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

Research output: Contribution to journalArticlepeer-review

50 Scopus citations


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.

Original languageEnglish
Pages (from-to)1129-1136
Number of pages8
JournalJournal of Computational Biology
Issue number8
StatePublished - Oct 2005


  • Error correction
  • Pooling designs


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