Discovering Motifs in Scientific Databases

9224602莎莎SDB：在科学数据库中发现主题这是为期三年的持续奖项的第一年资助。这项研究是与美国国立卫生研究院布鲁斯·夏皮罗合作进行的。科学进步往往源于发现可以解释相似行为的结构性共性。例如，在分子生物学中，一组蛋白质或DNA序列可能在自然界中表达类似的功能。这个项目旨在帮助科学家发现解释相似性的共同序列或拓扑模式。模式发现需要以系统的方式生成模式猜测并对其进行测试。这些测试基于产生距离度量的近似模式匹配算法。因此，共性可能是近似的。主要的研究里程碑是模式发现、查询处理、数据组织和索引操作的一系列算法。这些算法将在来自美国国立卫生研究院和公共基因组数据库的数据上进行测试。虽然有些算法是特定于生物学中存在的组合结构的，但许多技术应该推广到任何试图在数据库中寻找模式的应用程序。这个项目将帮助科学家在大型数据库中发现决定自然行为的模式。这样的模式可能会导致新的药物设计或新的治疗方法。***

9224602 Shasha SDB: Discovering Motifs in Scientific Databases This is the first year funding of a three-year continuing award. This research is carried out in collaboration with Bruce Shapiro, National Institutes of Health. Scientific progress often results from discovering structural commonalities that explain similar behavior. For example, in molecular biology, a set of proteins or DNA sequences may express similar functionality in nature. This project aims to help scientists discover common sequence or topological patterns that explain the similarity. Pattern discovery entails generating pattern guesses in a systematic way and testing them. The tests are based on approximate pattern matching algorithms that yield distance metrics. Thus, commonalities may be approximate. The main research milestones are a family of algorithms for pattern discovery, query processing, data organization and index manipulation. The algorithms are to be tested on data drawn from the National Institutes of Health and from public genome databases. Whereas some of the algorithms are specific to the combinatorial structures present in biology, many of the techniques should generalize to any application that seeks to find patterns in databases. This project will help scientists discover patterns in large databases that determine natural behavior. Such patterns may lead to new drug design or to new treatments. ***