The Ohio State University, Department of Biomedical Informatics, 3190 Graves Hall, 333 West 10th Avenue, Columbus, OH 43210, USA.
J Biomed Inform. 2011 Dec;44 Suppl 1(Suppl 1):S56-S62. doi: 10.1016/j.jbi.2011.07.006. Epub 2011 Jul 29.
Investigators in the translational research and systems medicine domains require highly usable, efficient and integrative tools and methods that allow for the navigation of and reasoning over emerging large-scale data sets. Such resources must cover a spectrum of granularity from bio-molecules to population phenotypes. Given such information needs, we report upon the initial design and evaluation of an ontology-anchored integrative query tool, Research-IQ, which employs a combination of conceptual knowledge engineering and information retrieval techniques to enable the intuitive and rapid construction of queries, in terms of semi-structured textual propositions, that can subsequently be applied to integrative data sets. Our initial results, based upon both quantitative and qualitative evaluations of the efficacy and usability of Research-IQ, demonstrate its potential to increase clinical and translational research throughput.
转化研究和系统医学领域的研究人员需要高度可用、高效和集成的工具和方法,这些工具和方法允许对新兴的大规模数据集进行导航和推理。这些资源必须涵盖从生物分子到人群表型的一系列粒度。鉴于这些信息需求,我们报告了初始设计和评估的基于本体的集成查询工具 Research-IQ,该工具采用了概念知识工程和信息检索技术的组合,以支持基于半结构化文本命题的直观和快速查询构建,随后可以将这些查询应用于集成数据集。我们的初步结果基于对 Research-IQ 的功效和可用性的定量和定性评估,证明了它有潜力提高临床和转化研究的效率。