Tusch Guenter, Tole Olvi
School of Computing and Information Systems, Grand Valley State University, Allendale, MI, USA.
Stud Health Technol Inform. 2012;180:1156-8.
Widespread use of microarray technology that led to highly complex datasets often is addressing similar or related biological questions. In translational medicine research is often based on measurements that have been obtained at different points in time. However, the researcher looks at them as a progression over time. If a biological stimulus shows an effect on a particular gene that is reversed over time, this would show, for instance, as a peak in the gene's temporal expression profile. Our program SPOT helps researchers find these patterns in large sets of microarray data. We created the software tool using open-source platforms and the Semantic Web tool Protégé-OWL.
导致产生高度复杂数据集的微阵列技术的广泛应用，往往针对的是相似或相关的生物学问题。在转化医学研究中，常常基于在不同时间点获得的测量数据。然而，研究人员将这些数据视为随时间的一种进展。例如，如果一种生物刺激对某个特定基因产生了一种随时间逆转的效应，这将表现为该基因的时间表达谱中的一个峰值。我们的程序SPOT帮助研究人员在大量微阵列数据集中找到这些模式。我们使用开源平台和语义网工具Protégé-OWL创建了这个软件工具。
