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

TL;DR

This paper introduces a generalized group testing framework with hybrid items, enabling efficient classification of defectives, inhibitors, and hybrids, with applications in neuroscience and practical test design.

Contribution

It extends existing group testing models by including hybrid items and provides a comprehensive non-adaptive classification framework for over 7 million instances.

Findings

Framework classifies all item types non-adaptively

Applicable to neuron classification in neuroscience

Practical implementation of the testing scheme

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.