A framework for generalized group testing with inhibitors and its
potential application in neuroscience
potential application in neuroscience
File(s)
Author(s)
Bui, Thach V
Kuribayashi, Minoru
Cheraghchi, Mahdi
Echizen, Isao
Type
Working Paper
Abstract
The main goal of group testing with inhibitors (GTI) is to efficiently
identify a small number of defective items and inhibitor items in a large set
of items. A test on a subset of items is positive if the subset satisfies some
specific properties. Inhibitor items cancel the effects of defective items,
which often make the outcome of a test containing defective items negative.
Different GTI models can be formulated by considering how specific properties
have different cancellation effects. This work introduces generalized GTI
(GGTI) in which a new type of items is added, i.e., hybrid items. A hybrid item
plays the roles of both defectives items and inhibitor items. Since the number
of instances of GGTI is large (more than 7 million), we introduce a framework
for classifying all types of items non-adaptively, i.e., all tests are designed
in advance. We then explain how GGTI can be used to classify neurons in
neuroscience. Finally, we show how to realize our proposed scheme in practice.
identify a small number of defective items and inhibitor items in a large set
of items. A test on a subset of items is positive if the subset satisfies some
specific properties. Inhibitor items cancel the effects of defective items,
which often make the outcome of a test containing defective items negative.
Different GTI models can be formulated by considering how specific properties
have different cancellation effects. This work introduces generalized GTI
(GGTI) in which a new type of items is added, i.e., hybrid items. A hybrid item
plays the roles of both defectives items and inhibitor items. Since the number
of instances of GGTI is large (more than 7 million), we introduce a framework
for classifying all types of items non-adaptively, i.e., all tests are designed
in advance. We then explain how GGTI can be used to classify neurons in
neuroscience. Finally, we show how to realize our proposed scheme in practice.
Date Issued
2019-02-10
Citation
2019
Identifier
http://arxiv.org/abs/1810.01086v2
Subjects
cs.IT
cs.IT
math.IT