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dc.contributor.authorChang, Huilanen_US
dc.contributor.authorFu, Hung-Linen_US
dc.contributor.authorShih, Chih-Huaien_US
dc.date.accessioned2014-12-08T15:31:31Z-
dc.date.available2014-12-08T15:31:31Z-
dc.date.issued2013-06-01en_US
dc.identifier.issn1066-5277en_US
dc.identifier.urihttp://dx.doi.org/10.1089/cmb.2012.0224en_US
dc.identifier.urihttp://hdl.handle.net/11536/22354-
dc.description.abstractIn 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.en_US
dc.language.isoen_USen_US
dc.subjectgroup testingen_US
dc.subjectinhibitoren_US
dc.subjectnonadaptive algorithmen_US
dc.subjectpooling designen_US
dc.subjectthreshold group testingen_US
dc.titleThreshold Group Testing on Inhibitor Modelen_US
dc.typeArticleen_US
dc.identifier.doi10.1089/cmb.2012.0224en_US
dc.identifier.journalJOURNAL OF COMPUTATIONAL BIOLOGYen_US
dc.citation.volume20en_US
dc.citation.issue6en_US
dc.citation.spage464en_US
dc.citation.epage470en_US
dc.contributor.department應用數學系zh_TW
dc.contributor.departmentDepartment of Applied Mathematicsen_US
dc.identifier.wosnumberWOS:000319654200006-
dc.citation.woscount1-
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