RUI: Group Testing for Complexes

RUI：复合体的分组测试

• 批准号: 0107179
• 负责人: Anthony Macula
• 金额: $ 6.02万
• 依托单位: SUNY College at Geneseo
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2001
• 资助国家: 美国
• 起止时间: 2001-09-01 至 2005-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0107179&HistoricalAwards=false
• 关键词: RUI; Group; Testing; Complexes

The screening of data sets is essential to modern technology. Whenever the objective is to find "positive elements" in a data set, a test indicating whether at least one positive is an element of a specific part of the data set can greatly facilitate their isolation. Such tests are called binary group tests and the general mathematical method behind the identification of the positives using such tests is known as classical group testing. In many applied settings, the use of classical group testing to isolate objects that are individually positive has become standard experimental procedure. However, very little work has been done in applying group testing techniques to the identification of objects that are collectively positive. Let C be an unknown collection of subsets or complexes in a population and let P be a pool taken from the population. A pool P is said to be positive if and only if a member of C is also a subset of P. The identification of C by the application of these binary tests is called group testing for complexes.The primary aim of this proposal is the development of efficient group testing methods that lead to the identification of positive combinations of objects, namely the positive complexes. There are many scientific and technical areas where the identification of positive combinations of objects is important. Computationally feasible methods of finding combinations of entities that produce some measurable outcome or are measurably linked to some function or disfunction would have important applications to medical genetics, computer security, software testing, data mining, communications, and marketing.

筛选数据集对现代技术至关重要。 无论何时，只要目标是在数据集中找到“肯定要素”，就可以通过测试来表明是否至少有一个肯定要素是数据集特定部分的要素，这将大大有助于将其分离出来。 这种测试被称为二元组测试，使用这种测试识别阳性的一般数学方法被称为经典组测试。 在许多应用环境中，使用经典的群体测试来隔离个体阳性的对象已成为标准的实验程序。 然而，很少有工作已经做了应用群体测试技术，以确定对象的集体是积极的。 令C是一个未知的集合的子集或复合体的人口和P是一个池采取的人口。 一个池P被称为是积极的，当且仅当一个成员的C也是一个子集的P。C的鉴定，通过应用这些二元测试被称为组测试的complexes.The主要目的，这一建议是有效的组测试方法，导致识别的积极组合的对象，即积极的复合物的发展。 在许多科学和技术领域，物体的正组合的识别是重要的。寻找产生某种可测量结果或与某种功能或功能障碍可测量相关的实体组合的计算可行方法将在医学遗传学、计算机安全、软件测试、数据挖掘、通信和营销方面具有重要应用。