A construction of pooling designs with some happy surprises.
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.
D'Yachkov
A
A
Department of Probability Theory, Faculty of Mechanics and Mathematics, Moscow State University, Moscow, 119992, Russia. agd-msu@yandex.ru
Hwang
Frank
F
Macula
Antony
A
Vilenkin
Pavel
P
Weng
Chih-Wen
CW
D019295:Computational Biology / Q000379:methods*; D004247:DNA / Q000032:analysis*; D015342:DNA Probes; D015698:Genomic Library; D006801:Humans; D008433:Mathematics; D008962:Models, Theoretical
D015342:DNA Probes; D004247:DNA