2D gene polymorphism determines high (HNK1) or low (LNK1) expression. MICA is the most polymorphic NKG2D ligand and stronger effector cell activation associates with methionine rather than valine at residue 129. We investigated correlation between cord blood (CB) NKG2D and MICA genotypes and haematopoietic stem cell (HSC) transplant outcome. Methods: We retrospectively studied 267 CB HSC recipients (178 adult and 87 paediatric) who underwent transplant for malignant disease between 2007 and 2018, analysing CB graft DNA for NKG2D and MICA polymorphisms using Sanger sequencing. Multivariate analysis was used to correlate these results with transplant outcomes. Results: In adult patients, LNK1 homozygous CB significantly improved 60‐day neutrophil engraftment (hazard ratio (HR) 0.6; 95% confidence interval (CI) 0.4–0.9; p =.003). In paediatrics, HNK1 homozygous CB improved 60‐day engraftment (HR 0.4; 95% CI 0.2–0.7; p =.003), as did MICA‐129 methionine+ CB grafts (HR 1.7 95% CI 1.1–2.6; p =.02). Conclusion: CB NKG2D and MICA genotypes potentially improve CB HSC engraftment. However, results contrast between adult and paediatric recipients and may reflect transplant procedure disparities between cohorts. [ABSTRACT FROM AUTHOR]

Objectives: Mobile applications (apps) are multiplying in laryngology, with little standardization of content, functionality, or accessibility. The purpose of this study is to evaluate the quality, functionality, health literacy, readability, accessibility, and inclusivity of laryngology mobile applications. Methods: Of the 3230 apps identified from the Apple and Google Play stores, 28 patient‐facing apps met inclusion criteria. Apps were evaluated using validated scales assessing quality and functionality: the Mobile App Rating Scale (MARS) and the Institute for Healthcare Informatics App Functionality Scale. The Clear Communication Index (CDC) Institute of Medicine Strategies for Creating Health Literate Mobile Applications, and Patient Education Materials Assessment Tool (PEMAT) were used to evaluate apps health literacy level. Readability was assessed using established readability formulas. Apps were evaluated for language, accessibility features, and representation of a diverse population. Results: Twenty‐six apps (92%) had adequate quality (MARS score > 3). The mean PEMAT score was 89% for actionability and 86% for understandability. On average, apps utilized 25/33 health literate strategies. Twenty‐two apps (79%) did not pass the CDC index threshold of 90% for health literacy. Twenty‐four app descriptions (86%) were above an 8th grade reading level. Only 4 apps (14%) showed diverse representation, 3 (11%) had non‐English language functions, and 2 (7%) offered subtitles. Inter‐rater reliability for MARS was adequate (CA‐ICC = 0.715). Conclusion: While most apps scored well in quality and functionality, many laryngology apps did not meet standards for health literacy. Most apps were written at a reading level above the national average, lacked accessibility features, and did not represent diverse populations. Laryngoscope, 133:1540–1549, 2023 [ABSTRACT FROM AUTHOR]