Department of Biomedical Informatics, The Ohio State University, Columbus, Ohio, USA.
J Am Med Inform Assoc. 2012 Nov-Dec;19(6):1110-4. doi: 10.1136/amiajnl-2011-000736. Epub 2012 May 30.
The conduct of clinical and translational research regularly involves the use of a variety of heterogeneous and large-scale data resources. Scalable methods for the integrative analysis of such resources, particularly when attempting to leverage computable domain knowledge in order to generate actionable hypotheses in a high-throughput manner, remain an open area of research. In this report, we describe both a generalizable design pattern for such integrative knowledge-anchored hypothesis discovery operations and our experience in applying that design pattern in the experimental context of a set of driving research questions related to the publicly available Osteoarthritis Initiative data repository. We believe that this 'test bed' project and the lessons learned during its execution are both generalizable and representative of common clinical and translational research paradigms.
临床和转化研究的开展通常涉及到多种异构的大规模数据资源的使用。针对此类资源的可扩展分析方法，尤其是当试图利用可计算的领域知识以高通量的方式生成可行的假说时，仍然是一个待研究的领域。在本报告中，我们描述了这种综合知识锚定假说发现操作的通用设计模式，以及我们在一组与公开可用的骨关节炎倡议（Osteoarthritis Initiative）数据存储库相关的研究问题的实验背景下应用该设计模式的经验。我们认为这个“测试床”项目及其执行过程中得到的经验教训是具有普遍性的，并且可以代表常见的临床和转化研究范例。
