Rafiq Mohammed I, O'Connor Martin J, Das Amar K
Stanford Medical Informatics, MSOB X233, Stanford, CA 94305, USA.
Proc IEEE Comput Syst Bioinform Conf. 2005:362-5. doi: 10.1109/csb.2005.25.
With the rapid growth of biomedical research databases, opportunities for scientific inquiry have expanded quickly and led to a demand for computational methods that can extract biologically relevant patterns among vast amounts of data. A significant challenge is identifying temporal relationships among genotypic and clinical (phenotypic) data. Few software tools are available for such pattern matching, and they are not interoperable with existing databases. We are developing and validating a novel software method for temporal pattern discovery in biomedical genomics. In this paper, we present an efficient and flexible query algorithm (called TEMF) to extract statistical patterns from time-oriented relational databases. We show that TEMF - as an extension to our modular temporal querying application (Chronus II) - can express a wide range of complex temporal aggregations without the need for data processing in a statistical software package. We show the expressivity of TEMF using example queries from the Stanford HIV Database.
随着生物医学研究数据库的迅速增长，科学探究的机会迅速扩大，从而产生了对能够在大量数据中提取生物学相关模式的计算方法的需求。一个重大挑战是识别基因型和临床（表型）数据之间的时间关系。用于这种模式匹配的软件工具很少，而且它们与现有数据库不兼容。我们正在开发和验证一种用于生物医学基因组学中时间模式发现的新型软件方法。在本文中，我们提出了一种高效且灵活的查询算法（称为TEMF），用于从面向时间的关系数据库中提取统计模式。我们表明，作为我们的模块化时间查询应用程序（Chronus II）的扩展，TEMF无需在统计软件包中进行数据处理，就能表达广泛的复杂时间聚合。我们使用来自斯坦福HIV数据库的示例查询展示了TEMF的表达能力。
