2,716 research outputs found
Learning Immune-Defectives Graph through Group Tests
This paper deals with an abstraction of a unified problem of drug discovery
and pathogen identification. Pathogen identification involves identification of
disease-causing biomolecules. Drug discovery involves finding chemical
compounds, called lead compounds, that bind to pathogenic proteins and
eventually inhibit the function of the protein. In this paper, the lead
compounds are abstracted as inhibitors, pathogenic proteins as defectives, and
the mixture of "ineffective" chemical compounds and non-pathogenic proteins as
normal items. A defective could be immune to the presence of an inhibitor in a
test. So, a test containing a defective is positive iff it does not contain its
"associated" inhibitor. The goal of this paper is to identify the defectives,
inhibitors, and their "associations" with high probability, or in other words,
learn the Immune Defectives Graph (IDG) efficiently through group tests. We
propose a probabilistic non-adaptive pooling design, a probabilistic two-stage
adaptive pooling design and decoding algorithms for learning the IDG. For the
two-stage adaptive-pooling design, we show that the sample complexity of the
number of tests required to guarantee recovery of the inhibitors, defectives,
and their associations with high probability, i.e., the upper bound, exceeds
the proposed lower bound by a logarithmic multiplicative factor in the number
of items. For the non-adaptive pooling design too, we show that the upper bound
exceeds the proposed lower bound by at most a logarithmic multiplicative factor
in the number of items.Comment: Double column, 17 pages. Updated with tighter lower bounds and other
minor edit
A framework for generalized group testing with inhibitors and its potential application in neuroscience
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
Concomitant Group Testing
In this paper, we introduce a variation of the group testing problem
capturing the idea that a positive test requires a combination of multiple
``types'' of item. Specifically, we assume that there are multiple disjoint
\emph{semi-defective sets}, and a test is positive if and only if it contains
at least one item from each of these sets. The goal is to reliably identify all
of the semi-defective sets using as few tests as possible, and we refer to this
problem as \textit{Concomitant Group Testing} (ConcGT). We derive a variety of
algorithms for this task, focusing primarily on the case that there are two
semi-defective sets. Our algorithms are distinguished by (i) whether they are
deterministic (zero-error) or randomized (small-error), and (ii) whether they
are non-adaptive, fully adaptive, or have limited adaptivity (e.g., 2 or 3
stages). Both our deterministic adaptive algorithm and our randomized
algorithms (non-adaptive or limited adaptivity) are order-optimal in broad
scaling regimes of interest, and improve significantly over baseline results
that are based on solving a more general problem as an intermediate step (e.g.,
hypergraph learning).Comment: 15 pages, 3 figures, 1 tabl
Frameworks for Evaluating Qualitative and Quantitative Information on Adverse Drug Events throughout Development through to Marketing
Significant public health issues caused by adverse drug reactions in the post-marketing phase, such as birth defects by thalidomide, have been well described. Unfortunately, subjects in clinical trials cannot completely avoid severe harm during drug development. TGN1412Â in 2006 and BIA 10-2474Â in 2016 were withdrawn from development due to severe adverse reactions in first-into-man studies. Thus, monitoring drug safety is important throughout all phases of development. In twenty-first century, minimizing drug development cost and time is a challenge for pharmaceutical companies. When a drug is approved with a smaller size and fewer number of clinical trials, pharmacovigilance and benefit-risk evaluation after marketing need to be sufficiently performed. Underpinned by understanding of the traditional methods of evaluating adverse drug reactions, new developments in IT and computing might well help us to detect drug safety signals earlier, enabling prompt intervention for protecting the rights of subjects and public health
Optimal Dorfman Group Testing For Symmetric Distributions
We study Dorfman's classical group testing protocol in a novel setting where
individual specimen statuses are modeled as exchangeable random variables. We
are motivated by infectious disease screening. In that case, specimens which
arrive together for testing often originate from the same community and so
their statuses may exhibit positive correlation. Dorfman's protocol screens a
population of n specimens for a binary trait by partitioning it into
nonoverlapping groups, testing these, and only individually retesting the
specimens of each positive group. The partition is chosen to minimize the
expected number of tests under a probabilistic model of specimen statuses. We
relax the typical assumption that these are independent and indentically
distributed and instead model them as exchangeable random variables. In this
case, their joint distribution is symmetric in the sense that it is invariant
under permutations. We give a characterization of such distributions in terms
of a function q where q(h) is the marginal probability that any group of size h
tests negative. We use this interpretable representation to show that the set
partitioning problem arising in Dorfman's protocol can be reduced to an integer
partitioning problem and efficiently solved. We apply these tools to an
empirical dataset from the COVID-19 pandemic. The methodology helps explain the
unexpectedly high empirical efficiency reported by the original investigators.Comment: 20 pages w/o references, 2 figure
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