J. Comput. Biol. - Threshold group testing on inhibitor model.

Tópicos

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Resumo

In classical group testing, one is given a population [Formula: see text] and an unknown subset [Formula: see text] of positive items, and the goal is to determine D by testing subsets of [Formula: see text]. 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.

Resumo Limpo

classic group test one given popul formula see text unknown subset formula see text posit item goal determin d test subset formula see text threshold group test general classic group test outcom group test determin number posit item test group test inhibitor model inhibitor third type item dictat test outcom negat regardless mani posit test threshold group test kinhibitor model natur combin threshold group test inhibitor model articl provid nonadapt algorithm conquer threshold group test kinhibitor model errortoler consid furthermor provid twostag algorithm identifi inhibitor find gapproxim set

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