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迈向通用生物医学数据翻译器的进展。

Progress toward a universal biomedical data translator.

作者信息

Fecho Karamarie, Thessen Anne E, Baranzini Sergio E, Bizon Chris, Hadlock Jennifer J, Huang Sui, Roper Ryan T, Southall Noel, Ta Casey, Watkins Paul B, Williams Mark D, Xu Hao, Byrd William, Dančík Vlado, Duby Marc P, Dumontier Michel, Glusman Gustavo, Harris Nomi L, Hinderer Eugene W, Hyde Greg, Johs Adam, Su Andrew I, Qin Guangrong, Zhu Qian

机构信息

Renaissance Computing Institute, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.

Center for Health AI, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA.

出版信息

Clin Transl Sci. 2022 Aug;15(8):1838-1847. doi: 10.1111/cts.13301. Epub 2022 Jun 6.

Abstract

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.

摘要

临床、生物医学和转化科学在可用数据的广度和多样性以及这些数据对改善人类健康和福祉的潜在影响方面已达到一个转折点。然而,由于术语和表示方式上的学科特定差异,数据往往是孤立的、无序的,并且无法广泛获取。为应对这些挑战,生物医学数据翻译联盟开发并测试了一个基于知识图谱的试点“翻译器”系统,该系统能够整合现有的生物医学数据集,并将这些数据“翻译”为旨在增强人类推理和加速转化科学的见解。在证明了翻译器系统的可行性之后,翻译器项目自此进入开发阶段,并且翻译器联盟在一个运营系统的研究、设计和实施方面取得了重大进展。在此,我们描述当前系统的架构、性能和结果质量。我们将翻译器应用于与主题专家合作开发的几个实际用例。最后,我们讨论翻译器的科学和技术特征,并将这些特征与其他基于生物医学图谱的最先进问答系统进行比较。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4eed/9372428/e7318d75081e/CTS-15-1838-g001.jpg

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