Progress toward a universal biomedical data translator

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

Tuberculosis: integrated studies for a complex disease 2050

Tuberculosis (TB) has been a disease for centuries with various challenges [1]. Like other places where challenges and opportunities come together, TB challenges were the inspiration for the scientific community to mobilize different groups for the purpose of interest. For example, with the emergence of drug resistance, there has been a huge volume of research on the discovery of new medicines and drug delivery methods and the repurposing of old drugs [2, 3]. Moreover, to enhance the capacity to detect TB cases, studies have sought diagnostics and biomarkers, with much hope recently expressed in the direction of point-of-care tests [4]. Despite all such efforts as being highlighted in 50 Chapters of this volume, we are still writing about TB and thinking about how to fight this old disease–implying that the problem of TB might be complex, so calling the need for an integrated science to deal with multiple dimensions in a simultaneous and effective manner. We are not the first one; there have been proposed integrated platform for TB research, integrated prevention services, integrated models for drug screening, integrated imaging protocol, integrated understanding of the disease pathogenesis, integrated control models, integrated mapping of the genome of the pathogen, etc. [5–12], to name some. These integrated jobs date back decades ago. So, a question arises: why is there a disease named TB yet? It might be due to the fact that this integration has happened to a scale that is not global, and so TB remains to be a problem, especially in resource-limited settings. Hope Tuberculosis: Integrated Studies for a Complex Disease helps to globalize the integrated science of TB.info:eu-repo/semantics/publishedVersio

Biolink Model: A universal schema for knowledge graphs in clinical, biomedical, and translational science

<h2>What's Changed</h2> <ul> <li>Documentation and repo hierarchy refactoring by @sierra-moxon in https://github.com/biolink/biolink-model/pull/1418</li> </ul> <p>Summary: 4.0.0 is a major release that includes many changes to the documentation for Biolink Model as well as the reorganization of the repository to support the new documentation structure and comply with LinkML best practices. The model itself has not changed significantly, but the documentation has been updated to reflect the current state of the model, and includes new visualizations of the model, additional text-based documentation, and a new gh-pages documentation layout.</p> <p><strong>Full Changelog</strong>: https://github.com/biolink/biolink-model/compare/v3.6.0...v4.0.0</p>Please cite the following works when using this software