A framework for generalized group testing with inhibitors and its potential application in neuroscience

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.

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• Near-Optimal Stochastic Threshold Group Testing
• On Multistage Learning a Hidden Hypergraph
• Sublinear decoding schemes for non-adaptive group testing with inhibitors
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Cited by in corpus (4)

• Improved encoding and decoding for non-adaptive threshold group testing
• Sublinear decoding schemes for non-adaptive group testing with inhibitors
• Improved non-adaptive algorithms for threshold group testing with a gap
• Efficient and error-tolerant schemes for non-adaptive complex group testing and its application in complex disease genetics