A construction of pooling designs with some happy surprises

A. D'Yachkov*, Frank Hwang, Antony Macula, Pavel Vilenkin, Chih-wen Weng

*Corresponding author for this work

研究成果: Article同行評審

49 引文 斯高帕斯(Scopus)

摘要

The screening of data sets for "positive data objects" is essential to modern technology. A (group) test that indicates whether a positive data object is in a specific subset or pool of the dataset can greatly facilitate the identification of all the positive data objects. A collection of tested pools is called a pooling design. Pooling designs are standard experimental tools in many biotechnical applications. In this paper, we use the (linear) subspace relation coupled with the general concept of a "containment matrix" to construct pooling designs with surprisingly high degrees of error correction (detection.) Error-correcting pooling designs are important to biotechnical applications where error rates often are as high as 15%. What is also surprising is that the rank of the pooling design containment matrix is independent of the number of positive data objects in the dataset.

原文English
頁(從 - 到)1129-1136
頁數8
期刊Journal of Computational Biology
12
發行號8
DOIs
出版狀態Published - 1 十月 2005

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