RUI: Group Testing for Complexes

数据集的筛选对现代技术至关重要。每当目标是在数据集中找到“正元素”时，表明至少有一个正元素是数据集中特定部分的元素的测试可以极大地促进它们的隔离。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，在将群体测试技术应用于识别集体积极的物体方面所做的工作很少。设C是种群中子集或复合体的未知集合，设P是种群中的一个池。当且仅当C中的一个成员也是P的子集时，池P被称为正的。通过应用这些二元检验来识别C称为复合体的群检验。本提案的主要目的是开发有效的群体测试方法，从而识别物体的积极组合，即积极的复合物。在许多科学和技术领域，识别物体的积极组合是很重要的。计算上可行的方法，可以找到产生某种可测量结果的实体组合，或者与某些功能或功能障碍有可测量的联系，这将在医学遗传学、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。

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.