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.
| Medienart: |
Artikel |
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| Erscheinungsjahr: |
2005 |
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| Erschienen: |
2005 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:12 |
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| Enthalten in: |
Journal of computational biology : a journal of computational molecular cell biology - 12(2005), 8 vom: 14. Okt., Seite 1129-36 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
D'Yachkov, A [VerfasserIn] |
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| Themen: |
9007-49-2 |
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| Anmerkungen: |
Date Completed 29.12.2005 Date Revised 15.11.2006 published: Print Citation Status MEDLINE |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM158509951 |
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| 245 | 1 | 2 | |a A construction of pooling designs with some happy surprises |
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| 520 | |a 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 | ||
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| 700 | 1 | |a Weng, Chih-Wen |e verfasserin |4 aut | |
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