Title: Threshold Group Testing on Inhibitor Model
Authors: Chang, Huilan
Fu, Hung-Lin
Shih, Chih-Huai
Department of Applied Mathematics
Keywords: group testing;inhibitor;nonadaptive algorithm;pooling design;threshold group testing
Issue Date: 1-Jun-2013
Abstract: In classical group testing, one is given a population N and an unknown subset D subset of N of positive items, and the goal is to determine D by testing subsets of N. Threshold group testing is a generalization of classical group testing, where the outcome of a group test is determined by the number of positive items in the test. In group testing on inhibitor model, inhibitors are the third type of item that dictate the test outcome to be negative regardless of how many positives are in the test. The threshold group testing on k-inhibitor model is a natural combination of threshold group testing and inhibitor model. In this article, we provide nonadaptive algorithms to conquer the threshold group testing on k-inhibitor model where error-tolerance is considered. Furthermore, we provide a two-stage algorithm to identify all inhibitors and find a g-approximate set.
URI: http://dx.doi.org/10.1089/cmb.2012.0224
ISSN: 1066-5277
DOI: 10.1089/cmb.2012.0224
Volume: 20
Issue: 6
Begin Page: 464
End Page: 470
Appears in Collections:Articles

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